Planning and decision-making technology at intersections is a comprehensive research
problem in intelligent transportation systems due to the uncertainties caused by a variety of …

Intersections are considered one of the most complex scenarios in a self-driving framework
due to the uncertainty in the behaviors of surrounding vehicles and the different types of …

Deep learning has been used to demonstrate end-to-end neural network learning for
autonomous vehicle control from raw sensory input. While LiDAR sensors provide reliably …

Left turning at unsignalized intersectionis one of the most challenging tasks for urban
automated driving, due to the various shapes of intersections and rapidly changing nature of …

The intersection is a critical traffic problem from the perspective of safety and traffic
efficiency. As wireless communication technology advances, vehicle infrastructure …

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

Urban autonomous driving is a difficult task because of its complex road scenarios and the
interaction between multiple vehicles. Autonomous vehicles need to balance multiple …

Reinforcement learning-based techniques, empowered by deep-structured neural nets,
have demonstrated superiority over rule-based methods in terms of making high-level …

Turning behaviour is one of the most challenging driving manoeuvres that take place at
intersections. Autonomous vehicles (AVs) are often overly conservative in these scenarios …

Intersections have attracted wide attention owing to their complexity and high rate of traffic
accidents. In the process of developing L3-and-above autonomous-driving techniques, it is …