In driving scenes, road agents often engage in frequent interaction and strive to understand
their surroundings. Ego-agent (each road agent itself) predicts what behavior will be …

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

This paper presents a novel interactive motion planning approach for left-turning of
autonomous vehicles at uncontrolled intersections. A typical left-turning scenario that …

In this paper, we propose a new state representation method, called encoding sum and
concatenation (ESC), to describe the environment observation for decision-making in …

Real-world driving involves intricate interactions among vehicles navigating through dense
traffic scenarios. Recent research focuses on enhancing the interaction awareness of …

This book details cutting-edge research into human-like driving technology, utilising game
theory to better suit a human and machine hybrid driving environment. Covering feature …

针对自动驾驶汽车运动规划中预测周围交通态势的问题ꎬ 提出一种考虑周围车辆间交互轨迹
预测的运动规划算法. 首先ꎬ 针对结构化道路信息ꎬ 构建改进社会力模型ꎬ …

Balancing safety and efficiency when navigating in dense traffic remains an open challenge
in autonomous driving. Safety envelopes restrict the allowed planning region and yield …

Autonomous driving in urban environments requires safe control policies that account for the
non-determinism of moving obstacles, for instance, the intention of other vehicles while …

Safety has been a key goal for autonomous driving since its inception, and we believe
recognizing and responding to risk is a key component of safety. In this work, we aim to …