Recently, the advancement in communications, intelligent transportation systems, and
computational systems has opened up new opportunities for intelligent traffic safety, comfort …

Humans drive in a holistic fashion which entails, in particular, understanding dynamic road
events and their evolution. Injecting these capabilities in autonomous vehicles can thus take …

Accurate trajectory prediction of surrounding road users is critical to autonomous driving
systems. In mixed traffic flows, road users with different kinds of behaviors and styles bring …

Decision and control are core functionalities of high-level automated vehicles. Current
mainstream methods, such as functional decomposition and end-to-end reinforcement …

Planning safe trajectories for autonomous vehicles in complex urban environments is
challenging since there are numerous semantic elements (such as dynamic agents, traffic …

Autonomous vehicles must operate safely in their dynamic and continuously-changing
environment. However, the operating environment of an autonomous vehicle is complicated …

Trajectory planning is of vital importance to decision-making for autonomous vehicles.
Currently, there are three popular classes of cost-based trajectory planning methods …

Safe and efficient decision-making and path planning is a challenging problem for
autonomous driving in highway because of numerous dynamic vehicles around the ego …

This paper introduces a local planner that synergizes the decision making and trajectory
planning modules towards autonomous driving. The decision making and trajectory …

Reinforcement Learning (RL) has made promising progress in planning and decision-
making for Autonomous Vehicles (AVs) in simple driving scenarios. However, existing RL …