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

In this work, we develop a scalable, local tra-jectory optimization algorithm that enables
robots to interact with other robots. It has been shown that agents' interactions can be …

This article proposes two decentralized multiagent optimal control methods that combine the
computational efficiency and scalability of differential dynamic programming (DDP) and the …

In this work, we propose a numerical method for the solution of local generalized Nash
equilibria (GNE) for the class of open-loop general-sum dynamic games for agents with …

Interactive behavior modeling of multiple agents is an essential challenge in simulation,
especially in scenarios when agents need to avoid collisions and cooperate at the same …

In this work, we consider the problem of autonomous racing with multiple agents where
agents must interact closely and influence each other to compete. We model interactions …

Although dynamic games provide a rich paradigm for modeling agents' interactions, solving
these games for real-world applications is often challenging. Many real-world interactive …

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

In this paper, we propose a distributed, first-order, feedback-based approach to solve
nonlinear optimal control problems with aggregative cost functions over networks of …

Game theoretic methods have become popular for planning and prediction in situations
involving rich multi-agent interactions. However, these methods often assume the existence …