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Applied Sciences
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Autonomous Vehicles: Technology and Application
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Special Issue "Autonomous Vehicles: Technology and Application"

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

Deadline for manuscript submissions: 31 October 2023 | Viewed by 2614

Special Issue Editors

Prof. Dr. Yi Han

E-Mail Website
Guest Editor
School of Automobile, Chang’an University, Xi’an 710064, China
Interests: new energy vehicles; autonomous vehicles
Prof. Dr. Xiaojun Tan

E-Mail Website
Guest Editor
School of Intelligent Systems Engineering, Sun Yat-sen University. Room.628, Block 1 of Engineering Building, #66 Gongchang Rd., Shenzhen 518107, China
Interests: new energy vehicles; smart driving; cluster control for unmanned systems application; computer vision and its application technology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We invite submissions to this Special Issue devoted to the technology and application of autonomous vehicles. Driverless driving helps improve traffic safety and efficiency, which is a hot topic in the development of artificial intelligence technology and intelligent transportation technology. Its aim is to drive more safely and reliably, free people's hands, and reduce physical labor. In the process of driving, people are the most uncertain factors, and the autonomous car works according to the computer control set by people, which can greatly reduce the occurrence of accidents, to a certain extent, while freeing hands. In general, driverless technology is a synthesis of many cutting-edge disciplines such as sensors, computers, artificial intelligence, communications, navigation and positioning, pattern recognition, machine vision, and intelligent control. According to the functional modules of driverless cars, the key technologies of driverless cars include environmental awareness, navigation and positioning, path planning, and decision control. In recent years, with the maturity of unmanned driving technology, the application and industrial system of the low-speed unmanned driving market have begun to see a larger scale. Semi-closed places with logistics transportation and personnel transfer have gradually become a typical application scenario where functional low-speed unmanned vehicles can quickly land. The application of unmanned driving will have a disruptive impact on the industry and encourage and promote the progress and technological upgrading of vehicle manufacturers.

For this Special Issue, we invite submissions exploring cutting-edge research and recent advances in the fields of autonomous vehicles. Both theoretical and experimental studies are welcome, as well as comprehensive reviews and survey papers.

Prof. Dr. Yi Han
Prof. Dr. Xiaojun Tan
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 2300 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 vehicles
  • new energy vehicles
  • cluster control for unmanned systems application
  • computer vision
  • smart driving

Published Papers (4 papers)

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Article
Wheel Odometry with Deep Learning-Based Error Prediction Model for Vehicle Localization
by
Ke He
,
Haitao Ding
,
Nan Xu
and
Konghui Guo
Appl. Sci. 2023, 13(9), 5588; https://doi.org/10.3390/app13095588 - 30 Apr 2023
Viewed by 639
Abstract
Wheel odometry is a simple and low-cost localization technique that can be used for localization in GNSS-deprived environments; however, its measurement accuracy is affected by many factors, such as wheel slip, wear, and tire pressure changes, resulting in unpredictable and variable errors, which [...] Read more.
Wheel odometry is a simple and low-cost localization technique that can be used for localization in GNSS-deprived environments; however, its measurement accuracy is affected by many factors, such as wheel slip, wear, and tire pressure changes, resulting in unpredictable and variable errors, which in turn affect positioning performance. To improve the localization performance of wheel odometry, this study developed a wheel odometry error prediction model based on a transformer neural network to learn the measurement uncertainty of wheel odometry and accurately predict the odometry error. Driving condition characteristics including features describing road types, road conditions, and vehicle driving operations were considered, and models both with and without driving condition characteristics were compared and analyzed. Tests were performed on a public dataset and an experimental vehicle. The experimental results demonstrate that the proposed model can predict the odometry error with higher accuracy, stability, and reliability than the LSTM and WhONet models under multiple challenging and longer GNSS outage driving conditions. At the same time, the transformer model’s overall performance can be improved in longer GNSS outage driving conditions by considering the driving condition characteristics. Tests on the experimental vehicle demonstrate the model’s generalization capability and the improved positioning performance of dead reckoning when using the proposed model. This study explored the possibility of applying a transformer model to wheel odometry and provides a new solution for using deep learning in localization. Full article
(This article belongs to the Special Issue Autonomous Vehicles: Technology and Application)
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Article
Multi-View Joint Learning and BEV Feature-Fusion Network for 3D Object Detection
by
Qunming Liu
,
Xiaodong Li
,
Xiaofei Zhang
,
Xiaojun Tan
and
Bodong Shi
Appl. Sci. 2023, 13(9), 5274; https://doi.org/10.3390/app13095274 - 23 Apr 2023
Viewed by 595
Abstract
Traditional 3D object detectors use BEV (bird’s eye view) feature maps to generate 3D object proposals, but in a single BEV feature map, there are inevitably the problems of high compression and information loss. To solve this problem, we propose a multi-view joint [...] Read more.
Traditional 3D object detectors use BEV (bird’s eye view) feature maps to generate 3D object proposals, but in a single BEV feature map, there are inevitably the problems of high compression and information loss. To solve this problem, we propose a multi-view joint learning and BEV feature-fusion network. In this network, we mainly propose two fusion modules: the multi-view feature-fusion module and the multi-BEV feature-fusion module. The multi-view feature fusion module performs joint learning from multiple views, fusing features learned from multiple views, and supplementing missing information in the BEV feature map. The multi-BEV feature-fusion module fuses BEV feature map outputs from different feature extractors to further enrich the feature information in the BEV feature map, in order to generate better quality 3D object proposals. We conducted experiments on a widely used KITTI dataset. The results show that our method has significantly improved the detection accuracy of the cyclist category.For cyclist detection tasks at the easy, moderate, and hard levels of the KITTI test dataset, our method improves by 1.57%, 2.03%, and 0.67%, respectively, compared to PV-RCNN. Full article
(This article belongs to the Special Issue Autonomous Vehicles: Technology and Application)
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Article
Cooperative Decision-Making for Mixed Traffic at an Unsignalized Intersection Based on Multi-Agent Reinforcement Learning
by
Huanbiao Zhuang
,
Chaofan Lei
,
Yuanhang Chen
and
Xiaojun Tan
Appl. Sci. 2023, 13(8), 5018; https://doi.org/10.3390/app13085018 - 17 Apr 2023
Viewed by 480
Abstract
Despite rapid advances in vehicle intelligence and connectivity, there is still a significant period in mixed traffic where connected, automated vehicles and human-driven vehicles coexist. The behavioral uncertainty of human-driven vehicles makes decision-making a challenging task in an unsignalized intersection scenario. In this [...] Read more.
Despite rapid advances in vehicle intelligence and connectivity, there is still a significant period in mixed traffic where connected, automated vehicles and human-driven vehicles coexist. The behavioral uncertainty of human-driven vehicles makes decision-making a challenging task in an unsignalized intersection scenario. In this paper, a decentralized multi-agent proximal policy optimization (MAPPO) based on an attention representations algorithm (Attn-MAPPO) was developed to make joint decisions at an intersection to avoid collisions and cross the intersection effectively. To implement this framework, by exploiting the shared information, the system was modeled as a model-free, fully cooperative, multi-agent system. The vehicle employed an attention module to extract the most valuable information from its neighbors. Based on the observation and traffic rules, a joint policy was identified to work more cooperatively based on the trajectory prediction of all the vehicles. To facilitate the collaboration between the vehicles, a weighted reward assignment scheme was proposed to focus more on the vehicles approaching intersections. The results presented the advantages of the Attn-MAPPO framework and validated the effectiveness of the designed reward function. Ultimately, the comparative experiments were conducted to demonstrate that the proposed approach was more adaptive and generalized than the heuristic rule-based model, which revealed its great potential for reinforcement learning in the decision-making of autonomous driving. Full article
(This article belongs to the Special Issue Autonomous Vehicles: Technology and Application)
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Article
Feature Identification, Solution Disassembly and Cost Comparison of Intelligent Driving under Different Technical Routes
by
Zongwei Liu
,
Wang Zhang
,
Hong Tan
and
Fuquan Zhao
Appl. Sci. 2023, 13(7), 4361; https://doi.org/10.3390/app13074361 - 29 Mar 2023
Viewed by 474
Abstract
Technical route decision making of intelligent driving has always been the focus of attention of automotive enterprises and even the industry. Firstly, this study combs the main technical routes of intelligent driving at different levels from three dimensions: development strategy, intelligence allocation and [...] Read more.
Technical route decision making of intelligent driving has always been the focus of attention of automotive enterprises and even the industry. Firstly, this study combs the main technical routes of intelligent driving at different levels from three dimensions: development strategy, intelligence allocation and sensor combination. Then, the methodology of technical component combination is designed to disassemble different technical routes into corresponding technical component combinations. Finally, an improved evaluation model of total cost of ownership of intelligent driving is developed and the total cost of ownership of intelligent driving system under different technical routes is compared. For the development strategy, even if the function superposition can follow some research and development achievements of low-level intelligent driving, scenario-driven is still the option with lower cost and better sustainability. For intelligence allocation, collaborative intelligence can effectively reduce the cost of the vehicle compared with single-vehicle intelligence by up to 46%, but the cost reduction depends on the original on-board hardware. For sensor combination, the multi-source fusion always has the cost advantage compared with vision-only, but the advantage is more obvious in the medium-level and high-level stage of single-vehicle intelligence. Full article
(This article belongs to the Special Issue Autonomous Vehicles: Technology and Application)
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