Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/90917
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Title: High-resolution traffic sensing with probe autonomous vehicles : a data-driven approach
Authors: Ma, W 
Qian, S
Issue Date: Jan-2021
Source: Sensors (Switzerland), Jan. 2021, v. 21, no. 2, 464, p. 1-35
Abstract: Recent decades have witnessed the breakthrough of autonomous vehicles (AVs), and the sensing capabilities of AVs have been dramatically improved. Various sensors installed on AVs will be collecting massive data and perceiving the surrounding traffic continuously. In fact, a fleet of AVs can serve as floating (or probe) sensors, which can be utilized to infer traffic information while cruising around the roadway networks. Unlike conventional traffic sensing methods relying on fixed location sensors or moving sensors that acquire only the information of their carrying vehicle, this paper leverages data from AVs carrying sensors for not only the information of the AVs, but also the characteristics of the surrounding traffic. A high-resolution data-driven traffic sensing framework is proposed, which estimates the fundamental traffic state characteristics, namely, flow, density and speed in high spatio-temporal resolutions and of each lane on a general road, and it is developed under different levels of AV perception capabilities and for any AV market penetration rate. Experimental results show that the proposed method achieves high accuracy even with a low AV market penetration rate. This study would help policymakers and private sectors (e.g., Waymo) to understand the values of massive data collected by AVs in traffic operation and management.
Keywords: Autonomous vehicle
Camera
Data-driven
LiDAR
NGSIM
State estimation
Traffic flow
Traffic sensing
Publisher: Molecular Diversity Preservation International (MDPI)
Journal: Sensors (Switzerland) 
ISSN: 1424-8220
DOI: 10.3390/s21020464
Rights: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
The following publication Ma, W.; Qian, S. High-Resolution Traffic Sensing with Probe Autonomous Vehicles: A Data-Driven Approach. Sensors 2021, 21, 464 is available at https://doi.org/10.3390/s21020464
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