Traditional methods for autonomous driving are implemented with many building blocks
from perception, planning and control, making them difficult to generalize to varied scenarios …

Reliable trajectory planning like human drivers in real-world dynamic urban environments is
a critical capability for autonomous driving. To this end, we develop a vision and imitation …

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 …

The reasonable trajectory prediction of surrounding traffic participants is crucial for
autonomous driving. Especially, how to predict multiple plausible trajectories is still a …

With the recent advancement of deep learning technology, data-driven approaches for
autonomous car prediction and planning have achieved extraordinary performance …

For driving safely and comfortably, the long-term trajectory prediction of surrounding
vehicles is essential for autonomous vehicles. For handling the uncertain nature of trajectory …

Learning-based approaches have achieved remarkable performance in the domain of
autonomous driving. Leveraging the impressive ability of neural networks and large …

Autonomous vehicles face numerous challenges to ensure safe operation in unpredictable
and hazardous conditions. The autonomous driving environment is characterized by high …

Predicting the future trajectories of surrounding vehicles is a key competence for safe and
efficient real-world autonomous driving systems. Previous works have presented deep …

This paper focuses on the real-time trajectory planning problem for autonomous vehicles
driving in realistic urban environments. To solve the complex navigation problem, we adopt …