In a typical traffic scenario, autonomous vehicles are required to share the road with other
road participants, eg, human driven vehicles, pedestrians, etc. To successfully navigate the …

Motion planning at urban intersections that accounts for the situation context, handles
occlusions, and deals with measurement and prediction uncertainty is a major challenge on …

This paper presents the lane-merging strategy for self-driving cars in dense traffic using the
Stackelberg game approach. From the perspective of the self-driving car, in order to make …

Optimizing static risk-averse objectives in Markov decision processes is difficult because
they do not admit standard dynamic programming equations common in Reinforcement …

Increasing penetration of renewable energy introduces significant uncertainty into power
systems. Traditional simulation‐based verification methods may not be applicable due to the …

In this article, we propose a novel safety specification tool, called the distributionally robust
risk map (DR-risk map), for a mobile robot operating in a learning-enabled environment …

The capabilities of automated vehicles are advancing rapidly, yet achieving full autonomy
remains a significant challenge, requiring ongoing human cognition in decision-making …

Safety is one of the main challenges when applying learning-based motion controllers to
practical robotic systems, especially when the dynamics of the robots and their surrounding …

Balancing safety and efficiency when planning in dense traffic is challenging. Interactive
behavior planners incorporate prediction uncertainty and interactivity inherent to these traffic …

The expected value (EV) based optimization principle often used in engineering ignores risk-
related human characteristics which are however important to human-robot interaction …