Ground robots navigating in complex, dynamic environments must compute collision-free
trajectories to avoid obstacles safely and efficiently. Nonconvex optimization is a popular …

Uncertainty in the behavior of other traffic participants is a crucial factor in collision
avoidance for automated driving; here, stochastic metrics could avoid overly conservative …

Mixed-integer quadratic programs (MIQPs) are a versatile way of formulating vehicle
decision making (DM) and motion planning problems, where the prediction model is a …

Standard Model Predictive Control (MPC) or trajectory optimization approaches perform only
a local search to solve a complex non-convex optimization problem. As a result, they cannot …

A trajectory planning method for local obstacle avoidance based on an improved artificial
potential field (APF) method is proposed, which is aimed at the problem for dual motor …

In this work, we present BiM-ACPPO, a bilevel multi-armed bandit-based hierarchical
reinforcement learning framework for interaction-aware decision-making and planning at …

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

In this article, we propose an optimization-based integrated behavior planning and motion
control scheme, which is an interpretable and adaptable urban autonomous driving solution …

Autonomous driving with active obstacle avoidance in dynamic urban environment has
attracted significant attention to improve road safety and traffic efficiency. In this paper, an …

This paper introduces a local planner that synergizes the decision making and trajectory
planning modules towards autonomous driving. The decision making and trajectory …