Informative Path Planning of Autonomous Vehicle for Parking Occupancy Estimation

TL;DR

This paper presents a novel approach for autonomous vehicles to efficiently plan informative paths for parking occupancy estimation by formulating it as a POMDP and introducing a Monte Carlo Bayes Filter Tree algorithm.

Contribution

It introduces the problem of informative path planning for parking occupancy estimation, formulates it as a POMDP, and proposes a new efficient algorithm outperforming benchmarks.

Findings

Outperforms benchmark methods in simulation environments

Balances optimality and computational efficiency

Effectively estimates parking occupancy using autonomous vehicles

Abstract

Parking occupancy estimation holds significant potential in facilitating parking resource management and mitigating traffic congestion. Existing approaches employ robotic systems to detect the occupancy status of individual parking spaces and primarily focus on enhancing detection accuracy through perception pipelines. However, these methods often overlook the crucial aspect of robot path planning, which can hinder the accurate estimation of the entire parking area. In light of these limitations, we introduce the problem of informative path planning for parking occupancy estimation using autonomous vehicles and formulate it as a Partially Observable Markov Decision Process (POMDP) task. Then, we develop an occupancy state transition model and introduce a Bayes filter to estimate occupancy based on noisy sensor measurements. Subsequently, we propose the Monte Carlo Bayes Filter Tree, a computationally efficient algorithm that leverages progressive widening to generate informative paths. We demonstrate that the proposed approach outperforms the benchmark methods in diverse simulation environments, effectively striking a balance between optimality and computational efficiency.