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Advanced Sensing Techniques for Autonomous Vehicles and Advanced Driver Assistance Systems (ADAS): 2nd Edition

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

Deadline for manuscript submissions: closed (30 June 2024) | Viewed by 13980

Special Issue Editors

Prof. Dr. Javier Alonso Ruiz

E-Mail Website
Guest Editor
Computer Engineering Department, INVETT Research Group, Universidad de Alcalá, Alcalá de Henares, Madrid, Spain
Interests: intelligent transportation systems; autonomous vehicles; control systems; driver assistance systems; artificial vision
Special Issues, Collections and Topics in MDPI journals
Dr. Iván García Daza

E-Mail Website
Guest Editor
Computer Engineering Department, Polytechnic School, University of Alcalá, Campus Universitario s/n, Alcalá de Henares, 288805 Madrid, Spain
Interests: accurate mapping systems based on optimal optimization algorithms; advanced driver assistance systems; assistive intelligent vehicles; driver and road user state and intent recognition; dynamic and cinematic car models; intelligent localization systems based on LiDAR odometry; intelligent navigation and localization systems based on inertial navigation systems; intelligent-vehicle-related image, radar, and LiDAR signal processing; sensor fusion systems for driverless cars
Special Issues, Collections and Topics in MDPI journals
Dr. Carlota Salinas

E-Mail Website
Guest Editor
Computer Engineering Department, Universidad de Alcalá, Alcalá de Henares, 28805 Madrid, Spain
Interests: computer vision; multi-sensory systems; 3D sensing; mapping and localization; autonomous vehicles and robotics
Special Issues, Collections and Topics in MDPI journals
Dr. Rubén Izquierdo

E-Mail Website
Guest Editor
INVETT Research Group, Universidad de Alcalá, Campus Universitario, Ctra, Madrid-Barcelona km, 33, 600, 28805 Alcalá de Henares, Spain
Interests: intelligent vehicles and traffic technologies; intelligent vehicles; user-based autonomous vehicle design; advanced vehicle and traffic perception and modeling systems; predictive perception systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Several systems are essential to autonomous vehicles, including localization, navigation, and obstacle avoidance systems. To be able to implement all of these systems, autonomous vehicles must be equipped with a multitude of sensors (GPS, inertial measurement units (IMUs), radars, cameras, LiDARs, etc.). All of these systems require the development of techniques that extract relevant information as efficiently as possible. This Special Issue focuses on exploring these techniques to apply them to autonomous vehicles or advanced driving assistance systems (ADAS). The topics include, but are not limited, to:

  • Inertial measurement units;
  • Artificial vision;
  • Accurate localization;
  • Mapping;
  • Simultaneous localization and mapping (SLAM);
  • LiDAR odometry;
  • Navigation;
  • Sensor fusion.

For more information, please refer to the publications in the first edition of this Special Issue.

Dr. Javier Alonso Ruiz
Dr. Iván García Daza
Dr. Carlota Salinas
Dr. Rubén Izquierdo
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

  • accurate localization
  • mapping
  • LiDAR odometry
  • navigation
  • sensor fusion

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Published Papers (4 papers)

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21 pages, 4077 KiB  
Article
Analysis of Advanced Driver-Assistance Systems for Safe and Comfortable Driving of Motor Vehicles
by Tomasz Neumann
Sensors 2024, 24(19), 6223; https://doi.org/10.3390/s24196223 - 26 Sep 2024
Cited by 5 | Viewed by 6894
Abstract
This paper aims to thoroughly examine and compare advanced driver-assistance systems (ADASs) in the context of their impact on safety and driving comfort. It also sought to determine the level of acceptance and trust drivers have in these systems. The first chapter of [...] Read more.
This paper aims to thoroughly examine and compare advanced driver-assistance systems (ADASs) in the context of their impact on safety and driving comfort. It also sought to determine the level of acceptance and trust drivers have in these systems. The first chapter of this document describes the sensory detectors used in ADASs, including radars, cameras, LiDAR, and ultrasonics. The subsequent chapter presents the most popular driver assistance systems, including adaptive cruise control (ACC), blind spot detection (BSD), lane keeping systems (LDW/LKS), intelligent headlamp control (IHC), and emergency brake assist (EBA). A key element of this work is the evaluation of the effectiveness of these systems in terms of safety and driving comfort, employing a survey conducted among drivers. Data analysis illustrates how these systems are perceived and identified areas requiring improvements. Overall, the paper shows drivers’ positive reception of ADASs, with most respondents confirming that these technologies increase their sense of safety and driving comfort. These systems prove to be particularly helpful in avoiding accidents and hazardous situations. However, there is a need for their further development, especially in terms of increasing their precision, reducing false alarms, and improving the user interface. ADASs significantly contribute to enhancing safety and driving comfort. Yet, they are still in development and require continuous optimization and driver education to fully harness their potential. Technological advancements are expected to make these systems even more effective and user-friendly. Full article
(This article belongs to the Special Issue Advanced Sensing Techniques for Autonomous Vehicles and Advanced Driver Assistance Systems (ADAS): 2nd Edition)
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16 pages, 1717 KiB  
Article
SDC-Net++: End-to-End Crash Detection and Action Control for Self-Driving Car Deep-IoT-Based System
by Mohammed Abdou Tolba and Hanan Ahmed Kamal
Sensors 2024, 24(12), 3805; https://doi.org/10.3390/s24123805 - 12 Jun 2024
Viewed by 1606
Abstract
Few prior works study self-driving cars by deep learning with IoT collaboration. SDC-Net, which is an end-to-end multitask self-driving car camera cocoon IoT-based system, is one of the research areas that tackles this direction. However, by design, SDC-Net is not able to identify [...] Read more.
Few prior works study self-driving cars by deep learning with IoT collaboration. SDC-Net, which is an end-to-end multitask self-driving car camera cocoon IoT-based system, is one of the research areas that tackles this direction. However, by design, SDC-Net is not able to identify the accident locations; it only classifies whether a scene is a crash scene or not. In this work, we introduce an enhanced design for the SDC-Net system by (1) replacing the classification network with a detection one, (2) adapting our benchmark dataset labels built on the CARLA simulator to include the vehicles’ bounding boxes while keeping the same training, validation, and testing samples, and (3) modifying the shared information via IoT to include the accident location. We keep the same path planning and automatic emergency braking network, the digital automation platform, and the input representations to formulate the comparative study. The SDC-Net++ system is proposed to (1) output the relevant control actions, especially in case of accidents: accelerate, decelerate, maneuver, and brake, and (2) share the most critical information to the connected vehicles via IoT, especially the accident locations. A comparative study is also conducted between SDC-Net and SDC-Net++ with the same input representations: front camera only, panorama and bird’s eye views, and with single-task networks, crash avoidance only, and multitask networks. The multitask network with a BEV input representation outperforms the nearest representation in precision, recall, f1-score, and accuracy by more than 15.134%, 12.046%, 13.593%, and 5%, respectively. The SDC-Net++ multitask network with BEV outperforms SDC-Net multitask with BEV in precision, recall, f1-score, accuracy, and average MSE by more than 2.201%, 2.8%, 2.505%, 2%, and 18.677%, respectively. Full article
(This article belongs to the Special Issue Advanced Sensing Techniques for Autonomous Vehicles and Advanced Driver Assistance Systems (ADAS): 2nd Edition)
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16 pages, 5213 KiB  
Article
Fuzzy Neural Network PID-Based Constant Deceleration Control for Automated Mine Electric Vehicles Using EMB System
by Jian Li, Chi Ma and Yuqiang Jiang
Sensors 2024, 24(7), 2129; https://doi.org/10.3390/s24072129 - 27 Mar 2024
Cited by 2 | Viewed by 2943
Abstract
It is urgent for automated electric transportation vehicles in coal mines to have the ability of self-adaptive tracking target constant deceleration to ensure stable and safe braking effects in long underground roadways. However, the current braking control system of underground electric trackless rubber-tired [...] Read more.
It is urgent for automated electric transportation vehicles in coal mines to have the ability of self-adaptive tracking target constant deceleration to ensure stable and safe braking effects in long underground roadways. However, the current braking control system of underground electric trackless rubber-tired vehicles (UETRVs) still adopts multi-level constant braking torque control, which cannot achieve target deceleration closed-loop control. To overcome the disadvantages of lower safety and comfort, and the non-precise stopping distance, this article describes the architecture and working principle of constant deceleration braking systems with an electro-mechanical braking actuator. Then, a deceleration closed-loop control algorithm based on fuzzy neural network PID is proposed and simulated in Matlab/Simulink. Finally, an actual brake control unit (BCU) is built and tested in a real industrial field setting. The test illustrates the feasibility of this constant deceleration control algorithm, which can achieve constant decelerations within a very short time and maintain a constant value of −2.5 m/s2 within a deviation of ±0.1 m/s2, compared with the deviation of 0.11 m/s2 of fuzzy PID and the deviation of 0.13 m/s2 of classic PID. This BCU can provide electric and automated mine vehicles with active and smooth deceleration performance, which improves the level of electrification and automation for mine transport machinery. Full article
(This article belongs to the Special Issue Advanced Sensing Techniques for Autonomous Vehicles and Advanced Driver Assistance Systems (ADAS): 2nd Edition)
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16 pages, 11990 KiB  
Article
Joint Object Detection and Re-Identification for 3D Obstacle Multi-Camera Systems
by Irene Cortés, Jorge Beltrán, Arturo de la Escalera and Fernando García
Sensors 2023, 23(23), 9395; https://doi.org/10.3390/s23239395 - 25 Nov 2023
Cited by 1 | Viewed by 1762
Abstract
The growing on-board processing capabilities have led to more complex sensor configurations, enabling autonomous car prototypes to expand their operational scope. Nowadays, the joint use of LiDAR data and multiple cameras is almost a standard and poses new challenges for existing multi-modal perception [...] Read more.
The growing on-board processing capabilities have led to more complex sensor configurations, enabling autonomous car prototypes to expand their operational scope. Nowadays, the joint use of LiDAR data and multiple cameras is almost a standard and poses new challenges for existing multi-modal perception pipelines, such as dealing with contradictory or redundant detections caused by inference on overlapping images. In this paper, we address this last issue in the context of sequential schemes like F-PointNets, where object candidates are obtained in the image space, and the final 3D bounding box is then inferred from point cloud information. To this end, we propose the inclusion of a re-identification branch into the 2D detector, i.e., Faster R-CNN, so that objects seen from adjacent cameras can be handled before the 3D box estimation takes place, removing duplicates and completing the object’s cloud. Extensive experimental evaluations covering both the 2D and 3D domains affirm the effectiveness of the suggested methodology. The findings indicate that our approach outperforms conventional Non-Maximum Suppression (NMS) methods. Particularly, we observed a significant gain of over 5% in terms of accuracy for cars in camera overlap regions. These results highlight the potential of our upgraded detection and re-identification system in practical scenarios for autonomous driving. Full article
(This article belongs to the Special Issue Advanced Sensing Techniques for Autonomous Vehicles and Advanced Driver Assistance Systems (ADAS): 2nd Edition)
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