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
Applicant filed amendments and remarks on 04/17/2026. Claims 1-20 were amended. Claims 1-20 are pending examination.
Response to Arguments
Regarding the claim rejections under 35 USC 101: applicant’s arguments filed 04/17/2026 (hereinafter referred to as the “Remarks”) have been fully considered and they are persuasive. The previously given claim rejections under 35 USC 101 are withdrawn.
Regarding the claim rejections under 35 USC 103: Applicant's arguments filed 04/17/2026 with respect to over Debeauwais et al. (US20230234585A1) in view of Ng et al. (US20240059285A1) have been fully considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument ) in view of Son et al. (US11926310B2), in view of Ting (US6275761B1).
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Debeauwais et al. (US20230234585A1) in view of Son et al. (US11926310B2) and further in view of Ting (US6275761B1), hereinafter referred to as Debeauwais, Son and Ting respectively.
Regarding claims 1 and 9, Debeauwais discloses a method of controlling an ego vehicle comprising at least one processor, the method comprising:
receiving, by the at least one processor, driving state information of the ego vehicle and a different vehicle that is in front of the ego vehicle from at least one sensor of the ego vehicle (“a first step of identifying traffic surrounding the ego vehicle in the traffic lane of the ego vehicle and in at least one adjacent parallel lane in the same traffic direction” [0023]–[0024],“In this driver assistance system according to the invention are found sensors C such as perception sensors enabling measurement of the dynamics of the ego but also perception of the environment… the determination of the environmental information Env that includes the kinematic attributes that are the presence in the lane, dynamic relative position, speed and acceleration of the surrounding vehicles by the sensors C is required…” [0052]).
Debeauwais does not explicitly teach generating, by the at least one processor, a virtual accelerator pedal sensor (APS) map for the ego vehicle using a first neural network model trained with first input data comprising the driving state information, wherein the virtual APS map includes information on a virtual APS corresponding to a torque and a vehicle speed of the ego vehicle
determining, by the at least one processor, a gear stage corresponding to the RPM and the virtual APS based on a shift pattern map;
determining, by the at least one processor, a final gear stage of the ego vehicle based on the gear stage and a preset gear stage;
in response to the determined final gear stage being out of a preset reference gear range, tuning, by the at least one processor, the shift pattern map by adjusting an RPM set in the shift pattern map; redetermining, by the at least one processor, the final gear stage based on the tuned shift pattern map;
and controlling, by the at least one processor, a gear shift of the ego vehicle based on the redetermined final gear stage.
However, Son does teach generating, by the at least one processor, a virtual accelerator pedal sensor (APS) map for the ego vehicle using a first neural network model trained with first input data comprising the driving state information, wherein the virtual APS map includes information on a virtual APS corresponding to a torque and a vehicle speed of the ego vehicle (“the torque-APS conversion map 244 transmits a virtual APS value APSvr to the virtual APS corrector 246”[Col.8 ln 40-45]“The virtual APS corrector 246 outputs the larger one of the APS value for constant-speed driving at the set limit speed Vset and the virtual APS value APS determined through the torque-APS conversion map 244 as a corrected virtual APS value APSvr,mod, and transmits the corrected virtual APS value APSvr,mod to the transmission control unit 250” [Col.9 ln 6-12])
determining, by the at least one processor, a gear stage corresponding to the RPM and the virtual APS based on a shift pattern map (“the transmission control TCU determines a transmission gear position by applying virtual APS value APS and a current vehicle speed V to a shifting map corresponding to the current set mode” [Col.3 ln 3-7];
determining, by the at least one processor, a final gear stage of the ego vehicle based on the gear stage and a preset gear stage (“the CD/CS mode corrector 247 may determine to transition in advance to the CD mode from a point in time at which the actual APS value and the virtual APS value APS become different (i.e., TqAPS>Tqcontroller). Thereby, upshifting to the gear position in the CD mode may be performed until the vehicle reaches the set limit speed Vset, and thus, busy shifting may be prevented.” [Col.9 ln 40-47];
in response to the determined final gear stage being out of a preset reference gear range, tuning, by the at least one processor, the shift pattern map by adjusting an RPM set in the shift pattern map; redetermining, by the at least one processor, the final gear stage based on the tuned shift pattern map (“transitioning to a second mode at a point in time at which an actual APS value and the second APS value become different … determining a transmission gear position by applying the determined virtual vehicle speed and the determined virtual APS value to one of a first shifting pattern corresponding to the first mode and a second shifting pattern corresponding to the second mode, depending on whether or not to transition to the second mode.” [Col.4-5 ln 58-67 & 1-2]);
and controlling, by the at least one processor, a gear shift of the ego vehicle based on the redetermined final gear stage (“the CD/CS mode corrector 247 may determine to transition in advance to the CD mode from a point in time at which the actual APS value and the virtual APS value APS become different (i.e., TqAPS>Tqcontroller). Thereby, upshifting to the gear position in the CD mode may be performed until the vehicle reaches the set limit speed Vset, and thus, busy shifting may be prevented.” [Col.9 ln 40-47]). Both Debeauwais and Son teach methods of controlling autonomous vehicles. However, Son explicitly teaches generating, by the at least one processor, a virtual accelerator pedal sensor (APS) map for the ego vehicle using a first neural network model trained with first input data comprising the driving state information, wherein the virtual APS map includes information on a virtual APS corresponding to a torque and a vehicle speed of the ego vehicle; determining, by the at least one processor, a gear stage corresponding to the RPM and the virtual APS based on a shift pattern map; determining, by the at least one processor, a final gear stage of the ego vehicle based on the gear stage and a preset gear stage; in response to the determined final gear stage being out of a preset reference gear range, tuning, by the at least one processor, the shift pattern map by adjusting an RPM set in the shift pattern map; redetermining, by the at least one processor, the final gear stage based on the tuned shift pattern map; and controlling, by the at least one processor, a gear shift of the ego vehicle based on the redetermined final gear stage.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the autonomous vehicle control method of Debeauwais to also include generating, by the at least one processor, a virtual accelerator pedal sensor (APS) map for the ego vehicle using a first neural network model trained with first input data comprising the driving state information, wherein the virtual APS map includes information on a virtual APS corresponding to a torque and a vehicle speed of the ego vehicle; determining, by the at least one processor, a gear stage corresponding to the RPM and the virtual APS based on a shift pattern map; determining, by the at least one processor, a final gear stage of the ego vehicle based on the gear stage and a preset gear stage; in response to the determined final gear stage being out of a preset reference gear range, tuning, by the at least one processor, the shift pattern map by adjusting an RPM set in the shift pattern map; redetermining, by the at least one processor, the final gear stage based on the tuned shift pattern map; and controlling, by the at least one processor, a gear shift of the ego vehicle based on the redetermined final gear stage, as taught by Son, with a reasonable expectation of success. Doing so improves safety for operating autonomous vehicles (With regard to this reasoning, see at least [Son, Col. 3-5 and 9]).
Debeauwais in view of Son does not explicitly teach generating, by the at least one processor, revolutions per minute (RPM) using a second neural network model trained with second input data comprising the virtual APS and the vehicle speed.
However, Ting does teach generating, by the at least one processor, revolutions per minute (RPM) using a second neural network model trained with second input data comprising the virtual APS and the vehicle speed (“A feature of this invention is development of a single, composite slip estimator which utilizes different neural network designs for different operating conditions of the vehicle.… a preferred composite virtual slip sensor comprises the use of a crude slip estimator in one mode of engine-transmission operation and two different neural network-based slip estimators in the other two modes of operation.” [Col.3 ln 10-32 ] and “a trial neural network for a virtual sensor may be constructed and its output, predicted torque converter slippage in rpm, may be compared with experimentally measured torque converter slippage of the powertrain system of interest.” [Col.2 ln 63-67] and “When this mode of operation is detected by the PCM, a neural network of a first structure and architecture is referenced in the memory of the module and applied. … Upon detection of this mode, the PCM looks to a second neural network to estimate slip.” [Col.3 ln 36-43]). Both Debeauwais in view Son and Ting teach methods of controlling autonomous vehicles. However, Ting explicitly teaches generating, by the at least one processor, revolutions per minute (RPM) using a second neural network model trained with second input data comprising the virtual APS and the vehicle speed.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the autonomous vehicle control method of Debeauwais to also include generating, by the at least one processor, revolutions per minute (RPM) using a second neural network model trained with second input data comprising the virtual APS and the vehicle speed, as taught by Ting, with a reasonable expectation of success. Doing so improves safety for operating autonomous vehicles (With regard to this reasoning, see at least [Ting, Col.3]).
Regarding claims 2, 10 and 18, Debeauwais discloses The method of claim 1,
wherein the driving state information comprises first driving state information of the different vehicle that is in front of the ego vehicle and second driving state information of the ego vehicle (“In this driver assistance system according to the invention are found sensors C such as perception sensors enabling measurement of the dynamics of the ego but also perception of the environment, these sensors C here make it possible not only to provide information as to the speed and the acceleration of the ego, as well as the position, speed and acceleration of the objects in the environment, but also to supply the trajectory prediction for those objects. In fact, the determination of the environmental information Env that includes the kinematic attributes that are the presence in the lane, dynamic relative position, speed and acceleration of the surrounding vehicles by the sensors C is required to be able to predict their trajectories correctly.” [0052]);
and wherein the method further comprises: determining, by the at least one processor, the torque for the ego vehicle based on the first driving state information and second driving state information (“based on the measured wheel speed, as soon as the lane is free in front of the ego vehicle, to increase its acceleration automatically to achieve the control speed selected by the driver, the torque of the actuators A (engine, brakes, etc.) being controlled by the torque setpoint Cc generated at the output of the torque control unit CC, being a function of the consolidated acceleration command on leaving the loop, thus making it possible to assist the driver in their driving task. However, this device considers only one target at a time, the one that is present in front of the ego in its lane, which renders it highly sensitive, in particular to merging of vehicles between the ego and the vehicle that precedes it as well as to a change of lane by the target vehicle, degrading the smoothness of control with sudden irregularities of setpoints.” [0020];
and determining, by the at least one processor and based on the virtual APS map, the virtual APS corresponding to the torque and the vehicle speed of the ego vehicle (“a third step of calculating a longitudinal speed setpoint of the ego vehicle, an acceleration setpoint and a torque setpoint…” [0025]).
Debeauwais does not explicitly teach generating, by the at least one processor, the virtual APS map based on the torque and the second driving state information.
However, Son does teach generating, by the at least one processor, the virtual APS map based on the torque and the second driving state information (“The virtual APS corrector 246 outputs the larger one of the APS value for constant-speed driving at the set limit speed Vset and the virtual APS value APS determined through the torque-APS conversion map 244 as a corrected virtual APS value APSvr,mod” [Col.9 ln 6-12] and “the CD shifting pattern is applied, and vehicle speeds and virtual APS values are present between a boundary 540 between the first and second gear positions and a boundary 550 between the second and third gear positions, and thus, the hybrid electric vehicle is downshifted again from the third gear to the second gear. Thereafter, although the vehicle speed exceeds the set limit speed Vset and the virtual APS value is lowered, the vehicle speed is increased and reaches the boundary 550 between the second and third gear positions, the hybrid electric vehicle is upshifted again to the third gear, and when the vehicle speed is decreased again, the hybrid electric vehicle is downshifted to the second gear position, thereby causing unnecessary busy shifting.” [Col.3-4 ln 65-67 & ln 1-11]). Both Debeauwais and Son teach methods of controlling autonomous vehicles. However, Son explicitly teaches generating, by the at least one processor, the virtual APS map based on the torque and the second driving state information.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the autonomous vehicle control method of Debeauwais to also include generating, by the at least one processor, the virtual APS map based on the torque and the second driving state information, as taught by Son, with a reasonable expectation of success. Doing so improves safety for operating autonomous vehicles (With regard to this reasoning, see at least [Son, Col.9]).
Regarding claims 3, 11 and 19,
Debeauwais does not explicitly teach further comprising: extracting, by the at least one processor, feature values from the vehicle speed and the virtual APS.
However, Son does teach further comprising: extracting, by the at least one processor, feature values from the vehicle speed and the virtual APS (“determining a transmission gear position by applying the determined virtual vehicle speed and the determined virtual APS value to one of a first shifting pattern corresponding to the first mode and a second shifting pattern corresponding to the second mode, depending on whether or not to transition to the second mode.” [Col.4-5 ln 58-67 & 1-2]). Both Debeauwais and Son teach methods of controlling autonomous vehicles. However, Son explicitly teaches extracting, by the at least one processor, feature values from the vehicle speed and the virtual APS.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the autonomous vehicle control method of Debeauwais to also include extracting, by the at least one processor, feature values from the vehicle speed and the virtual APS, as taught by Son, with a reasonable expectation of success. Doing so improves safety for operating autonomous vehicles (With regard to this reasoning, see at least [Son, Col.4-5]).
Debeauwais in view of Son does not explicitly teach generating, by the at least one processor, the second neural network model which is a multiple linear regression model configured to predict the RPM based on the feature values by determining a correlation coefficient between the vehicle speed, the virtual APS, and the RPM.
However, Ting does teach generating, by the at least one processor, the second neural network model which is a multiple linear regression model configured to predict the RPM based on the feature values by determining a correlation coefficient between the vehicle speed, the virtual APS, and the RPM (“There was one hidden layer with 16 hidden nodes and one network output variable—a scaled estimated slip value (in RPM). The activation functions in the hidden and output layers were chosen to be a logarithmic sigmoid function and a pure linear function, respectively. The logarithmic sigmoid function provided output values between 0 and 1. Therefore, instead of estimating the actual slip value in rpm, the neural network output variable estimated a scaled slip value given by
Scaled slip value=Actual slip value/3000,
where the normalizing factor 3000 was selected to ensure that the scaled slip values had a magnitude of less than 1.
Synthesizing the weights of a neural network design is a user interactive, iterative process which systematically searches through the space of all possible network weight combinations to obtain the best match to the desired input/output properties of the network. These desired properties are represented by user specified sets of input/output data, referred to as the training data. As discussed earlier, the “quality” of the match in this example is represented by the sum-squared error between the predicted and desired outputs of the network. The most fundamental network training methods (often referred to as backpropagation) are gradient based and attempt to minimize this error by adjusting each weight in a network proportional to the derivative of the error with respect to that value.” [Col.7 ln 25-51]
“a trial neural network for a virtual sensor may be constructed and its output, predicted torque converter slippage in rpm, may be compared with experimentally measured torque converter slippage of the powertrain system of interest. Engine and transmission operating parameters affecting torque converter slip are selected for input neuron data. The construction and evaluation of the network is assisted using available software. The network is revised until its output suitably simulates the test system.” [Col.2-3 ln 63-67 & 1-5]). Both Debeauwais in view Son and Ting teach methods of controlling autonomous vehicles. However, Ting explicitly teaches generating, by the at least one processor, the second neural network model which is a multiple linear regression model configured to predict the RPM based on the feature values by determining a correlation coefficient between the vehicle speed, the virtual APS, and the RPM.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the autonomous vehicle control method of Debeauwais to also include generating, by the at least one processor, the second neural network model which is a multiple linear regression model configured to predict the RPM based on the feature values by determining a correlation coefficient between the vehicle speed, the virtual APS, and the RPM, as taught by Ting, with a reasonable expectation of success. Doing so improves safety for operating autonomous vehicles (With regard to this reasoning, see at least [Ting, Col.2-3 & 7]).
Regarding claims 4 and 12,
Debeauwais in view of Son does not explicitly teach further comprising training the second neural network model with the second input data until a determination coefficient reaches a preset reference value, wherein the determination coefficient is a result value of the correlation coefficient.
However, Ting does teach further comprising training the second neural network model with the second input data until a determination coefficient reaches a preset reference value, wherein the determination coefficient is a result value of the correlation coefficient (“When this mode of operation is detected by the PCM, a neural network of a first structure and architecture is referenced in the memory of the module and applied. A second mode is during vehicle deceleration and the torque converter is in an “over running” mode and experiencing negative slip. Upon detection of this mode, the PCM looks to a second neural network to estimate slip. Finally, when the vehicle is operating at a relatively high steady speed and the torque converter is locked up, there is little or no slippage and the PCM applies a simple slip estimator calculation for this mode of operation.” [Col.3 ln 35-46],
“When the torque converter clutch is engaged in the transmission used in this embodiment, it operates in what is referred to as the Electronic Controlled Converter Clutch (EC-Cubed) mode. This mode is activated under two separate vehicle operating conditions: (1) The vehicle is in third gear and in a low-mid throttle range (i.e., <50% throttle but >10% throttle) or (2) the vehicle is in fourth gear. In either case, the EC-Cubed mode regulates the slip to roughly 20 RPM or less. From the analysis of the simulation results, the crude slip estimator performed well when the torque converter was operating in EC-Cubed mode” [Col.9 ln17-27],
“Synthesizing the weights of a neural network design is a user interactive, iterative process which systematically searches through the space of all possible network weight combinations to obtain the best match to the desired input/output properties of the network. These desired properties are represented by user specified sets of input/output data, referred to as the training data. As discussed earlier, the “quality” of the match in this example is represented by the sum-squared error between the predicted and desired outputs of the network. The most fundamental network training methods (often referred to as backpropagation) are gradient based and attempt to minimize this error by adjusting each weight in a network proportional to the derivative of the error with respect to that value.”[Col.7 ln 39-52]). Both Debeauwais in view Son and Ting teach methods of controlling autonomous vehicles. However, Ting explicitly teaches further comprising training the second neural network model with the second input data until a determination coefficient reaches a preset reference value, wherein the determination coefficient is a result value of the correlation coefficient.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the autonomous vehicle control method of Debeauwais to also include further comprising training the second neural network model with the second input data until a determination coefficient reaches a preset reference value, wherein the determination coefficient is a result value of the correlation coefficient, as taught by Ting, with a reasonable expectation of success. Doing so improves safety for operating autonomous vehicles (With regard to this reasoning, see at least [Ting, Col.3 and 7]).
Regarding claims 5 and 13,
Debeauwais does not explicitly teach further comprising: subdividing, by the at least one processor, the virtual APS map;
extracting, by the at least one processor, a subdivided virtual APS based on the subdivided virtual APS map.
However, Son does teach further comprising: subdividing, by the at least one processor, the virtual APS map (“the transmission control TCU determines a transmission gear position by applying virtual APS value APS and a current vehicle speed V to a shifting map corresponding to the current set mode, among a shifting map corresponding to the CD mode and a shifting map corresponding to the CS mode. The two shifting maps (shifting patterns) used in the plug-in hybrid electric vehicle (PHEV) will be described with reference to FIG. 4” [Col.3 ln 3-11], The CS and CD shifting patterns are shown in Fig. 4 as discrete maps of APS (%) versus vehicle speed (kph) that define gear regions (1, 2, 3…);
extracting, by the at least one processor, a subdivided virtual APS based on the subdivided virtual APS map (“determining a transmission gear position by applying the determined virtual vehicle speed and the determined virtual APS value to one of a first shifting pattern corresponding to the first mode and a second shifting pattern corresponding to the second mode, depending on whether or not to transition to the second mode.” [Col.4-5 ln 58-67 & 1-2]). Both Debeauwais and Son teach methods of controlling autonomous vehicles. However, Son explicitly teaches subdividing, by the at least one processor, the virtual APS map; extracting, by the at least one processor, a subdivided virtual APS based on the subdivided virtual APS map.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the autonomous vehicle control method of Debeauwais to also include subdividing, by the at least one processor, the virtual APS map; extracting, by the at least one processor, a subdivided virtual APS based on the subdivided virtual APS map, as taught by Son, with a reasonable expectation of success. Doing so improves safety for operating autonomous vehicles (With regard to this reasoning, see at least [Son, Col.3-5]).
Debeauwais in view of Son does not explicitly teach predicting, by the at least one processor, an RPM per index based on the subdivided virtual APS.
However, Ting does teach predicting, by the at least one processor, an RPM per index based on the subdivided virtual APS (“a trial neural network for a virtual sensor may be constructed and its output, predicted torque converter slippage in rpm, may be compared with experimentally measured torque converter slippage of the powertrain system of interest. Engine and transmission operating parameters affecting torque converter slip are selected for input neuron data. The construction and evaluation of the network is assisted using available software. The network is revised until its output suitably simulates the test system.” [Col.2-3 ln 63-67 & 1-5]). Both Debeauwais in view Son and Ting teach methods of controlling autonomous vehicles. However, Ting explicitly teaches predicting, by the at least one processor, an RPM per index based on the subdivided virtual APS.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the autonomous vehicle control method of Debeauwais to also include predicting, by the at least one processor, an RPM per index based on the subdivided virtual APS, as taught by Ting, with a reasonable expectation of success. Doing so improves safety for operating autonomous vehicles (With regard to this reasoning, see at least [Ting, Col.2-3]).
Regarding claims 6, 14 and 20,
Debeauwais does not explicitly teach wherein the preset reference gear range is defined as vehicle speeds corresponding to RPMs that do not cause noise perceived by a driver while driving at each gear stage.
and determining whether to tune the shift pattern map based on a result of the determining whether the final gear stage per index is suitable or not.
However, Son does teach wherein the preset reference gear range is defined as vehicle speeds corresponding to RPMs that do not cause noise perceived by a driver while driving at each gear stage (“Referring to FIG. 4 , starting of the engine 110 is assumed in the CS mode and thus the CS shifting pattern is configured to maintain a low RPM in consideration of the efficiency of the engine 110, and the motor 140 is mainly used in the CD mode and thus the CD shifting pattern is configured to allow a high RPM in consideration of the efficiency of the motor 140. Therefore, at the same vehicle speed, the same APS value 410 corresponds to the third gear in the CS shifting pattern and corresponds to the second gear in the CD shifting pattern, i.e., a desired gear position may vary depending on the current mode.” [Col.4 ln 14-24],
“the CD/CS mode corrector 247 may determine to transition in advance to the CD mode from a point in time at which the actual APS value and the virtual APS value APS become different (i.e., TqAPS>Tqcontroller). Thereby, upshifting to the gear position in the CD mode may be performed until the vehicle reaches the set limit speed Vset, and thus, busy shifting may be prevented.” [Col.9 ln 40-47]).
determining whether to tune the shift pattern map based on a result of the determining whether the final gear stage per index is suitable or not (“transitioning to a second mode at a point in time at which an actual APS value and the second APS value become different … determining a transmission gear position by applying the determined virtual vehicle speed and the determined virtual APS value to one of a first shifting pattern corresponding to the first mode and a second shifting pattern corresponding to the second mode, depending on whether or not to transition to the second mode.” [Col.4-5 ln 58-67 & 1-2]). Both Debeauwais and Son teach methods of controlling autonomous vehicles. However, Son explicitly teaches wherein the preset reference gear range is defined as vehicle speeds corresponding to RPMs that do not cause noise perceived by a driver while driving at each gear stage and determining whether to tune the shift pattern map based on a result of the determining whether the final gear stage per index is suitable or not.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the autonomous vehicle control method of Debeauwais to also include wherein the preset reference gear range is defined as vehicle speeds corresponding to RPMs that do not cause noise perceived by a driver while driving at each gear stage and determining whether to tune the shift pattern map based on a result of the determining whether the final gear stage per index is suitable or not, as taught by Son, with a reasonable expectation of success. Doing so improves safety for operating autonomous vehicles (With regard to this reasoning, see at least [Son, Col.4 and 9]).
Regarding claims 7 and 15,
Debeauwais does not explicitly teach further comprising, in response to the determined final gear stage being within the preset reference gear range, determining, by
However, Son does teach further comprising, in response to the determined final gear stage being within the preset reference gear range, determining, bystage as a current gear stage (“Referring to FIG. 4 , starting of the engine 110 is assumed in the CS mode and thus the CS shifting pattern is configured to maintain a low RPM in consideration of the efficiency of the engine 110, and the motor 140 is mainly used in the CD mode and thus the CD shifting pattern is configured to allow a high RPM in consideration of the efficiency of the motor 140. Therefore, at the same vehicle speed, the same APS value 410 corresponds to the third gear in the CS shifting pattern and corresponds to the second gear in the CD shifting pattern, i.e., a desired gear position may vary depending on the current mode.” [Col.4 ln 14-24] and “the transmission control TCU determines a transmission gear position by applying virtual APS value APS and a current vehicle speed V to a shifting map corresponding to the current set mode, among a shifting map corresponding to the CD mode and a shifting map corresponding to the CS mode.” [Col.3 ln 3-10]). Both Debeauwais and Son teach methods of controlling autonomous vehicles. However, Son explicitly teaches in response to the determined final gear stage being within the preset reference gear range, determining, by the at least one processor, the final gear stage as a current gear stage.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the autonomous vehicle control method of Debeauwais to also include in response to the determined final gear stage being within the preset reference gear range, determining, by the at least one processor, the final gear stage as a current gear stage, as taught by Son, with a reasonable expectation of success. Doing so improves safety for operating autonomous vehicles (With regard to this reasoning, see at least [Son, Col.4]).
Regarding claims 8 and 16,
Debeauwais does not explicitly teach wherein tuning, by the at least one processor, the shift pattern map comprises lowering the RPM set in the shift pattern map.
However, Son does teach wherein tuning, by the at least one processor, the shift pattern map comprises lowering the RPM set in the shift pattern map (“Referring to FIG. 8 , the vehicle speed corrector 245 outputs the lower one of a set limit speed Vset and a vehicle speed V as a virtual speed Vvir, and the virtual speed Vvir is transmitted to the transmission control unit 250 instead of the existing vehicle speed V. Thereby, although the actual vehicle speed V exceeds the set limit speed Vset, the virtual speed Vvir remains under the set limit speed Vset, and thus, shifting due to an increase in the vehicle speed V may be prevented.” [Col.8 ln 52-60]). Both Debeauwais and Son teach methods of controlling autonomous vehicles. However, Son explicitly teaches wherein tuning, by the at least one processor, the shift pattern map comprises lowering the RPM set in the shift pattern map.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the autonomous vehicle control method of Debeauwais to also include wherein tuning, by the at least one processor, the shift pattern map comprises lowering the RPM set in the shift pattern map, as taught by Son, with a reasonable expectation of success. Doing so improves safety for operating autonomous vehicles (With regard to this reasoning, see at least [Son, Col.8]).
Regarding claim 17, Debeauwais discloses a method of controlling an ego vehicle, the method comprising: determining a torque for the ego vehicle based on first driving state information of a different vehicle that is in front of the ego vehicle and second driving state information of the ego vehicle(“a first step of identifying traffic surrounding the ego vehicle in the traffic lane of the ego vehicle and in at least one adjacent parallel lane in the same traffic direction” and “a third step of calculating a longitudinal speed setpoint of the ego vehicle, an acceleration setpoint and a torque setpoint, said longitudinal speed setpoint being a function of the position of the virtual barycentric target, the speed of the virtual barycentric target and the acceleration of the virtual barycentric target.” [0023]–[0025],“In this driver assistance system according to the invention are found sensors C such as perception sensors enabling measurement of the dynamics of the ego but also perception of the environment… the determination of the environmental information Env that includes the kinematic attributes that are the presence in the lane, dynamic relative position, speed and acceleration of the surrounding vehicles by the sensors C is required…” [0052]);
Debeauwais does not explicitly discloses generating a virtual accelerator pedal sensor (APS) map for the ego vehicle based on the torque and the second driving state information of the ego vehicle,
determining revolutions per minute (RPM) and a gear stage of the ego vehicle based on the virtual APS map and a shift pattern map;
determining a final gear stage of the ego vehicle by comparing the gear stage and a preset gear stage;
in response to a determination that the final gear stage is out of a preset reference gear range, tuning the shift pattern map by lowering an RPM set in the shift pattern map and redetermining the final gear stage based on the tuned shift pattern map;
in response to a determination that the final gear stage is within the preset reference gear range, determining the final gear stage as a current gear stage;
and controlling a gear shift of the ego vehicle based on the redetermined final gear stage or the current gear stage.
However, Son teaches generating a virtual accelerator pedal sensor (APS) map for the ego vehicle based on the torque and the second driving state information of the ego vehicle (“the torque-APS conversion map 244 transmits a virtual APS value APSvr to the virtual APS corrector 246”[Col.8 ln 40-45]“The virtual APS corrector 246 outputs the larger one of the APS value for constant-speed driving at the set limit speed Vset and the virtual APS value APS determined through the torque-APS conversion map 244 as a corrected virtual APS value APSvr,mod, and transmits the corrected virtual APS value APSvr,mod to the transmission control unit 250” [Col.9 ln 6-12]);
determining revolutions per minute (RPM) and a gear stage of the ego vehicle based on the virtual APS map and a shift pattern map (“the transmission control TCU determines a transmission gear position by applying virtual APS value APS and a current vehicle speed V to a shifting map corresponding to the current set mode” [Col.3 ln 3-7];;
determining a final gear stage of the ego vehicle by comparing the gear stage and a preset gear stage (“the CD/CS mode corrector 247 may determine to transition in advance to the CD mode from a point in time at which the actual APS value and the virtual APS value APS become different (i.e., TqAPS>Tqcontroller). Thereby, upshifting to the gear position in the CD mode may be performed until the vehicle reaches the set limit speed Vset, and thus, busy shifting may be prevented.” [Col.9 ln 40-47];
in response to a determination that the final gear stage is out of a preset reference gear range, tuning the shift pattern map by lowering an RPM set in the shift pattern map and redetermining the final gear stage based on the tuned shift pattern map (“transitioning to a second mode at a point in time at which an actual APS value and the second APS value become different … determining a transmission gear position by applying the determined virtual vehicle speed and the determined virtual APS value to one of a first shifting pattern corresponding to the first mode and a second shifting pattern corresponding to the second mode, depending on whether or not to transition to the second mode.” [Col.4-5 ln 58-67 & 1-2]);
in response to a determination that the final gear stage is within the preset reference gear range, determining the final gear stage as a current gear stage (“Referring to FIG. 4 , starting of the engine 110 is assumed in the CS mode and thus the CS shifting pattern is configured to maintain a low RPM in consideration of the efficiency of the engine 110, and the motor 140 is mainly used in the CD mode and thus the CD shifting pattern is configured to allow a high RPM in consideration of the efficiency of the motor 140. Therefore, at the same vehicle speed, the same APS value 410 corresponds to the third gear in the CS shifting pattern and corresponds to the second gear in the CD shifting pattern, i.e., a desired gear position may vary depending on the current mode.” [Col.4 ln 14-24] and “the transmission control TCU determines a transmission gear position by applying virtual APS value APS and a current vehicle speed V to a shifting map corresponding to the current set mode, among a shifting map corresponding to the CD mode and a shifting map corresponding to the CS mode.” [Col.3 ln 3-10]);
and controlling a gear shift of the ego vehicle based on the redetermined final gear stage or the current gear stage (“the CD/CS mode corrector 247 may determine to transition in advance to the CD mode from a point in time at which the actual APS value and the virtual APS value APS become different (i.e., TqAPS>Tqcontroller). Thereby, upshifting to the gear position in the CD mode may be performed until the vehicle reaches the set limit speed Vset, and thus, busy shifting may be prevented.” [Col.9 ln 40-47]). Both Debeauwais and Son teach methods of controlling autonomous vehicles. However, Son explicitly teaches generating a virtual accelerator pedal sensor (APS) map for the ego vehicle based on the torque and the second driving state information of the ego vehicle; determining revolutions per minute (RPM) and a gear stage of the ego vehicle based on the virtual APS map and a shift pattern map; determining a final gear stage of the ego vehicle by comparing the gear stage and a preset gear stage; in response to a determination that the final gear stage is out of a preset reference gear range, tuning the shift pattern map by lowering an RPM set in the shift pattern map and redetermining the final gear stage based on the tuned shift pattern map; in response to a determination that the final gear stage is within the preset reference gear range, determining the final gear stage as a current gear stage and controlling a gear shift of the ego vehicle based on the redetermined final gear stage or the current gear stage.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the autonomous vehicle control method of Debeauwais to also include generating a virtual accelerator pedal sensor (APS) map for the ego vehicle based on the torque and the second driving state information of the ego vehicle; determining revolutions per minute (RPM) and a gear stage of the ego vehicle based on the virtual APS map and a shift pattern map; determining a final gear stage of the ego vehicle by comparing the gear stage and a preset gear stage; in response to a determination that the final gear stage is out of a preset reference gear range, tuning the shift pattern map by lowering an RPM set in the shift pattern map and redetermining the final gear stage based on the tuned shift pattern map; in response to a determination that the final gear stage is within the preset reference gear range, determining the final gear stage as a current gear stage and controlling a gear shift of the ego vehicle based on the redetermined final gear stage or the current gear stage, as taught by Son, with a reasonable expectation of success. Doing so improves safety for operating autonomous vehicles (With regard to this reasoning, see at least [Son, Col.3-7 and 9]).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
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/A.A./Examiner, Art Unit 3668
/MOHAMED ABDO ALGEHAIM/Primary Examiner, Art Unit 3668
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