Intelligent Vehicle
Intelligent vehicles aim to enhance safety and efficiency in transportation through advanced automation and decision-making capabilities. Current research heavily focuses on improving perception (using cameras, LiDAR, and radar data, often processed by convolutional neural networks and transformers), predicting the behavior of other road users (leveraging graph neural networks and reinforcement learning), and planning safe and efficient maneuvers (employing optimization algorithms and imitation learning). These advancements are crucial for enabling autonomous driving and improving existing driver-assistance systems, with significant implications for traffic safety, transportation efficiency, and the broader automotive industry.
Papers
Identify, Estimate and Bound the Uncertainty of Reinforcement Learning for Autonomous Driving
Weitao Zhou, Zhong Cao, Nanshan Deng, Kun Jiang, Diange Yang
Milestones in Autonomous Driving and Intelligent Vehicles Part I: Control, Computing System Design, Communication, HD Map, Testing, and Human Behaviors
Long Chen, Yuchen Li, Chao Huang, Yang Xing, Daxin Tian, Li Li, Zhongxu Hu, Siyu Teng, Chen Lv, Jinjun Wang, Dongpu Cao, Nanning Zheng, Fei-Yue Wang
RSG-Net: Towards Rich Sematic Relationship Prediction for Intelligent Vehicle in Complex Environments
Yafu Tian, Alexander Carballo, Ruifeng Li, Kazuya Takeda
Robust AI Driving Strategy for Autonomous Vehicles
Subramanya Nageshrao, Yousaf Rahman, Vladimir Ivanovic, Mrdjan Jankovic, Eric Tseng, Michael Hafner, Dimitar Filev