In driving scenes, road agents often engage in frequent interaction and strive to understand
their surroundings. Ego-agent (each road agent itself) predicts what behavior will be …

Imitation learning (IL) is a simple and powerful way to use high-quality human driving data,
which can be collected at scale, to produce human-like behavior. However, policies based …

Autonomous driving is considered one of the revolutionary technologies shaping humanity's
future mobility and quality of life. However, safety remains a critical hurdle in the way of …

Deep neural networks (DNNs) have achieved tremendous success in making accurate
predictions for computer vision, natural language processing, as well as science and …

Vehicle Trajectory Prediction (VTP) is one of the key issues in the field of autonomous
driving. In recent years, more researchers have tried applying Deep Learning methods and …

Deep reinforcement learning (DRL) has emerged as a promising approach for developing
more intelligent autonomous vehicles (AVs). A typical DRL application on AVs is to train a …

Recent advances in the development of vision-language models (VLMs) are yielding
remarkable success in recognizing visual semantic content, including impressive instances …

The combination of lifelong learning algorithms with autonomous intelligent systems (AIS) is
gaining popularity due to its ability to enhance AIS performance, but the existing summaries …

Self-driving vehicles (SDVs) are becoming reality but still suffer from “long-tail” challenges
during natural driving: the SDVs will continually encounter rare, safety-critical cases that may …

Range-extended electric vehicles combine the benefits of electric propulsion with the
convenience and range flexibility of conventional internal combustion engine vehicles. In the …