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Advances in Navigation and Control of Autonomous Vehicles
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Advances in Navigation and Control of Autonomous Vehicles

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Robotics and Automation".

Deadline for manuscript submissions: closed (20 September 2023) | Viewed by 5202

Special Issue Editors

Dr. Daofei Li

E-Mail Website
Guest Editor
Institute of Power Machinery and Vehicular Engineering, Zhejiang University, Hangzhou 310027, China
Interests: vehicle dynamics and control; driver model; automated driving
Dr. Weichao Zhuang

E-Mail Website
Guest Editor
School of Mechanical Engineering, Southeast University, Nanjing 211189, China
Interests: vehicle dynamics and control; automotive powertrain design and optimization; clean energy vehicles; connected and automated vehicles; multi-agent control
Special Issues, Collections and Topics in MDPI journals
Dr. Yafei Wang

E-Mail Website
Guest Editor
Institute of Intelligent Vehicle, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
Interests: vehicle dynamics and control; connected and automated vehicles
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Duanfeng Chu

E-Mail Website
Guest Editor
School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China
Interests: connected and automated vehicles; machine learning

Special Issue Information

Dear Colleagues,

In the past five years, we have witnessed the rapid development of autonomous driving. Indeed, new cars equipped with SAE Level 1–2 automation functions have accounted for about 20% of the new car market. Several OEMs have launched the automated navigation function for highway driving, while the more challenging urban self-navigated driving function has become a hot topic in the last two years.

However, this field still faces many unsolved problems of safe navigation and control in complex environments. Typical challenges include unprotected left turns in dense traffic, ramp merging to a busy lane, automous driving in unstructured environments, time-efficient parking in a small space, safe and reliable motion control on poorly built or even unpaved surfaces, interactive driving with other manually or automatically driven vehicles, etc.

We believe that only by solving these problems can we truly achieve reliable and trustworthy autonomous driving vehicles. This Special Issue will be dedicated to new advances in the navigation and control of autonomous vehicles.

This Special Issue will focus not only on new methods and algorithms of navigation and control, but also their implementation and validation in important applications.

Dr. Daofei Li
Dr. Weichao Zhuang
Dr. Yafei Wang
Prof. Dr. Duanfeng 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. Applied Sciences 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

  • sensing, localization and mapping, perception, prediction of driving environment
  • navigation, decision-making, behavioral planning, motion planning
  • path tracking, vehicle motion control for longitudinal, lateral and vertical dynamics
  • connectivity-assisted driving intelligence, e.g., vehicle-to-everything (V2X)
  • AI applications in autonomous driving, machine/deep learning, reinforcement learning
  • implementation, test, and validation for autonomous vehicles

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Published Papers (2 papers)

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Research

23 pages, 9217 KiB  
Article
Trajectory Tracking Control Study of Unmanned Fully Line-Controlled Distributed Drive Electric Vehicles
by Tian Tian, Gang Li, Yuzhi Li, Ning Li and Hongfei Bai
Appl. Sci. 2023, 13(11), 6465; https://doi.org/10.3390/app13116465 - 25 May 2023
Viewed by 1365
Abstract
Unmanned fully line-controlled distributed drive electric vehicles with four-wheel independent drive, dependent braking and dependent steering have significant advantages over conventional vehicles in terms of dynamic control, but at the same time multiple actuators with multiple degrees of freedom also pose the risk [...] Read more.
Unmanned fully line-controlled distributed drive electric vehicles with four-wheel independent drive, dependent braking and dependent steering have significant advantages over conventional vehicles in terms of dynamic control, but at the same time multiple actuators with multiple degrees of freedom also pose the risk of failure in the steering system, which is studied in this paper for trajectory tracking control. Rational control of multiple systems such as drive, braking, steering and fault tolerance of the unmanned fully line-controlled distributed drive electric vehicles are carried out. For longitudinal control, a fuzzy PI algorithm is used to input velocity error and velocity error rate of change, and to solve the required drive torque of the vehicle based on fuzzy rules; for lateral control, according to model prediction control theory, the exact model is predicted and an optimized search is performed to reasonably allocate the forward and backward wheels turning corners ensuring the accuracy and roadholding of trajectory tracking; for fault-tolerant control, differential drive and other methods of control, when a fault is detected, the number and position information of the faulty steering motor is transmitted to the fault-tolerant decision module, which outputs control commands according to the decision. The outcomes demonstrate that the presented trajectory following the control policy enhances the precision, roadholding and safety of trajectory following in an effective way. Full article
(This article belongs to the Special Issue Advances in Navigation and Control of Autonomous Vehicles)
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18 pages, 7223 KiB  
Article
Usability of Perception Sensors to Determine the Obstacles of Unmanned Ground Vehicles Operating in Off-Road Environments
by Marek Nowakowski and Jakub Kurylo
Appl. Sci. 2023, 13(8), 4892; https://doi.org/10.3390/app13084892 - 13 Apr 2023
Cited by 9 | Viewed by 2947
Abstract
This article presents the essential abilities and limitations of various sensors used for object recognition in the operation environment of unmanned ground vehicles (UGVs). The use of autonomous and unmanned vehicles for reconnaissance and logistics purposes has attracted attention in many countries. There [...] Read more.
This article presents the essential abilities and limitations of various sensors used for object recognition in the operation environment of unmanned ground vehicles (UGVs). The use of autonomous and unmanned vehicles for reconnaissance and logistics purposes has attracted attention in many countries. There are many different applications of mobile platforms in both civilian and military fields. Herein, we introduce a newly developed manned–unmanned high-mobility vehicle called TAERO that was designed for public roads and off-road operation. Detection for unmanned mode is required in both on-road and off-road environments, but the approach to identify drivable pathway and obstacles around a mobile platform is different in each environment. Dense vegetation and trees can affect the perception system of the vehicle, causing safety risks or even collisions. The main aim was to define the limitations of the perception system in off-road environments, as well as associated challenges and possible future directions for practical applications, to improve the performance of the UGV in all-terrain conditions. Recorded datasets were used to verify vision and laser-based sensors in practical application. The future directions of work to overcome or minimize the indicated challenges are also discussed. Full article
(This article belongs to the Special Issue Advances in Navigation and Control of Autonomous Vehicles)
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