Machine Learning Enabled Traffic Prediction for Speed Optimization of Connected and Autonomous Electric Vehicles

Yunli Shao, Yuan Zheng, Zongxuan Sun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Connected and autonomous vehicles (CAVs) can bring in energy, mobility, and safety benefits to transportation. The optimal control strategies of CAVs are usually determined for a look-ahead horizon using previewed traffic information. This requires the development of an effective future traffic prediction algorithm and its integration to the CAV control framework. However, it is challenging for short-term traffic prediction using information from connectivity, especially for mixed traffic scenarios. In this work, a novel machine learning enabled traffic prediction method is developed and integrated with a speed optimization algorithm for connected and autonomous electric vehicles. The traffic prediction is based on a hybrid macroscopic traffic flow model, in which the most challenging nonlinear terms are modeled with neural networks (NNs). The traffic prediction method can be readily applied to various mixed traffic scenarios. Information from connected vehicles is used as partial measurement of the traffic states and the rest unknown traffic states are estimated using a state observer. Then, the preceding vehicle's future trajectory is obtained to formulate the car-following distance constraint of the energy optimization problem. In a simulated scenario of 70% penetration rate of connectivity, the NN-based traffic prediction algorithm can reduce the root-mean-square errors of the prediction of preceding vehicle speed by near 50%, compared to the conventional traffic flow model. The energy benefit is 12.5%, which is satisfactory compared to 16.5% of the scenario with perfect prediction.

Original languageEnglish (US)
Title of host publication2021 American Control Conference, ACC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages172-177
Number of pages6
ISBN (Electronic)9781665441971
DOIs
StatePublished - May 25 2021
Event2021 American Control Conference, ACC 2021 - Virtual, New Orleans, United States
Duration: May 25 2021May 28 2021

Publication series

Name2021 American Control Conference (ACC)

Conference

Conference2021 American Control Conference, ACC 2021
Country/TerritoryUnited States
CityVirtual, New Orleans
Period5/25/215/28/21

Bibliographical note

Publisher Copyright:
© 2021 American Automatic Control Council.

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