Energy-Efficient Autonomous Driving Using Cognitive Driver Behavioral Models and Reinforcement Learning

Huayi Li; Nan Li; Ilya Kolmanovsky; Anouck Girard

doi:10.1007/978-3-031-06780-8_10

Energy-Efficient Autonomous Driving Using Cognitive Driver Behavioral Models and Reinforcement Learning

Huayi Li, Nan Li, Ilya Kolmanovsky, Anouck Girard

Mechanical and Aerospace Engineering

Producción científica: Chapter › revisión exhaustiva

1 Cita (Scopus)

Resumen

Autonomous driving technologies are expected to not only improve mobility and road safety but also bring energy efficiency benefits. In the foreseeable future, autonomous vehicles (AVs) will operate on roads shared with human-driven vehicles. To maintain safety and liveness while simultaneously minimizing energy consumption, the AV planning and decision-making process should account for interactions between the autonomous ego vehicle and surrounding human-driven vehicles. In this chapter, we describe a framework for developing energy-efficient autonomous driving policies on shared roads by exploiting human-driver behavior modeling based on cognitive hierarchy theory and reinforcement learning.

Idioma original	English
Título de la publicación alojada	Lecture Notes in Intelligent Transportation and Infrastructure
Páginas	283-305
Número de páginas	23
DOI	https://doi.org/10.1007/978-3-031-06780-8_10
Estado	Published - 2023

Serie de la publicación

Nombre	Lecture Notes in Intelligent Transportation and Infrastructure
Volumen	Part F1376
ISSN (versión impresa)	2523-3440
ISSN (versión digital)	2523-3459

Nota bibliográfica

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Financiación

Acknowledgements This research was supported by Mcity, University of Michigan. This research was also supported in part through computational resources and services provided by Advanced Research Computing at the University of Michigan, Ann Arbor.

Financiadores	Número del financiador
Michigan Retirement Research Center, University of Michigan

ASJC Scopus subject areas

Computer Science Applications
Automotive Engineering
Control and Systems Engineering
Transportation

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

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Energy-Efficient Autonomous Driving Using Cognitive Driver Behavioral Models and Reinforcement Learning. / Li, Huayi; Li, Nan; Kolmanovsky, Ilya et al.
Lecture Notes in Intelligent Transportation and Infrastructure. 2023. p. 283-305 (Lecture Notes in Intelligent Transportation and Infrastructure; Vol. Part F1376).

Producción científica: Chapter › revisión exhaustiva

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