Multi-robot environmental coverage can be widely used in many applications like search
and rescue. However, it is challenging to coordinate the robot team for high coverage …

For autonomous vehicles, scene understanding is still one of the major challenges, which
needs to be well handled to avoid jittery decisions and unsmooth trajectories. Furthermore …

Intersections are key nodes and also bottlenecks of urban road networks, so improving the
traffic efficiency at intersections is beneficial to improving overall traffic throughput and …

Autonomous driving requires efficient and safe decision making and motion planning in
dynamic and uncertain environments. Future movement of surrounding vehicles is often …

Autonomous vehicles face challenges in ensuring cyber-physical security due to their
reliance on image data from cameras processed by machine learning. These algorithms …

Multi-vehicle coordinated motion planning has always been challenged to safely and
efficiently resolve conflicts under non-holonomic dynamic constraints. Constructing spatial …

In this paper, we develop a framework to enhance the control of autonomous vehicles within
signalized intersections by integrating system dynamics with imperfect sensor data …

Deep reinforcement learning (DRL) has become a powerful method for autonomous driving
while often lacking safety guarantees. In this paper, we propose a Risk-fused Constraint …

In this work, we present a reward-driven automated curriculum reinforcement learning
approach for interaction-aware self-driving at unsignalized intersections, taking into account …

This paper addresses the traffic management problem for autonomous vehicles at
intersections without traffic signals. In the current system, a road junction has no traffic …