Provided are methods for learning to identify safety-critical scenarios for autonomous vehicles. First state information representing a first state of a driving scenario is received. The information includes a state of a vehicle and a state of an agent in the vehicle's environment. The first state information is processed with a neural network to determine at least one action to be performed by the agent, including a perception degradation action causing misperception of the agent by a perception system of the vehicle. Second state information representing a second state of the driving scenario is received after performance of the at least one action. A reward for the action is determined. First and second distances between the vehicle and the agent are determined and compared to determine the reward for the at least one action. At least one weight of the neural network is adjusted based on the reward.
Learning to identify safety-critical scenarios for an autonomous vehicle
29.11.2023
Patent
Elektronische Ressource
Englisch
LEARNING TO IDENTIFY SAFETY-CRITICAL SCENARIOS FOR AN AUTONOMOUS VEHICLE
Europäisches Patentamt | 2025
LEARNING TO IDENTIFY SAFETY-CRITICAL SCENARIOS FOR AN AUTONOMOUS VEHICLE
Europäisches Patentamt | 2023
|Learning to identify safety-critical scenarios for an autonomous vehicle
Europäisches Patentamt | 2022
|Suicidal Pedestrian: Generation of Safety-Critical Scenarios for Autonomous Vehicles
ArXiv | 2023
|