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

Wei Ma¹, Sean Qian^2,3

¹Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong, China.

Sensors (Basel, Switzerland)

|January 14, 2021

Summary

Autonomous vehicles (AVs) can act as floating sensors, providing valuable traffic data. A new framework uses AV sensor data to accurately estimate traffic flow, density, and speed, even with low AV adoption.

Keywords:

LiDAR NGSIM autonomous vehicle camera data-driven state estimation traffic flow traffic sensing

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Area of Science:

Intelligent Transportation Systems
Data Science
Traffic Engineering

Background:

Autonomous vehicles (AVs) are increasingly equipped with advanced sensors, generating vast amounts of data.
Existing traffic sensing methods have limitations in spatial and temporal resolution.
AVs present an opportunity to function as mobile sensing platforms for comprehensive traffic monitoring.

Purpose of the Study:

To propose a high-resolution, data-driven traffic sensing framework utilizing AV sensor data.
To estimate fundamental traffic state characteristics (flow, density, speed) for each lane.
To develop a framework adaptable to varying AV perception capabilities and market penetration rates.

Main Methods:

Leveraging data from AVs, including their own sensor information and surrounding traffic data.
Developing a data-driven framework to infer traffic flow, density, and speed at high spatio-temporal resolutions.
Validating the framework under diverse AV perception capabilities and market penetration scenarios.

Main Results:

The proposed framework accurately estimates traffic flow, density, and speed in real-time.
High accuracy is maintained even with low autonomous vehicle (AV) market penetration rates.
The system effectively utilizes AV sensor data for comprehensive traffic state characterization.

Conclusions:

AVs can serve as effective floating sensors for advanced traffic monitoring.
The developed framework offers a scalable and accurate solution for traffic state estimation.
This research highlights the significant value of AV-generated data for traffic management and urban planning.