Autonomous robots will soon enter our everyday life as self-driving cars. These vehicles are
designed to behave according to certain sets of cooperative rules, such as traffic ones, and …

In future smart cities supported by cyber-physical social intelligence, autonomous behavioral
decision for vehicular agents is going to become a general demand. Despite much progress …

Despite the advances in the autonomous driving domain, autonomous vehicles (AVs) are
still inefficient and limited in terms of cooperating with each other or coordinating with …

Trajectory prediction is crucial for autonomous vehicles. The planning system not only needs
to know the current state of the surrounding objects but also their possible states in the …

This paper proposes a two-stage trajectory planning method for self-driving vehicles (SDVs)
in intersection scenarios with uncertain social circumstances while considering other traffic …

Reinforcement learning (RL) can be used to design smart driving policies in complex
situations where traditional methods cannot. However, they are frequently black-box in …

Cooperative driving behavior is essential for driving in traffic, especially for ramp merging,
lane changing or navigating intersections. Autonomous vehicles should also man-age these …

It is critical to predict the motion of surrounding vehicles for self-driving planning, especially
in a socially compliant and flexible way. However, future prediction is challenging due to the …

The driving behavior of autonomous vehicles has a significant impact on safety for all traffic
participants. Unlike current traffic participants, autonomous vehicles in the future will also …

Although autonomous driving techniques have achieved great improvements, challenges
still exist in decision making for variety of different scenarios under uncertain and interactive …