Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims 1, 3, 4, 6, 14, 16, 17 and 21-25 are amended.
Claims 1-25 are pending.
Response to Arguments/Remarks
Claim Rejections - 35 USC 103
Applicant continues to argue that the zone or escape zone or space or area (in these arguments, an emergency escape zone) is not described in the current art. The arguments indicated that these spaces/zones are not the same as those indicated in the ART of record.
Examiner has used the Broadest reasonable interpretation of the claims and the amendments. The new amendments do not change the scope of the claims, thus the art of record still holds. Under a broadest reasonable interpretation (BRI), words of the claim must be given their plain meaning, unless such meaning is inconsistent with the specification. The plain meaning of a term means the ordinary and customary meaning given to the term by those of ordinary skill in the art at the relevant time. The ordinary and customary meaning of a term may be evidenced by a variety of sources, including the words of the claims themselves, the specification, drawings, and prior art. However, the best source for determining the meaning of a claim term is the specification - the greatest clarity is obtained when the specification serves as a glossary for the claim terms. The words of the claim must be given their plain meaning unless the plain meaning is inconsistent with the specification. 2111.01 (I). See also In re Marosi, 710 F.2d 799, 802, 218 USPQ 289, 292 (Fed. Cir. 1983) ("'[C]laims are not to be read in a vacuum, and limitations therein are to be interpreted in light of the specification in giving them their ‘broadest reasonable interpretation.'"2111.01 (II).
The instant claims, in their Broadest Reasonable Interpretation, are determining the best and safest space or zone to move when in traffic. This can include a lane space, a safety zone, emergency zone and/or escape zone. The instant claims do this by determining the best zone from one or more, that fulfill the parameters and guidelines that are calculated.
Examiner respectfully disagrees with the arguments. Samara in Table 1 shows two possible choices for a “zone” to move to. Also in Section 2, Related Work, also shows “allocation of a zone” and the “choice of an ideal zone.” Thus, Samara determines a number of zones available to move into thus is providing the information needed to fulfill the actions of the vehicles.
Oguro teaches [0010] ("the plurality of retreat destination candidates may include a first retreat destination candidate and a second retreat destination candidate that is located farther away than the first retreat destination candidate when seen from the vehicle, and the action plan generating unit may generate a retreat action plan for the vehicle (this means the same as an escape zone) to retreat to the second retreat destination candidate in a case in which a degree of safety of the second retreat destination candidate is higher than a degree of safety of the first retreat destination candidate."). This is determining multiple escape zones and has vehicle controllers to guide vehicle to the proper, and safest space.
Healy also teaches techniques for sensing and controlling a vehicle to the best of the free space surrounding the vehicle [0045].
Lastly, Ramasamy teaches coordinated lane-change Negotiation’s which also provides “zones,” safe areas for a vehicle to move, or indicated and provide a “zone” for the vehicle to move to. [“the responding vehicles 142 a-b determine whether to accommodate the requested lane change by helping to establish sufficient space in the lane”)], thus having multiple determined spaces
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-25 are rejected under 35 U.S.C. 103 as being unpatentable over Ghassan Samara “Lane prediction optimization in VANET” Egyptian Informatics Journal, 2020 [Article history: Received 14 June 2020 Revised 21 November 2020 Accepted 14 December 2020] [Now Samara]; in view of Oguro et al. [US20200086860, now Oguro], in view of Healey et al. [US20190049957, now Healey], and further in view of Ramasamy, et al. [US10089876, now Ramasamy].
Claim 1
Samara discloses a system for cooperative escape zone detection for a group of vehicle [see at least Samara, Abstract, Fig. 1; Pp. 3-4, sections 3-5];
determine an emergency escape zone status for the vehicle based on the one or more distances associated with the vehicle, the driving environment of the vehicle, and the vehicle condition of the vehicle, the emergency escape zone status indicating at least a number of physically unobstructed emergency escape zones, each being a contiguous region of roadway immediately adjacent to the vehicle that is free of obstacles and sufficient for the vehicle to enter without colliding with another vehicle or object; [see at least Samara, P2, Sect. 2 (“A number of vehicle prediction techniques were created on the basis of the Markov chain [28,35]. The Markov (binary) system was used mostly for determining the vehicle state of the next interval depending on the signal models. In conjunction with the Markov variable-length strings, for instance, the closest neighboring ranking was used to forecast traffic patterns [28]. Following classification into a group of the vehicle status for each fresh time step, a specific speed score is calculated using the suitable weighted regression model tracked only with information from the corresponding group. In order to ensure the elevated predictability of the short-term traffic flow predicting technique [35] a mixed fore- cast technique centered on Markov’s loop hypothesis and Grey Verhulst’s model was also suggested. The volatility of the information was addressed in the Markov chain principle, which is based on the Gray Verhulst model, in an attempt to enhance the precision of the forecasts.”); P. 3 sect. 3 (“Note that road conditions change over a moment, so vibrant designs are necessary to predict traffic conditions. The forecast for the road level can be performed by using regression designs. The optimization problem is developed to determine what room (i.e., part of the range) the car should occupy at some stage in order to find the finest range route for an application.”)];
determine whether the number of emergency escape zones available to any of the vehicles is less than a predetermined minimum number of two emergency zones: and in response to the emergency escape zone status of one in the group of vehicles indicating that the number of emergency escape zones of the one in the group of vehicles is less than the predetermined minimum number,, [see at least Samara, Abstract , Pp. 3-5, sect. 3-6 (“…the simulation results indicate that the heavy-traffic scenarios with richer traffic information can lead to optimal decisions.”) Note: is suggesting].
Samara does not specifically disclose but Oguro teaches sensors configured to obtain driving condition information for the group of vehicles, the driving condition information indicating driving environments and vehicle conditions of the group of vehicles; [see at least Oguro, ¶ 0038, 0045 ("The vehicle sensor 40 includes a vehicle speed sensor detecting a speed of the subject vehicle M, an acceleration sensor detecting an acceleration, a yaw rate sensor detecting an angular velocity around a vertical axis, an azimuth sensor detecting an azimuth of the subject vehicle M, and the like. The vehicle sensor 40 outputs detected information (a speed, an acceleration, an angular velocity, an azimuth, and the like) to the automated driving control unit 100.")]; and
a controller configured to: for each vehicle in the group of vehicles, determine, based on the driving environment of the vehicle, one or more distances associated with the vehicle that are between the vehicle and one or more obstacles that surround the vehicle [see at least Oguro, abstract ("a risk determining unit configured to determine a degree of risk to a vehicle of the obstacle detected by the detection unit; and an action plan generating unit configured to search for a retreat destination candidate for the vehicle, determine a degree of safety of the retreat destination candidate, and generate a retreat action plan for the vehicle on the basis of a result of the determination of the degree of safety of the retreat destination candidate in a case in which the degree of risk determined by the risk determining unit is equal to or higher than a threshold."); ¶ 0008 ("a vehicle control system is provided, including: a detection unit configured to detect an obstacle in front of a vehicle; a risk determining unit configured to determine a degree of risk of a vehicle for the obstacle detected by the detection unit; and an action plan generating unit configured to search for a retreat destination candidate for the vehicle, determine a degree of safety of the retreat destination candidate, and generate a retreat action plan for the vehicle on the basis of a result of the determination of the degree of safety of the retreat destination candidate in a case in which the degree of risk determined by the risk determining unit is equal to or higher than a threshold."). Note: a retreat destination and an escape route provide a place for the vehicle to go safely. An obstacle can be another vehicle.]; and
Oguro also teaches in response to the zone status of one in the group of vehicles indicating the zones [available], send one or more control signals configured to cause one or more cooperative physical movements of the one or more different vehicles in the group of vehicles to create an additional emergency escape zone for the one in the group of vehicles that are not the vehicle with the less-than-two emergency escape zones, [see at least Oguro, ¶ 0010 ("the plurality of retreat destination candidates may include a first retreat destination candidate and a second retreat destination candidate that is located farther away than the first retreat destination candidate when seen from the vehicle, and the action plan generating unit may generate a retreat action plan for the vehicle to retreat to the second retreat destination candidate in a case in which a degree of safety of the second retreat destination candidate is higher than a degree of safety of the first retreat destination candidate.") Note: multiple “retreat destination candidates = multiple escape/safety zones].
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the lane prediction optimization of Samara with the vehicle control system and method for determining a retreat [escape] destination or route of Oguro. Thus providing a more effective, efficient way to “improve car security and safety [Samara, Intro)” of vehicles when a change of lanes within a group/fleet/convoy/platoon of autonomous vehicles.
Healey also teaches sensors configured to obtain driving condition information for the group of vehicles, the driving condition information indicating driving environments [see at least Healey, Abstract (“In one example a system for emotional adaptive driving policies for automated driving vehicles, comprising a first plurality of sensors to detect environmental information relating to at least one passenger in a vehicle and a controller communicatively coupled to the plurality of sensors and comprising processing circuitry, to receive the environmental information from the first plurality of sensors, determine, from the environmental information, an emotional state of the at least one passenger, and implement a driving policy based at least in part on the emotional state of the at least one passenger. Other examples may be described.”); Figs. 5(A-D)].
determine, based on the driving environment of the vehicle, one or more distances associated with the vehicle that are between the vehicle and one or more obstacles that surround the vehicle [see at least Healey, ¶ 0068 ("a weighted sum of terms: e.g. positively weighted speed (u.sub.t in km/h), distance traveled towards the goal (d.sub.t in km) and emotional state valence (eval.sub.tE[-1,1]), and negatively weighted distance to front object (c.sub.t), overlap with left lane shoulder (s.sub.tE[0,1]), overlap with the opposite lane (o.sub.tE[0,1]) and emotional state arousal (earo.sub.t):"); 0077 ("The previously described implementation can be applied during automated or cooperative driving environments to customize the vehicle response and behavior to road events. FIG. SA, SB, SC and FIG. SD are diagrams illustrating driving maneuvers in accordance with some examples. Referring to FIG. SA illustrates a take-over situation in which short head distance (di) leads to stressed situation. FIG. SB illustrates an optimized take over behavior based on emotional state with longer head distance and smoother lane changing FIG. SC illustrates a simple lane following driving policy in urban scenarios. Due to low vehicle speed this scenario generates frustration. FIG. SD illustrates an updated driving policy in which average speed is increased by allowing for multiple lane changes to overtake slower vehicles.")];
in response to status… indicating the number of zones of the one in the group of vehicles being less than the integer of at least 1, send to one or more vehicles in the group of vehicles, one or more control signals configured to cause one or more physical movements of the one or more vehicles in the group of vehicles [see at least Healey, ¶ 0053 ("Since there can be multiple paths in the free space communicating the starting and end points so we define optimal path planning through a cost function C:P->N.sub.o where P denotes the set of all possible paths. Optimal path planning is defined as a process cp' that uses the previous cost function to restrict the solution to the optimal solution o' so that C(p')=min (C(P)). The main parameter to the cost function is usually shortest path as it saves fuel/energy but other parameters can be added to optimize route to specific parameters.")].
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the lane prediction optimization of Samara with the vehicle control system and method for determining a retreat [escape] destination or route of Oguro with the more specific adaptive driving techniques of Healey. Thus providing a more effective, efficient way to “improve car security and safety [Samara, Intro)” of vehicles when a change of lanes within a group/fleet/convoy/platoon of autonomous vehicles.
Ramasamy teaches in more detail determine whether the number of emergency escape zones available to any of the vehicles [see at least Ramasamy, Abstract, Col. 3, lines 17-48 (“14) In this example, the responding vehicle 142a-b identify other vehicles in the lane 120 using a light detection and ranging (LIDAR); radio detection and ranging (RADAR); wireless communication techniques, including broadcast and point-to-point techniques; or a computer vision (CV) system. After identifying nearby vehicles, if any, the responding vehicles 142a-b determine whether to accommodate the requested lane change by helping to establish sufficient space in the lane. (15) In this example, the responding vehicles 142a-b determine to allow the requesting vehicle 140 to merge into the lane ahead of responding vehicle 142b. The responding vehicles 142a-b then exchange messages to coordinate the lane change request. In this example, vehicle 142a accelerates by three miles per hour for a time until the distance between vehicle 142b and vehicle 142a is at least 50 feet. Vehicle 142b also reduces its speed by 3 miles per hour. After a period of time, the space between the vehicles 142a-b expands to at least 50 feet, at which time, one or both responding vehicles 142a-c transmit a message to the requesting vehicle 140 indicating that a space has been created between the vehicles 142a-b, and that the requesting vehicle 140 may merge into it. The requesting vehicle 140 then adjusts its speed and position until it is alongside the created space, and then changes lanes into the space. The requesting vehicle 140 then transmits a response to the responding vehicles 142a-b indicating that the requesting vehicle 140 has merged into the space, which each respond with an acknowledgement message. Each of the three vehicles then resumes ordinary operation, which may include further increasing the space between the three vehicles to a safe amount of inter-vehicle separation.”); See note below];
Ramasamy more specifically teaches send, to one or more vehicles in the group of vehicles, one or more control signals configured to cause one or more physical movements of the one or more vehicles in the group of vehicles to create at least one additional emergency escape zone for the one in the group of vehicles [see at least Ramasamy, Abstract, Fig. 5; Col 9, line 33 to Col. 10, line 2 (“(39) Referring now to FIG. 5, FIG. 5 shows an example method 500 for coordinated lane-change negotiations between vehicles. The method 500 of FIG. 5 will be discussed with respect to the example vehicle 200 shown in FIG. 2, the example computing device 210 shown in FIG. 3, and the example roadway scenario shown in FIGS. 6A-6C. However, it should be appreciated that any suitable vehicle, computing device, or driving environment according to this disclosure may be employed. (40) At block 510, a vehicle receives a lane change request from a requesting vehicle. In this example, the lane change request is transmitted via an RF transmitter mounted on vehicle 640, and multiple vehicles 642b-c (i.e., vehicles 642b and 642c), 644 receive the lane change request. Each of the receiving vehicles 642b-c, 644 may therefore perform part or all of this method 500. In contrast, vehicle 642a is not equipped with an RF receiver configured to receive such lane change requests, and thus does not receive the lane change request. (41) In other examples, however, a lane change request may be communicated via other means or multiple means. For example, the requesting vehicle 640 may transmit an RF message including a lane change request and may also activate a turn signal indicators or the driver of the vehicle may make an arm gesture indicating a desired lane change. Many vehicles are equipped with turn signal indicators both on the front and rear of the vehicle, and often on the sides as well, that will flash a light or lights corresponding to the direction of the desired lane change. Turn signal indicators are typically in the yellow/orange range of light and flash at a periodic rate, and therefore may be relatively easy to detect, even in a congested environment. Such flashing lights can be detected by nearby vehicles using image or light sensors, such as cameras or other light detectors. Detecting such turn signal indicators may be a receipt of a lane change request from a requesting vehicle in some examples according to this disclosure.”); See note below].
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the lane prediction optimization of Samara with the vehicle control system and method for determining a retreat [escape] destination or route of Oguro, with the more specific adaptive driving techniques of Healey, further with the method of coordinating lane-changes between vehicles of Ramasamy. Thus providing a more effective, efficient way to “improve car security and safety [Samara, Intro)” of vehicles when a change of lanes within a group/fleet/convoy/platoon of autonomous vehicles.
Examiners Note: In the broadest Reasonable interpretation the claims, these limitations are indicating that there is a space (zone or zones) available or not. The art in combination is indicating an available space (zone) to move into and then if not available, safe area to move to, having other autonomous vehicles alter their actions in order to make a zone if needed.
Claim 2
Samara with Oguro, Healey and further with Ramasamy disclose/teach the system of Claim 1.
Samara does not specifically teach/suggest but Oguro teaches the vehicle condition of the vehicle comprises one or more of a brake condition, a tire condition, and a speed of the vehicle [see at least Oguro, ¶ 0014 ("automated driving realized by an automated driving control unit executing at least one of speed control and steering control of the vehicle.")].
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the lane prediction optimization of Samara with the vehicle control system and method for determining a retreat [escape] destination or route of Oguro. Thus providing a more effective, efficient way to provide a safer change of lanes within a group/fleet/convoy/platoon of autonomous vehicles.
Healey also teaches the vehicle condition of the vehicle comprises one or more of a brake condition, a tire condition, and a speed of the vehicle [see at least Healey, ¶ 0021 ("speed and force of acceleration or braking (in level 4 autonomous vehicle), etc."); 0041, 0055].
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the lane prediction optimization of Samara with the vehicle control system and method for determining a retreat [escape] destination or route of Oguro with the more specific adaptive driving techniques of Healey. Thus providing a more effective, efficient way to “improve car security and safety [Samara, Intro)” of vehicles when a change of lanes within a group/fleet/convoy/platoon of autonomous vehicles.
Claim 3
Samara with Oguro, Healey and further with Ramasamy disclose/teach the system of Claim 2.
Samara further teaches determine whether emergency escape zones are available [see at least Samara, abstract, Fig. 1].
Samara does not specifically disclose but Oguro teaches the driving environments include one or more of at least one road condition, at least one road type, and a weather condition of the group of vehicles [see at least Oguro, ¶ 0046 ("information in which a road form is represented by respective links representing a road and respective nodes connected using the links. The first map information 54 may include a curvature of each road, point of interest (POI) information, and the like."); 0048 ("road information, traffic regulations information, address information (address and zip code), facilities information, telephone information, and the like may be included. In the road information, information representing a type of road such as an expressway, a toll road, a national highway, or a prefectural road and information such as the number of lanes of a road, a width of each lane, a gradient of a road, a position of a road (three- dimensional coordinates including longitude, latitude, and a height), a curvature of the curve of a lane, locations of merging and branching points of lanes, a sign installed on a road, and the like are included."); 0056], and
the controller is further configured to: determine a threshold distance based on one or more of a respective one of the at least one road condition, a respective one of the at least one road type, the weather condition, and the vehicle condition for the vehicle [see at least Oguro, ¶ 0016 -0017 ("According to one aspect of the present invention, a vehicle control method using an in-vehicle computer is provided, the vehicle control method including: detecting an obstacle in front of a vehicle; determining a degree of risk to a vehicle of the obstacle; and searching for a retreat destination candidate for the vehicle, determining a degree of safety of the retreat destination candidate, and generating a retreat action plan for the vehicle on the basis of a result of the determination of the degree of safety of the retreat destination candidate in a case in which the degree of risk is equal to or higher than a threshold. [0017] (10) According to one aspect of the present invention, a vehicle control program is provided causing an in-vehicle computer to execute: detecting an obstacle in front of a vehicle; determining a degree of risk to a vehicle of the obstacle; and searching for a retreat destination candidate for the vehicle, determining a degree of safety of the retreat destination candidate, and generating a retreat action plan for the vehicle on the basis of a result of the determination of the degree of safety of the retreat destination candidate in a case in which the degree of risk is equal to or higher than a threshold.")].
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the lane prediction optimization of Samara with the vehicle control system and method for determining a retreat [escape] destination or route of Oguro. Thus providing a more effective, efficient way to “improve car security and safety [Samara, Intro)” of vehicles when a change of lanes within a group/fleet/convoy/platoon of autonomous vehicles.
Healey also teaches distance based on one or more of a respective one of the at least one road condition, a respective one of the at least one road type, the weather condition, and the vehicle condition for the vehicle [see at least Healey, ¶ 0044 ("Referring to FIG. 3, at operation 310 localization data is retrieved, e.g., from localization module 244. At operation 312 information about one or more obstacles is retrieved, e.g., from sensor based object recognition module 242. At operation 314 information about the road(s) on which the vehicle is traveling, e.g., from the various sensors in the outward sensor suite 220 alone or in combination with external sources, e.g., mapping resources."); 0077 (:he previously described implementation can be applied during automated or cooperative driving environments to customize the vehicle response and behavior to road events. FIG. SA, SB, SC and FIG. SD are diagrams illustrating driving maneuvers in accordance with some examples. Referring to FIG. SA illustrates a take- over situation in which short head distance (di) leads to stressed situation. FIG. SB illustrates an optimized take over behavior based on emotional state with longer head distance and smoother lane changing FIG. SC illustrates a simple lane following driving policy in urban scenarios. Due to low vehicle speed this scenario generates frustration. FIG. SD illustrates an updated driving policy in which average speed is increased by allowing for multiple lane changes to overtake slower vehicles.")].
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the lane prediction optimization of Samara with the vehicle control system and method for determining a retreat [escape] destination or route of Oguro with the more specific adaptive driving techniques of Healey. Thus providing a more effective, efficient way to “improve car security and safety [Samara, Intro)” of vehicles when a change of lanes within a group/fleet/convoy/platoon of autonomous vehicles.
Ramasamy teaches with more specificity, determine whether emergency escape zones are available to the vehicle based on a comparison of the one or more distances and the threshold distance [see at least Ramasamy, Fig. 5; Col 9, line 33 to Col. 10, line 2 (“(39) Referring now to FIG. 5, FIG. 5 shows an example method 500 for coordinated lane-change negotiations between vehicles. The method 500 of FIG. 5 will be discussed with respect to the example vehicle 200 shown in FIG. 2, the example computing device 210 shown in FIG. 3, and the example roadway scenario shown in FIGS. 6A-6C. However, it should be appreciated that any suitable vehicle, computing device, or driving environment according to this disclosure may be employed. (40) At block 510, a vehicle receives a lane change request from a requesting vehicle. In this example, the lane change request is transmitted via an RF transmitter mounted on vehicle 640, and multiple vehicles 642b-c (i.e., vehicles 642b and 642c), 644 receive the lane change request. Each of the receiving vehicles 642b-c, 644 may therefore perform part or all of this method 500. In contrast, vehicle 642a is not equipped with an RF receiver configured to receive such lane change requests, and thus does not receive the lane change request. (41) In other examples, however, a lane change request may be communicated via other means or multiple means. For example, the requesting vehicle 640 may transmit an RF message including a lane change request and may also activate a turn signal indicators or the driver of the vehicle may make an arm gesture indicating a desired lane change. Many vehicles are equipped with turn signal indicators both on the front and rear of the vehicle, and often on the sides as well, that will flash a light or lights corresponding to the direction of the desired lane change. Turn signal indicators are typically in the yellow/orange range of light and flash at a periodic rate, and therefore may be relatively easy to detect, even in a congested environment. Such flashing lights can be detected by nearby vehicles using image or light sensors, such as cameras or other light detectors. Detecting such turn signal indicators may be a receipt of a lane change request from a requesting vehicle in some examples according to this disclosure.”); Note. Thus determining the safest area for the vehicle to move – to an escape/safe zone].
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the lane prediction optimization of Samara, with the vehicle control system and method for determining a retreat [escape] destination or route of Oguro, with the more specific adaptive driving techniques of Healey, further with the method of coordinating lane-changes between vehicles of Ramasamy. Thus providing a more effective, efficient way to “improve car security and safety [Samara, Intro)” of vehicles when a change of lanes within a group/fleet/convoy/platoon of autonomous vehicles.
Claim 4
Samara with Oguro, Healey and further with Ramasamy disclose/teach the system of Claim 3.
Samara, in general terms, discloses each vehicle is associated with four sides… determine whether the emergency escape zones are available for each of the four sides based on a comparison distance(s) [see at least Samara, abstract, Fig. 1, P. 3, sect. 3].
Samara does not specifically disclose but Oguro teaches each vehicle is associated with four sides that include a front side, a rear side, a left side, and a right side [see at least Oguro, ¶ 0054 ("The subject vehicle position recognizing unit 122, for example, recognizes a lane (running lane) in which the subject vehicle M runs and a relative position and a posture of the subject vehicle M with respect to the running lane. The subject vehicle position recognizing unit 122, for example, by comparing a pattern (for example, an array of solid lines and broken lines) of a road partition line that is acquired from the second map information 62 with a pattern of the road partition line in the vicinity of the subject vehicle M that is recognized from an image captured by the camera 10, recognizes a running lane. In the recognition, the position of the subject vehicle M acquired from the navigation device 50 and a processing result acquired using the INS may be added.") Note: Checking all sides of a vehicle.],
the one or more obstacles includes a front obstacle, a rear obstacle, a left obstacle, and a right obstacle, the one or more distances associated with the vehicle include a front distance, a rear distance, a left distance, and a right distance between the vehicle and the front obstacle, the rear obstacle, the left obstacle, and the right obstacle, respectively [see at least Oguro, Figs. 3, 6; ¶ 0008 ("detection unit configured to detect an obstacle in front of a vehicle; a risk determining unit configured to determine a degree of risk of a vehicle for the obstacle detected by the detection unit; and an action plan generating unit configured to search for a retreat destination candidate for the vehicle, determine a degree of safety of the retreat destination candidate, and generate a retreat action plan for the vehicle on the basis of a result of the determination of the degree of safety of the retreat destination candidate in a case in which the degree of risk determined by the risk determining unit is equal to or higher than a threshold."); 0058 ("avoidance locus is generated."); 0068 ("The obstacle detecting unit 121A may detect the presence/absence, type, size, possibility of a secondary disaster, and the like of an obstacle on the basis of information received from a vehicle involved in an accident or a nearby vehicle running ahead of the subject vehicle M through the communication device 20, information received from a communication facility installed on a road through the communication device 20, or the like instead of or in addition to information input from the camera 10 and the like. The obstacle detecting unit 121A outputs a detection result acquired by the obstacle detecting unit 121A to the risk determining unit 124.")];
for each vehicle in the group of vehicles, the controller is further configured to: determine whether the emergency escape zones are available for each of the four sides based on a comparison of the front distance, the rear distance, the left distance, and the right distance with the threshold distance; [see at least Oguro, Figs. 3, 6; ¶ 0047 ("The recommended lane determining unit 61 divides a path provided from the navigation device 50 into a plurality of blocks (for example, divides the path into blocks of 100 m in the advancement direction of the vehicle) and determines a recommended lane for each block by referring to the second map information 62..."); 0055 ("During the execution of such an event, there are cases in which an action for avoidance is planned on the basis of surrounding statuses of the subject vehicle M (the presence/absence of nearby vehicles and pedestrians, lane contraction according to road construction, and the like)."); 0058; Note: if can find one free space can find multiple free spaces to move the vehicle into].
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the lane prediction optimization of Samara with the vehicle control system and method for determining a retreat [escape] destination or route of Oguro. Thus providing a more effective, efficient way to “improve car security and safety [Samara, Intro)” of vehicles when a change of lanes within a group/fleet/convoy/platoon of autonomous vehicles.
Healey also teaches one or more obstacles and determine the status that indicates a number of zones available to the vehicle and/or a location of an emergency escape zone [see at least Healey, ¶ 0044 ("from sensor based object recognition module"); 0045 ("an amount of free space surrounding the vehicle is estimated based on the obstacle information and the road information. At operation 318 it is determined whether the current trajectory is acceptable...")].
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the lane prediction optimization of Samara with the vehicle control system and method for determining a retreat [escape] destination or route of Oguro with the more specific adaptive driving techniques of Healey. Thus providing a more effective, efficient way to “improve car security and safety [Samara, Intro)” of vehicles when a change of lanes within a group/fleet/convoy/platoon of autonomous vehicles.
Neither Samara, Oguro or Healey specifically teach management of a group of vehicles [fleet], but they do teach all the other concepts of the limitations.
Ramasamy does teach communication between vehicles in order to determine availability of a space or zone [see at least Ramasamy, title; Fig. 5; Col 9, line 33 to Col. 10, line 2].
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the lane prediction optimization of Samara with the vehicle control system and method for determining a retreat [escape] destination or route of Oguro, with the more specific adaptive driving techniques of Healey, further with the method of coordinating lane-changes between vehicles of Ramasamy. Thus providing a more effective, efficient way to “improve car security and safety [Samara, Intro)” of vehicles when a change of lanes within a group/fleet/convoy/platoon of autonomous vehicles.
Claim 5
Samara with Oguro, Healey and further with Ramasamy disclose/teach the system of Claim 3.
Samara discloses the group of vehicles travels on at least one road, the at least one road condition indicates one of… and the at least one road type at least one speed limit of the at least one road [see at least Samara, Fig. 1; P.2, section 2; P. 3, section 3 and 4].
Oguro more specifically teaches the group of vehicles travels on at least one road, the at least one road condition indicates one of: dryness, quality, or curvature of the at least one road, and the at least one road type at least one speed limit of the at least one road [see at least Oguro, ¶ 0018 ("in a case in which there is an obstacle of which a degree of risk is high in front of a vehicle, a retreat action plan for the vehicle is generated on the basis of the degree of safety of the retrieved retreat destination candidate."); 0042 ("thereby recognizing a position, a type, a speed, and the like of an object. The object recognizing device 16 outputs a result of recognition to the automated driving control unit 100"); 0045 ("The vehicle sensor 40 outputs detected information (a speed, an acceleration, an angular velocity, an azimuth, and the like) to the automated driving control unit 100."); 0046].
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the lane prediction optimization of Samara with the vehicle control system and method for determining a retreat [escape] destination or route of Oguro. Thus providing a more effective, efficient way to “improve car security and safety [Samara, Intro)” of vehicles when a change of lanes within a group/fleet/convoy/platoon of autonomous vehicles.
Healey also teaches speed as traffic rules [see at least Healey, ¶ 0041 ("The traffic rules module 258B may comprise knowledge base system containing traffic regulations, such as maximum speed allowed on the road segment and right of way behavior. The emotion rules module 256 may also comprise a knowledge base system containing emotional states mappings, e.g. reduce acceleration delta when passenger is startled or increase speed if user is frustrated and speed is low.")].
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the lane prediction optimization of Samara with the vehicle control system and method for determining a retreat [escape] destination or route of Oguro with the more specific adaptive driving techniques of Healey. Thus providing a more effective, efficient way to “improve car security and safety [Samara, Intro)” of vehicles when a change of lanes within a group/fleet/convoy/platoon of autonomous vehicles.
Neither, Samara, Oguro or Healey specifically teach management of a group of vehicles [fleet], but they do teach all the other concepts of the limitations.
Ramasamy does teach communication between more than one vehicle in order to determine availability of a space or zone [see at least Ramasamy, title; Fig. 5; Col 9, line 33 to Col. 10, line 2; Note: if communicating between more than one vehicle, is similar to fleet/vehicle group communication and management].
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the lane prediction optimization of Samara with the vehicle control system and method for determining a retreat [escape] destination or route of Oguro with the more specific adaptive driving techniques of Healey. Thus providing a more effective, efficient way to “improve car security and safety [Samara, Intro)” of vehicles when a change of lanes within a group/fleet/convoy/platoon of autonomous vehicles.
Claim 6
Samara with Oguro, Healey and further with Ramasamy disclose/teach the system of Claim 1.
Samara further discloses zone status of the one in the group of vehicles [see at least Samara, abstract, Fig. 1; P. 3, sect. 3].
Samara does not specifically disclose but Oguro teaches zone status of the one in the group of vehicles the emergency escape zones not being located at pre-defined locations [see at least Oguro, ¶ 0009 ("the action plan generating unit may search for a plurality of retreat destination candidates, determine a degree of safety of each of the plurality of retreat destination candidates, and generate the retreat action plan on the basis of a result of the determination of the degree of safety of each of the plurality of retreat destination candidates.") 0055 ("he action plan generating unit 123 determines events to be sequentially executed in automated driving such that the subject vehicle M runs in a recommended lane determined by the recommended lane determining unit 61 and deals with a surrounding status of the subject vehicle M. As the events, for example, there are a constant-speed running event in which the subject vehicle runs at a constant speed in the same running lane, a following running event in which the subject vehicle follows a vehicle running ahead, a lane changing event, a merging event, a branching event, an emergency stop event, a handover event for ending automated driving and switching to manual driving, and the like. During the execution of such an event, there are cases in which an action for avoidance is planned on the basis of surrounding statuses of the subject vehicle M (the presence/absence of nearby vehicles and pedestrians, lane contraction according to road construction, and the like).")].
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the lane prediction optimization of Samara with the vehicle control system and method for determining a retreat [escape] destination or route of Oguro. Thus providing a more effective, efficient way to “improve car security and safety [Samara, Intro)” of vehicles when a change of lanes within a group/fleet/convoy/platoon of autonomous vehicles.
Neither Samara, Oguro or Healey specifically teach management of a group of vehicles [fleet], but they do teach all the other concepts of the limitations.
Ramasamy does teach communication between more than one vehicle in order to determine availability of a space or zone [see at least Ramasamy, title; Fig. 5; Col 9, line 33 to Col. 10, line 2; Note: if communicating between more than one vehicle, is similar to fleet/vehicle group communication and management].
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the lane prediction optimization of Samara with the vehicle control system and method for determining a retreat [escape] destination or route of Oguro, with the more specific adaptive driving techniques of Healey, further with the method of coordinating lane-changes between vehicles of Ramasamy. Thus providing a more effective, efficient way to “improve car security and safety [Samara, Intro)” of vehicles when a change of lanes within a group/fleet/convoy/platoon of autonomous vehicles.
Claim 7
Samara with Oguro, Healey and further with Ramasamy disclose/teach the system of Claim 1.
Samara does not specifically disclose but Oguro teaches the one or more control signals includes a plurality of signal, and the controller is further configured to send the plurality of signals to the plurality of vehicles, respectively [see at least Oguro, abstract (control); 0009, 0019, 0088 ("The automated driving control unit 100 may transmit information relating to an obstacle detected by the obstacle detecting unit 121A, the magnitude of a degree of risk determined by the risk determining unit 124, and the like to a nearby vehicle through inter-vehicle communication performed through the communication device 20. In addition, the automated driving control unit 100 may generate a retreat action plan for other vehicles that are nearby vehicles using the action plan generating unit 123 and transmit the retreat action plan to other vehicles.")].
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the lane prediction optimization of Samara with the vehicle control system and method for determining a retreat [escape] destination or route of Oguro. Thus providing a more effective, efficient way to “improve car security and safety [Samara, Intro)” of vehicles when a change of lanes within a group/fleet/convoy/platoon of autonomous vehicles.
Neither Samara, Oguro or Healey specifically teach management of a group of vehicles [fleet], but they do teach all the other concepts of the limitations.
Ramasamy does teach communication between more than one vehicle in order to determine availability of a space or zone [see at least Ramasamy, title; Fig. 5; Col 9, line 33 to Col. 10, line 2; Note: Communicating between vehicles, is similar to fleet/vehicle group communication and management].
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the lane prediction optimization of Samara with the vehicle control system and method for determining a retreat [escape] destination or route of Oguro, with the more specific adaptive driving techniques of Healey, further with the method of coordinating lane-changes between vehicles of Ramasamy. Thus providing a more effective, efficient way to “improve car security and safety [Samara, Intro)” of vehicles when a change of lanes within a group/fleet/convoy/platoon of autonomous vehicles.
Claim 8
Samara with Oguro, Healey and further with Ramasamy disclose/teach the system of Claim 1.
Samara does not specifically disclose but Oguro teaches in general terms the one or more vehicles includes the one in the group of vehicles [see at least Oguro, abstract, ¶ 0088 (nearby vehicle, inter-vehicle communication); Note: if communication between vehicles is possible then more than one vehicle can be controlled].
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the lane prediction optimization of Samara with the vehicle control system and method for determining a retreat [escape] destination or route of Oguro. Thus providing a more effective, efficient way to provide a safer change of lanes within a group/fleet/convoy/platoon of autonomous vehicles.
Neither Samara, Oguro or Healey specifically teach management of a group of vehicles [fleet], but they do teach all the other concepts of the limitations.
Ramasamy does teach communication between more than one vehicle in order to determine availability of a space or zone [see at least Ramasamy, title; Fig. 5; Col 9, line 33 to Col. 10, line 2; Note: Communicating between vehicles, is similar to fleet/vehicle group communication and management].
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the lane prediction optimization of Samara with the vehicle control system and method for determining a retreat [escape] destination or route of Oguro, with the more specific adaptive driving techniques of Healey, further with the method of coordinating lane-changes between vehicles of Ramasamy. Thus providing a more effective, efficient way to provide a safer change of lanes within a group/fleet/convoy/platoon of autonomous vehicles.
Claim 9
Samara with Oguro, Healey and further with Ramasamy disclose/teach the system of Claim 1.
Samara does not specifically disclose but Healey does teach controller is configured to determine the one or more distances using an artificial neural network [see at least Healey, Figs. 12, 13 ("FIG. 13 illustrates training and deployment of a deep neural network in accordance with one or more embodiments."); ¶ 0057 ("The neural network approach has received particular interest in the robotics and autonomous driving community in particular with the successful application of deep learning techniques to the perception-decision- actuation pipeline. Within deep learning there are multiple approaches such as Supervised Learning, where the driving policy is directly presented a sequence of independent examples of correct predictions to make in different circumstances, Imitation Learning, where the driving policy is provided demonstrations of actions of a good strategy to follow in given situations and Reinforcement Learning (RL), where the driving policy explores the space of possible strategies and receives feedback on the outcome of the choices made to deduce a "good" -or ideally optimal-policy (i.e., strategy or controller)."); 0143 ("FIG. 12 is a generalized diagram of a machine learning software stack 1200. A machine learning application 1202 can be configured to train a neural network using a training dataset or to use a trained deep neural network to implement machine intelligence. The machine learning application 1202 can include training and inference functionality for a neural network and/or specialized software that can be used to train a neural network before deployment. The machine learning application 1202 can implement any type of machine intelligence including but not limited to image recognition, mapping and localization, autonomous navigation, speech synthesis, medical imaging, or language translation.")].
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the lane prediction optimization of Samara with the vehicle control system and method for determining a retreat [escape] destination or route of Oguro with the more specific adaptive driving techniques of Healey. Thus providing a more effective, efficient way to provide a safer change of lanes within a group/fleet/convoy/platoon of autonomous vehicles.
Neither Samara, Oguro or Healey specifically teach management of a group of vehicles [fleet], but they do teach all the other concepts of the limitations.
Ramasamy does teach communication between more than one vehicle in order to determine availability of a space or zone [see at least Ramasamy, title; Fig. 5; Col 9, line 33 to Col. 10, line 2; Note: Communicating between vehicles, is similar to fleet/vehicle group communication and management].
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the lane prediction optimization of Samara with the vehicle control system and method for determining a retreat [escape] destination or route of Oguro, with the more specific adaptive driving techniques of Healey, further with the method of coordinating lane-changes between vehicles of Ramasamy. Thus providing a more effective, efficient way to provide a safer change of lanes within a group/fleet/convoy/platoon of autonomous vehicles.
Claim 10
Samara with Oguro, Healey and further with Ramasamy disclose/teach the system of Claim 1.
Samara further discloses, in general terms interface circuitry configured to obtain a training dataset including driving condition information of multiple vehicles and corresponding distances associated with each of the multiple vehicles, the corresponding distances being between the vehicle and obstacles that surround the vehicle; and the controller is further configured to modify the artificial neural network based on the training dataset [see at least Samara, P. 2 Sect. 2 (“Another way of estimating and predicting the road state is to use an enhanced Kalman Filter ensemble to assess and forecast realistic highway network which, thanks to lower matrix reversals, can decrease computing time [32]. However, Kalman Filter considers both equations between the device and the monitoring model to be linear, which in many real-life circumstances, is not realistic. In [34] the development of the road state in a highway was modelized through a computer training neural network. All these methods are concentrated instead of lane point on the forecast of link-level congestion state.”)]
Samara discloses the limitation in general terms, Oguro does not specifically disclose but Healey does teach the system further includes interface circuitry configured to obtain a training dataset including driving condition information of multiple vehicles and corresponding distances associated with each of the multiple vehicles, the corresponding distances being between the vehicle and obstacles that surround the vehicle; and the controller is further configured to modify the artificial neural network based on the training dataset [see at least Healey, Figs. 12, 13; ¶ 0057, 0143].
Another way of estimating and predicting the road state is to use an enhanced Kalman Filter ensemble to assess and forecast realistic highway network which, thanks to lower matrix reversals, can decrease computing time [32]. However, Kalman Filter considers both equations between the device and the monitoring model to be linear, which in many real-life circumstances, is not realistic. In [34] the development of the road state in a highway was modelized through a computer training neural network. All these methods are concentrated instead of lane point on the forecast of link-level congestion state.
Neither Samara, Oguro or Healey specifically teach management of a group of vehicles [fleet], but they do teach all the other concepts of the limitations.
Ramasamy does teach communication between more than one vehicle in order to determine availability of a space or zone [see at least Ramasamy, title; Fig. 5; Col 9, line 33 to Col. 10, line 2; Note: Communicating between vehicles, is similar to fleet/vehicle group communication and management].
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the lane prediction optimization of Samara with the vehicle control system and method for determining a retreat [escape] destination or route of Oguro, with the more specific adaptive driving techniques of Healey, further with the method of coordinating lane-changes between vehicles of Ramasamy. Thus providing a more effective, efficient way to provide a safer change of lanes within a group/fleet/convoy/platoon of autonomous vehicles.
Claim 11
Samara with Oguro, Healey and further with Ramasamy disclose/teach the system of Claim 1.
Neither Samara or Oguro specifically disclose/teach but Healey does teach includes a centralized controller having another artificial neural network, and the controller is configured to update the artificial neural network in the controller based on the other artificial neural network [see at least Healey, Figs. 12, 13; ¶ 0057, 0143].
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the lane prediction optimization of Samara with the vehicle control system and method for determining a retreat [escape] destination or route of Oguro with the more specific adaptive driving techniques of Healey. Thus providing a more effective, efficient way to provide a safer change of lanes within a group/fleet/convoy/platoon of autonomous vehicles.
Claim 12
Samara with Oguro, Healey and further with Ramasamy disclose/teach the system of Claim 1.
Samara does not specifically disclose but Oguro teaches controller is one of (i) a centralized controller in a cloud or (ii) a decentralized controller associated with the group of vehicles [see at least Oguro, ¶ 0038 ("The vehicle system 1, for example, includes a camera 10, a radar device 12, a finder 14, an object recognizing device 16, a communication device 20, a human machine interface (HMI) 30, a vehicle sensor 40, a navigation device 50, a micro-processing unit (MPU) 60, a driving operator 80, an automated driving control unit 100, a running driving force output device 200, a brake device 210, and a steering device 220. Such devices and units are interconnected using a multiplex communication line such as a controller area network (CAN) communication line, a serial communication line, a radio communication network, or the like. The configuration illustrated in FIG. 1 is merely one example, and thus, some components may be omitted, and, furthermore, another component may be added thereto. A "vehicle control system", for example, includes a camera 10, a radar device 12, a finder 14, an object recognizing device 16, a communication device 20, a human machine interface (HMI) 30, a vehicle sensor 40, a navigation device 50, a micro-processing unit (MPU) 60, and an automated driving control unit 100")].
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the lane prediction optimization of Samara with the vehicle control system and method for determining a retreat [escape] destination or route of Oguro. Thus providing a more effective, efficient way to provide a safer change of lanes within a group/fleet/convoy/platoon of autonomous vehicles.
Healey more specifically teaches controller is one of (i) a centralized controller in a cloud or (ii) a decentralized controller associated with the group of vehicles [see at least Healey, Figs. 6, 8, 12; ¶ 0078 ("one or more Central Processing Unit (CPU) cores 620, one or more Graphics Processor Unit (GPU) cores 630, an Input/output (1/0) interface 640, and a memory controller 642. Various components of the SOC package 602 may be coupled to an interconnect or bus such as discussed herein with reference to the other figures. Also, the SOC package 602 may include more or less components, such as those discussed herein with reference to the other figures. Further, each component of the SOC package 620 may include one or more other components, e.g., as discussed with reference to the other figures herein. In one embodiment, SOC package 602 (and its components) is provided on one or more Integrated Circuit (IC) die, e.g., which are packaged into a single semiconductor device."); 0119].
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the lane prediction optimization of Samara with the vehicle control system and method for determining a retreat [escape] destination or route of Oguro. Thus providing a more effective, efficient way to provide a safer change of lanes within a group/fleet/convoy/platoon of autonomous vehicles.
Neither Samara, Oguro or Healey specifically teach management of a group of vehicles [fleet], but they do teach all the other concepts of the limitations.
Ramasamy does teach communication between more than one vehicle in order to determine availability of a space or zone [see at least Ramasamy, title; Fig. 5; Col 9, line 33 to Col. 10, line 2; Note: Communicating between vehicles, is similar to fleet/vehicle group communication and management].
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the lane prediction optimization of Samara with the vehicle control system and method for determining a retreat [escape] destination or route of Oguro, with the more specific adaptive driving techniques of Healey, further with the method of coordinating lane-changes between vehicles of Ramasamy. Thus providing a more effective, efficient way to provide a safer change of lanes within a group/fleet/convoy/platoon of autonomous vehicles.
Claim 13
Samara with Oguro, Healey and further with Ramasamy disclose/teach the system of Claim 1.
Samara does not disclose but Oguro teaches in general terms the controller is the centralized controller in the cloud, the system further includes a decentralized controller associated with the group of vehicles, and the decentralized controller is configured to preprocess the driving condition information to obtain the driving environments and the vehicle conditions of the group of vehicles [see at least Oguro, ¶ 0038 (“In one example a system for emotional adaptive driving policies for automated driving vehicles, comprising a first plurality of sensors to detect environmental information relating to at least one passenger in a vehicle and a controller communicatively coupled to the plurality of sensors and comprising processing circuitry, to receive the environmental information from the first plurality of sensors, determine, from the environmental information, an emotional state of the at least one passenger, and implement a driving policy based at least in part on the emotional state of the at least one passenger. Other examples may be described.”)].
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the lane prediction optimization of Samara with the vehicle control system and method for determining a retreat [escape] destination or route of Oguro. Thus providing a more effective, efficient way to provide a safer change of lanes within a group/fleet/convoy/platoon of autonomous vehicles.
Healey also teaches the controller is the centralized controller in the cloud, the system further includes a decentralized controller associated with the group of vehicles, and the decentralized controller is configured to preprocess the driving condition information to obtain the driving environments and the vehicle conditions of the group of vehicles [see at least Healey, Figs. 6, 8, 12; ¶ 0078].
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the lane prediction optimization of Samara with the vehicle control system and method for determining a retreat [escape] destination or route of Oguro with the more specific adaptive driving techniques of Healey. Thus providing a more effective, efficient way to provide a safer change of lanes within a group/fleet/convoy/platoon of autonomous vehicles.
Neither Samara, Oguro or Healey specifically teach management of a group of vehicles [fleet], but they do teach all the other concepts of the limitations.
Ramasamy does teach communication between more than one vehicle in order to determine availability of a space or zone [see at least Ramasamy, Title; Fig. 5; Col 9, line 33 to Col. 10, line 2; Note: if communicating between more than one vehicle, is similar to fleet/vehicle group communication and management].
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the lane prediction optimization of Samara with the vehicle control system and method for determining a retreat [escape] destination or route of Oguro, with the more specific adaptive driving techniques of Healey, further with the method of coordinating lane-changes between vehicles of Ramasamy. Thus providing a more effective, efficient way to provide a safer change of lanes within a group/fleet/convoy/platoon of autonomous vehicles.
Claim 14
Claim 14 has the method for the system of claim 1 and has similar limitations to claim 1, therefore claim 14 is rejected with the same rationale as claim 1.
Claim 15
Claim 15 has similar limitations to claim 2, therefore claim 15 is rejected with the same rationale as claim 2.
Claim 16
Claim 16 has similar limitations to claim 3, therefore claim 16 is rejected with the same rationale as claim 3.
Claim 17
Claim 17 has similar limitations to claim 4, therefore claim 17 is rejected with the same rationale as claim 4.
Claim 18
Claim 18 has similar limitations to claim 7, therefore claim 18 is rejected with the same rationale as claim 7.
Claim 19
Claim 19 has similar limitations to claim 9, therefore claim 19 is rejected with the same rationale as claim 9.
Claim 20
Claim 20 has similar limitations to claim 10, therefore claim 20 is rejected with the same rationale as claim 10.
Claim 21
Samara with Oguro, Healey and further with Ramasamy disclose/teach the system of Claim 3.
Samara further discloses wherein the emergency escape zone status includes a location of an emergency escape zone [see at least Samara, Fig. 1; P.2, Sect. 2].
Oguro also teaches wherein the emergency escape zone status includes a location of an emergency escape zone [see at least Oguro, ¶ 0048].
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the lane prediction optimization of Samara with the vehicle control system and method for determining a retreat [escape] destination or route of Oguro. Thus providing a more effective, efficient way to provide a safer change of lanes within a group/fleet/convoy/platoon of autonomous vehicles.
Healey also teaches determining the location of the vehicle, which helps in locating zones to move into. [see at least Healey, ¶ 0032 (“generate a location and context state of the vehicle.”)].
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the lane prediction optimization of Samara with the vehicle control system and method for determining a retreat [escape] destination or route of Oguro with the more specific adaptive driving techniques of Healey. Thus providing a more effective, efficient way to provide a safer change of lanes within a group/fleet/convoy/platoon of autonomous vehicles.
Ramasamy more specifically teaches determining location that is used to determine lane-change availability [see at least Ramasamy, Figs 4a-c; Col. 4, lines 58-64 (“The computing device 210 may further function as, or be in communication with, a navigation system of the vehicle 200. The computing device 210 may be configured in some examples to access navigation information, such as preprogrammed route information or location information that may be employed for coordinated lane-change negotiations between vehicles.”).
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the lane prediction optimization of Samara with the vehicle control system and method for determining a retreat [escape] destination or route of Oguro, with the more specific adaptive driving techniques of Healey, further with the method of coordinating lane-changes between vehicles of Ramasamy. Thus providing a more effective, efficient way to provide a safer change of lanes within a group/fleet/convoy/platoon of autonomous vehicles.
Claim 22
Claim 22 has similar limitations to claim 21, therefore claim 22 is rejected with the same rationale as claim 21.
Claims 23- 25 are rejected under 35 U.S.C. 103 as being unpatentable over Ghassan Samara “Lane prediction optimization in VANET” Egyptian Informatics Journal, 2020 [Article history: Received 14 June 2020 Revised 21 November 2020 Accepted 14 December 2020] [Now Samara]; in view of Oguro et al. [US20200086860, now Oguro], in view of Healey et al. [US20190049957, now Healey], in view of Ramasamy, et al. [US10089876, now Ramasamy] and further in view of Laubinger et al. [US20170344023, now Laubinger].
Claim 23
Samara with Oguro, Healey and further with Ramasamy disclose/teach the system of Claim 1.
Neither Samara, Oguro, Healey or Ramasamy specifically teach but Laubinger does teach controller is further configured to determine the emergency escape zone status for each vehicle in the group of vehicles based on distances in both a longitudinal direction and a lateral direction with respect to the each vehicle [see at least Laubinger, ¶ 0111 (“ the permitted speed differential may be based in part on the longitudinal distance that the vehicles are separated by.”); 0192: 0201; 0265.
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the lane prediction optimization of Samara with the vehicle control system and method for determining a retreat [escape] destination or route of Oguro, with the more specific adaptive driving techniques of Healey, with the method of coordinating lane-changes between vehicles of Ramasamy, further with the use of longitudinal and lateral calculations. Thus providing a more effective, efficient way to provide a safer change of lanes within a group/fleet/convoy/platoon of autonomous vehicles.
Claim 24
Samara with Oguro, Healey and further with Ramasamy disclose/teach the system of Claim 1.
Neither Samara, Oguro, Healey or Ramasamy specifically teach but Laubinger does teach wherein the controller is further configured to cause output of a message to a driver of a vehicle in the group of vehicles when the emergency escape zone status indicates that no emergency escape zones are available to the vehicle [see at least Laubinger, ¶ 0141-0144 (“ The physical distances between the vehicles during each of the states can be represented as a series of zones are in FIGS. 24A-24C, together with activities in accordance with the present invention that are possible within each of those zones. It will be appreciated by those skilled in the art that the numerical distances for each of these zones can vary depending upon the vehicle's speeds, road conditions, environmental conditions, communications quality, network policies, fleet management policies, and other factors as discussed herein. For example, the correlation between speed and safe distances during pull-in is discussed in connection with FIGS. 17A-17B, above. FIGS. 25A-25C through 35A-35C then display the relevant zones, the visual display seen by the driver of the follow vehicle, and the physical inputs available to the user at each state. For simplicity and clarity, FIGS. 25A-35C reflect the perspective of the follow vehicle, but those skilled in the art will appreciate from the following discussion that the displays for the lead vehicle are analogous and readily ascertained. [0142] Referring first to FIG. 24A, a lead vehicle 2400 and a follow vehicle 2405 proceeding in the same lane on roadway 2410. The right-pointing arrow in front of follow vehicle 2405 indicates that the follow vehicle is proceeding at a relatively higher speed than lead vehicle 2400, in order to close the distance between them, although in many instances both vehicles are moving at substantially highway speeds as discussed elsewhere in this specification. A zone 2415 designates the distance, measured from the back of the lead vehicle (for these examples, all zones are measure from the back of the lead vehicle), over which robust DSRC communications exists between the vehicles. It will be appreciated that, for this example, DSRC communications are assumed, but any suitably reliable communications protocol of adequate bandwidth can be substituted for, or used in addition to, DSRC. Zone 2420 shows the distance over which the vehicle's Collision Mitigation System (“CMS”) of warnings and Automated Emergency Braking (“AEB”) may be enabled to intervene during normal and non-platooning operation. Zone 2425 shows the distance over with Cooperative Adaptive Cruise Control operates. Zone 2430 shows the commanded gap distance during which the torque and braking, and thus the speed, of the vehicles 2400 and 2405 operate under the control of the systems of the present invention when the platoon is operating normally in state 2355. Zone 2435 illustrates a platooning tolerance zone, which represents a variable distance over which the commanded gap may vary while the platoon continues to operate in state 2355. For simplicity and improved clarity, Zone 2435 is omitted from FIGS. 24B-24C. [0143] FIG. 24B augments FIG. 24A by adding zones that are relevant when the vehicles have begun engagement of a platoon, or states 2335 through 2355 of FIG. 23. Thus, a zone 2440 illustrates the distance during which engagement of a platoon is an option for both drivers or, in the case of fully automated vehicles, for the command program operating the vehicles. Further, zone 2445 illustrates an “engagement green light zone” or preferred distance during which the vehicle operators or command program can initiate platooning and thus transition from state 2340 to state 2350 of FIG. 23. It will be appreciated that the green light zone 2445 may be identical to or nearly identical to zone 2440, depending upon the implementation, especially for fully autonomous vehicles. [0144] FIG. 24C augments FIG. 24A by adding zones that are relevant when the platoon is being dissolved, i.e., state 2360 of FIG. 23. During dissolve, as discussed previously herein, the vehicles separate and the distance between them increases, for example until the gap between them is sufficient for the drivers to resume manual control of the speed of the vehicles. Thus, FIG. 24C shows a left-pointing arrow behind follow vehicle 2405 to indicate that the relative speed between them is increasing and, correspondingly, the distance is increasing. In addition, FIG. 24C includes a platooning re-engagement zone 2450, or a distance during which the drivers/command program are permitted to re-engage the platoon. FIG. 24C also illustrates a re-engagement green light zone 2455, which, in at least some embodiments, is a somewhat smaller distance, separated from the end of the zone 2450 nearest the lead vehicle by a hysteresis distance or gap 2460 and separated from end of the zone 2450 farther from the lead vehicle by a policy distance or gap 2465. The hysteresis gap allows for comparatively slight variation that occurs with the latency of the system, human response time, and so forth. The policy gap is an arbitrary safety factor. It will be appreciated that, in some embodiments, the gaps 2460 and 2465 can be zero or approximate zero, particularly for autonomous operation. In addition, FIG. 24C includes a manual takeover zone 2470, which depicts the distances that are sufficient to permit the driver to control the vehicle manually, but are still within there-engagement distance. If the distance extends beyond the manual takeover zone, re-engagement is no longer permitted and manual takeover is required, such that the driver is in primary control as shown at 2475.”); 0165 (“If any of those checks fail at any time while the controller is in the Front System Ready state, the front controller informs the trailing vehicle that it is no longer ready for platooning (e.g., by sending a NOT SYSTEM_READY message) and transitions back to Front Rendezvous state 3623.”); Note that if can determine a size of a zone that the message can indicate the size and that there is not room for another vehicle to use that space.”)].
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the lane prediction optimization of Samara with the vehicle control system and method for determining a retreat [escape] destination or route of Oguro, with the more specific adaptive driving techniques of Healey, with the method of coordinating lane-changes between vehicles of Ramasamy, further with the use of longitudinal and lateral calculations. Thus providing a more effective, efficient way to provide a safer change of lanes within a group/fleet/convoy/platoon of autonomous vehicles.
Claim 25
Samara with Oguro, Healey and further with Ramasamy disclose/teach the system of Claim 1.
Neither Samara, Oguro, or Healey specifically teach but Ramasamy does teach wherein the controller is further configured to detect that a previously available emergency escape zone is no longer available and to update the emergency escape zone status accordingly [see at least Ramasamy, Col. 13, line 66- col. 14, line 232 (“In this example, the vehicles 642b-c employ a peer-to-peer technique to establish a space in their lane 620. Such a peer-to-peer technique may involve the transmission or exchange of multiple messages between the receiving vehicles 642b-c, and in some examples, may involve the transmission or exchange of messages with other vehicles travelling in the lane 620. For example, during the course of the coordination, the receiving vehicles 642b-c may exchange traffic information, e.g., braking or acceleration information or congestion information, position updates, vehicle spacing information, etc. Some messages may be periodic, such as position updates, while others may be event driven, such as messages indicating braking or acceleration or a new vehicle being detected, e.g., if such a vehicle merges into the lane 620. Thus, while certain messages and negotiations are discussed below, these additional messages may be exchanged in addition to the messaging discussed below, and information obtained from such messaging may be incorporated into the coordination techniques discussed below. For example, when the receiving vehicles 642b-c negotiate to establish a space or determine distances between them or the requesting vehicle 640, such negotiations or determinations may be based on the most current position or other information obtained, rather than on the initial lane change request or other initial messaging.”)].
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the lane prediction optimization of Samara with the vehicle control system and method for determining a retreat [escape] destination or route of Oguro, with the more specific adaptive driving techniques of Healey, further with the method of coordinating lane-changes between vehicles of Ramasamy. Thus providing a more effective, efficient way to provide a safer change of lanes within a group/fleet/convoy/platoon of autonomous vehicles.
Laubinger also teaches this limitation [see at least Laubinger, ¶ 0078 (“updated”); 0092; 0093 (update)].
Therefore, it would be obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the lane prediction optimization of Samara with the vehicle control system and method for determining a retreat [escape] destination or route of Oguro, with the more specific adaptive driving techniques of Healey, with the method of coordinating lane-changes between vehicles of Ramasamy, further with the use of longitudinal and lateral calculations. Thus providing a more effective, efficient way to provide a safer change of lanes within a group/fleet/convoy/platoon of autonomous vehicles.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kahn; Michael Robert [US20200094852]
VEHICLE WITH ESCAPE FEATURE USING SYNTHESIZED VEHICLE VIEW [Title]
Embodiments of the present invention provide an autonomous vehicle with an emergency escape mode. When fleeing a scene is critical, embodiments provide an AV that can operate in an emergency escape mode (EEM) to enable the AV to flee a scene, protecting its occupants. Typically, a passenger or operator invokes EEM in an AV when they are in imminent danger from criminal activity such as carjacking. A least resistance route can be computed to determine an escape route that provides for reduced chance of injury and/or increased probability of a successful escape. [Abstract]
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/JOAN T GOODBODY/
Examiner, Art Unit 3667
(571) 270-7952