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 …
We present POLAR (The source code can be found at https://github. com/ChaoHuang2018/ POLAR_Tool. The full version of this paper can be found at https://arxiv …
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 …
Neural networks have shown great promises in planning, control, and general decision making for learning-enabled cyber-physical systems (LE-CPSs), especially in improving …
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 …
Deep Neural Networks (DNNs) are extensively deployed in today's safety-critical autonomous systems thanks to their excellent performance. However, they are known 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 …
R Jiao, X Liu, B Zheng, D Liang… - 2022 IEEE/RSJ …, 2022 - ieeexplore.ieee.org
Trajectory generation and prediction are two in-terwoven tasks that play important roles in planner evaluation and decision making for intelligent vehicles. Most existing methods focus …