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公开(公告)号：US20240416929A1
公开(公告)日：2024-12-19
申请号：US18335758
申请日：2023-06-15
Applicant: GM Global Technology Operations LLC
Inventor： Rodolfo Valiente Romero , Hyukseong Kwon , Marcus James Huber , Alireza Esna Ashari Esfahani , Michael Cui
Abstract: An end-to-end perception perturbation modeling system for a vehicle includes one or more controllers storing a detection model in memory. The detection model includes a plurality of detection model plots that each indicate a probability value that an object in an environment surrounding the vehicle is detected based on a current weather condition and a distance measured between the vehicle and the object detected in the environment surrounding the vehicle. The one or more controllers execute instructions to receive an input state of the vehicle that is observed during non-inclement weather conditions and indicates one or more vehicle states, the current weather condition, and the distance. The controllers determine a perturbed state of the vehicle observed during the current weather condition.
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公开(公告)号：US20230192118A1
公开(公告)日：2023-06-22
申请号：US17555973
申请日：2021-12-20
Applicant: GM Global Technology Operations LLC
CPC classification number: B60W60/001 , B60W40/09 , G05D1/10 , G05D1/0088 , G05D1/0221 , B60W2540/30 , G05D2201/0213
Abstract: A system comprises a computer including a processor and a memory. The memory includes instructions such that the processor is programmed to: receive sensor data representing a perceived driving environment, select a reinforcement learning agent from a plurality of reinforcement learning agents based on a challenge score calculated using the sensor data and a desired driving style, and generate, via the selected reinforcement learning agent, a driving action based on the sensor data.
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Patent Agency Ranking
公开(公告)号：US20230339517A1
公开(公告)日：2023-10-26
申请号：US17726785
申请日：2022-04-22
Applicant: GM Global Technology Operations LLC
Inventor： Syed Bilal Mehdi , Marcus James Huber , Sayyed Rouhollah Jafari Tafti , Jeremy A. Salinger
CPC classification number: B60W60/0053 , G07C5/02 , G06F30/20 , B60W2554/406 , B60W2555/60
Abstract: A system comprises a computer including a processor and a memory. The memory includes instructions such that the processor is programmed to: execute an autonomous vehicle algorithm simulating vehicle operations within a simulated environment. The simulated environment represents a plurality of driving situations. The memory also includes instructions such that the processor is programmed to: determine a challenge rating for the driving situation, determine an autonomous vehicle performance assessment score corresponding to the simulated environment, compare the autonomous vehicle performance assessment score with a human driving score corresponding to the simulated environment, and generate a performance profile based on the comparison. In some implementations, a vehicle computer can determine a challenge rating and generate at least one of a driver takeover recommendation or an alert indicating a presence of a fault based on a comparison of vehicle performance with the challenge rating.
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公开(公告)号：US20250044118A1
公开(公告)日：2025-02-06
申请号：US18362109
申请日：2023-07-31
Applicant: GM Global Technology Operations LLC
Inventor： Michael Cui , Hyukseong Kwon , Rodolfo Valiente Romero , Marcus James Huber , Alireza Esna Ashari Esfahani , Andrew Howe
Abstract: A system for updating a road map for a vehicle includes a plurality of vehicle sensors and a vehicle controller in electrical communication with the plurality of vehicle sensors. The vehicle controller is programmed to gather input data about an environment surrounding the vehicle using the plurality of vehicle sensors. The input data includes at least one abnormal traffic pattern indication. The vehicle controller is further programmed to generate an input label map based at least in part on the input data. The vehicle controller is further programmed to generate a vehicle output label map based at least in part on the input label map. The vehicle output label map is generated using a machine learning algorithm. The vehicle controller is further programmed to perform a first action based at least in part on the vehicle output label map.
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公开(公告)号：US12172669B2
公开(公告)日：2024-12-24
申请号：US17555973
申请日：2021-12-20
Applicant: GM Global Technology Operations LLC
Abstract: A system comprises a computer including a processor and a memory. The memory includes instructions such that the processor is programmed to: receive sensor data representing a perceived driving environment, select a reinforcement learning agent from a plurality of reinforcement learning agents based on a challenge score calculated using the sensor data and a desired driving style, and generate, via the selected reinforcement learning agent, a driving action based on the sensor data.
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公开(公告)号：US20250065917A1
公开(公告)日：2025-02-27
申请号：US18453489
申请日：2023-08-22
Applicant: GM Global Technology Operations LLC
Inventor： Michael Cui , Hyukseong Kwon , Rodolfo Valiente Romero , Andrew Howe , Alexander Waagen , Alexei Kopylov , Marcus James Huber , Alireza Esna Ashari Esfahani
IPC: B60W60/00 , B60W30/09 , B60W30/095
Abstract: According to several aspects, a method for path planning for a vehicle includes determining a predicted trajectory of a remote vehicle. The predicted trajectory of the remote vehicle includes a plurality of predicted trajectory nodes. The method further includes determining a plurality of possible trajectories for the vehicle. The plurality of possible trajectories includes a plurality of possible trajectory nodes. The method further includes determining one or more evaluation metrics of each of the plurality of possible trajectory nodes based at least in part on a weather condition in an environment surrounding the vehicle. The method further includes selecting an optimal trajectory for the vehicle from the plurality of possible trajectories based at least in part on the one or more evaluation metrics of each of the plurality of possible trajectory nodes. The method further includes performing a first action based at least in part on the optimal trajectory.
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公开(公告)号：US20240425050A1
公开(公告)日：2024-12-26
申请号：US18340249
申请日：2023-06-23
Applicant: GM Global Technology Operations LLC
Inventor： Rodolfo Valiente Romero , Hyukseong Kwon , Marcus James Huber , Alireza Esna Ashari Esfahani , Michael Cui
Abstract: A hybrid probabilistic driving behavior modeling system conditioned on weather for a vehicle includes one or more controllers executing instructions to determine, by a longitudinal driving model stored by the one or more controllers, a probabilistic longitudinal velocity of the vehicle with respect to a current weather condition based on a car-following model, a semantic rule system, and a speed and visibility model. The current weather condition indicates an adverse weather condition impacting driving conditions for the vehicle. The one or more controllers determine, by a probabilistic lateral driving model by the one or more controllers, one or more lane choices for the vehicle with respect to the current weather condition based on a route plan of the vehicle and perception data indicative of an environment surrounding the vehicle and selects a final lane choice from the one or more lane choices.
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