It is quite challenging to ensure the safety of reinforcement learning (RL) agents in an unknown and stochastic environment under hard constraints that require the system state …
R Jiao, J Bai, X Liu, T Sato, X Yuan… - 2023 IEEE/RSJ …, 2023 - ieeexplore.ieee.org
Predicting the future trajectories of surrounding vehicles based on their history trajectories is a critical task in autonomous driving. However, when small crafted perturbations are …
X Liu, R Jiao, Y Wang, Y Han… - 2023 IEEE/RSJ …, 2023 - ieeexplore.ieee.org
Recently significant progress has been made in vehicle prediction and planning algorithms for autonomous driving. However, it remains quite challenging for an autonomous vehicle to …
X Liu, Y Luo, A Goeckner, T Chakraborty… - 2023 60th ACM/IEEE …, 2023 - ieeexplore.ieee.org
The rapid advancement of edge and cloud computing platforms, vehicular ad-hoc networks, and machine learning techniques have brought both opportunities and challenges for next …
Neural network-based driving planners have shown great promises in improving task performance of autonomous driving. However, it is critical and yet very challenging to ensure …
Model-based reinforcement learning has been widely studied for controller synthesis in cyber-physical systems (CPSs). In particular, for safety-critical CPSs, it is important to …
Connectivity technology has shown great potentials in improving the safety and efficiency of transportation systems by providing information beyond the perception and prediction …
Predicting the trajectories of surrounding objects is a critical task for self-driving vehicles and many other autonomous systems. Recent works demonstrate that adversarial attacks on …
B Xia, H Luan, Z Zhao, X Gao, P Xie… - 2023 IEEE/RSJ …, 2023 - ieeexplore.ieee.org
Cooperative object transportation using multiple robots has been intensively studied in the control and robotics literature, but most approaches are either only applicable to …