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Special Issue "Application of Deep Learning in Intelligent Transportation"

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

Deadline for manuscript submissions: 30 November 2022 | Viewed by 6636

Special Issue Editors

Prof. Dr. Dieter Schramm
E-Mail Website
Guest Editor
Department Mechanical Engineering, University of Duisburg-Essen, Forsthausweg 2, 47057 Duisburg, Germany
Interests: Vehicle dynamics; driver assistance systems; vehicle simulators
Dr. Philipp Sieberg
E-Mail Website
Guest Editor
Department of Mechanics, Universitat Duisburg-Essen, Essen, Germany
Interests: Intelligent Transportation Systems (ITS); Vehicle Dynamics; Machine Learning; Hybride Methoden und Ansätze; Applied Artificial Intelligence

Special Issue Information

Dear Colleagues,

For some time now, deep learning has been a promising approach for the study of various systems, and this is also the case in the field of intelligent transportation systems. Due to their multi-layered structure, deep networks outperform classical machine learning approaches in processing data and learning correlations. This advancement is also apparent for the purpose of intelligent transportation systems. Applications include navigation and localization, signal and image processing, connected and automated vehicles, as well as virtual sensors, among many others.

This Special Issue encourages authors from academia and industry to submit new research results about technological innovations and novel ideas for the application of deep learning in intelligent transportation systems.

Prof. Dr. Dieter Schramm
Dr. Philipp Sieberg
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 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

  • advanced driver assistance systems
  • artificial neural networks
  • connected and automated vehicles
  • control systems
  • deep learning
  • intelligent transportation systems
  • navigation and localization
  • signal and image processing
  • virtual sensors

Published Papers (9 papers)

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Research

Article
Single Camera Face Position-Invariant Driver’s Gaze Zone Classifier Based on Frame-Sequence Recognition Using 3D Convolutional Neural Networks
Sensors 2022, 22(15), 5857; https://doi.org/10.3390/s22155857 - 05 Aug 2022
Viewed by 283
Abstract
Estimating the driver’s gaze in a natural real-world setting can be problematic for different challenging scenario conditions. For example, faces will undergo facial occlusions, illumination, or various face positions while driving. In this effort, we aim to reduce misclassifications in driving situations when [...] Read more.
Estimating the driver’s gaze in a natural real-world setting can be problematic for different challenging scenario conditions. For example, faces will undergo facial occlusions, illumination, or various face positions while driving. In this effort, we aim to reduce misclassifications in driving situations when the driver has different face distances regarding the camera. Three-dimensional Convolutional Neural Networks (CNN) models can make a spatio-temporal driver’s representation that extracts features encoded in multiple adjacent frames that can describe motions. This characteristic may help ease the deficiencies of a per-frame recognition system due to the lack of context information. For example, the front, navigator, right window, left window, back mirror, and speed meter are part of the known common areas to be checked by drivers. Based on this, we implement and evaluate a model that is able to detect the head direction toward these regions having various distances from the camera. In our evaluation, the 2D CNN model had a mean average recall of 74.96% across the three models, whereas the 3D CNN model had a mean average recall of 87.02%. This result show that our proposed 3D CNN-based approach outperforms a 2D CNN per-frame recognition approach in driving situations when the driver’s face has different distances from the camera. Full article
(This article belongs to the Special Issue Application of Deep Learning in Intelligent Transportation)
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Article
Short-Term Drift Prediction of Multi-Functional Buoys in Inland Rivers Based on Deep Learning
Sensors 2022, 22(14), 5120; https://doi.org/10.3390/s22145120 - 07 Jul 2022
Viewed by 371
Abstract
The multi-functional buoy is an important facility for assisting the navigation of inland waterway ships. Therefore, real-time tracking of its position is an essential process to ensure the safety of ship navigation. Aiming at the problem of the low accuracy of multi-functional buoy [...] Read more.
The multi-functional buoy is an important facility for assisting the navigation of inland waterway ships. Therefore, real-time tracking of its position is an essential process to ensure the safety of ship navigation. Aiming at the problem of the low accuracy of multi-functional buoy drift prediction, an integrated deep learning model incorporating the attention mechanism and ResNet-GRU (RGA) to predict short-term drift values of buoys is proposed. The model has the strong feature expression capability of ResNet and the temporal memory capability of GRU, and the attention mechanism can capture important information adaptively, which can solve the nonlinear time series drift prediction problem well. In this paper, the data collected from multi-functional buoy #4 at Nantong anchorage No. 2 in the Yangtze River waters in China were studied as an example, and first linear interpolation was used for filling in missing values; then, input variables were selected based on Pearson correlation analysis, and finally, the model structure was designed for training and testing. The experimental results show that the mean square error, mean absolute error, root mean square error and mean percentage error of the RGA model on the test set are 5.113036, 1.609969, 2.261202 and 15.575886, respectively, which are significantly better than other models. This study provides a new idea for predicting the short-term drift of multi-functional buoys, which is helpful for their tracking and management. Full article
(This article belongs to the Special Issue Application of Deep Learning in Intelligent Transportation)
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Article
TA-Unet: Integrating Triplet Attention Module for Drivable Road Region Segmentation
Sensors 2022, 22(12), 4438; https://doi.org/10.3390/s22124438 - 12 Jun 2022
Viewed by 497
Abstract
Road segmentation has been one of the leading research areas in the realm of autonomous driving cars due to the possible benefits autonomous vehicles can offer. Significant reduction of crashes, greater independence for the people with disabilities, and reduced traffic congestion on the [...] Read more.
Road segmentation has been one of the leading research areas in the realm of autonomous driving cars due to the possible benefits autonomous vehicles can offer. Significant reduction of crashes, greater independence for the people with disabilities, and reduced traffic congestion on the roads are some of the vivid examples of them. Considering the importance of self-driving cars, it is vital to develop models that can accurately segment drivable regions of roads. The recent advances in the area of deep learning have presented effective methods and techniques to tackle road segmentation tasks effectively. However, the results of most of them are not satisfactory for implementing them into practice. To tackle this issue, in this paper, we propose a novel model, dubbed as TA-Unet, that is able to produce quality drivable road region segmentation maps. The proposed model incorporates a triplet attention module into the encoding stage of the U-Net network to compute attention weights through the triplet branch structure. Additionally, to overcome the class-imbalance problem, we experiment on different loss functions, and confirm that using a mixed loss function leads to a boost in performance. To validate the performance and efficiency of the proposed method, we adopt the publicly available UAS dataset, and compare its results to the framework of the dataset and also to four state-of-the-art segmentation models. Extensive experiments demonstrate that the proposed TA-Unet outperforms baseline methods both in terms of pixel accuracy and mIoU, with 98.74% and 97.41%, respectively. Finally, the proposed method yields clearer segmentation maps on different sample sets compared to other baseline methods. Full article
(This article belongs to the Special Issue Application of Deep Learning in Intelligent Transportation)
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Article
Real-Time Vehicle Classification and Tracking Using a Transfer Learning-Improved Deep Learning Network
Sensors 2022, 22(10), 3813; https://doi.org/10.3390/s22103813 - 18 May 2022
Viewed by 710
Abstract
Accurate vehicle classification and tracking are increasingly important subjects for intelligent transport systems (ITSs) and for planning that utilizes precise location intelligence. Deep learning (DL) and computer vision are intelligent methods; however, accurate real-time classification and tracking come with problems. We tackle three [...] Read more.
Accurate vehicle classification and tracking are increasingly important subjects for intelligent transport systems (ITSs) and for planning that utilizes precise location intelligence. Deep learning (DL) and computer vision are intelligent methods; however, accurate real-time classification and tracking come with problems. We tackle three prominent problems (P1, P2, and P3): the need for a large training dataset (P1), the domain-shift problem (P2), and coupling a real-time multi-vehicle tracking algorithm with DL (P3). To address P1, we created a training dataset of nearly 30,000 samples from existing cameras with seven classes of vehicles. To tackle P2, we trained and applied transfer learning-based fine-tuning on several state-of-the-art YOLO (You Only Look Once) networks. For P3, we propose a multi-vehicle tracking algorithm that obtains the per-lane count, classification, and speed of vehicles in real time. The experiments showed that accuracy doubled after fine-tuning (71% vs. up to 30%). Based on a comparison of four YOLO networks, coupling the YOLOv5-large network to our tracking algorithm provided a trade-off between overall accuracy (95% vs. up to 90%), loss (0.033 vs. up to 0.036), and model size (91.6 MB vs. up to 120.6 MB). The implications of these results are in spatial information management and sensing for intelligent transport planning. Full article
(This article belongs to the Special Issue Application of Deep Learning in Intelligent Transportation)
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Communication
Ensuring the Reliability of Virtual Sensors Based on Artificial Intelligence within Vehicle Dynamics Control Systems
Sensors 2022, 22(9), 3513; https://doi.org/10.3390/s22093513 - 05 May 2022
Viewed by 463
Abstract
The use of virtual sensors in vehicles represents a cost-effective alternative to the installation of physical hardware. In addition to physical models resulting from theoretical modeling, artificial intelligence and machine learning approaches are increasingly used, which incorporate experimental modeling. Due to the resulting [...] Read more.
The use of virtual sensors in vehicles represents a cost-effective alternative to the installation of physical hardware. In addition to physical models resulting from theoretical modeling, artificial intelligence and machine learning approaches are increasingly used, which incorporate experimental modeling. Due to the resulting black-box characteristics, virtual sensors based on artificial intelligence are not fully reliable, which can have fatal consequences in safety-critical applications. Therefore, a hybrid method is presented that safeguards the reliability of artificial intelligence-based estimations. The application example is the state estimation of the vehicle roll angle. The state estimation is coupled with a central predictive vehicle dynamics control. The implementation and validation is performed by a co-simulation between IPG CarMaker and MATLAB/Simulink. By using the hybrid method, unreliable estimations by the artificial intelligence-based model resulting from erroneous input signals are detected and handled. Thus, a valid and reliable state estimate is available throughout. Full article
(This article belongs to the Special Issue Application of Deep Learning in Intelligent Transportation)
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Article
Applying Hybrid Lstm-Gru Model Based on Heterogeneous Data Sources for Traffic Speed Prediction in Urban Areas
Sensors 2022, 22(9), 3348; https://doi.org/10.3390/s22093348 - 27 Apr 2022
Viewed by 516
Abstract
With the advent of the Internet of Things (IoT), it has become possible to have a variety of data sets generated through numerous types of sensors deployed across large urban areas, thus empowering the notion of smart cities. In smart cities, various types [...] Read more.
With the advent of the Internet of Things (IoT), it has become possible to have a variety of data sets generated through numerous types of sensors deployed across large urban areas, thus empowering the notion of smart cities. In smart cities, various types of sensors may fall into different administrative domains and may be accessible through exposed Application Program Interfaces (APIs). In such setups, for traffic prediction in Intelligent Transport Systems (ITS), one of the major prerequisites is the integration of heterogeneous data sources within a preprocessing data pipeline resulting into hybrid feature space. In this paper, we first present a comprehensive algorithm to integrate heterogeneous data obtained from sensors, services, and exogenous data sources into a hybrid spatial–temporal feature space. Following a rigorous exploratory data analysis, we apply a variety of deep learning algorithms specialized for time series geospatial data and perform a comparative analysis of Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and their hybrid combinations. The hybrid LSTM–GRU model outperforms the rest with Root Mean Squared Error (RMSE) of 4.5 and Mean Absolute Percentage Error (MAPE) of 6.67%. Full article
(This article belongs to the Special Issue Application of Deep Learning in Intelligent Transportation)
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Article
Bike-Sharing Demand Prediction at Community Level under COVID-19 Using Deep Learning
Sensors 2022, 22(3), 1060; https://doi.org/10.3390/s22031060 - 29 Jan 2022
Cited by 2 | Viewed by 846
Abstract
An important question in planning and designing bike-sharing services is to support the user’s travel demand by allocating bikes at the stations in an efficient and reliable manner which may require accurate short-time demand prediction. This study focuses on the short-term forecasting, 15 [...] Read more.
An important question in planning and designing bike-sharing services is to support the user’s travel demand by allocating bikes at the stations in an efficient and reliable manner which may require accurate short-time demand prediction. This study focuses on the short-term forecasting, 15 min ahead, of the shared bikes demand in Montreal using a deep learning approach. Having a set of bike trips, the study first identifies 6 communities in the bike-sharing network using the Louvain algorithm. Then, four groups of LSTM-based architectures are adopted to predict pickup demand in each community. A univariate ARIMA model is also used to compare results as a benchmark. The historical trip data from 2017 to 2021 are used in addition to the extra inputs of demand related engineered features, weather conditions, and temporal variables. The selected timespan allows predicting bike demand during the COVID-19 pandemic. Results show that the deep learning models significantly outperform the ARIMA one. The hybrid CNN-LSTM achieves the highest prediction accuracy. Furthermore, adding the extra variables improves the model performance regardless of its architecture. Thus, using the hybrid structure enriched with additional input features provides a better insight into the bike demand patterns, in support of bike-sharing operational management. Full article
(This article belongs to the Special Issue Application of Deep Learning in Intelligent Transportation)
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Article
Hyperparameter Optimization Techniques for Designing Software Sensors Based on Artificial Neural Networks
Sensors 2021, 21(24), 8435; https://doi.org/10.3390/s21248435 - 17 Dec 2021
Cited by 1 | Viewed by 801
Abstract
Software sensors are playing an increasingly important role in current vehicle development. Such soft sensors can be based on both physical modeling and data-based modeling. Data-driven modeling is based on building a model purely on captured data which means that no system knowledge [...] Read more.
Software sensors are playing an increasingly important role in current vehicle development. Such soft sensors can be based on both physical modeling and data-based modeling. Data-driven modeling is based on building a model purely on captured data which means that no system knowledge is required for the application. At the same time, hyperparameters have a particularly large influence on the quality of the model. These parameters influence the architecture and the training process of the machine learning algorithm. This paper deals with the comparison of different hyperparameter optimization methods for the design of a roll angle estimator based on an artificial neural network. The comparison is drawn based on a pre-generated simulation data set created with ISO standard driving maneuvers. Four different optimization methods are used for the comparison. Random Search and Hyperband are two similar methods based purely on randomness, whereas Bayesian Optimization and the genetic algorithm are knowledge-based methods, i.e., they process information from previous iterations. The objective function for all optimization methods consists of the root mean square error of the training process and the reference data generated in the simulation. To guarantee a meaningful result, k-fold cross-validation is integrated for the training process. Finally, all methods are applied to the predefined parameter space. It is shown that the knowledge-based methods lead to better results. In particular, the Genetic Algorithm leads to promising solutions in this application. Full article
(This article belongs to the Special Issue Application of Deep Learning in Intelligent Transportation)
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Article
Hourly Origin–Destination Matrix Estimation Using Intelligent Transportation Systems Data and Deep Learning
Sensors 2021, 21(21), 7080; https://doi.org/10.3390/s21217080 - 26 Oct 2021
Cited by 2 | Viewed by 1022
Abstract
Predicting the travel demand plays an indispensable role in urban transportation planning. Data collection methods for estimating the origin–destination (OD) demand matrix are being extensively shifted from traditional survey techniques to the pre-collected data from intelligent transportation systems (ITSs). This shift is partly [...] Read more.
Predicting the travel demand plays an indispensable role in urban transportation planning. Data collection methods for estimating the origin–destination (OD) demand matrix are being extensively shifted from traditional survey techniques to the pre-collected data from intelligent transportation systems (ITSs). This shift is partly due to the high cost of conducting traditional surveys and partly due to the diversity of scattered data produced by ITSs and the opportunity to derive extra benefits out of this big data. This study attempts to predict the OD matrix of Tehran metropolis using a set of ITS data, including the data extracted from automatic number plate recognition (ANPR) cameras, smart fare cards, loop detectors at intersections, global positioning systems (GPS) of navigation software, socio-economic and demographic characteristics as well as land-use features of zones. For this purpose, five models based on machine learning (ML) techniques are developed for training and test. In evaluating the performance of the models, the statistical methods show that the convolutional neural network (CNN) leads to the best performance in terms of accuracy in predicting the OD matrix and has the lowest error in terms of root mean square error (RMSE) and mean absolute percentage error (MAPE). Moreover, the predicted OD matrix was structurally compared with the ground truth matrix, and the CNN model also shows the highest structural similarity with the ground truth OD matrix in the presented case. Full article
(This article belongs to the Special Issue Application of Deep Learning in Intelligent Transportation)
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