DETAILED ACTION
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
This communication is in response to application No. 18/885,279, filed on 09/13/2024. Claims 1-10 are currently pending and have been examined. Claims 1-10 have been rejected as follows.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) filed on 09/13/2024 has been acknowledged.
Claim Objections
Claim 8 objected to because of the following informalities: “wherein the swarm data include an external trajectory” should be “wherein the swarm data includes an external trajectory”. Appropriate correction is required.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1-3, 5, 7, and 9-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kang (US 20180247138 A1).
Regarding claim 1, Kang teaches a method for validating a driving corridor for lateral guidance of a motor vehicle by a driver assistance system (par. 64, “In an example, the virtual lane generating device displays the valid lane detection information to a user, or generates a driving route for a vehicle such as, for example, an autonomous driving vehicle”), the method comprising:
determining, based on environmental data corresponding to an environment of the motor vehicle, corridor boundaries of the driving corridor (Fig. 1 operation 110, “lane detection information extracted from external image of a forward view of vehicle), wherein the corridor boundaries specify a first track width for the lateral guidance of the motor vehicle (par. 60, “The lane detection information includes information, such as, for example, a lane boundary line and a lane region in the external image”);
and validating, based on a result of the checking, the driving corridor by using the first track width specified by the corridor boundaries for the lateral guidance of the motor vehicle (par. 64, “When it is determined that the lane detection information is valid, the virtual lane generating device uses the valid lane detection information to generate the virtual lane”; see Fig. 1).
Kang fails to explicitly teach checking whether more than one driving trajectory is provided within the corridor boundaries, by determining whether a plurality of data points is located along a predetermined lateral axis of the motor vehicle relative to a direction of travel of the motor vehicle, wherein each of the data points is assigned to a different driving trajectory and indicates at least geocoordinates of the different driving trajectory.
However, Kang does teach validating the driving corridor (Fig. 1 operation 110), and does so by calculating a validity score of the lane detection information using information such as “a luminance level around the vehicle, weather information, time information, and image quality information of the image” (claim 9), “verifying whether a movement route of a target object matches a lane that is based on the lane detection information” (claim 15), and many other metrics. If a driving corridor is determined to be invalid, then the system generates a new driving corridor (Fig. 1 operation 120). Kang does this by “generating at least one driving group by clustering objects present in the image, and generating the virtual lane based on the at least one driving group. The generating of the virtual lane based on the at least one driving group may include estimating lane regions based on a region occupied by each of the at least one driving group, in response to the at least one driving group including a plurality of driving groups, generating a virtual lane boundary line between each of the estimated lane regions, and generating the virtual lane based on the virtual lane boundary lines” (par. 8-9). It is obvious that a driving corridor should only contain one vehicle laterally, and such criteria is commonly used in determining lane boundaries (examples include Del Pero (US 20210403001 A1) and Jammoussi (US 10121367 B2)). It would have been obvious that this criteria could also be used in the validation step instead of just the generation step. Kang already uses the trajectories of other vehicles in validating the lane detection information (claim 15).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kang to add the feature of checking whether more than one driving trajectory is provided within the corridor boundaries, by determining whether a plurality of data points (driving groups) is located along a predetermined lateral axis of the motor vehicle relative to a direction of travel of the motor vehicle (Kang specifies it uses a forward view of the vehicle. However, the axis could be the front lateral axis of the vehicle and the driving groups could be laterally adjacent to the vehicle in a way that Kang’s forward facing sensors would detect them. Regardless, 1) many driving assistance systems use cameras or sensors that detect more than just the front-view and would have been an obvious improvement to Kang, and 2) the predetermined lateral axis does not necessarily have to cross the motor vehicle), wherein each of the data points is assigned to a different driving trajectory and indicates at least geocoordinates of the different driving trajectory (a plurality of driving groups would mean a plurality of lanes, so if the lane detection information reveals there are a plurality of driving groups in one lane, the validity score of the lane detection information would decrease). Doing so would increase the accuracy of Kang’s validity check by adding another method to its long list of method for determining validity of the lane detection information.
Regarding claim 2, Kang, as modified above, teaches the method according to claim 1. Kang further teaches the driving corridor is only validated if exactly one data point within the driving corridor is determined according to the checking (par. 8-9, “generating at least one driving group by clustering objects present in the image, and generating the virtual lane based on the at least one driving group. The generating of the virtual lane based on the at least one driving group may include estimating lane regions based on a region occupied by each of the at least one driving group, in response to the at least one driving group including a plurality of driving groups, generating a virtual lane boundary line between each of the estimated lane regions, and generating the virtual lane based on the virtual lane boundary lines”—if only one driving group is detected, then the system validates the lane detection information).
Regarding claim 3, Kang, as modified above, teaches the method according to claim 1. Kang further teaches falsifying the driving corridor based on a result of the checking when at least two data points within the driving corridor are determined according to the determining, wherein the falsifying includes determining a second track width different from the first track width and using the second track width for the lateral guidance of the motor vehicle (par. 9, "The generating of the virtual lane based on the at least one driving group may include estimating lane regions based on a region occupied by each of the at least one driving group, in response to the at least one driving group including a plurality of driving groups, generating a virtual lane boundary line between each of the estimated lane regions, and generating the virtual lane based on the virtual lane boundary lines"—if multiple driving groups are detected, then the system determines the lanes are not valid).
Regarding claim 5, Kang, as modified above, teaches the method according to claim 3. Kang further teaches the second track width is narrower than the first track width (Kang determines the lane detection information is not valid when there are multiple driving groups in one lane, and then recalculates the lanes so that each driving group has their own lane. This would obviously result in a narrower lane).
Regarding claim 7, Kang, as modified above, teaches the method according to claim 3. Kang further teaches each of the data points contains information for an authorized driving direction along the different driving trajectory and the method further includes determining the second track width based on the authorized driving direction relative to a driving direction of the motor vehicle (par 83, "In an example, when a lane boundary line on a road is not readily recognizable due to, for example, a heavy rain, the virtual lane generating device may estimate a movement route of a nearby vehicle based on the nearby vehicle and a feature portion of the nearby vehicle. In an example, the virtual lane generating device generates a virtual lane boundary line based on the estimated movement route and provides the generated virtual lane boundary line to aid a driver or an autonomous driving vehicle in driving").
Regarding claim 9, Kang teaches a driver assistance system for lateral guidance of a motor vehicle, the driver assistance system (par. 64, “In an example, the virtual lane generating device displays the valid lane detection information to a user, or generates a driving route for a vehicle such as, for example, an autonomous driving vehicle”) comprising:
at least one processor (Fig. 16, processor 1620); and at least one memory storing program code (par. 34, “a memory configured to store instructions”) that, when executed by the at least one processor, causes the driver assistance system to:
determine, based on environmental data corresponding to an environment of the motor vehicle, corridor boundaries of a driving corridor (Fig. 1 operation 110, “lane detection information extracted from external image of a forward view of vehicle), wherein the corridor boundaries specify a first track width for the lateral guidance of the motor vehicle (par. 60, “The lane detection information includes information, such as, for example, a lane boundary line and a lane region in the external image”);
and validate, based on a result of the check, the driving corridor by using the first track width specified by the corridor boundaries for the lateral guidance of the motor vehicle (par. 64, “When it is determined that the lane detection information is valid, the virtual lane generating device uses the valid lane detection information to generate the virtual lane”; see Fig. 1).
Kang fails to explicitly teach check whether more than one driving trajectory is provided within the corridor boundaries, by determining whether a plurality of data points is located along a predetermined lateral axis of the motor vehicle relative to a direction of travel of the motor vehicle, wherein each of the data points is assigned to a different driving trajectory and indicates at least geocoordinates of the different driving trajectory.
However, Kang does teach validating the driving corridor (Fig. 1 operation 110), and does so by calculating a validity score of the lane detection information using information such as “a luminance level around the vehicle, weather information, time information, and image quality information of the image” (claim 9), “verifying whether a movement route of a target object matches a lane that is based on the lane detection information” (claim 15), and many other metrics. If a driving corridor is determined to be invalid, then the system generates a new driving corridor (Fig. 1 operation 120). Kang does this by “generating at least one driving group by clustering objects present in the image, and generating the virtual lane based on the at least one driving group. The generating of the virtual lane based on the at least one driving group may include estimating lane regions based on a region occupied by each of the at least one driving group, in response to the at least one driving group including a plurality of driving groups, generating a virtual lane boundary line between each of the estimated lane regions, and generating the virtual lane based on the virtual lane boundary lines” (par. 8-9). It is obvious that a driving corridor should only contain one vehicle laterally, and such criteria is commonly used in determining lane boundaries (examples include Del Pero (US 20210403001 A1) and Jammoussi (US 10121367 B2)). It would have been obvious that this criteria could also be used in the validation step instead of just the generation step. Kang already uses the trajectories of other vehicles in validating the lane detection information (claim 15).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kang to add the feature of checking whether more than one driving trajectory is provided within the corridor boundaries, by determining whether a plurality of data points (driving groups) is located along a predetermined lateral axis of the motor vehicle relative to a direction of travel of the motor vehicle (Kang specifies it uses a forward view of the vehicle. However, the axis could be the front lateral axis of the vehicle and the driving groups could be laterally adjacent to the vehicle in a way that Kang’s forward facing sensors would detect them. Regardless, 1) many driving assistance systems use cameras or sensors that detect more than just the front-view and would have been an obvious improvement to Kang, and 2) the predetermined lateral axis does not necessarily have to cross the motor vehicle), wherein each of the data points is assigned to a different driving trajectory and indicates at least geocoordinates of the different driving trajectory (a plurality of driving groups would mean a plurality of lanes, so if the lane detection information reveals there are a plurality of driving groups in one lane, the validity score of the lane detection information would decrease). Doing so would increase the accuracy of Kang’s validity check by adding another method to its long list of method for determining validity of the lane detection information.
Regarding claim 10, Kang, as modified above, teaches a motor vehicle including the driver assistance system according to claim 9 (par. 56, “the virtual lane generating device is provided in the vehicle. The vehicle refers to any mode of transportation, delivery, or communication such as, for example, an automobile, a truck, a tractor, a scooter, a motorcycle, a cycle, an amphibious vehicle, a snowmobile, a public transit vehicle, a bus, a monorail, a train, a tram, an unmanned aerial vehicle, or a drone”).
Claim(s) 4 and 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kang in view of Jammoussi (US 10121367 B2).
Regarding claim 4, Kang, as modified above, teaches the method according to claim 3. Kang fails to teach determining a data point spacing of the at least two data points along the predetermined lateral axis; and determining the second track width based on the data point spacing.
However, Jammoussi teaches determining a data point spacing of the at least two data points along the predetermined lateral axis; and determining the second track width based on the data point spacing (column 7 line 17, “the computer 112 may estimate width of lane(s), i.e., width of a lane in which a respective cluster of second vehicles 120 travel. In one embodiment, estimation of lane width may include computing an average distance between the curves fitted between the centers C of second vehicles 120 two adjacent cluster paths”).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kang to incorporate the teachings of Jammoussi. Jammoussi states, “[s]uch estimation may be dependent on an assumption that vehicles in a cluster drive on average substantially in a middle of the respective lane, e.g., a center C of second vehicles is assumed to be within a 5% deviation from a center of the respective lane” (column 7 line 23). It would be obvious to assume that must vehicles drive in the center of the lane, and therefore could be used to determine lane width.
Regarding claim 6, Kang, as modified above, teaches the method according to claim 3. Kang further teaches a value of the first track width and/or the second track width is selected according to a predetermined selection criterion as a function of a parameter of at least two travel trajectories within the driving corridor or as a function of a parameter of the driving corridor (second track width is based on the driving groups).
Alternatively, Jammoussi also teaches a value of the first track width and/or the second track width is selected according to a predetermined selection criterion as a function of a parameter of at least two travel trajectories within the driving corridor or as a function of a parameter of the driving corridor (column 7 line 17, “the computer 112 may estimate width of lane(s), i.e., width of a lane in which a respective cluster of second vehicles 120 travel. In one embodiment, estimation of lane width may include computing an average distance between the curves fitted between the centers C of second vehicles 120 two adjacent cluster paths”).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kang to incorporate the teachings of Jammoussi. Jammoussi states, “[s]uch estimation may be dependent on an assumption that vehicles in a cluster drive on average substantially in a middle of the respective lane, e.g., a center C of second vehicles is assumed to be within a 5% deviation from a center of the respective lane” (column 7 line 23). It would be obvious to assume that must vehicles drive in the center of the lane, and therefore could be used to determine lane width.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kang in view of Del Pero (US 20210403001 A1).
Regarding claim 8, Kang, as modified above, teaches the method according to claim 1. Kang fails to teach determining the driving trajectory or an additional driving trajectory of the motor vehicle from swarm data, wherein the swarm data include an external trajectory of at least one external vehicle that has already passed through the driving corridor.
However, Del Pero teaches determining the driving trajectory or an additional driving trajectory of the motor vehicle from swarm data, wherein the swarm data include an external trajectory of at least one external vehicle that has already passed through the driving corridor (Fig. 3A-3M, lanes are determined based on a set of trajectory data; par. 3, “each vehicle trajectory in the set of vehicle trajectories may comprise a time-sequence of pose values that have been derived from sensor data captured by a vehicle while driving along a segment of the road network”).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kang to incorporate the teachings of Del Pero to use trajectory data of past vehicles instead of current surrounding vehicles. Del Pero states that solely using sensor data to derive lane data “can lead to undesirable driving behavior in situations when the sensor data does not provide the on-board computing system with enough information to confidently determine the geospatial positioning of the right lane's boundaries” (par. 50). Furthermore, using trajectory data for vehicles that have previously traveled within the road network can lead to a more accurate and scalable method of generating lane data (par. 55-58).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MINATO LEE HORNER whose telephone number is (571)272-5425. The examiner can normally be reached M-F 8-5.
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/M.L.H./Examiner, Art Unit 3665 /CHRISTIAN CHACE/Supervisory Patent Examiner, Art Unit 3665