Real-time, accurate, and robust localisation is critical for autonomous vehicles (AVs) to
achieve safe, efficient driving, whilst real-time performance is essential for AVs to achieve …

In the context of autonomous driving, the iterative linear quadratic regulator (iLQR) is known
to be an efficient approach to deal with the nonlinear vehicle model in motion planning …

Cooperative trajectory planning of connected autonomous vehicles (CAVs) generally admits
strong nonlinearity and non-convexity, rendering great difficulties in finding the optimal …

The number of research papers on decision-making systems in automated driving has
increased significantly over the last few years. Decision-making for automated driving can …

Motion planning under uncertainty is critical for safe self-driving. This paper proposes a
unified obstacle avoidance framework that deals with 1) uncertainty in ego-vehicle motion; …

Safety-guaranteed motion planning is critical for self-driving cars to generate collision-free
trajectories. A layered motion planning approach with decoupled path and speed planning …

To efficiently generate safe trajectories for an autonomous vehicle in dynamic environments,
a layered motion planning method with decoupled path and speed planning is widely used …

Traversing intersections is a challenging problem for autonomous vehicles, especially when
the intersections do not have traffic control. Recently deep reinforcement learning has …

Lane-change trajectory planning of autonomous light trucks is closely related to safety,
driving stability and transportation efficiency, especially in complex road and traffic …

Safety guarantees in motion planning for autonomous driving typically involve certifying the
trajectory to be collision-free under any motion of the uncontrollable participants in the …