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

How to make a controller robust and stable to reject the disturbance of uncertainty is an
inevitable challenge. Aiming at addressing the lateral control problem for an autonomous road …

Reinforcement learning is applied to automatic parking in order to eliminate errors caused
by dead reckoning and path tracking during automatic parking. However, the reinforcement …

The obstacle-avoidance problem of intelligent vehicles is one of the challenges that we face
in path planning. In order to tackle it, this paper proposes a real-time path planning …

In this paper, we develop a unified trajectory planning and tracking control framework for
autonomous overtaking in structured roads. The framework consists of two individual MPC to …

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 …

Motion planning in autonomous driving is a major challenge, as it needs to consider the
continuity between the actions output by internal modules and the need for consistency in …

Autonomous Vehicles (AV) driving is one of the latest research points in variety fields, a high-qualified
decision method is an essential part of the autonomous driving system. …

In this paper, a real-time local path planner for obstacle avoidance of autonomous vehicles
is proposed. Based on the tentacle algorithm, a novel tentacle curve that can ensure the …

A novel dual‐pH sensitive charge‐reversal strategy is designed to deliver antitumor drugs
targeting to tumor cells and to further promote the nuclei internalization by a stepwise …