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Advances, Opportunities and Challenges in AI-enabled Technologies for Autonomous and Connected Vehicles Read chapter Read first chapter

2023 | OriginalPaper | Chapter

Towards Learning-Based Control of Connected and Automated Vehicles: Challenges and Perspectives

Author : Alexander Katriniok

Published in: AI-enabled Technologies for Autonomous and Connected Vehicles

Publisher: Springer International Publishing

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Abstract

The exploitation of communication technologies enables connected and automated vehicles (CAVs) to operate more collaboratively, that is, by exchanging or even negotiating future trajectories and control actions. That way, CAVs (or agents) can establish a networked control system such as to safely automate road traffic in a collaborative fashion. A rich body of literature is available, e.g., on intersection automation, automated lane change or lane merging scenarios. These control concepts, though, are most tailored to the particular application and are in general not applicable to multiple scenarios. This chapter conveys the challenges and perspectives of modeling and optimization-based control techniques for the safe coordination of multiple connected agents in road traffic scenarios. Along these lines, the perspective of generalizing controller design to serve multiple use cases simultaneously instead of designing separate controllers for every use case is discussed. Moreover, the opportunities of learning-based control in case of model uncertainties and mixed-traffic scenarios, involving connected and non-connected agents, are outlined.

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previous chapter Factors Influencing Driver Behavior and Advances in Monitoring Methods
next chapter Virtual Rings on Highways: Traffic Control by Connected Automated Vehicles
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Metadata
Title
Towards Learning-Based Control of Connected and Automated Vehicles: Challenges and Perspectives
Author
Alexander Katriniok
Copyright Year
2023
Book
AI-enabled Technologies for Autonomous and Connected Vehicles

Print ISBN: 978-3-031-06779-2

Electronic ISBN: 978-3-031-06780-8

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-3-031-06780-8_15

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