State-of-the-Art Technologies for Connected and Automated Vehicles

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electrical and Autonomous Vehicles".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 1696

Special Issue Editors


E-Mail Website
Guest Editor
National Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
Interests: electrical/hybrid driven system; battery safety management system; big data analysis on electric vehicles; V2G control system
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
National Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
Interests: automated and connected vehicles; vehicle dynamics and control; battery management techniques
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Connected and automated vehicles (CAVs) have attracted tremendous attention in recent decades due to their potential for improving the fuel economy, safety, and capacity of the transport system. Enabling decision making, planning, and control holds the key to efficient, safe, and reliable operations of CAVs. Thus, accurate CAV modeling and robust vehicle dynamics control are fundamental. Moreover, to achieve improved traffic efficiency and energy economy, cooperative control of CAVs is an effective solution. This extends the control object from a single CAV to a CAV platoon.

This Special Issue is focused on state-of-the-art technologies for CAVs. It will include decision making, trajectory planning, and motion control for single CAVs or CAV platoons. Attention will also be paid to the related technologies in any other aspects.

The topics of interest include but are not limited to the following:

  • Environmental perception;
  • Behavior prediction of surrounding vehicles;
  • Lane change or turning decision of CAVs;
  • Trajectory planning of CAVs;
  • Eco-driving of CAVs;
  • Vehicle modeling and state estimation;
  • Vehicle dynamics control;
  • Advanced driver assistance systems;
  • Platoon control.

Prof. Dr. Zhenpo Wang
Dr. Lei Zhang
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. Electronics is an international peer-reviewed open access semimonthly 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.

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

16 pages, 6491 KiB  
Article
A Hierarchical Estimation Method for Road Friction Coefficient Combining Single-Step Moving Horizon Estimation and Inverse Tire Model
by Guodong Wang, Guoxing Bai, Yu Meng, Li Liu, Qing Gu and Zhiping Ma
Electronics 2023, 12(3), 525; https://doi.org/10.3390/electronics12030525 - 19 Jan 2023
Cited by 1 | Viewed by 1068
Abstract
To improve the real-time performance of the estimation method of road friction coefficient (RFC) based on moving horizon estimation (MHE), a hierarchical estimation method for RFC combining single-step MHE (S-MHE) and inverse tire model (ITM) based on lateral vehicle dynamics is proposed in [...] Read more.
To improve the real-time performance of the estimation method of road friction coefficient (RFC) based on moving horizon estimation (MHE), a hierarchical estimation method for RFC combining single-step MHE (S-MHE) and inverse tire model (ITM) based on lateral vehicle dynamics is proposed in this study. Firstly, a hierarchical estimation framework is designed to decouple vehicle and tire systems. Secondly, the S-MHE estimator is designed based on the nonlinear vehicle model to estimate the lateral tire force. Thirdly, the ITM is deduced based on the Pacejka model, and the estimator for the RFC based on the ITM is designed. Finally, the estimation accuracy, convergence speed, and real-time performance of the proposed method and the traditional MHE method are compared and discussed through different tests based on CarSim and Simulink. The results show that compared with the traditional MHE method, the proposed method reduces the average computation time to about 0.125 s and improves the real-time performance by more than 30% while ensuring the estimation accuracy and convergence speed. Full article
(This article belongs to the Special Issue State-of-the-Art Technologies for Connected and Automated Vehicles)
Show Figures

Figure 1

Back to TopTop