This paper introduces a novel social preference-aware decentralized safe control framework
to address the responsibility allocation problem in multi-agent collision avoidance …

Decentralized control schemes are increasingly favored in various domains that involve
multi-agent systems due to the need for computational efficiency as well as general …

Within a robot autonomy stack, the planner and controller are typically designed separately,
and serve different purposes. As such, there is often a diffusion of responsibilities when it …

Drivers have a responsibility to exercise reasonable care to avoid collision with other road
users. This assumed responsibility allows interacting agents to maintain safety without …

Connected Autonomous Vehicles (CAVs) are expected to enable reliable and efficient
transportation systems. Most motion planning algorithms for multi-agent systems are not …

Interactive decision-making and motion planning are important to safety-critical autonomous
agents, particularly when they interact with humans. Many different interaction strategies can …

This paper considers the multi-vehicle automated driving coordination problem. We develop
a distributed, hybrid decision-making framework for safe and efficient autonomous driving of …

We propose a hybrid decision-making framework for safe and efficient autonomous driving
of selfish vehicles on highways. Specifically, we model the dynamics of each vehicle as a …

This contribution introduces a centralized input constrained optimal control framework based
on multiple control barrier functions (CBFs) to coordinate connected and automated agents …

This article proposes a novel decision-making framework for autonomous vehicles (AVs),
called predictor–corrector potential game (PCPG), composed of a predictor and a corrector …