Thanks to their road safety potential, automated vehicles are rapidly becoming a reality. In
the last decade, automated driving has been the focus of intensive automotive engineering …

Autonomous driving has the ability to reshape mobility and transportation by reducing road
accidents, traffic jams, and air pollution. This can yield energy efficiency, convenience, and …

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

Complex, dynamic, and interactive environment brings huge challenges to autonomous
driving technologies. Because of the strong interactions between different traffic participants …

Parking in a small parking lot within limited space poses a difficult task. It often leads to
deviations between the final parking posture and the target posture. These deviations can …

Autonomous driving confronts great challenges in complex traffic scenarios, where the
SOTIF risk can be triggered by the dynamic operational environment and system …

To successfully implement the platoon control of connected and automated vehicles, it is
necessary to address motion control issues to achieve longitudinal and lateral collaborative …

A driving-safety-zone-model-oriented motion planning framework (DSZMF) is proposed for
autonomous platoons in heterogeneous driving environments with complex driving …

This paper presents a comparison among path tracking controllers on low-friction roads for
autonomous vehicles. There are two goals in this paper. The first is to check the …

For automobiles, the innovation of autopilot, connected communication, and new energy has
been treated as the trend. This article proposed a deep reinforcement learning (DRL)-based …