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

Navigating safely and efficiently in dense and heterogeneous traffic scenarios is challenging
for autonomous vehicles (AVs) due to their inability to infer the behaviors or intentions of …

For autonomous vehicles integrating onto road-ways with human traffic participants, it
requires understanding and adapting to the participants' intention by responding in …

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

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 …

Traditional planning and control methods could fail to find a feasible trajectory for an
autonomous vehicle to execute amongst dense traffic on roads. This is because the obstacle …

Autonomous driving in multi-agent dynamic traffic scenarios is challenging: the behaviors of
road users are uncertain and are hard to model explicitly, and the ego-vehicle should apply …

Safe motion planning in complex and interactive environments is one of the major
challenges for developing autonomous vehicles. In this paper, we propose an interaction …

An intelligent agent operating in the real-world must balance achieving its goal with
maintaining the safety and comfort of not only itself, but also other participants within the …