Situation-aware Autonomous Driving Decision Making with Cooperative Perception on Demand

TL;DR

This paper presents a situation-aware decision-making framework for autonomous vehicles that leverages cooperative perception on demand, improving safety and efficiency by activating perception only when necessary and modeling decision-making as a POMDP.

Contribution

It introduces a novel Cooperative Perception on Demand (CPoD) strategy and models decision-making as a POMDP for urban autonomous driving.

Findings

Enhanced decision-making performance with cooperative perception

Efficient perception activation reduces communication load

Safe and effective urban driving demonstrated in evaluations

Abstract

This paper investigates the impact of cooperative perception on autonomous driving decision making on urban roads. The extended perception range contributed by the cooperative perception can be properly leveraged to address the implicit dependencies within the vehicles, thereby the vehicle decision making performance can be improved. Meanwhile, we acknowledge the inherent limitation of wireless communication and propose a Cooperative Perception on Demand (CPoD) strategy, where the cooperative perception will only be activated when the extended perception range is necessary for proper situation-awareness. The situation-aware decision making with CPoD is modeled as a Partially Observable Markov Decision Process (POMDP) and solved in an online manner. The evaluation results demonstrate that the proposed approach can function safely and efficiently for autonomous driving on urban roads.