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Advances in Braking System for Better Autonomous Driving Safety and User Experience

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Transportation".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 25213

Special Issue Editors

Dr. Wei Tian

E-Mail Website
Guest Editor
School of Automotive Studies, Tongji University, Shanghai 201804, China
Interests: behavior recognition; trajectory prediction
Dr. Hongqing Chu

E-Mail Website
Guest Editor
School of Automotive Studies, Tongji University, Shanghai 201804, China
Interests: optimal control of intelligent vehicles; advanced motion control of electrified chassis; advanced driving assistance system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the persistent progress in autonomous driving, advanced braking technologies, especially active braking systems, have been introduced to reduce traffic accidents and increase transportation efficiency.

However, the development of braking systems in autonomous driving is still challenging due to the difficulty in predicting the behavior of environmental objects, the optimization problem of planning and control algorithms, the complexity of the design of collision avoidance systems and driver or passenger protection strategies.

The aim of this Special Issue is to address these challenges by presenting the related research advances in the hope of promoting sustainable development of braking systems in autonomous driving, as well as increasing both its safety and user experience.  

In this Special Issue, original research articles and reviews are welcome. Topics may include (but are not limited to) the following:

  • Insights on the challenges in the development of advanced braking systems, their causes and potential opportunities;
  • Improvements in motion estimation, trajectory prediction and behavior recognition of road traffic participants;
  • Novel planning, decision and control approaches to autonomous vehicles in braking related scenarios;
  • Advanced concepts for warning and collision avoidance systems;
  • New designs of sensors and actuators relating to braking systems;
  • Emergency protection strategies for drivers or passengers;
  • Simulation and evaluation tool related to braking systems.

I look forward to receiving your contributions.

Dr. Wei Tian
Dr. Hongqing Chu
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. Sustainability 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.

Keywords

  • autonomous driving
  • advanced braking system
  • behavior analysis
  • planning and control
  • decision making
  • collision warning and avoidance
  • emergency protection

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Published Papers (4 papers)

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Research

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31 pages, 2355 KiB  
Article
A Review of One-Box Electro-Hydraulic Braking System: Architecture, Control, and Application
by Xinyu Zhao, Lu Xiong, Guirong Zhuo, Wei Tian, Jing Li, Qiang Shu, Xuanbai Zhao and Guodong Xu
Sustainability 2024, 16(3), 1049; https://doi.org/10.3390/su16031049 - 25 Jan 2024
Cited by 1 | Viewed by 3693
Abstract
With the development of automobile electrification and intelligence, new requirements have been put forward for automotive braking technologies. Under this background, the One-box EHB (Electro-Hydraulic Braking system) brake-by-wire technology has emerged, which combines the electric booster and wheel-cylinder control module into one box [...] Read more.
With the development of automobile electrification and intelligence, new requirements have been put forward for automotive braking technologies. Under this background, the One-box EHB (Electro-Hydraulic Braking system) brake-by-wire technology has emerged, which combines the electric booster and wheel-cylinder control module into one box and can realize vehicle stability and comfort functions such as service brake, pedal feel simulation, brake decoupling, failure backup, active braking, and wheel-cylinder pressure control. This article reviews the current research of key technologies of One-box EHB, including system architecture design and applications under high-level autonomous driving, master cylinder pressure control algorithm design, wheel-cylinder pressure control algorithm design, and electro-hydraulic composite braking control algorithm design. Finally, this article summarizes the current research status of One-box EHB key technologies and puts forward suggestions for future research directions. Full article
(This article belongs to the Special Issue Advances in Braking System for Better Autonomous Driving Safety and User Experience)
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16 pages, 1843 KiB  
Article
Driveline Oscillation Damping for Hybrid Electric Vehicles Using Extended-State-Observer-Based Compensator
by Hongqing Chu, Wentong Shi, Yuyao Jiang and Bingzhao Gao
Sustainability 2023, 15(10), 8143; https://doi.org/10.3390/su15108143 - 17 May 2023
Cited by 3 | Viewed by 1727
Abstract
Driveline oscillation is a significant concern in the context of hybrid electric vehicles (HEVs), because it can adversely affect the vehicles’ sustainability. The reason for this is that the oscillation not only diminishes the longevity of components due to high mechanical contact stress [...] Read more.
Driveline oscillation is a significant concern in the context of hybrid electric vehicles (HEVs), because it can adversely affect the vehicles’ sustainability. The reason for this is that the oscillation not only diminishes the longevity of components due to high mechanical contact stress but also results in poor driving comfort, which in turn reduces customer satisfaction. To address the issue of driveline oscillation effectively, two critical challenges, namely the time-varying torque load and driveline backlash, need to be tackled. To this end, this study constructs a control-oriented model of a second-order system plus a dead zone for the driveline backlash. An extended state observer is designed in order to estimate the unmeasurable load torque. As such, an extended-state-observer-based compensator is proposed to suppress driveline oscillations for HEVs. To evaluate the control and observation performance of the proposed extended-state-observer-based compensator, simulation and engine-in-loop experiments are conducted. Results obtained in the time and frequency domains reveal that the proposed control scheme substantially reduces driveline oscillation. Full article
(This article belongs to the Special Issue Advances in Braking System for Better Autonomous Driving Safety and User Experience)
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38 pages, 5692 KiB  
Article
A Review of Electro-Mechanical Brake (EMB) System: Structure, Control and Application
by Congcong Li, Guirong Zhuo, Chen Tang, Lu Xiong, Wei Tian, Le Qiao, Yulin Cheng and Yanlong Duan
Sustainability 2023, 15(5), 4514; https://doi.org/10.3390/su15054514 - 2 Mar 2023
Cited by 20 | Viewed by 13796
Abstract
With the development of automobile electrification and intelligence, the demand for electro-mechanical braking (EMB) systems is increasing rapidly. This paper reviews the development status of the EMB actuator on the basis of extensive patent and literature research. By analyzing the basic structure of [...] Read more.
With the development of automobile electrification and intelligence, the demand for electro-mechanical braking (EMB) systems is increasing rapidly. This paper reviews the development status of the EMB actuator on the basis of extensive patent and literature research. By analyzing the basic structure of the EMB actuator, this paper decomposes the actuator into five modules: service brake module, parking brake module, brake clearance compensation module, quick-return module and sensor module. On the basis of basic structure, the estimation algorithm for indirect clamping force control and the direct clamping force control algorithm of the actuator are summarized. In addition, the requirements of the EMB system for intelligent vehicles and its typical architecture are analyzed, and the preliminary application of the EMB system in intelligent driving is summarized. Full article
(This article belongs to the Special Issue Advances in Braking System for Better Autonomous Driving Safety and User Experience)
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Review

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43 pages, 2007 KiB  
Review
A Review of Deep Learning-Based Vehicle Motion Prediction for Autonomous Driving
by Renbo Huang, Guirong Zhuo, Lu Xiong, Shouyi Lu and Wei Tian
Sustainability 2023, 15(20), 14716; https://doi.org/10.3390/su152014716 - 10 Oct 2023
Cited by 4 | Viewed by 4586
Abstract
Autonomous driving vehicles can effectively improve traffic conditions and promote the development of intelligent transportation systems. An autonomous vehicle can be divided into four parts: environment perception, motion prediction, motion planning, and motion control, among which the motion prediction module plays an essential [...] Read more.
Autonomous driving vehicles can effectively improve traffic conditions and promote the development of intelligent transportation systems. An autonomous vehicle can be divided into four parts: environment perception, motion prediction, motion planning, and motion control, among which the motion prediction module plays an essential role in the sustainability of autonomous driving vehicles. Vehicle motion prediction improves autonomous vehicles’ understanding of the surrounding dynamic environment, which reduces the uncertainty in the decision-making system and facilitates the implementation of an active braking system for autonomous vehicles. Currently, deep learning-based methods have become prevalent in this field as they can efficiently process complex scene information and achieve long-term prediction. These methods often follow a similar paradigm: encoding scene input to obtain the context feature, then decoding the context feature to output predictions. Recent research has proposed innovative improvement designs to enhance the primary paradigm. Thus, we review recent works based on their improvement designs and summarize them based on three criteria: scene input representation, context refinement, and prediction rationality improvement. Although most works focus on trajectory prediction, this paper also discusses new occupancy flow prediction methods. Additionally, this paper outlines commonly used datasets, evaluation metrics, and potential research directions. Full article
(This article belongs to the Special Issue Advances in Braking System for Better Autonomous Driving Safety and User Experience)
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