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 …

Automated driving in urban settings is challenging. Human participant behavior is difficult to
model, and conventional, rule-based Automated Driving Systems (ADSs) tend to fail when …

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

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

Achieving a proper balance between planning quality, safety and efficiency is a major
challenge for autonomous driving. Optimisation-based motion planners are capable of …

Planning safe trajectories under uncertain and dynamic conditions makes the autonomous
driving problem significantly complex. Current heuristic-based algorithms such as the slot …

Autonomous vehicles need to handle various traffic conditions and make safe and efficient
decisions and maneuvers. However, on the one hand, a single optimization/sampling-based …

We present a learning-based planner that aims to robustly drive a vehicle by mimicking
human drivers' driving behavior. We leverage a mid-to-mid approach that allows us to …

Predicting the future states of surrounding traffic participants and planning a safe, smooth,
and socially compliant trajectory accordingly are crucial for autonomous vehicles (AVs) …

Autonomous navigation in dense traffic scenarios remains challenging for autonomous
vehicles (AVs) because the intentions of other drivers are not directly observable and AVs …