As more and more autonomous vehicles (AVs) are being deployed on public roads,
designing socially compatible behaviors for them is becoming increasingly important. In …

Lane change and crash risk prediction are critical technologies for autonomous driving. An
attention-based LSTM model is proposed in this paper for lane change behavior prediction …

Autonomous vehicles are sharing the road with human drivers. In order to facilitate
interactive driving and cooperative behavior in dense traffic, a thorough understanding and …

Understanding human drivers' intentions and predicting their future motions are significant to
connected and autonomous vehicles and traffic safety and surveillance systems. Predicting …

In this work, a synergistic combination of deep reinforcement learning and hierarchical game
theory is proposed as a modeling framework for behavioral predictions of drivers in highway …

Personalization in driving behavior research is crucial for developing intelligent vehicles that
can safely coexist with human-driven vehicles in mixed-traffic environments. By accounting …

In the foreseeable future, connected and auto-mated vehicles (CAVs) and human-driven
vehicles will share the road networks together. In such a mixed traffic environment, CAVs …

Autonomous cars have to navigate in dynamic environment which can be full of
uncertainties. The uncertainties can come either from sensor limitations such as occlusions …

This paper describes the design, implementation, and evaluation of a virtual target-based
overtaking decision, motion planning, and control algorithm for autonomous vehicles. Both …

Merge scenarios on highway are often challenging for autonomous driving, due to its lack of
sufficient tacit understanding on and subtle interaction with human drivers in the traffic flow …