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Advances in Sensing, Imaging and Computing for Autonomous Driving: 2nd Edition

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: 20 November 2025 | Viewed by 1505

Special Issue Editors

Dr. Wei Li

E-Mail Website
Guest Editor
Department of Computer Science, Georgia State University, Atlanta, GA 30302, USA
Interests: secure and privacy-aware computing; Internet of Things; big data; game theory
Special Issues, Collections and Topics in MDPI journals
Dr. Honglu Jiang

E-Mail Website
Guest Editor
Computer Science and Software Engineering, Miami University, Oxford, OH 45056, USA
Interests: wireless and mobile security; IoT; big data; privacy preservation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Autonomous driving techniques have been experiencing profound innovation and becoming increasingly mature, which greatly accelerates the development of the automobile industry. The success of autonomous driving vehicles mainly benefits from their high-performance perception and decision-making systems guided by huge amounts of perceptual data collected via various onboard sensors. For instance, GPS sensors collect real-time location data with exact coordinates, radar sensors detect surrounding objects and their distance to vehicles, behavior-relevant sensors monitor the environment inside the car and record passenger operations, and camera sensors work as the eyes of vehicles to gain a visual view and instruct driving behaviors. These sensory data can not only facilitate autonomous vehicles but also serve as a precious resource for smart city, smart transportation, and many other real-world applications.

This Special Issue solicits high-quality contributions that focus on the design and development of new technologies, algorithms, and tools to advance autonomous driving techniques. In particular, we encourage original and high-quality submissions related (but not limited) to one or more of the topics below:

  • Autonomous-driving vehicles;
  • Sensory data acquisition;
  • Sensory data processing;
  • Computer vision;
  • Motion planning and decision making;
  • Object detection, perception, and prediction;
  • Attack and defense in autonomous driving;
  • Safety, security, and privacy in autonomous driving;
  • Anomaly detection in autonomous driving;
  • Cooperative and coordinated autonomous driving.

Dr. Wei Li
Dr. Honglu Jiang
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. Sensors 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 2600 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 vehicles
  • sensory data acquisition
  • sensory data processing
  • computer vision
  • motion planning and decision making
  • object detection, perception, and prediction
  • attack and defense in autonomous driving
  • safety, security, and privacy in autonomous driving
  • anomaly detection in autonomous driving
  • cooperative and coordinated autonomous driving

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Related Special Issue

Published Papers (2 papers)

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Research

20 pages, 2077 KB  
Article
OTVLD-Net: An Omni-Dimensional Dynamic Convolution-Transformer Network for Lane Detection
by Yunhao Wu, Ziyao Zhang, Haifeng Chen and Li Jian
Sensors 2025, 25(17), 5475; https://doi.org/10.3390/s25175475 - 3 Sep 2025
Abstract
With the vigorous development of deep learning technology, lane detection tasks have achieved phased results. However, existing lane detection models do not consider the unique geometric and visual features of lanes when dealing with some challenging scenarios, resulting in many difficulties and limitations. [...] Read more.
With the vigorous development of deep learning technology, lane detection tasks have achieved phased results. However, existing lane detection models do not consider the unique geometric and visual features of lanes when dealing with some challenging scenarios, resulting in many difficulties and limitations. To this end, we propose a lane detection network based on full-dimensional convolutional Transformer (OTVLD-Net) to improve the adaptability of the model under extreme road conditions and better handle complex lane topology. In order to extract richer contextual features, we designed ODVT-Net, which uses full-dimensional dynamic convolution combined with improved feature flip fusion layer and non-local network layer, and aggregates lane symmetry features by utilizing the horizontal symmetry of lanes. A feature weight generation mechanism based on Transformer is designed, and a cross-attention mechanism between feature maps and lane requests is added in the decoding stage to enable the network to aggregate global feature information. At the same time, a vanishing point detection module is introduced, and a joint weighted loss function is designed to be trained in coordination with the lane detection task to improve the generalization ability of the lane detection model. Experimental results on the OpenLane and CurveLanes datasets show that the detection effect of the OTVLD-Net model has reached the current advanced level. In particular, the accuracy on the OpenLane dataset is 6.4% higher than the F1 score of the second-ranked model, and the average performance in different challenging scenarios is also improved by 8.9%. At the same time, when ResNet-18 is used as the template feature extraction network, the model achieves a speed of 103FPS and a computing power of 14.2 GFlops, achieving good performance while ensuring real-time performance. Full article
(This article belongs to the Special Issue Advances in Sensing, Imaging and Computing for Autonomous Driving: 2nd Edition)
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16 pages, 1435 KB  
Article
Vehicle Re-Identification Method Based on Efficient Self-Attention CNN-Transformer and Multi-Task Learning Optimization
by Yu Wang, Rui Li and Yihan Shao
Sensors 2025, 25(10), 2977; https://doi.org/10.3390/s25102977 - 8 May 2025
Cited by 1 | Viewed by 1139
Abstract
To address the challenges of low accuracy in vehicle re-identification caused by intra-class variations, inter-class similarities and environmental factors, this paper proposes a CNN-Transformer architecture (IBNT-Net) for vehicle re-identification. The method builds upon a ResNet50-IBN backbone network and incorporates an improved multi-head self-attention [...] Read more.
To address the challenges of low accuracy in vehicle re-identification caused by intra-class variations, inter-class similarities and environmental factors, this paper proposes a CNN-Transformer architecture (IBNT-Net) for vehicle re-identification. The method builds upon a ResNet50-IBN backbone network and incorporates an improved multi-head self-attention mechanism to aggregate contextual information. It constructs a multi-branch vehicle re-identification network that combines both global and local features. Furthermore, a multi-task learning strategy is adopted, creating specialized learning pathways for classification tasks and metric learning tasks. Group convolution techniques are utilized to reduce model complexity, making it suitable for resource-constrained environments. On the VeRi-776 and VehicleID dataset, the proposed method achieves state-of-the-art performance with less parameters. The experimental results show that the proposed method has better re-identification performance and the extracted features are more discriminative. Full article
(This article belongs to the Special Issue Advances in Sensing, Imaging and Computing for Autonomous Driving: 2nd Edition)
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