Learning-based approaches have achieved remarkable performance in the domain of
autonomous driving. Leveraging the impressive ability of neural networks and large …

Automated driving has the potential to revolutionize personal, public, and freight mobility.
Besides the enormous challenge of perception, ie accurately perceiving the environment …

End-to-end motion planning models equipped with deep neural networks have shown great
potential for enabling full autonomous driving. However the oversized neural networks …

Ensuring the safety of autonomous vehicles (AV s) in uncertain traffic scenarios is a major
challenge. In this paper, we address the problem of computing the risk that AV s violate a …

Human trajectory forecasting in crowds presents the challenges of modelling social
interactions and outputting collision-free multimodal distribution. Following the success of …

Reinforcement learning (RL) methods have gained popularity in the field of motion planning
for autonomous vehicles due to their success in robotics and computer games. However, no …

Autonomous vehicles need to plan at the task level to compute a sequence of symbolic
actions, such as merging left and turning right, to fulfill people's service requests, where …

Trajectory planning has generally been framed as finding the lowest cost one from a set of
trajectory candidates, where the cost function has been hand-crafted with carefully tuned …

One of the challenges of developing autonomous vehicles is planning in an inhabited
environment under sensing uncertainty as well as limited perception and computational …

Generating diverse and comprehensive interacting agents to evaluate the decision-making
modules is essential for the safe and robust planning of autonomous vehicles (AV). Due to …