DETAILED ACTION
This is the first office action regarding application number 18/664,712, filed May 15, 2024. This is a Non-Final Office Action on the merits, Claims 1-20 are currently pending and are addressed below.
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 .
Priority
Acknowledgement is made of applicants claim for domestic priority based on an provisional application filed on May 16, 2023.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “mission planner” and “vehicle control interface” in claims 15-19.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Regarding “mission planner” in claims 15-19, the specification recites the structure of “In some embodiments, some portion or all of the mission planner 910, the control decoupler 920 and/or the longitudinal MPC 930 may be implemented in the processor 902.” in at least paragraph [0091], therefore the function of the mission planner is implemented by the structure of a processor.
and “vehicle control interface” in claims 15-19, the specification recites the structure of “An in-vehicle control computer 1050, which may be referred to as a vehicle control unit or VCU, can include, for example, any one or more of: a vehicle subsystem interface 1060, a map data sharing module 1065, a driving operation module 1068, one or more processors 1070, and/or memory 1075.” in at least paragraph [0099], therefore the function of the vehicle control is implemented by the structure of a processor and memory.
Claim Rejections - 35 USC § 102
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 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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 3-10, 12-15, and 19-20 is/are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being anticipated by Jokela (US-20210009128).
Regarding claim 1, Jokela teaches a method for controlling a vehicle comprising (Paragraph [0001], "The present disclosure relates to a vehicle control method and apparatus.")
obtaining reference information relating to an operation parameter of the vehicle (Paragraph [0023], "The method may comprise identifying a speed limit applicable for at least a part of the route between the current position of the host vehicle and the first location. The speed limit may define at least a portion of the upper limit of the target operational speed band.")
the operation parameter describing mission waypoints of the vehicle at a plurality of time points during which the vehicle is to traverse a path the reference information including a plurality of reference values of the operation parameter of the vehicle, each of the plurality of reference values corresponding to one of the plurality of time points (Paragraph [0157], "A static speed constraint due to road topology/speed limits is represented by a first continuous line 43 in the 2D optimization grid 40, and by a continuous surface 44 within the 3D optimization grid 41.") (See Figure 7A and 7B showing a speed limit reference value describing the mission waypoints at a plurality of time points during the operation of the vehicle)
obtaining context information of the vehicle that relates to a state of the vehicle during an operation of the vehicle at the plurality of time points or an environment enclosing the path (Paragraph [0024], "The method may comprise identifying a plurality of obstacles on the route. The target operational speed band may be determined in respect of each obstacle identified on the route,” here the system is collecting context information relating to obstacles in the environment of the vehicle path)
determining a tolerable range of the operation parameter for each of the plurality of time points based on the reference information and the context information (Paragraph [0160], "The first and second acceleration limits define upper and lower speed trajectories 51, 52 for the host vehicle 1. The upper and lower speed trajectories 51, 52 define a target operational speed band 53.") (See Figure 8 showing the tolerable range for the operation parameter/speed)
obtaining penalty information (Paragraph [0157], "The first trajectory 45 is valid since it results in the host vehicle 1 traversing the location of the traffic control signals 18-n during a green phase. The second trajectory 46 is invalid since it results in the host vehicle 1 traversing the location of the traffic control signals 18-n during a red phase. Thus, only the first trajectory 45 (extending from A to B.sub.2) is feasible with regards to traffic control signal constraints.")
including a plurality of penalty weights each of which corresponds to a modulation bandwidth indicating a difference between a tolerable range and a constraint at one of the plurality of time points (Paragraph [0013-0014], "The first speed trajectory may form a lower limit of the target operational speed band. A cost penalty may be applied to a target speed trajectory which is less than the first speed trajectory … A cost penalty may be applied to a target speed trajectory which is greater than the first speed trajectory.") (Paragraph [0168], "The cost function g.sub.TL increases the associated cost as the violation a.sub.viol increases, the cost decreases as the distance remaining to the traffic control signal d.sub.rem,TL increases and as the number of traffic control signals between the currently considered traffic control signal and the host vehicle 1 M.sub.TL increases. The weightings of these different considerations can be tuned with the following coefficients: WTL.sub.1 ∈(0, ∞), WTL.sub.2 ∈ [0, ∞) and WTL.sub.3 ∈ [0, ∞)," here the system is using a cost function including penalties in order to determine optimal control signal, this cost function uses weighting coefficients in order to tune the penalties based on different considerations)
determining a control instruction based on the tolerable ranges and the penalty information (Paragraph [0190], "The VMC 36 generates a propulsion torque request suitable for maintaining the host vehicle 1 within the target operational speed band 67.")
and operating the vehicle based on the control instruction such that a value of the operation parameter of the vehicle at each of the at least one of the plurality of time points falls within or close to a tolerable range at the time point so as to satisfy the constraint (Paragraph [0070], "As described herein, the controller 2 is configured to implement a dynamic programming algorithm for controlling the target operational speed of the host vehicle 1 as it travels along a route R (illustrated in FIG. 3). The control algorithm may be implemented as part of an autonomous control function, for example comprising one or more of the following: Adaptive Cruise Control (ACC), Intelligent Cruise Control (ICC), Green Light Optimized Speed Advisory (GLOSA), and Traffic Jam Assist (TJA).").
Regarding claim 3, Jokela teaches the method as discussed above in claim 1, Jokela further teaches determining that a value of the operation parameter violates the constraint at a specific time point of the plurality of time points (Paragraph [0157], "The first trajectory 45 is valid since it results in the host vehicle 1 traversing the location of the traffic control signals 18-n during a green phase. The second trajectory 46 is invalid since it results in the host vehicle 1 traversing the location of the traffic control signals 18-n during a red phase. Thus, only the first trajectory 45 (extending from A to B.sub.2) is feasible with regards to traffic control signal constraints," here the system is comparing the speed trajectory to the traffic signal penalty and determining that the speed trajectory violates the constraint)
adjusting the penalty information with respect to the specific time point or at least one time point following the specific time point (Paragraph [0157], “The traffic control signals 18-n impose a speed constraint during a red phase when the progress of the host vehicle 1 would be impeded. The traffic control signals 18-n do not impose a speed constraint during a green phase when the progress of the host vehicle 1 would be at least substantially unhindered,” here the system is adjusting the penalty information at specific time points according to the traffic signal violations)
adjusting the control instruction based on the adjusted penalty information (Paragraph [0157], "The first trajectory 45 is valid since it results in the host vehicle 1 traversing the location of the traffic control signals 18-n during a green phase. The second trajectory 46 is invalid since it results in the host vehicle 1 traversing the location of the traffic control signals 18-n during a red phase. Thus, only the first trajectory 45 (extending from A to B.sub.2) is feasible with regards to traffic control signal constraints," here because the second trajectory violates the traffic signal penalty information, the system will not use that trajectory in controlling the vehicle as it is not feasible)
and operating the vehicle based on the adjusted control instruction such that a value of the operation parameter of the vehicle changes so as to satisfy the constraint or that a value of the operation parameter at a subsequent time point satisfies the constraint (Paragraph [0070], "As described herein, the controller 2 is configured to implement a dynamic programming algorithm for controlling the target operational speed of the host vehicle 1 as it travels along a route R (illustrated in FIG. 3). The control algorithm may be implemented as part of an autonomous control function, for example comprising one or more of the following: Adaptive Cruise Control (ACC), Intelligent Cruise Control (ICC), Green Light Optimized Speed Advisory (GLOSA), and Traffic Jam Assist (TJA).").
Regarding claim 4, Jokela teaches the method as discussed above in claim 1, Jokela further teaches determining that a value of the operation parameter violates the constraint at a specific time point of the plurality of time points (Paragraph [0159], “The dynamic programming algorithm is calculated forwards, i.e. from the current time at the beginning of algorithm execution, to determine whether or not time-variant constraints, such as the traffic control signal 18-n, will be violated (i.e. whether or not one or more traffic control signal 18-n on the route R will impede progress of the host vehicle 1).”)
switching to a tracking based control mode in which the context information is ignored and the control instruction is determined based on the reference information adjusting the control instruction according to the tracking-based control mode (Paragraph [0178], “As illustrated in FIG. 9, the headway 62 is maintained between the host vehicle 1 and the first target vehicle 15-1 as a safety consideration. As speeds of the host vehicle 1 and the first target vehicle 15-1 are at least substantially equal to each other, the headway 62 remains constant. In order to avoid an unnecessarily large headway 62, a small violation of the acceleration limit a.sub.OV may be permitted. Thus, no cost may be applied for a deceleration of the host vehicle 1 which is lower than the acceleration limit a.sub.OV within a predetermined margin, for example expressed as a proportion of the acceleration limit a.sub.OV. The dynamic programming algorithm may flag any trajectory as infeasible which would result in the host vehicle 1 getting closer to the first target vehicle 15-1 than a predetermined minimum headway. In this example, the first speed trajectory 61 defines an upper limit of a target operational speed trajectory band 63. The operational speed trajectory band 63 is the area below the first speed trajectory 61 shown in FIG. 9. Other static constraints could be applied to reduce the target speed trajectory band,” here the system is adjusting control to a different mode in which some situational contexts are suppressed/ignored and the vehicle is controlled based on the modified control instructions)
and operating the vehicle based on the adjusted control instruction such that a value of the operation parameter changes so as to satisfy the constraint or that a value of the operation parameter at a subsequent time point satisfies the constraint (Paragraph [0178], “As illustrated in FIG. 9, the headway 62 is maintained between the host vehicle 1 and the first target vehicle 15-1 as a safety consideration. As speeds of the host vehicle 1 and the first target vehicle 15-1 are at least substantially equal to each other, the headway 62 remains constant. In order to avoid an unnecessarily large headway 62, a small violation of the acceleration limit a.sub.OV may be permitted. Thus, no cost may be applied for a deceleration of the host vehicle 1 which is lower than the acceleration limit a.sub.OV within a predetermined margin, for example expressed as a proportion of the acceleration limit a.sub.OV. The dynamic programming algorithm may flag any trajectory as infeasible which would result in the host vehicle 1 getting closer to the first target vehicle 15-1 than a predetermined minimum headway. In this example, the first speed trajectory 61 defines an upper limit of a target operational speed trajectory band 63. The operational speed trajectory band 63 is the area below the first speed trajectory 61 shown in FIG. 9. Other static constraints could be applied to reduce the target speed trajectory band,” here the system is adjusting control to a different mode in which some situational contexts are suppressed/ignored and the vehicle is controlled based on the modified control instructions by satisfying one constraint while ignoring a different constraint with a lower priority).
Regarding claim 5, Jokela teaches the method as discussed above in claim 1, Jokela further teaches wherein the operation parameter comprises a velocity or a position of the vehicle (Paragraph [0004], “The speed of the host vehicle may be controlled in dependence on the target operational speed band. The target operational speed band may define a speed range for the host vehicle”).
Regarding claim 6, Jokela teaches the method as discussed above in claim 1, Jokela further teaches wherein the control instruction relates to a wheel domain parameter that comprises at least one of a wheel speed, a wheel drive torque, a wheel brake torque, a road grade angle, a longitudinal torque-acceleration response model, or a fuel consumption estimation model (Paragraph [0071], “When referring to power, torque, and speed signals, the subscript wh is used herein to indicate the wheel frame of reference”) (Paragraph [0087], “The torque T.sub.erad at the output shaft of the ERAD 5 is calculated from the corresponding torque at the wheel, T.sub.wh,drv,rr after considering all lumped driveline losses η.sub.gb,erad and transmission ratio v.sub.gb,erad:”).
Regarding claim 7, Jokela teaches the method as discussed above in claim 1, Jokela further teaches wherein the control instruction relates to an engine domain parameter that comprises at least one of an engine speed, an engine flywheel torque, a foundation air pressure, a gear position, a transmission efficiency gain set, a clutch engagement status, a gear ratio set, or a final drive ratio (Paragraph [0087], “The torque T.sub.erad at the output shaft of the ERAD 5 is calculated from the corresponding torque at the wheel, T.sub.wh,drv,rr after considering all lumped driveline losses η.sub.gb,erad and transmission ratio v.sub.gb,erad:”).
Regarding claim 8, Jokela teaches the method as discussed above in claim 1, Jokela further teaches wherein the constraint relates to a limit on a mechanical capacity of the vehicle (Paragraph [0133], “The maximum speed road curvature module 31 calculates a speed limit due to lateral acceleration exerted on the host vehicle 1 when travelling around a bend in a road (the curvature of the bend being determined with reference to geographical map data). The maximum speed constraint is identified as the smaller of the speed determined by the maximum speed limit arbitration module 32 and the maximum speed road curvature module 31,” here the system uses the road curvature in order to determine a maximum speed constraint that the vehicle is capable of achieving according to its mechanical capacity).
Regarding claim 9, Jokela teaches the method as discussed above in claim 1, Jokela further teaches wherein a performance parameter of the vehicle when the vehicle traverses the path according to values of the operation parameter that are determined based on the tolerable ranges and the penalty information improves than when the vehicle traverses the path according to the reference information without the context information (Paragraph [0009], “The target operational speed band may define a speed range for the host vehicle. At least in certain embodiments, this may improve operating efficiency of the vehicle and/or reduce a time required to complete a trip,” here the system is improving the operating efficiency of the vehicle when the system uses the tolerable ranges and determined penalty information) (Paragraph [0002], “An example of how V2I information can be used for energy savings is when a traffic light communicates its current and future states to approaching vehicles. This allows approaching vehicles to adapt their approach speed profile so as to potentially avoid stopping at the traffic light, thus saving energy and increasing driver comfort. An example of how V2V information can be used for energy savings is when vehicles ahead of the host vehicle communicate their movements to other vehicles around them. For instance, if the host vehicle is approaching a congested area, it could anticipate the congestion and adapt its approach speed and propulsion system usage to improve energy efficiency, for example by increasing the amount of vehicle coasting and decreasing the use of friction brakes.”).
Regarding claim 10, Jokela teaches the method as discussed above in claim 1, Jokela further teaches wherein the performance parameter comprises at least one of fuel efficiency or acceleration jerkiness (Paragraph [0002], “An example of how V2I information can be used for energy savings is when a traffic light communicates its current and future states to approaching vehicles. This allows approaching vehicles to adapt their approach speed profile so as to potentially avoid stopping at the traffic light, thus saving energy and increasing driver comfort. An example of how V2V information can be used for energy savings is when vehicles ahead of the host vehicle communicate their movements to other vehicles around them. For instance, if the host vehicle is approaching a congested area, it could anticipate the congestion and adapt its approach speed and propulsion system usage to improve energy efficiency, for example by increasing the amount of vehicle coasting and decreasing the use of friction brakes.”).
Regarding claim 12, Jokela teaches the method as discussed above in claim 1, Jokela further teaches wherein the plurality of time points correspond to a time horizon for which at least one of the reference information or the context information is available (Paragraph [0135], “The prediction is conducted for each target vehicle 15-n, one target vehicle 15-n at a time, within an optimization horizon of the host vehicle 1 along the route R.”).
Regarding claim 13, Jokela teaches the method as discussed above in claim 1, Jokela further teaches wherein the control instruction is configured to control at least one of longitudinal motion or lateral motion of the vehicle (Paragraph [0195], “The results of the optimization may be used in a semi-autonomous longitudinal control feature that directly actuates the optimized speed trajectory and/or powertrain usage optimization.”).
Regarding claim 14, Jokela teaches the method as discussed above in claim 1, Jokela further teaches wherein the reference information further comprises a plurality of second reference values of a second operation parameter of the vehicle, each of the plurality of second reference values corresponding to one of the plurality of time points, the operation parameter and the second operation parameter collectively defining a state of the vehicle at each of the plurality of time points (Paragraph [0037], “The first speed trajectory may be generated for a first acceleration limit of the host vehicle,” here the generated speed trajectories may use other reference information such as acceleration limits for the plurality of time points)
the method further comprises determining a tolerable range of the second operation parameter for each of the plurality of time points based on the reference information and the context information (Paragraph [0160], “First and second acceleration limits for the host vehicle 1 are calculated to arrive at the first location K during a time period corresponding to a green phase of the first traffic control signal 18-1 (represented by a double-headed arrow l in FIG. 8),” here the system can determine acceleration limits/tolerable range for the plurality of time points so that the vehicle meets the traffic control signal constraints/context information)
and the penalty information further comprises a plurality of second penalty weights each of which corresponds to a second modulation bandwidth indicating a difference between a tolerable range of the second operation parameter and a second constraint at one of the plurality of time points (Paragraph [0160], “First and second acceleration limits for the host vehicle 1 are calculated to arrive at the first location K during a time period corresponding to a green phase of the first traffic control signal 18-1 (represented by a double-headed arrow l in FIG. 8). The first acceleration limit a.sub.OV corresponds to the host vehicle 1 arriving at the first traffic control signal 18-1 concurrent with the beginning of the green phase l, i.e. as the first traffic control signal 18-1 turns green (a.sub.TL.sup.l,green). The first acceleration limit a.sub.TL corresponds to a constant acceleration or deceleration that would cause the host vehicle 1 to arrive at the first location K at a first arrival time corresponding to a time when the first traffic control signal 18-1 enters a first green phase. The second acceleration limit corresponds to the host vehicle 1 arriving at the first traffic control signal 18-1 contemporaneous with the end of the green phase l, i.e. as the first traffic control signal 18-1 turns red (a.sub.TL.sup.l,red).”).
Regarding claim 15, Jokela teaches a system for controlling a vehicle, comprising: (Paragraph [0001], "The present disclosure relates to a vehicle control method and apparatus.")
a mission planner configured to provide reference information and context information of the vehicle, the reference information relating to an operation parameter of the vehicle that describes mission waypoints of the vehicle at a plurality of time points during which the vehicle is to traverse a path (Paragraph [0023], "The method may comprise identifying a speed limit applicable for at least a part of the route between the current position of the host vehicle and the first location. The speed limit may define at least a portion of the upper limit of the target operational speed band.") (Paragraph [0157], "A static speed constraint due to road topology/speed limits is represented by a first continuous line 43 in the 2D optimization grid 40, and by a continuous surface 44 within the 3D optimization grid 41.") (See Figure 7A and 7B showing a speed limit reference value describing the mission waypoints at a plurality of time points during the operation of the vehicle)
and context information, and the context information relating to a state of the vehicle during an operation of the vehicle at the plurality of time points or an environment enclosing the path (Paragraph [0024], "The method may comprise identifying a plurality of obstacles on the route. The target operational speed band may be determined in respect of each obstacle identified on the route,” here the system is collecting context information relating to obstacles in the environment of the vehicle path)
a model predictive control (MPC) controller coupled to the mission planner and configured to perform operations including: (Paragraph [0124], “The predictive control algorithms comprise a vehicle speed control unit 20 and a hybrid powertrain control unit 21. The supporting functions comprise a route-based predictive optimizer 22, a static speed constraints calculator 23, an energy recuperation estimator 24, a target vehicle speed trajectory predictor 25, an auxiliary load estimator 26 and a road-load estimator 27. The vehicle on-board controllers comprise a route preview calculator 28, such as an eHorizon module available from Continental AG; a powertrain control module 29 and a V2I communication module 30.”)
obtaining the reference information and the context information from the mission planner (Paragraph [0169], “A similar cost function can be applied with regards a target vehicle 15-n, for example to calculate a target speed trajectory band for the host vehicle that avoids approaching a target vehicle 15-n in front of the host vehicle 1 with a large speed difference. The progress of the target vehicle 15-n along the route R is predicted, for example utilising the model described herein with reference to FIG. 6. The application of a suitable cost generates a speed profile that results in the host vehicle 1 gradually reducing speed to maintain a target headway between the host vehicle and the target vehicle 15-n.,” here the cost function is receiving reference information such as the speed limite, and context information relating to obstacles such as other vehicles)
determining a tolerable range of the operation parameter for each of the plurality of time points based on the reference information and the context information (Paragraph [0160], "The first and second acceleration limits define upper and lower speed trajectories 51, 52 for the host vehicle 1. The upper and lower speed trajectories 51, 52 define a target operational speed band 53.") (See Figure 8 showing the tolerable range for the operation parameter/speed)
obtaining penalty information including a plurality of penalty weights each of which corresponds to a modulation bandwidth indicating a difference between a tolerable range of the operation parameter and a constraint at one of the plurality of time points (Paragraph [0157], "The first trajectory 45 is valid since it results in the host vehicle 1 traversing the location of the traffic control signals 18-n during a green phase. The second trajectory 46 is invalid since it results in the host vehicle 1 traversing the location of the traffic control signals 18-n during a red phase. Thus, only the first trajectory 45 (extending from A to B.sub.2) is feasible with regards to traffic control signal constraints.") (Paragraph [0013-0014], "The first speed trajectory may form a lower limit of the target operational speed band. A cost penalty may be applied to a target speed trajectory which is less than the first speed trajectory … A cost penalty may be applied to a target speed trajectory which is greater than the first speed trajectory.") (Paragraph [0168], "The cost function g.sub.TL increases the associated cost as the violation a.sub.viol increases, the cost decreases as the distance remaining to the traffic control signal d.sub.rem,TL increases and as the number of traffic control signals between the currently considered traffic control signal and the host vehicle 1 M.sub.TL increases. The weightings of these different considerations can be tuned with the following coefficients: WTL.sub.1 ∈(0, ∞), WTL.sub.2 ∈ [0, ∞) and WTL.sub.3 ∈ [0, ∞)," here the system is using a cost function including penalties in order to determine optimal control signal, this cost function uses weighting coefficients in order to tune the penalties based on different considerations)
and determining a control instruction based on the tolerable ranges and the penalty information (Paragraph [0190], "The VMC 36 generates a propulsion torque request suitable for maintaining the host vehicle 1 within the target operational speed band 67.")
and a vehicle control interface coupled to the MPC controller to obtain the control instruction and configured to cause the vehicle to operate based on the control instruction (Paragraph [0070], "As described herein, the controller 2 is configured to implement a dynamic programming algorithm for controlling the target operational speed of the host vehicle 1 as it travels along a route R (illustrated in FIG. 3). The control algorithm may be implemented as part of an autonomous control function, for example comprising one or more of the following: Adaptive Cruise Control (ACC), Intelligent Cruise Control (ICC), Green Light Optimized Speed Advisory (GLOSA), and Traffic Jam Assist (TJA).").
Regarding claim 19, claim 19 is similar in scope to claim 9 and therefore is rejected under similar rationale.
Regarding claim 20, Jokela teaches an apparatus for controlling a vehicle (Paragraph [0001], "The present disclosure relates to a vehicle control method and apparatus.")
comprising a processor configured to perform steps including: (Paragraph [0035], “The processing means may be in the form of a processor, such as an electronic processor. The memory means may be in the form a memory device.”)
obtaining reference information of an operation parameter of the vehicle (Paragraph [0023], "The method may comprise identifying a speed limit applicable for at least a part of the route between the current position of the host vehicle and the first location. The speed limit may define at least a portion of the upper limit of the target operational speed band.")
the reference information including a plurality of reference values of the operation parameter, the operation parameter describing mission waypoints of the vehicle at a plurality of time points during which the vehicle is to traverse a path, each of the plurality of reference values corresponding to one of the plurality of time points (Paragraph [0157], "A static speed constraint due to road topology/speed limits is represented by a first continuous line 43 in the 2D optimization grid 40, and by a continuous surface 44 within the 3D optimization grid 41.") (See Figure 7A and 7B showing a speed limit reference value describing the mission waypoints at a plurality of time points during the operation of the vehicle)
obtaining context information of the vehicle that relates to a state of the vehicle during an operation of the vehicle at the plurality of time points or an environment enclosing the path (Paragraph [0024], "The method may comprise identifying a plurality of obstacles on the route. The target operational speed band may be determined in respect of each obstacle identified on the route,” here the system is collecting context information relating to obstacles in the environment of the vehicle path)
determining a tolerable range of the operation parameter for each of the plurality of time points based on the reference information and the context information (Paragraph [0160], "The first and second acceleration limits define upper and lower speed trajectories 51, 52 for the host vehicle 1. The upper and lower speed trajectories 51, 52 define a target operational speed band 53.") (See Figure 8 showing the tolerable range for the operation parameter/speed)
obtaining penalty information including a plurality of penalty weights each of which corresponds to a modulation bandwidth indicating a difference between a tolerable range of the operation parameter and a constraint at one of the plurality of time points (Paragraph [0157], "The first trajectory 45 is valid since it results in the host vehicle 1 traversing the location of the traffic control signals 18-n during a green phase. The second trajectory 46 is invalid since it results in the host vehicle 1 traversing the location of the traffic control signals 18-n during a red phase. Thus, only the first trajectory 45 (extending from A to B.sub.2) is feasible with regards to traffic control signal constraints.") (Paragraph [0013-0014], "The first speed trajectory may form a lower limit of the target operational speed band. A cost penalty may be applied to a target speed trajectory which is less than the first speed trajectory … A cost penalty may be applied to a target speed trajectory which is greater than the first speed trajectory.") (Paragraph [0168], "The cost function g.sub.TL increases the associated cost as the violation a.sub.viol increases, the cost decreases as the distance remaining to the traffic control signal d.sub.rem,TL increases and as the number of traffic control signals between the currently considered traffic control signal and the host vehicle 1 M.sub.TL increases. The weightings of these different considerations can be tuned with the following coefficients: WTL.sub.1 ∈(0, ∞), WTL.sub.2 ∈ [0, ∞) and WTL.sub.3 ∈ [0, ∞)," here the system is using a cost function including penalties in order to determine optimal control signal, this cost function uses weighting coefficients in order to tune the penalties based on different considerations)
determining a control instruction based on the tolerable ranges and the penalty information (Paragraph [0190], "The VMC 36 generates a propulsion torque request suitable for maintaining the host vehicle 1 within the target operational speed band 67.")
and operating the vehicle based on the control instruction such that a value of the operation parameter of the vehicle at each of at least one of the plurality of time points falls within or close to a tolerable range at the time point so as to satisfy the constraint (Paragraph [0070], "As described herein, the controller 2 is configured to implement a dynamic programming algorithm for controlling the target operational speed of the host vehicle 1 as it travels along a route R (illustrated in FIG. 3). The control algorithm may be implemented as part of an autonomous control function, for example comprising one or more of the following: Adaptive Cruise Control (ACC), Intelligent Cruise Control (ICC), Green Light Optimized Speed Advisory (GLOSA), and Traffic Jam Assist (TJA).").
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 2 and 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jokela (US-20210009128) in view of Berntorp (US-9568915) and further in view of Jardine (US-20220135039).
Regarding claim 2, Jokela teaches the method as discussed above in claim 1, Jokela further teaches wherein the reference information comprises a value of the operation parameter at a prior time point that precedes the plurality of time points (Paragraph [0143], “The current vehicle speed (as initial condition) and SOC,” here the system is using the current vehicle speed as an initial condition prior to the plurality of time points)
the context information comprises at least one of a mechanical capacity of the vehicle or environmental information of the environment enclosing the path (Paragraph [0024], "The method may comprise identifying a plurality of obstacles on the route. The target operational speed band may be determined in respect of each obstacle identified on the route,” here the system is collecting context information relating to obstacles in the environment of the vehicle path)
and determining a tolerable range of the operation parameter for each of the plurality of time points based on the reference information and the context information comprises (Paragraph [0160], "The first and second acceleration limits define upper and lower speed trajectories 51, 52 for the host vehicle 1. The upper and lower speed trajectories 51, 52 define a target operational speed band 53,” here the speed trajectories and acceleration limits use the vehicle current speed as an initial parameter) (See Figure 8 showing the tolerable range for the operation parameter/speed).
However Jokela does not explicitly teach inputting the reference information and the context information into an uncertainty model, the uncertainty model comprises a machine learning model trained to predict substantially in real time a tolerable range of the operation parameter at a specific time point based on at least one of a value of the operation parameter at a prior time point that precedes the specific time point, the mechanical capacity of the vehicle, or the environmental information.
Berntorp teaches systems and method for controlling an autonomous vehicle wherein the system controls a motion of a vehicle including
inputting the reference information (Column 6, lines 35-45, “In one embodiment, the motion-planning system determines the future motion of the vehicle, using a probabilistic motion model of the vehicle, that with highest probability satisfies various constraints on the motion of the vehicle, such as a bound on a deviation of a location of the vehicle from a middle of a road, a bound on a change from a current acceleration and a heading angle of the vehicle, a bound on a deviation from a desired velocity profile of the vehicle, and a minimal distance to an obstacle on the road,” here the system is using reference information in the form of various constraints)
and the context information (Column 6, lines 45-60, “the motion-planning system 240 receives information 231 about the surroundings 250, such as obstacles, drivable, nondrivable, or illegal areas, for the vehicle,” here the system uses vehicle context information)
into an uncertainty model (Column 2, lines 40-45, “The method includes sampling a control space of possible control inputs to a model of the motion of the vehicle to produce a set of sampled control inputs, wherein the model of the motion of the vehicle includes an uncertainty”)
trained to predict substantially in real time a tolerable range of the operation parameter at a specific time point (Column 7, lines 15-30, “For example, the computed motions are searched for intersection with the obstacle such that a collision can occur, and one embodiment assigns a low probability, or even discards, those that are predicted to collide with the obstacle. The modified future motion is determined starting from the remaining stored set of possible motions, computed from previous iterations. Some of the embodiments of the invention are based on that the sensor information 231 obtained from the sensing system 230 can be uncertain and with errors, and that predictions of obstacle motion are uncertain, even when the obstacle prediction 243 accounts for uncertainties,” here the system is predicting ranges of motions for motion parameters of the vehicle that do not violate constraints such as colliding with objects)
based on at least one of a value of the operation parameter at a prior time point that precedes the specific time point, the mechanical capacity of the vehicle, or the environmental information (Column 7, lines 1-15, “In one embodiment of the invention, the motion-planning system includes predictions 243 of motions of obstacles, detected by the sensing system 230 and received 231 by the motion planner. In response to predicting an obstacle on the future predicted motion of the vehicle with high probability, some embodiments of the invention compute a modified future motion of the vehicle. An obstacle can be another vehicle or a pedestrian, or a virtual obstacle representing illegal driving behavior, such as the line delimiting the allowed driving lane, a stop line, a yield sign, or from inputs from a driver or passenger.”).
Jokela and Berntorp are analogous art as they are both generally related to systems for monitoring the surrounds of a vehicle and determining autonomous controls.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include inputting the reference information and the context information into an uncertainty model, the uncertainty model comprises a machine learning model trained to predict substantially in real time a tolerable range of the operation parameter at a specific time point based on at least one of a value of the operation parameter at a prior time point that precedes the specific time point, the mechanical capacity of the vehicle, or the environmental information of Berntorp in the system for controlling an autonomous vehicle of Jokela with a reasonable expectation of success in order to improve safety of the vehicle system by maintain safety distances from obstacles including accounting for the uncertainty in the safety envelope (Column 9, lines 30-45, “Surrounding obstacles can be detected by the sensing system 230 and the future behavior of the obstacles can be predicted by the prediction system 243. In addition, another possible specification is to maintain safety distance to vehicles in the same lane 440, which can be, but in general are not, the same as 430. For reasons of passenger comfort, fuel consumption, wear-and-tear, or other reasons, the specification can mandate a smooth drive 450 of the vehicle.”).
However while Bertorp teaches the uncertainty model uses a decision tree algorithm (Column 17, lines 15-25, “For each edge that intersects with obstacles, the corresponding child endpoint node is marked as invalid. Next, the tree is trimmed and regrown. “) (Column 8, lines 40-50, “FIG. 3 shows a schematic of a tree of state transitions defining the motion of the vehicle according to some embodiments of the invention. The current tree in the drivable space 330 is shown with root node 300 indicating the current state of the vehicle and includes the states as nodes and the state transitions as edges in state space, arising from control inputs chosen according to other embodiments of the invention. For example, edge 321 is the motion generated by applying a control input for a predefined time from root node 300 to state 320. The tree can include a target state 310 and target region 340 of the vehicle.”).
However the combination does not explicitly teach the use of a machine learning model.
Jardine teaches a control system and a method for a host vehicle operable in an autonomous mode including use of a machine learning model (Paragraph [0229], “The machine learning algorithm is for controlling one or more parameters of one or more of the other algorithms, in dependence on information indicative of past use of the host vehicle 10. The information may be indicative of past use of the host vehicle 10 in the autonomous mode and/or the non-autonomous mode. The information may be indicative of inputs such as steering inputs, acceleration inputs and braking inputs. The information may be indicative of environment characteristics. The information may be associated with information from the sensing means. The information may be associated with traffic conditions, road works or the like. The information may be indicative of locations of the past use. The information may be indicative of a temporal pattern of use of the host vehicle 10. For example, the times of the past use may have been recorded. The temporal pattern may enable locations visited at a recurring time and/or day and/or date to be established, such as a workplace. The information may be used for training of the machine learning algorithm. Machine learning enables an optimization of vehicle behavior for repeated journeys. Further, at least some of the parameters may be user-settable using HMI according to preference,” here the system is applying a machine learning algorithm for controlling other system algorithms of the vehicle in order to optimize vehicle behavior, this technique could be combined with the uncertainty algorithms used in the Berntorp reference in order to teach the limitation of a uncertainty model comprising machine learning).
Jokela, Berntorp, and Jardine are analogous art as they are all generally related to systems for monitoring the surrounds of a vehicle and determining autonomous controls.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include use of a machine learning model of Jardine in the system for controlling an autonomous vehicle of Jokela and Berntorp with a reasonable expectation of success in order to further optimize the behavior of the vehicle using historical data to improve the system algorithms (Paragraph [0229], “The information may be used for training of the machine learning algorithm. Machine learning enables an optimization of vehicle behavior for repeated journeys. Further, at least some of the parameters may be user-settable using HMI according to preference”).
Regarding claim 17, Jokela teaches the system as discussed above in claim 15, however Jokela does not explicitly teach wherein the MPC controller comprises an uncertainty model trained to predict substantially in real time a tolerable range of the operation parameter at a specific time point based on at least one of a value of the operation parameter of the vehicle at a prior time point that precedes the specific time point, the reference information, or the context information.
Berntorp further teaches wherein the MPC controller comprises an uncertainty model (Column 2, lines 40-45, “The method includes sampling a control space of possible control inputs to a model of the motion of the vehicle to produce a set of sampled control inputs, wherein the model of the motion of the vehicle includes an uncertainty”)
trained to predict substantially in real time a tolerable range of the operation parameter at a specific time point (Column 7, lines 15-30, “For example, the computed motions are searched for intersection with the obstacle such that a collision can occur, and one embodiment assigns a low probability, or even discards, those that are predicted to collide with the obstacle. The modified future motion is determined starting from the remaining stored set of possible motions, computed from previous iterations. Some of the embodiments of the invention are based on that the sensor information 231 obtained from the sensing system 230 can be uncertain and with errors, and that predictions of obstacle motion are uncertain, even when the obstacle prediction 243 accounts for uncertainties,” here the system is predicting ranges of motions for motion parameters of the vehicle that do not violate constraints such as colliding with objects)
based on at least one of a value of the operation parameter of the vehicle at a prior time point that precedes the specific time point, the reference information, or the context information (Column 7, lines 1-15, “In one embodiment of the invention, the motion-planning system includes predictions 243 of motions of obstacles, detected by the sensing system 230 and received 231 by the motion planner. In response to predicting an obstacle on the future predicted motion of the vehicle with high probability, some embodiments of the invention compute a modified future motion of the vehicle. An obstacle can be another vehicle or a pedestrian, or a virtual obstacle representing illegal driving behavior, such as the line delimiting the allowed driving lane, a stop line, a yield sign, or from inputs from a driver or passenger.”).
Jokela and Berntorp are analogous art as they are both generally related to systems for monitoring the surrounds of a vehicle and determining autonomous controls.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include wherein the MPC controller comprises an uncertainty model trained to predict substantially in real time a tolerable range of the operation parameter at a specific time point based on at least one of a value of the operation parameter of the vehicle at a prior time point that precedes the specific time point, the reference information, or the context information of Berntorp in the system for controlling an autonomous vehicle of Jokela with a reasonable expectation of success in order to improve safety of the vehicle system by maintain safety distances from obstacles including accounting for the uncertainty in the safety envelope (Column 9, lines 30-45, “Surrounding obstacles can be detected by the sensing system 230 and the future behavior of the obstacles can be predicted by the prediction system 243. In addition, another possible specification is to maintain safety distance to vehicles in the same lane 440, which can be, but in general are not, the same as 430. For reasons of passenger comfort, fuel consumption, wear-and-tear, or other reasons, the specification can mandate a smooth drive 450 of the vehicle.”).
However the combination does not explicitly teach the model is trained.
Jardine further teaches a control system and a method for a host vehicle operable in an autonomous mode including use of a trained machine learning model (Paragraph [0229], “The machine learning algorithm is for controlling one or more parameters of one or more of the other algorithms, in dependence on information indicative of past use of the host vehicle 10. The information may be indicative of past use of the host vehicle 10 in the autonomous mode and/or the non-autonomous mode. The information may be indicative of inputs such as steering inputs, acceleration inputs and braking inputs. The information may be indicative of environment characteristics. The information may be associated with information from the sensing means. The information may be associated with traffic conditions, road works or the like. The information may be indicative of locations of the past use. The information may be indicative of a temporal pattern of use of the host vehicle 10. For example, the times of the past use may have been recorded. The temporal pattern may enable locations visited at a recurring time and/or day and/or date to be established, such as a workplace. The information may be used for training of the machine learning algorithm. Machine learning enables an optimization of vehicle behavior for repeated journeys. Further, at least some of the parameters may be user-settable using HMI according to preference,” here the system is applying a machine learning algorithm for controlling other system algorithms of the vehicle in order to optimize vehicle behavior by training the model using historical information, this technique could be combined with the uncertainty algorithms used in the Berntorp reference in order to teach the limitation of a uncertainty model comprising machine learning).
Jokela, Berntorp, and Jardine are analogous art as they are all generally related to systems for monitoring the surrounds of a vehicle and determining autonomous controls.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include use of a trained machine learning model of Jardine in the system for controlling an autonomous vehicle of Jokela and Berntorp with a reasonable expectation of success in order to further optimize the behavior of the vehicle using historical data to improve the system algorithms (Paragraph [0229], “The information may be used for training of the machine learning algorithm. Machine learning enables an optimization of vehicle behavior for repeated journeys. Further, at least some of the parameters may be user-settable using HMI according to preference”).
Regarding claim 18, the combination of Jokela, Berntorp, and Jardine teaches the system as discussed above in claim 17, Jardine further wherein the uncertainty model comprises a multivariate model trained based on balanced training data that represent multiple types of events relating to the operation of the vehicle or the path (Paragraph [0229], “The machine learning algorithm is for controlling one or more parameters of one or more of the other algorithms, in dependence on information indicative of past use of the host vehicle 10. The information may be indicative of past use of the host vehicle 10 in the autonomous mode and/or the non-autonomous mode. The information may be indicative of inputs such as steering inputs, acceleration inputs and braking inputs. The information may be indicative of environment characteristics. The information may be associated with information from the sensing means. The information may be associated with traffic conditions, road works or the like. The information may be indicative of locations of the past use. The information may be indicative of a temporal pattern of use of the host vehicle 10. For example, the times of the past use may have been recorded. The temporal pattern may enable locations visited at a recurring time and/or day and/or date to be established, such as a workplace. The information may be used for training of the machine learning algorithm. Machine learning enables an optimization of vehicle behavior for repeated journeys. Further, at least some of the parameters may be user-settable using HMI according to preference,” here the system is applying a machine learning algorithm for controlling other system algorithms of the vehicle in order to optimize vehicle behavior by training the model using multiple inputs and multiple events, this technique could be combined with the uncertainty algorithms used in the Berntorp reference in order to teach the limitation of a uncertainty model comprising machine learning).
Jokela, Berntorp, and Jardine are analogous art as they are all generally related to systems for monitoring the surrounds of a vehicle and determining autonomous controls.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include wherein the uncertainty model comprises a multivariate model trained based on balanced training data that represent multiple types of events relating to the operation of the vehicle or the path of Jardine in the system for controlling an autonomous vehicle of Jokela and Berntorp with a reasonable expectation of success in order to further optimize the behavior of the vehicle using historical data to improve the system algorithms (Paragraph [0229], “The information may be used for training of the machine learning algorithm. Machine learning enables an optimization of vehicle behavior for repeated journeys. Further, at least some of the parameters may be user-settable using HMI according to preference”).
Claim 11 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jokela (US-20210009128) in view of Jardine (US-20220135039).
Regarding claim 11, Jokela teaches the method as discussed above in claim 1, however Jokela does not explicitly teach wherein the vehicle is an autonomous vehicle that is operating in a Society of Automotive Engineers (SAE) Level 4 (L4) automation mode.
Jardine teaches a control system and a method for a host vehicle operable in an autonomous mode including wherein the vehicle is an autonomous vehicle that is operating in a Society of Automotive Engineers (SAE) Level 4 (L4) automation mode (Paragraph [0215], “Once the transition phase is complete, the control system 200 controls the host vehicle 10 in the autonomous mode. SAE International's J3016 defines six levels of driving automation for on-road vehicles. The term autonomous mode as used herein will be understood to cover any of the SAE levels three or higher, such that the control system 200 will control all aspects of the dynamic driving task. At levels four or five, one or more aspects of one or more of the handover processes described herein for transitioning to and/or from the autonomous mode may not be implemented.”).
Jokela and Jardine are analogous art as they are all generally related to systems for monitoring the surrounds of a vehicle and determining autonomous controls.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include wherein the vehicle is an autonomous vehicle that is operating in a Society of Automotive Engineers (SAE) Level 4 (L4) automation mode of Jardine in the system for controlling an autonomous vehicle of Jokela with a reasonable expectation of success in order to improve the experience of the passenger and reduce passenger workload by fully autonomously controlling the vehicle (Paragraph [0217], “In the autonomous mode the occupant may not be required to keep one or more hands on the steering wheel, so a monitoring step requiring the occupant to keep one or more hands on the steering wheel may be omitted.”).
Regarding claim 16, Jokela teaches the system as discussed above in claim 15, Jokela further teaches a perception module configured to acquire environmental information of the environment (Paragraph [0074], “The processor 12 is configured to receive a second electrical input signal SIN2 from at least one vehicle sensor 16 provided on-board the host vehicle 1. The at least one vehicle sensor 16 in the present embodiment comprises a forward-looking radar 16 provided on the host vehicle 1.”)
and the plurality of time points correspond to a time horizon that relates to operations of the perception module and the mission planner (Paragraph [0194], “The optimization algorithm described herein combines inputs including: traffic control timing, behaviour of other vehicles, drivability considerations as well as road profile and traffic signage. The information originates from a variety of sources which may include V2I communication with traffic lights and other infrastructure, V2V communication with other vehicles, communication with an e-Horizon digital map database and in-vehicle sensors,” here the vehicle control uses a plurality of inputs including perception module/sensor) (Paragraph [0135], “The prediction is conducted for each target vehicle 15-n, one target vehicle 15-n at a time, within an optimization horizon of the host vehicle 1 along the route R.”).
However Jokela does not explicitly teach wherein the vehicle is an autonomous vehicle operating in a Society of Automotive Engineers (SAE) Level 4 (L4) automation mode.
.
Jardine teaches a control system and a method for a host vehicle operable in an autonomous mode including wherein the vehicle is an autonomous vehicle operating in a Society of Automotive Engineers (SAE) Level 4 (L4) automation mode (Paragraph [0215], “Once the transition phase is complete, the control system 200 controls the host vehicle 10 in the autonomous mode. SAE International's J3016 defines six levels of driving automation for on-road vehicles. The term autonomous mode as used herein will be understood to cover any of the SAE levels three or higher, such that the control system 200 will control all aspects of the dynamic driving task. At levels four or five, one or more aspects of one or more of the handover processes described herein for transitioning to and/or from the autonomous mode may not be implemented.”).
Jokela and Jardine are analogous art as they are all generally related to systems for monitoring the surrounds of a vehicle and determining autonomous controls.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include wherein the vehicle is an autonomous vehicle operating in a Society of Automotive Engineers (SAE) Level 4 (L4) automation mode of Jardine in the system for controlling an autonomous vehicle of Jokela with a reasonable expectation of success in order to improve the experience of the passenger and reduce passenger workload by fully autonomously controlling the vehicle (Paragraph [0217], “In the autonomous mode the occupant may not be required to keep one or more hands on the steering wheel, so a monitoring step requiring the occupant to keep one or more hands on the steering wheel may be omitted.”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Hammoud (US-20220126875) teaches controlling the trajectory of the autonomous vehicle in response to the confidence level determined for the perception data and regional map. Yu (US-20210262819) teaches motion planning and control of an autonomous vehicle including a plurality of motion control modes. Whang (US-20230044965) teaches a control method of a vehicle in which maximum and minimum vehicle speeds are determined which for a tolerable range of vehicle operation.
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/CHRISTOPHER GEORGE FEES/Examiner, Art Unit 3662