Autonomous Vehicles as a Sensor: Simulating Data Collection Process

TL;DR

This paper advances traffic state estimation by simulating autonomous vehicles as sensors, leveraging real-world sensor data to accurately estimate traffic at multiple levels, demonstrated through a case study in Ingolstadt.

Contribution

It refines the AVaaS concept by incorporating realistic sensor attributes and simulation, enabling high-resolution traffic estimation at various network levels.

Findings

AVaaS can estimate microscopic and macroscopic traffic states.

Simulation results confirm the viability of AVaaS in real-world scenarios.

Potential for improved traffic management using autonomous vehicle data.

Abstract

Urban traffic state estimation is pivotal in furnishing precise and reliable insights into traffic flow characteristics, thereby enabling efficient traffic management. Traditional traffic estimation methodologies have predominantly hinged on labor-intensive and costly techniques such as loop detectors and floating car data. Nevertheless, the relentless progression in autonomous driving technology has catalyzed an increasing interest in capitalizing on the extensive potential of on-board sensor data, giving rise to a novel concept known as "Autonomous Vehicles as a Sensor" (AVaaS). This paper innovatively refines the AVaaS concept by simulating the data collection process. We take real-world sensor attributes into account and employ more accurate estimation techniques based on the on-board sensor data. Such data can facilitate the estimation of high-resolution, link-level traffic states and, more extensively, online cluster- and network-level traffic states. We substantiate the viability of the AVaaS concept through a case study conducted using a real-world traffic simulation in Ingolstadt, Germany. The results attest to the ability of AVaaS in estimating both microscopic (link-level) and macroscopic (cluster- and network-level) traffic states, thereby highlighting the immense potential of the AVaaS concept in effecting precise and reliable traffic state estimation and also further applications.