mlsanityData to 7 Oct 2026

Adversarial Driving Behavior Generation Incorporating Human Risk Cognition for Autonomous Vehicle Evaluation

arXiv 2310.00029 ↗First posted 29 Sep 2023cs.AI cs.GT cs.LGcited by 0

On the map: End-to-end autonomous driving → Adversarial scenarios and safety testing

Abstract (arXiv)

Autonomous vehicle (AV) evaluation has been the subject of increased interest in recent years both in industry and in academia. This paper focuses on the development of a novel framework for generating adversarial driving behavior of background vehicle interfering against the AV to expose effective and rational risky events. Specifically, the adversarial behavior is learned by a reinforcement learning (RL) approach incorporated with the cumulative prospect theory (CPT) which allows representation of human risk cognition. Then, the extended version of deep deterministic policy gradient (DDPG) technique is proposed for training the adversarial policy while ensuring training stability as the CPT action-value function is leveraged. A comparative case study regarding the cut-in scenario is conducted on a high fidelity Hardware-in-the-Loop (HiL) platform and the results demonstrate the adversarial effectiveness to infer the weakness of the tested AV.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Adversarial scenarios and safety testing, one of 9 directions in End-to-end autonomous driving. Builds on 8 of its 28 references that are in the map. First posted on arXiv 29 Sep 2023.

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8 of 28 references.

References · 8

  1. 2015Continuous control with deep reinforcement learningcited by 3,279
  2. 2017Robust Adversarial Reinforcement Learningcited by 439
  3. 2020Learning to Collide: An Adaptive Safety-Critical Scenarios Generating Methodcited by 74
  4. 2016Accelerated Evaluation of Automated Vehicles Safety in Lane Change Scenarios Based on Importance Sampling Techniquescited by 44
  5. 2020Adversarial Evaluation of Autonomous Vehicles in Lane-Change Scenarioscited by 42
  6. 2015Cumulative Prospect Theory Meets Reinforcement Learning: Prediction and Controlcited by 34
  7. 2019Interpretable Modelling of Driving Behaviors in Interactive Driving Scenarios based on Cumulative Prospect Theorycited by 5
  8. 2020Generating Socially Acceptable Perturbations for Efficient Evaluation of Autonomous Vehiclescited by 3

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  4. 2024Adversarial Safety-Critical Scenario Generation using Naturalistic Human Driving Priors
  5. 2021Adversarial Deep Reinforcement Learning for Improving the Robustness of Multi-agent Autonomous Driving Policies
  6. 2023ReMAV: Reward Modeling of Autonomous Vehicles for Finding Likely Failure Events
  7. 2024VCAT: Vulnerability-aware and Curiosity-driven Adversarial Training for Enhancing Autonomous Vehicle Robustness
  8. 2024Less is More: A Stealthy and Efficient Adversarial Attack Method for DRL-based Autonomous Driving Policies