The focus of this paper is to propose a driver model that incorporates human reasoning
levels as actions during interactions with other drivers. Different from earlier work using …

The past decade has witnessed significant breakthroughs in autonomous driving
technologies. We are heading toward an intelligent and efficient transportation system …

As automated driving technology advances, drivers are gradually taking the role of
passengers. During this transition of the driver's role, learning methods to adapt the planned …

Road user behavior prediction is one of the most critical components in trajectory planning
for autonomous driving, especially in urban scenarios involving traffic signals. In this paper …

Trajectory planning is a key component of the Connected and Automated Vehicle (CAV)
autonomy stack. It is a challenging task to plan a trajectory for a CAV that ensures safety …

Reliably predicting the motion of contestant vehicles surrounding an autonomous racecar is
crucial for effective and performant planning. Although highly expressive, deep neural …

Ensuring the safe and efficient operation of self-driving vehicles relies heavily on accurately
predicting their future trajectories. Existing approaches commonly employ an encoder …

A behavior prediction method for autonomous driving vehicles based on the improved
Hidden Markov Model is proposed in this paper. Firstly, several groups of initial model …

As autonomous vehicles (AVs) become increasingly prevalent, their interaction with human
drivers presents a critical challenge. Current AVs lack social awareness, causing behavior …

This paper proposes a method for modeling human driver interactions that relies on multi-
output gaussian processes. The proposed method is developed as a refinement of the game …