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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Predictive neural motion planner for autonomous driving using graph networks by Mo, Xiaoyu, Lv, Chen

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Prioritized experience-based reinforcement learning with human guidance for autonomous driving by Wu, Jingda, Huang, Zhiyu, Huang, Wenhui, Lv, Chen

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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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An integrated decision-making framework for highway autonomous driving using combined learning and rule-based algorithm by Xu, Can, Zhao, Wanzhong, Liu, Jinqiang, Wang, Chunyan, Lv, Chen

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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 human-in-the-loop AI: enhancing deep reinforcement learning via real-time human guidance for autonomous driving by Wu, Jingda, Huang, Zhiyu, Hu, Zhongxu, Lv, Chen

“…In this study, a real-time human-guidance-based (Hug)-deep reinforcement learning (DRL) method is developed for policy training in an end-to-end autonomous driving case. With our newly designed mechanism for control transfer between humans and automation, humans are able to intervene and correct the agent's unreasonable actions in real time when necessary during the model training process. …”
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A personalized behavior learning system for human-like longitudinal speed control of autonomous vehicles by Lu, Chao, Gong, Jianwei, Lv, Chen, Chen, Xin, Cao, Dongpu, Chen, Yimin

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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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Deep reinforcement learning-based energy-efficient decision-making for autonomous electric vehicle in dynamic traffic environments by Wu, Jingda, Song, Ziyou, Lv, Chen

“…Autonomous driving techniques are promising for improving the energy efficiency of electrified vehicles (EVs) by adjusting driving decisions and optimizing energy requirements. …”
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Occlusion-free road segmentation leveraging semantics for autonomous vehicles by Wang, Kewei, Yan, Fuwu, Zou, Bin, Tang, Luqi, Yuan, Quan, Lv, Chen

“…The deep convolutional neural network has led the trend of vision-based road detection, however, obtaining a full road area despite the occlusion from monocular vision remains challenging due to the dynamic scenes in autonomous driving. Inferring the occluded road area requires a comprehensive understanding of the geometry and the semantics of the visible scene. …”
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LiDAR-based multi-task road perception network for autonomous vehicles by Yan, Fuwu, Wang, Kewei, Zou, Bin, Tang, Luqi, Li, Wenbo, Lv, Chen

“…A comprehensive perception of the surrounding road should cover the accurate detection of the entire road area despite occlusion, the 3D geometry and the types of road topology in order to facilitate the practical applications in autonomous driving. To this end, we propose a lightweight and efficient LiDAR-based multi-task road perception network (LMRoadNet) to conduct occlusion-free road segmentation, road ground height estimation, and road topology recognition simultaneously. …”
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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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