首页|Toward Trustworthy Decision-Making for Autonomous Vehicles:A Robust Reinforcement Learning Approach with Safety Guarantees

Toward Trustworthy Decision-Making for Autonomous Vehicles:A Robust Reinforcement Learning Approach with Safety Guarantees

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While autonomous vehicles are vital components of intelligent transportation systems,ensuring the trustworthiness of decision-making remains a substantial challenge in realizing autonomous driving.Therefore,we present a novel robust reinforcement learning approach with safety guarantees to attain trustworthy decision-making for autonomous vehicles.The proposed technique ensures decision trust-worthiness in terms of policy robustness and collision safety.Specifically,an adversary model is learned online to simulate the worst-case uncertainty by approximating the optimal adversarial perturbations on the observed states and environmental dynamics.In addition,an adversarial robust actor-critic algorithm is developed to enable the agent to learn robust policies against perturbations in observations and dynamics.Moreover,we devise a safety mask to guarantee the collision safety of the autonomous driving agent during both the training and testing processes using an interpretable knowledge model known as the Responsibility-Sensitive Safety Model.Finally,the proposed approach is evaluated through both simulations and experiments.These results indicate that the autonomous driving agent can make trustworthy decisions and drastically reduce the number of collisions through robust safety policies.

Autonomous vehicleDecision-makingReinforcement learningAdversarial attackSafety guarantee

Xiangkun He、Wenhui Huang、Chen Lv

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School of Mechanical and Aerospace Engineering,Nanyang Technological University,Singapore 639798,Singapore

Start-Up Grant-Nanyang Assistant Professorship Grant of Nanyang Technological UniversityAgency for Science,Technology and Research(A*STAR)under Advanced Manufacturing and Engineering(AME)Young Individual ResearchMTC Individual Research GrantANR-NRF Joint GrantMinistry of Education(MOE)under the Tier 2 Grant

A2084c0156M22K2c0079NRF2021-NRF-ANR003 HM ScienceMOE-T2EP50222-0002

2024

10.1016/j.eng.2023.10.005

工程(英文)

工程(英文)

CSTPCDEI
ISSN：2095-8099
年,卷(期)：2024.33(2)
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