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Sensors for Intelligent Vehicles and Autonomous Driving
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Sensors for Intelligent Vehicles and Autonomous Driving

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 4663

Special Issue Editors

Dr. Xin Xia

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, University of California, Los Angeles, CA 90095, USA
Interests: cooperative driving automation; state estimation; cooperative localization; cooperative perception; the dynamic control of connected autonomous vehicles
Special Issues, Collections and Topics in MDPI journals
Dr. Letian Gao

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, University of California, Los Angeles, CA 90095, USA
Interests: vehicle localization; state estimation; the dynamic control of intelligent vehicles

Special Issue Information

Dear Colleagues,

The rapid development of intelligent vehicles and autonomous driving technologies has garnered significant attention from both academia and industry in recent years. A critical component in the development of these systems is the integration of various sensor technologies that enable vehicles to perceive and understand their surroundings, make informed decisions, and navigate safely in complex environments. The increasing demand for higher levels of autonomy and safety in modern transportation systems has spurred substantial research and innovations in sensor technologies for intelligent vehicles and autonomous driving.

The primary motivation behind this Special Issue is to address the growing need for a comprehensive understanding of the latest sensor technologies, their applications, and the challenges associated with their implementation in intelligent vehicles and autonomous driving systems. By collating experts from various disciplines, this Special Issue aims to foster interdisciplinary collaboration and drive innovation in the design, development, and deployment of sensor technologies for intelligent vehicles and autonomous driving.

Dr. Xin Xia
Dr. Letian Gao
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

  • vehicle localization and mapping
  • obstacle detection and avoidance
  • lane detection and tracking
  • traffic sign and signal recognition
  • pedestrian and cyclist detection
  • vehicle-to-everything (V2X) communication
  • driver monitoring and assistance systems
  • advanced driver assistance systems (ADASs)

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Published Papers (5 papers)

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Research

18 pages, 11067 KiB  
Article
Enhancing Deep Learning-Based Segmentation Accuracy through Intensity Rendering and 3D Point Interpolation Techniques to Mitigate Sensor Variability
by Myeong-Jun Kim, Suyeon Kim, Banghyon Lee and Jungha Kim
Sensors 2024, 24(14), 4475; https://doi.org/10.3390/s24144475 - 11 Jul 2024
Cited by 1 | Viewed by 464
Abstract
In the context of LiDAR sensor-based autonomous vehicles, segmentation networks play a crucial role in accurately identifying and classifying objects. However, discrepancies between the types of LiDAR sensors used for training the network and those deployed in real-world driving environments can lead to [...] Read more.
In the context of LiDAR sensor-based autonomous vehicles, segmentation networks play a crucial role in accurately identifying and classifying objects. However, discrepancies between the types of LiDAR sensors used for training the network and those deployed in real-world driving environments can lead to performance degradation due to differences in the input tensor attributes, such as x, y, and z coordinates, and intensity. To address this issue, we propose novel intensity rendering and data interpolation techniques. Our study evaluates the effectiveness of these methods by applying them to object tracking in real-world scenarios. The proposed solutions aim to harmonize the differences between sensor data, thereby enhancing the performance and reliability of deep learning networks for autonomous vehicle perception systems. Additionally, our algorithms prevent performance degradation, even when different types of sensors are used for the training data and real-world applications. This approach allows for the use of publicly available open datasets without the need to spend extensive time on dataset construction and annotation using the actual sensors deployed, thus significantly saving time and resources. When applying the proposed methods, we observed an approximate 20% improvement in mIoU performance compared to scenarios without these enhancements. Full article
(This article belongs to the Special Issue Sensors for Intelligent Vehicles and Autonomous Driving)
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19 pages, 18663 KiB  
Article
Synthetic Data Enhancement and Network Compression Technology of Monocular Depth Estimation for Real-Time Autonomous Driving System
by Woomin Jun, Jisang Yoo and Sungjin Lee
Sensors 2024, 24(13), 4205; https://doi.org/10.3390/s24134205 - 28 Jun 2024
Cited by 1 | Viewed by 535
Abstract
Accurate 3D image recognition, critical for autonomous driving safety, is shifting from the LIDAR-based point cloud to camera-based depth estimation technologies driven by cost considerations and the point cloud’s limitations in detecting distant small objects. This research aims to enhance MDE (Monocular Depth [...] Read more.
Accurate 3D image recognition, critical for autonomous driving safety, is shifting from the LIDAR-based point cloud to camera-based depth estimation technologies driven by cost considerations and the point cloud’s limitations in detecting distant small objects. This research aims to enhance MDE (Monocular Depth Estimation) using a single camera, offering extreme cost-effectiveness in acquiring 3D environmental data. In particular, this paper focuses on novel data augmentation methods designed to enhance the accuracy of MDE. Our research addresses the challenge of limited MDE data quantities by proposing the use of synthetic-based augmentation techniques: Mask, Mask-Scale, and CutFlip. The implementation of these synthetic-based data augmentation strategies has demonstrably enhanced the accuracy of MDE models by 4.0% compared to the original dataset. Furthermore, this study introduces the RMS (Real-time Monocular Depth Estimation configuration considering Resolution, Efficiency, and Latency) algorithm, designed for the optimization of neural networks to augment the performance of contemporary monocular depth estimation technologies through a three-step process. Initially, it selects a model based on minimum latency and REL criteria, followed by refining the model’s accuracy using various data augmentation techniques and loss functions. Finally, the refined model is compressed using quantization and pruning techniques to minimize its size for efficient on-device real-time applications. Experimental results from implementing the RMS algorithm indicated that, within the required latency and size constraints, the IEBins model exhibited the most accurate REL (absolute RELative error) performance, achieving a 0.0480 REL. Furthermore, the data augmentation combination of the original dataset with Flip, Mask, and CutFlip, alongside the SigLoss loss function, displayed the best REL performance, with a score of 0.0461. The network compression technique using FP16 was analyzed as the most effective, reducing the model size by 83.4% compared to the original while maintaining the least impact on REL performance and latency. Finally, the performance of the RMS algorithm was validated on the on-device autonomous driving platform, NVIDIA Jetson AGX Orin, through which optimal deployment strategies were derived for various applications and scenarios requiring autonomous driving technologies. Full article
(This article belongs to the Special Issue Sensors for Intelligent Vehicles and Autonomous Driving)
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21 pages, 12670 KiB  
Article
An Edge Computing System with AMD Xilinx FPGA AI Customer Platform for Advanced Driver Assistance System
by Tsun-Kuang Chi, Tsung-Yi Chen, Yu-Chen Lin, Ting-Lan Lin, Jun-Ting Zhang, Cheng-Lin Lu, Shih-Lun Chen, Kuo-Chen Li and Patricia Angela R. Abu
Sensors 2024, 24(10), 3098; https://doi.org/10.3390/s24103098 - 13 May 2024
Viewed by 949
Abstract
The convergence of edge computing systems with Field-Programmable Gate Array (FPGA) technology has shown considerable promise in enhancing real-time applications across various domains. This paper presents an innovative edge computing system design specifically tailored for pavement defect detection within the Advanced Driver-Assistance Systems [...] Read more.
The convergence of edge computing systems with Field-Programmable Gate Array (FPGA) technology has shown considerable promise in enhancing real-time applications across various domains. This paper presents an innovative edge computing system design specifically tailored for pavement defect detection within the Advanced Driver-Assistance Systems (ADASs) domain. The system seamlessly integrates the AMD Xilinx AI platform into a customized circuit configuration, capitalizing on its capabilities. Utilizing cameras as input sensors to capture road scenes, the system employs a Deep Learning Processing Unit (DPU) to execute the YOLOv3 model, enabling the identification of three distinct types of pavement defects with high accuracy and efficiency. Following defect detection, the system efficiently transmits detailed information about the type and location of detected defects via the Controller Area Network (CAN) interface. This integration of FPGA-based edge computing not only enhances the speed and accuracy of defect detection, but also facilitates real-time communication between the vehicle’s onboard controller and external systems. Moreover, the successful integration of the proposed system transforms ADAS into a sophisticated edge computing device, empowering the vehicle’s onboard controller to make informed decisions in real time. These decisions are aimed at enhancing the overall driving experience by improving safety and performance metrics. The synergy between edge computing and FPGA technology not only advances ADAS capabilities, but also paves the way for future innovations in automotive safety and assistance systems. Full article
(This article belongs to the Special Issue Sensors for Intelligent Vehicles and Autonomous Driving)
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17 pages, 13759 KiB  
Article
Explaining Bounding Boxes in Deep Object Detectors Using Post Hoc Methods for Autonomous Driving Systems
by Caio Nogueira, Luís Fernandes, João N. D. Fernandes and Jaime S. Cardoso
Sensors 2024, 24(2), 516; https://doi.org/10.3390/s24020516 - 14 Jan 2024
Cited by 1 | Viewed by 1105
Abstract
Deep learning has rapidly increased in popularity, leading to the development of perception solutions for autonomous driving. The latter field leverages techniques developed for computer vision in other domains for accomplishing perception tasks such as object detection. However, the black-box nature of deep [...] Read more.
Deep learning has rapidly increased in popularity, leading to the development of perception solutions for autonomous driving. The latter field leverages techniques developed for computer vision in other domains for accomplishing perception tasks such as object detection. However, the black-box nature of deep neural models and the complexity of the autonomous driving context motivates the study of explainability in these models that perform perception tasks. Moreover, this work explores explainable AI techniques for the object detection task in the context of autonomous driving. An extensive and detailed comparison is carried out between gradient-based and perturbation-based methods (e.g., D-RISE). Moreover, several experimental setups are used with different backbone architectures and different datasets to observe the influence of these aspects in the explanations. All the techniques explored consist of saliency methods, making their interpretation and evaluation primarily visual. Nevertheless, numerical assessment methods are also used. Overall, D-RISE and guided backpropagation obtain more localized explanations. However, D-RISE highlights more meaningful regions, providing more human-understandable explanations. To the best of our knowledge, this is the first approach to obtaining explanations focusing on the regression of the bounding box coordinates. Full article
(This article belongs to the Special Issue Sensors for Intelligent Vehicles and Autonomous Driving)
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19 pages, 3459 KiB  
Article
Method of Evaluating Multiple Scenarios in a Single Simulation Run for Automated Vehicle Assessment
by Inyoung Kim, Donghyo Kang, Harim Jeong, Soomok Lee and Ilsoo Yun
Sensors 2023, 23(19), 8271; https://doi.org/10.3390/s23198271 - 6 Oct 2023
Viewed by 1125
Abstract
With advances in the technology applied to automated driving systems (ADSs), active efforts have been made to evaluate the safety of ADS in various complex situations using simulations. In accordance with these efforts, numerous institutions have developed single-scenario pools that reflect a variety [...] Read more.
With advances in the technology applied to automated driving systems (ADSs), active efforts have been made to evaluate the safety of ADS in various complex situations using simulations. In accordance with these efforts, numerous institutions have developed single-scenario pools that reflect a variety of road and traffic characteristics and ADS performances. However, a single scenario has limitations in comprehensively evaluating the performance of complex ADS. Therefore, this study proposed a methodology that combines and transforms single scenarios into multiple scenarios. This aided in continuously evaluating the ADS performance over entire road segments and implemented this methodology in the simulations. Full article
(This article belongs to the Special Issue Sensors for Intelligent Vehicles and Autonomous Driving)
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