Autonomous driving (AD) agents generate driving policies based on online perception
results, which are obtained at multiple levels of abstraction, eg, behavior planning, motion …

The development of autonomous vehicles (AVs) is becoming increasingly important as the
need for reliable and safe transportation grows. However, in order to achieve level 5 …

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 …

Autonomous driving has achieved significant progress in recent years, but autonomous cars
are still unable to tackle high-risk situations where a potential accident is likely. In such near …

Autonomous navigation in dense traffic scenarios remains challenging for autonomous
vehicles (AVs) because the intentions of other drivers are not directly observable and AVs …

We present a review and taxonomy of 200 models from the literature on driver behavior
modeling. We begin by introducing a mathematical framework for describing the dynamics of …

Driving at an unsignalized intersection is a complex traffic scenario that requires both traffic
safety and efficiency. At the unsignalized intersection, the driving policy does not simply …

Deep reinforcement learning (RL) has been widely applied to motion planning problems of
autonomous vehicles in urban traffic. However, traditional deep RL algorithms cannot …

Autonomous navigation in dense traffic scenarios remains challenging for autonomous
vehicles (AVs) because the intentions of other drivers are not directly observable and AVs …

Unsignalized intersection driving is challenging for automated vehicles. For safe and
efficient performances, the diverse and dynamic behaviors of interacting vehicles should be …