Multi-Uncertainty Aware Autonomous Cooperative Planning

TL;DR

This paper introduces MUACP, a framework for autonomous cooperative planning that effectively manages perception, motion, and communication uncertainties, enhancing multi-vehicle coordination in uncertain environments.

Contribution

It presents a novel multi-uncertainty aware ACP framework using RC-MPC that incorporates various uncertainties based on confidence, weather, and outage data.

Findings

Outperforms benchmark methods in CARLA simulations.

Achieves real-time cooperative formation under uncertainty.

Effectively handles perception, motion, and communication uncertainties.

Abstract

Autonomous cooperative planning (ACP) is a promising technique to improve the efficiency and safety of multi-vehicle interactions for future intelligent transportation systems. However, realizing robust ACP is a challenge due to the aggregation of perception, motion, and communication uncertainties. This paper proposes a novel multi-uncertainty aware ACP (MUACP) framework that simultaneously accounts for multiple types of uncertainties via regularized cooperative model predictive control (RC-MPC). The regularizers and constraints for perception, motion, and communication are constructed according to the confidence levels, weather conditions, and outage probabilities, respectively. The effectiveness of the proposed method is evaluated in the Car Learning to Act (CARLA) simulation platform. Results demonstrate that the proposed MUACP efficiently performs cooperative formation in real time and outperforms other benchmark approaches in various scenarios under imperfect knowledge of the environment.