Comfortable and energy-efficient speed control of autonomous vehicles on rough pavements using deep reinforcement learning. (January 2022)

• Record Type: Journal Article
• Authors: Du, Yuchuan; Chen, Jing; Zhao, Cong; Liu, Chenglong; Liao, Feixiong; Chan, Ching-Yao
• Is Part Of: Transportation research. Volume 134(2022)
• Publication Date: 2022-01
• Subjects: Autonomous vehicle -- Ride comfort -- Energy efficiency -- Deep reinforcement learning -- Speed control; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2021.103489
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: We propose a speed control framework for AVs on rough pavements in a vehicle-to-infrastructure communication system. The concept of 'maximum comfortable speed' is proposed to represent the vertical ride comfort of oncoming roads. A deep reinforcement learning algorithm is designed to learn comfortable and energy-efficient speed control strategies. The results indicate that the proposed approach is advantageous and applicable in real-time speed controls on rough pavements. Abstract: Rough pavements cause ride discomfort and energy inefficiency for road vehicles. Existing methods to address these problems are time-consuming and not adaptive to changing driving conditions on rough pavements. With the development of sensor and communication technologies, crowdsourced road and dynamic traffic information become available for enhancing driving performance, particularly addressing the discomfort and inefficiency issues by controlling driving speeds. This study proposes a speed control framework on rough pavements, envisioning the operation of autonomous vehicles based on the crowdsourced data. We suggest the concept of 'maximum comfortable speed' for representing the vertical ride comfort of oncoming roads. A deep reinforcement learning (DRL) algorithm is designed to learn comfortable and energy-efficient speed control strategies. The DRL-based speed control model is trained using real-world rough pavement data in Shanghai, China. The experimental results show that the vertical ride comfort, energy efficiency, and computation efficiency increase by 8.22%, 24.37%, and 94.38%, respectively, compared to an optimization-based speed control model. The results indicate that the proposed framework is effective for real-time speed controls of autonomous vehicles on rough pavements.