Interaction-Aware Trajectory Prediction and Planning in Dense Highway Traffic using Distributed Model Predictive Control
Paper in proceeding, 2023

In this paper we treat optimal trajectory planning for an autonomous vehicle (AV) operating in dense traffic, where vehicles closely interact with each other. To tackle this problem, we present a novel framework that couples trajectory prediction and planning in multi-agent environments, using distributed model predictive control. A demonstration of our framework is presented in simulation, employing a trajectory planner using non-linear model predictive control. We analyze performance and convergence of our framework, subject to different prediction errors. The results indicate that the obtained locally optimal solutions are improved, compared with decoupled prediction and planning.

Author

Erik Börve

Chalmers, Electrical Engineering, Systems and control

Nikolce Murgovski

Chalmers, Electrical Engineering, Systems and control

Leo Laine

Chalmers, Mechanics and Maritime Sciences (M2)

Proceedings of the IEEE Conference on Decision and Control

07431546 (ISSN) 25762370 (eISSN)

6124-6129
9798350301243 (ISBN)

62nd IEEE Conference on Decision and Control, CDC 2023
Singapore, Singapore,

Subject Categories

Robotics

DOI

10.1109/CDC49753.2023.10384178

More information

Latest update

2/23/2024