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

2021/11/27 by Huayi Li, Nan Li, Li, Huayi +5
Engineering · Psychology · #FOS: Computer and information sciences #FOS: Electrical engineering #Human-Automation Interaction and Safety #Robotics (cs.RO) #Systems and Control (eess.SY) #Traffic control and management #Transportation and Mobility Innovations #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2111.13966

openalex publication_date 2021/11/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

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