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Intelligent Vehicle Sensing and Monitoring
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Intelligent Vehicle Sensing and Monitoring

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

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 5718

Special Issue Editors

Dr. Pedro Carvalho

E-Mail Website
Guest Editor
Centre for Telecommunications and Multimedia at INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal
Interests: computer vision; image processing; multimedia; machine learning; video analytics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jaime S. Cardoso

E-Mail Website
Guest Editor
Faculty of Engineering, University of Porto (FEUP), 4200-465 Porto, Portugal
Interests: computer vision; machine learning; medical image analysis; medical decision support systems; biometrics
Special Issues, Collections and Topics in MDPI journals
Dr. Paula Viana

E-Mail Website
Guest Editor
School of Engineering, Polytechnic of Porto, 4200-072 Porto, Portugal
Interests: multimedia; content annotation; computer vision; machine learning; visualization

Special Issue Information

Dear Colleagues,

Driver assistance and autonomous driving technologies have made significant progress in the last decade, following the increased integration of technology in vehicles. Much of the research has been devoted to monitoring the external environment, but more recent efforts have dedicated attention to the interior. Outside monitoring is strongly aligned with ensuring effective and safe driving, while interior monitoring increases safety, comfort, and convenience for all vehicle occupants, especially in the case of autonomous shared vehicles.

This Special Issue focuses on the intelligent processing of data collected in the vehicle for integrated monitoring and event detection, with a special focus on autonomous vehicles. It covers topics such as obstacle detection, activity classification, emotional monitoring, identification of undesired behaviors, damage detection, and many other topics related to the automatic supervision of vehicles and their occupants.

We invite contributions that address themes related to intelligent vehicle sensing and monitoring. Works that cope with challenging learning settings, such as weak annotations, domain generalization, uncertainty estimation, edge cases, data integration, etc., in the context of autonomous vehicles will be valued.

Topics include but are not limited to:

  • Obstacle detection;
  • Damage detection;
  • Detection and prediction of a degraded driver state, including inattention, fatigue, cognitive load, intoxication, and sudden illness;
  • Driver activity recognition, including mobile phone use, eating, applying makeup/grooming, and interacting with passengers;
  • Situation-dependent and personalized driver state detection and activity recognition;
  • Driver/occupant intention prediction;
  • Ego-motion;
  • Activity recognition and emotional monitoring;
  • Identification of undesired behaviors;
  • Weather classification;
  • Semantic segmentation (lane, traffic signs, etc.);
  • Driver and passenger biometrics;
  • Human activity recording, simulation, and generation (i.e., database acquisition, synthetic data) for vehicle scenarios;
  • Motion and tracking for driver, passengers, and pedestrians;
  • Datasets for driver assistance and autonomous driving;
  • Computer vision and other modalities for a vehicle’s environment analysis;
  • LiDAR, RADAR, and RGB data integration.

Dr. Pedro Carvalho
Prof. Dr. Jaime S. Cardoso
Dr. Paula Viana
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.

Published Papers (4 papers)

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Research

19 pages, 5725 KiB  
Article
Fully Convolutional Neural Network for Vehicle Speed and Emergency-Brake Prediction
by Razvan Itu and Radu Danescu
Sensors 2024, 24(1), 212; https://doi.org/10.3390/s24010212 - 29 Dec 2023
Viewed by 933
Abstract
Ego-vehicle state prediction represents a complex and challenging problem for self-driving and autonomous vehicles. Sensorial information and on-board cameras are used in perception-based solutions in order to understand the state of the vehicle and the surrounding traffic conditions. Monocular camera-based methods are becoming [...] Read more.
Ego-vehicle state prediction represents a complex and challenging problem for self-driving and autonomous vehicles. Sensorial information and on-board cameras are used in perception-based solutions in order to understand the state of the vehicle and the surrounding traffic conditions. Monocular camera-based methods are becoming increasingly popular for driver assistance, with precise predictions of vehicle speed and emergency braking being important for road safety enhancement, especially in the prevention of speed-related accidents. In this research paper, we introduce the implementation of a convolutional neural network (CNN) model tailored for the prediction of vehicle velocity, braking events, and emergency braking, employing sequential image sequences and velocity data as inputs. The CNN model is trained on a dataset featuring sequences of 20 consecutive images and corresponding velocity values, all obtained from a moving vehicle navigating through road-traffic scenarios. The model’s primary objective is to predict the current vehicle speed, braking actions, and the occurrence of an emergency-brake situation using the information encoded in the preceding 20 frames. We subject our proposed model to an evaluation on a dataset using regression and classification metrics, and comparative analysis with existing published work based on recurrent neural networks (RNNs). Through our efforts to improve the prediction accuracy for velocity, braking behavior, and emergency-brake events, we make a substantial contribution to improving road safety and offer valuable insights for the development of perception-based techniques in the field of autonomous vehicles. Full article
(This article belongs to the Special Issue Intelligent Vehicle Sensing and Monitoring)
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17 pages, 9400 KiB  
Communication
A Study on Wheel Member Condition Recognition Using 1D–CNN
by Jin-Han Lee, Jun-Hee Lee, Chang-Jae Lee, Seung-Lok Lee, Jin-Pyung Kim and Jae-Hoon Jeong
Sensors 2023, 23(23), 9501; https://doi.org/10.3390/s23239501 - 29 Nov 2023
Viewed by 696
Abstract
The condition of a railway vehicle’s wheels is an essential factor for safe operation. However, the current inspection of railway vehicle wheels is limited to periodic major and minor maintenance, where physical anomalies such as vibrations and noise are visually checked by maintenance [...] Read more.
The condition of a railway vehicle’s wheels is an essential factor for safe operation. However, the current inspection of railway vehicle wheels is limited to periodic major and minor maintenance, where physical anomalies such as vibrations and noise are visually checked by maintenance personnel and addressed after detection. As a result, there is a need for predictive technology concerning wheel conditions to prevent railway vehicle damage and potential accidents due to wheel defects. Insufficient predictive technology for railway vehicle’s wheel conditions forms the background for this study. In this research, a real-time tire wear classification system for light-rail rubber tires was proposed to reduce operational costs, enhance safety, and prevent service delays. To perform real-time condition classification of rubber tires, operational data from railway vehicles, including temperature, pressure, and acceleration, were collected. These data were processed and analyzed to generate training data. A 1D–CNN model was employed to classify tire conditions, and it demonstrated exceptionally high performance with a 99.4% accuracy rate. Full article
(This article belongs to the Special Issue Intelligent Vehicle Sensing and Monitoring)
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23 pages, 6175 KiB  
Communication
A Study on Wheel Member Condition Recognition Using Machine Learning (Support Vector Machine)
by Jin-Han Lee, Jun-Hee Lee, Kwang-Su Yun, Han Byeol Bae, Sun Young Kim, Jae-Hoon Jeong and Jin-Pyung Kim
Sensors 2023, 23(20), 8455; https://doi.org/10.3390/s23208455 - 13 Oct 2023
Cited by 1 | Viewed by 776
Abstract
The wheels of railway vehicles are of paramount importance in relation to railroad operations and safety. Currently, the management of railway vehicle wheels is restricted to post-event inspections of the wheels whenever physical phenomena, such as abnormal vibrations and noise, occur during the [...] Read more.
The wheels of railway vehicles are of paramount importance in relation to railroad operations and safety. Currently, the management of railway vehicle wheels is restricted to post-event inspections of the wheels whenever physical phenomena, such as abnormal vibrations and noise, occur during the operation of railway vehicles. To address this issue, this paper proposes a method for predicting abnormalities in railway wheels in advance and enhancing the learning and prediction performance of machine learning algorithms. Data were collected during the operation of Line 4 of the Busan Metro in South Korea by directly attaching sensors to the railway vehicles. Through the analysis of key factors in the collected data, factors that can be used for tire condition classification were derived. Additionally, through data distribution analysis and correlation analysis, factors for classifying tire conditions were identified. As a result, it was determined that the z-axis of acceleration has a significant impact, and machine learning techniques such as SVM (Linear Kernel, RBF Kernel) and Random Forest were utilized based on acceleration data to classify tire conditions into in-service and defective states. The SVM (Linear Kernel) yielded the highest recognition rate at 98.70%. Full article
(This article belongs to the Special Issue Intelligent Vehicle Sensing and Monitoring)
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20 pages, 5315 KiB  
Article
Correlation Analysis of In-Vehicle Sensors Data and Driver Signals in Identifying Driving and Driver Behaviors
by Lucas V. Bonfati, José J. A. Mendes Junior, Hugo Valadares Siqueira and Sergio L. Stevan, Jr.
Sensors 2023, 23(1), 263; https://doi.org/10.3390/s23010263 - 27 Dec 2022
Cited by 4 | Viewed by 2516
Abstract
Today’s cars have dozens of sensors to monitor vehicle performance through different systems, most of which communicate via vehicular networks (CAN). Many of these sensors can be used for applications other than the original ones, such as improving the driver experience or creating [...] Read more.
Today’s cars have dozens of sensors to monitor vehicle performance through different systems, most of which communicate via vehicular networks (CAN). Many of these sensors can be used for applications other than the original ones, such as improving the driver experience or creating new safety tools. An example is monitoring variables that describe the driver’s behavior. Interactions with the pedals, speed, and steering wheel, among other signals, carry driving characteristics. However, not always all variables related to these interactions are available in all vehicles; for example, the excursion of the brake pedal. Using an acquisition module, data from the in-vehicle sensors were obtained from the CAN bus, the brake pedal (externally instrumented), and the driver’s signals (instrumented with an inertial sensor and electromyography of their leg), to observe the driver and car information and evaluate the correlation hypothesis between these data, as well as the importance of the brake pedal signal not usually available in all car models. Different sets of sensors were evaluated to analyze the performance of three classifiers when analyzing the driver’s driving mode. It was found that there are superior results in classifying identity or behavior when driver signals are included. When the vehicle and driver attributes were used, hits above 0.93 were obtained in the identification of behavior and 0.96 in the identification of the driver; without driver signals, accuracy was more significant than 0.80 in identifying behavior. The results show a good correlation between vehicle data and data obtained from the driver, suggesting that further studies may be promising to improve the accuracy of rates based exclusively on vehicle characteristics, both for behavior identification and driver identification, thus allowing practical applications in embedded systems for local signaling and/or storing information about the driving mode, which is important for logistics companies. Full article
(This article belongs to the Special Issue Intelligent Vehicle Sensing and Monitoring)
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