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Survey details security risks in deep reinforcement learning for autonomous driving

By PulseAugur Editorial · [1 sources] ·

A new survey paper published on arXiv examines the security vulnerabilities of deep reinforcement learning (RL) algorithms when applied to autonomous driving systems. The paper categorizes various attacks and defenses based on threat models and agent settings, aiming to provide a more comprehensive guide than existing literature. It specifically analyzes the applicability of these security measures within the context of autonomous driving to aid in the development of more robust RL systems. AI

IMPACT Highlights security challenges in applying deep reinforcement learning to safety-critical autonomous driving systems.

RANK_REASON The cluster contains an academic survey paper on a specific research topic. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Survey details security risks in deep reinforcement learning for autonomous driving

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The cluster contains an academic survey paper on a specific research topic. [lever_c_demoted from research: ic=1 ai=1.0]
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paper, safety
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Ambra Demontis, Srishti Gupta, Maura Pintor, Luca Demetrio, Kathrin Grosse, Hsiao-Ying Lin, Chengfang Fang, Battista Biggio, Fabio Roli ·

    Security of Deep Reinforcement Learning for Autonomous Driving: A Survey

    arXiv:2212.06123v4 Announce Type: replace Abstract: Reinforcement learning (RL) enables agents to learn optimal behaviors through interaction with their environment and has been increasingly deployed in safety-critical applications, including autonomous driving. Despite its promi…

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