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Article

High-Resolution Traffic Sensing with Probe Autonomous Vehicles: A Data-Driven Approach

by 1 and 2,3,*
1
Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong, China
2
Department of Civil and Environmental Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA
3
H. John Heinz III Heinz College, Carnegie Mellon University, Pittsburgh, PA 15213, USA
*
Author to whom correspondence should be addressed.
Sensors 2021, 21(2), 464; https://doi.org/10.3390/s21020464
Received: 18 December 2020 / Revised: 4 January 2021 / Accepted: 5 January 2021 / Published: 11 January 2021
(This article belongs to the Section Sensing and Imaging)
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. View Full-Text
Keywords: autonomous vehicle; LiDAR; camera; state estimation; traffic sensing; data-driven; traffic flow; NGSIM autonomous vehicle; LiDAR; camera; state estimation; traffic sensing; data-driven; traffic flow; NGSIM
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MDPI and ACS Style

Ma, W.; Qian, S. High-Resolution Traffic Sensing with Probe Autonomous Vehicles: A Data-Driven Approach. Sensors 2021, 21, 464. https://doi.org/10.3390/s21020464

AMA Style

Ma W, Qian S. High-Resolution Traffic Sensing with Probe Autonomous Vehicles: A Data-Driven Approach. Sensors. 2021; 21(2):464. https://doi.org/10.3390/s21020464

Chicago/Turabian Style

Ma, Wei, and Sean Qian. 2021. "High-Resolution Traffic Sensing with Probe Autonomous Vehicles: A Data-Driven Approach" Sensors 21, no. 2: 464. https://doi.org/10.3390/s21020464

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