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Electronics
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Development and Advances in Autonomous Driving Technology
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Development and Advances in Autonomous Driving Technology

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

Deadline for manuscript submissions: 15 October 2025 | Viewed by 3184

Special Issue Editors

Dr. Zhangyu Wang

E-Mail Website
Guest Editor
School of Transportation Science and Engineering, Beihang University, Beijing 100191, China
Interests: automatic driving; intelligent perception; intelligent transportation
Special Issues, Collections and Topics in MDPI journals
Dr. Peng Chen

E-Mail Website
Guest Editor
School of Transportation Science and Engineering, Beihang University, Beijing 100191, China
Interests: inteligent traffic operation and control; traffic system modeling and simulation; decision making and trajectory planning for CAVs
Dr. Bin Zhou

E-Mail Website
Guest Editor
School of Transportation Science and Engineering, Beihang University, Beijing 100191, China
Interests: special vehicle unmanned vehicle multi-sensor fusion perception; vehicle driving state identification; vehicle road collaborative intelligent control

Special Issue Information

Dear Colleagues,

In recent years, automatic driving has developed rapidly and has gradually realized its industrial application potential. With the expansion of application scenarios and the increase in application scale, the challenges faced by automatic driving in complex scenarios also become prominent. This Special Issue will publish the papers that reflect research and innovation in the area of autonomous driving in complex scenarios, which include, but are not limited to, the following main topics: vehicle–vehicle/vehicle–road collaborative perception, vehicle–vehicle/vehicle–road collaborative decision and control, intelligent perception and control methods in complex environments, intelligent perception in severe weather, high precision mapping and positioning in complex environments, acceleration tests and simulations for automatic driving, and end-to-end autonomous driving.

Dr. Zhangyu Wang
Dr. Peng Chen
Dr. Bin Zhou
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.

Keywords

  • intelligent perception
  • intelligent control
  • severe weather and complex environments
  • end-to-end autonomous driving

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

26 pages, 8883 KiB  
Article
Enhancing Machine Learning Techniques in VSLAM for Robust Autonomous Unmanned Aerial Vehicle Navigation
by Hussam Rostum and József Vásárhelyi
Electronics 2025, 14(7), 1440; https://doi.org/10.3390/electronics14071440 - 2 Apr 2025
Viewed by 298
Abstract
This study introduces a visual SLAM real-time system designed for small indoor environments. The system demonstrates resilience against significant motion clutter and supports wide-baseline loop closing, re-localization, and automatic initialization. Leveraging state-of-the-art algorithms, the approach presented in this article utilizes adapted Oriented FAST [...] Read more.
This study introduces a visual SLAM real-time system designed for small indoor environments. The system demonstrates resilience against significant motion clutter and supports wide-baseline loop closing, re-localization, and automatic initialization. Leveraging state-of-the-art algorithms, the approach presented in this article utilizes adapted Oriented FAST and Rotated BRIEF features for tracking, mapping, re-localization, and loop closing. In addition, the research uses an adaptive threshold to find putative feature matches that provide efficient map initialization and accurate tracking. The assignment is to process visual information from the camera of a DJI Tello drone for the construction of an indoor map and the estimation of the trajectory of the camera. In a ’survival of the fittest’ style, the algorithms selectively pick adaptive points and keyframes for reconstruction. This leads to robustness and a concise traceable map that develops as scene content emerges, making lifelong operation possible. The results give an improvement in the RMSE for the adaptive ORB algorithm and the adaptive threshold (3.280). However, the standard ORB algorithm failed to achieve the mapping process. Full article
(This article belongs to the Special Issue Development and Advances in Autonomous Driving Technology)
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17 pages, 9001 KiB  
Article
A Multi-Regime Car-Following Model Capturing Traffic Breakdown
by Zhenhua Li, Zuojun Wang and Yanyue Liu
Electronics 2025, 14(2), 304; https://doi.org/10.3390/electronics14020304 - 14 Jan 2025
Viewed by 598
Abstract
Traffic breakdown refers to the first-order transition from free flow to synchronized flow. It is characterized by a rapid decrease in speed, suddenly increasing density, and abruptly plummeting capacity in most of the relevant observations. To understand its cause and model its empirical [...] Read more.
Traffic breakdown refers to the first-order transition from free flow to synchronized flow. It is characterized by a rapid decrease in speed, suddenly increasing density, and abruptly plummeting capacity in most of the relevant observations. To understand its cause and model its empirical observations, a multi-regime car-following model is proposed, which classifies the car-following state into four regimes, i.e., Free driving, High-speed following, Low-speed following, and Emergency. Simulation results demonstrate that traffic breakdown and spontaneous jam formation can be reproduced simultaneously by the new model. Experimental verification has shown that the new model can successfully simulate the observed concave growth pattern of traffic oscillations. Full article
(This article belongs to the Special Issue Development and Advances in Autonomous Driving Technology)
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17 pages, 4500 KiB  
Article
Collision Avoidance Trajectory Planning Based on Dynamic Spatio-Temporal Corridor Search in Curvy Road Scenarios for Intelligent Vehicles
by Mingfang Zhang, Lianghao Tong, Leyuan Zhao and Pangwei Wang
Electronics 2024, 13(24), 4959; https://doi.org/10.3390/electronics13244959 - 16 Dec 2024
Viewed by 860
Abstract
To avoid collisions and ensure driving safety, comfort, and efficiency, in this study, we propose a trajectory planning strategy for intelligent vehicles navigating curvy road scenarios. This strategy is based on a dynamic spatio-temporal corridor search. First, an obstacle space expansion module is [...] Read more.
To avoid collisions and ensure driving safety, comfort, and efficiency, in this study, we propose a trajectory planning strategy for intelligent vehicles navigating curvy road scenarios. This strategy is based on a dynamic spatio-temporal corridor search. First, an obstacle space expansion module is constructed using a critical safety distance model to generate a searchable spatio-temporal corridor. Next, a dynamic step expansion is performed to improve the traditional hybrid A* search algorithm by the discretization of front-wheel steering angles and acceleration. The bisection method is applied to iteratively optimize the child nodes at each step, and the child node with the lowest cost is selected as the rough search node. Subsequently, a locally weighted dual-regression fitting algorithm is employed for segment trajectory fitting, and the optimal trajectory is generated. Finally, the performance of the proposed trajectory planning strategy is validated on the Carla simulation platform. The results show the effectiveness and efficiency of our strategy in three typical scenarios. Full article
(This article belongs to the Special Issue Development and Advances in Autonomous Driving Technology)
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20 pages, 1529 KiB  
Article
Data-Driven Bus Trajectory Tracking Based on Feedforward–Feedback Model-Free Adaptive Iterative Learning Control
by Weijie Xiu, Yongqiang Xie, Ye Ren and Li Wang
Electronics 2024, 13(23), 4673; https://doi.org/10.3390/electronics13234673 - 26 Nov 2024
Viewed by 737
Abstract
This paper presents a scheme for the feedforward–feedback longitudinal trajectory tracking control of buses. The scheme is specifically designed to address the periodic and repetitive nature of bus operations. First, the vehicle’s longitudinal dynamics are linearized along the iterative axis via full-form dynamic [...] Read more.
This paper presents a scheme for the feedforward–feedback longitudinal trajectory tracking control of buses. The scheme is specifically designed to address the periodic and repetitive nature of bus operations. First, the vehicle’s longitudinal dynamics are linearized along the iterative axis via full-form dynamic linearization (FFDL), and parameters such as the pseudo-gradient are estimated with data and a projection algorithm to grasp the dynamic characteristics of the system. To better handle complex real-world traffic conditions, we then propose the forward and backward structure. At the same time, the iterative axis design performance index is verified, and the forward partial control law, namely, model-free adaptive iterative learning control (MFAILC), is derived. In order to further enhance the robustness to disturbance and other factors, the control law of the feedback part is designed with active disturbance rejection control (ADRC). A key advantage of this control approach is its sole reliance on the data generated during vehicle operation, without the need for specific information about the controlled vehicle. This feature enables the method to be adaptable to different vehicle types and resilient to various disturbances. Finally, MATLAB simulations are used to verify the practicality of the proposed method. Full article
(This article belongs to the Special Issue Development and Advances in Autonomous Driving Technology)
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