Guardauto: a decentralized runtime protection system for autonomous driving by Cheng, Kun, Zhou, Yuan, Chen, Bihuan, Wang, Rui, Bai, Yuebin, Liu, Yang

Subjects: Get full text

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. …”
Get full text

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

Get full text

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

Subjects: Get full text

A deep learning-based semantic segmentation architecture for autonomous driving applications by Masood, Sharjeel, Ahmed, Fawad, Alsuhibany, Suliman A., Ghadi, Yazeed Yasin, Siyal, M. Y., Kumar, Harish, Khan, Khyber, Ahmad, Jawad

Subjects: Get full text

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.…”
Get full text

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

Get full text

Towards autonomous driving : review and perspectives on configuration and control of four-wheel independent drive/steering electric vehicles by Hang, Peng, Chen, Xinbo

Subjects: Get full text

AutoDRIVE: a comprehensive, flexible and integrated digital twin ecosystem for enhancing autonomous driving research and education by Samak, Tanmay, Samak, Chinmay, Kandhasamy, Sivanathan, Krovi, Venkat, Xie, Ming

“…This work presents our attempt towards developing such a comprehensive research and education ecosystem, called AutoDRIVE, for synergistically prototyping, simulating and deploying cyber-physical solutions pertaining to autonomous driving as well as smart city management. AutoDRIVE features both software as well as hardware-in-the-loop testing interfaces with openly accessible scaled vehicle and infrastructure components. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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

Subjects: Get full text

Contrastive learning-based knowledge distillation for RGB-thermal urban scene semantic segmentation by Guo, Xiaodong, Zhou, Wujie, Liu, Tong

Subjects: Get full text

Influence of multi-modal warning interface on takeover efficiency of autonomous high-speed train by Jing, Chunhui, Dai, Haohong, Yao, Xing, Du, Dandan, Yu, Kaidi, Yu, Dongyu, Zhi, Jinyi

Subjects: Get full text

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. …”
Get full text

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.…”
Get full text

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. …”
Get full text

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. …”
Get full text

A framework for fast and robust visual odometry by Wu, Meiqing, Lam, Siew-Kei, Srikanthan, Thambipillai

“…Knowledge of the ego-vehicle's motion state is essential for assessing the collision risk in advanced driver assistance systems or autonomous driving. Vision-based methods for estimating the ego-motion of vehicle, i.e., visual odometry, face a number of challenges in uncontrolled realistic urban environments. …”
Get full text

6G internet of things: a comprehensive survey by Nguyen, Dinh C., Ding, Ming, Pathirana, Pubudu N., Seneviratne, Aruna, Li, Jun, Niyato, Dusit, Dobre, Octavia, Poor, H. Vincent

“…Particularly, compared to the other related survey papers, we provide an in-depth discussion of the roles of 6G in a wide range of prospective IoT applications via five key domains, namely Healthcare Internet of Things, Vehicular Internet of Things and Autonomous Driving, Unmanned Aerial Vehicles, Satellite Internet of Things, and Industrial Internet of Things. …”
Get full text