Fear-neuro-inspired reinforcement learning for safe autonomous driving by He, Xiangkun, Wu, Jingda, Huang, Zhiyu, Hu, Zhongxu, Wang, Jun, Sangiovanni-Vincentelli, Alberto, Lv, Chen

“…Drawing inspiration from this scientific discovery, we present a fear-neuro-inspired reinforcement learning framework to realize safe autonomous driving through modeling the amygdala functionality. …”
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Towards safe autonomous driving: decision making with observation-robust reinforcement learning by He, Xiangkun, Lv, Chen

“…The results show that the developed approach can ensure autonomous driving performance, as well as the policy robustness against adversarial attacks on observations.…”
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Trustworthy autonomous driving via defense-aware robust reinforcement learning against worst-case observational perturbations by He, Xiangkun, Huang, Wenhui, Lv, Chen

“…Despite the substantial advancements in reinforcement learning (RL) in recent years, ensuring trustworthiness remains a formidable challenge when applying this technology to safety-critical autonomous driving domains. One pivotal bottleneck is that well-trained driving policy models may be particularly vulnerable to observational perturbations or perceptual uncertainties, potentially leading to severe failures. …”
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Toward personalized decision making for autonomous vehicles: a constrained multi-objective reinforcement learning technique by He, Xiangkun, Lv, Chen

“…Reinforcement learning promises to provide a state-of-the-art solution to the decision making problem of autonomous driving. Nonetheless, numerous real-world decision making problems involve balancing multiple conflicting or competing objectives. …”
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Toward trustworthy decision-making for autonomous vehicles: a robust reinforcement learning approach with safety guarantees by He, Xiangkun, Huang, Wenhui, Lv, Chen

“…These results indicate that the autonomous driving agent can make trustworthy decisions and drastically reduce the number of collisions through robust safety policies.…”
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Safe decision-making for lane-change of autonomous vehicles via human demonstration-aided reinforcement learning by Wu, Jingda, Huang, Wenhui, de Boer, Niels, Mo, Yanghui, He, Xiangkun, Lv, Chen

“…Decision-making is critical for lane change in autonomous driving. Reinforcement learning (RL) algorithms aim to identify the values of behaviors in various situations and thus they become a promising pathway to address the decision- making problem. …”
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