In this paper, we propose a neural motion planner for learning to drive autonomously in
complex urban scenarios that include traffic-light handling, yielding, and interactions with …

Many problems in robotics involve multiple decision making agents. To operate efficiently in
such settings, a robot must reason about the impact of its decisions on the behavior of other …

Motion planning is a core technique for autonomous driving. Nowadays, there still exists a
lot of challenges in motion planning for autonomous driving in complicated environments …

This letter presents a method for local motion planning in unstructured environments with
static and moving obstacles, such as humans. Given a reference path and speed, our …

In the past decades, we have witnessed significant progress in the domain of autonomous
driving. Advanced techniques based on optimization and reinforcement learning become …

The decision and planning system for autonomous driving in urban environments is hard to
design. Most current methods manually design the driving policy, which can be expensive to …

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 …

Generating safe and non-conservative behaviors in dense, dynamic environments remains
challenging for automated vehicles due to the stochastic nature of traffic participants' …

This paper presents a decoupled trajectory planning framework based on the integration of
lattice searching and convex optimization for autonomous driving in structured …

This paper investigates the cooperative planning and control problem for multiple connected
autonomous vehicles (CAVs) in different scenarios. In the existing literature, most of the …