Autonomous urban driving navigation with complex multi-agent dynamics is under-explored
due to the difficulty of learning an optimal driving policy. The traditional modular pipeline …

Realizing autonomy is a hot research topic for automatic vehicles in recent years. For a long
time, most of the efforts to this goal concentrate on understanding the scenes surrounding …

Currently, autonomous vehicles (AVs) have gained considerable research interest in motion
planning (MP) to control driving. Deep learning (DL) is a subset of machine learning …

The ability of artificial intelligence to drive toward an intended destination is a key
component of an autonomous vehicle. Different paradigms are now being employed to …

Parallel end-to-end driving aims to improve the performance of end-to-end driving models
using both simulated-and real-world data. However, how to efficiently utilize the data from …

Imitation learning for the end-to-end autonomous driving has drawn renewed attention from
academic communities. Current methods either only use images as the input, which will …

Human drivers have excellent perception and reaction abilities in complex environments
such as dangerous highways, busy intersections, and harsh weather conditions. To achieve …

Learning to drive faithfully in highly stochastic urban settings remains an open problem. To
that end, we propose a Multi-task Learning from Demonstration (MT-LfD) framework which …

Human drivers are remarkably robust against various unexpected occurring variations and
corruptions by understanding temporal changes and traffic scenes. In contrast, the neural …

The interest in autonomous vehicles has increased exponentially in recent years. While
Lidar is a proven autonomous driving technology, end-to-end learning approaches have …