From the driving strategy point of view, a major challenge for autonomous vehicles in urban
environment is to behave defensively to potential dangers, yet to not overreact to threats with …

Generating safe and non-conservative behaviors in dense, dynamic environments remains
challenging for automated vehicles due to the stochastic nature of traffic participants' …

Although autonomous driving techniques have achieved great improvements, challenges
still exist in decision making for variety of different scenarios under uncertain and interactive …

We present a motion planning framework for autonomous on-road driving considering both
the uncertainty caused by an autonomous vehicle and other traffic participants. The future …

The autonomous vehicle is regarded as a promising technology with the potential to
reshape mobility and solve many traffic issues, such as accessibility, efficiency …

This paper proposes a two-stage trajectory planning method for self-driving vehicles (SDVs)
in intersection scenarios with uncertain social circumstances while considering other traffic …

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 …

Reinforcement learning (RL) methods are commonly regarded as effective solutions for
designing intelligent driving policies. Nonetheless, even if the RL policy is converged after …

In this paper, we propose a novel planning framework that can greatly improve the level of
intelligence and driving quality of autonomous vehicles. A reference planning layer first …

Formally verified methods for motion planning are required in order to guarantee safety for
autonomous vehicles. In particular, we consider trajectory generation by considering the …