Data-Driven and Learning-Based Control for Vehicle Applications

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Vehicle Engineering".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 1302

Special Issue Editors


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Guest Editor
Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
Interests: control design and automation; connected and automated vehicles; electric and hybrid vehicles; optimization; artificial intelligence

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Co-Guest Editor
Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou 26467, China
Interests: reinforcement learning; intelligent vehicles; hybrid electric vehicles

Special Issue Information

Dear Colleagues,

This Special Issue seeks new and creative applications of emerging data-driven and learning-based control techniques to vehicle systems. The pace of innovation in the auto industry is accelerating quickly to include and merge connectivity, automation, and electrification in ways that will significantly transform the next generation of vehicles. The complexity of modern vehicles requires the use of advanced control methods applicable to challenging problems involving systems with unknown and changing dynamics, or interactions with highly uncertain environments, where specific safety or performance constraints must be satisfied at the same time. Data-driven and learning-based control techniques leverage online or offline data obtained from a complex system, using learning techniques to identify a proper data-driven system model for control design, or to derive appropriate control laws directly.

For this Special Issue, we are looking for original contributions or comprehensive tutorial and survey articles involving elegant machine learning techniques, advanced variants of reinforcement learning approaches, or other state-of-the-art data-driven methods to create suitable system models for control design, or to generate high-performance control laws directly for challenging automotive problems, including, but not limited to: automation and advanced driver assistance systems, energy management systems, battery management systems, thermal comfort control, and other complex vehicle control systems.     

Dr. Nasser Lashgarian Azad
Dr. Yuan Lin
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • automation and advanced driver assistance systems
  • energy management systems
  • battery management systems
  • thermal comfort control
  • complex vehicle control systems

Published Papers (1 paper)

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Research

14 pages, 665 KiB  
Article
Discretionary Lane-Change Decision and Control via Parameterized Soft Actor–Critic for Hybrid Action Space
by Yuan Lin, Xiao Liu and Zishun Zheng
Machines 2024, 12(4), 213; https://doi.org/10.3390/machines12040213 - 22 Mar 2024
Cited by 2 | Viewed by 690
Abstract
This study focuses on a crucial task in the field of autonomous driving, autonomous lane change. Autonomous lane change plays a pivotal role in improving traffic flow, alleviating driver burden, and reducing the risk of traffic accidents. However, due to the complexity and [...] Read more.
This study focuses on a crucial task in the field of autonomous driving, autonomous lane change. Autonomous lane change plays a pivotal role in improving traffic flow, alleviating driver burden, and reducing the risk of traffic accidents. However, due to the complexity and uncertainty of lane-change scenarios, the functionality of autonomous lane change still faces challenges. In this research, we conducted autonomous lane-change simulations using both deep reinforcement learning (DRL) and model predictive control (MPC). Specifically, we used the parameterized soft actor–critic (PASAC) algorithm to train a DRL-based lane-change strategy to output both discrete lane-change decisions and continuous longitudinal vehicle acceleration. We also used MPC for lane selection based on the smallest predictive car-following costs for the different lanes. For the first time, we compared the performance of DRL and MPC in the context of lane-change decisions. The simulation results indicated that, under the same reward/cost function and traffic flow, both MPC and PASAC achieved a collision rate of 0%. PASAC demonstrated a comparable performance to MPC in terms of average rewards/costs and vehicle speeds. Full article
(This article belongs to the Special Issue Data-Driven and Learning-Based Control for Vehicle Applications)
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