Motion prediction modules are crucial for autonomous vehicles to forecast the future
behavior of surrounding road users. Failures in prediction modules can mislead a …

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

This article proposes an interaction and safety-aware motion-planning method for an
autonomous vehicle in uncertain multi-vehicle traffic environments. The method integrates …

We propose an integrated behavior and motion planning framework for the lane-merging
problem. The behavior planner combines search-based planning with game theory to model …

This paper proposes a real-time motion planning scheme for safe human–robot workspace
sharing relying on scenario-based nonlinear model predictive control (NMPC), a well-known …

Motion planning for autonomous vehicles sharing the road with human drivers remains
challenging. The difficulty arises from three challenging aspects: human drivers are 1) multi …

Enforcing safety while preventing overly conservative behaviors is essential for autonomous
vehicles to achieve high task performance. In this paper, we propose a barrier-enhanced …

In this paper, we develop an uncertainty-aware decision-making and motion-planning
method for an autonomous ego vehicle in forced merging scenarios, considering the motion …

This study introduces the Metacognitive-Attribution Inspired Reinforcement Learning
(MAIRL) approach, designed to address unprotected interactive left turns at intersections …

Enforcing safety while preventing overly conservative behaviors is essential for autonomous
vehicles to achieve high task performance. In this paper, we propose a barrier-enhanced …