Abstract Recent advances in Intelligent Transport Systems (ITS) and Artificial Intelligence
(AI) have stimulated and paved the way toward the widespread introduction of Autonomous …

A fundamental task in robotics is to plan collision‐free motions among a set of obstacles.
Recently, learning‐based motion‐planning methods have shown significant advantages in …

Efficient reasoning about the semantic, spatial, and temporal structure of a scene is a crucial
prerequisite for autonomous driving. We present NEural ATtention fields (NEAT), a novel …

End-to-end autonomous driving has great potential in the transportation industry. However,
the lack of transparency and interpretability of the automatic decision-making process …

This article considers the problem of diagnosing certain common errors in reward design. Its
insights are also applicable to the design of cost functions and performance metrics more …

Reinforcement learning (RL) has shown excellent performance in the sequential decision-
making problem, where safety in the form of state constraints is of great significance in the …

This paper proposes an adaptive lane change trajectory planning scheme to road friction
and vehicle speed for autonomous driving, while considering both the maneuver safety and …

Decision-making for urban autonomous driving is challenging due to the stochastic nature of
interactive traffic participants and the complexity of road structures. Although reinforcement …

With the maturation of autonomous driving technology, the use of autonomous vehicles in a
socially acceptable manner has become a growing demand of the public. Human-like …

Traffic microsimulation has a functional role in understanding the traffic performance on the
road network. This study originated with intent to understand traffic microsimulation and its …