Intelligent Transportation Systems Safety Research Articles

Nighttime Pothole Detection: A Benchmark

In the field of computer vision, the detection of road potholes at night represents a critical challenge in enhancing the safety of intelligent transportation systems. Ensuring road safety is of paramount importance, particularly in promptly repairing pothole issues. These abrupt road depressions can easily lead to vehicle skidding, loss of control, and even traffic accidents, especially when water has pooled in or submerged the potholes. Therefore, the detection and recognition of road potholes can significantly reduce vehicle damage and the incidence of safety incidents. However, research on road pothole detection lacks high-quality annotated datasets, particularly under low-light conditions at night. To address this issue, this study introduces a novel Nighttime Pothole Dataset (NPD), independently collected and comprising 3831 images that capture diverse scene variations. The construction of this dataset aims to counteract the insufficiency of existing data resources and strives to provide a richer and more realistic benchmark. Additionally, we develop a baseline detector, termed WT-YOLOv8, for the proposed dataset, based on YOLOv8. We also evaluate the performance of the improved WT-YOLOv8 method and eight state-of-the-art object detection methods on the NPD and the COCO dataset. The experimental results on the NPD demonstrate that WT-YOLOv8 achieves a 2.3% improvement in mean Average Precision (mAP) over YOLOv8. In terms of the key metrics—AP@0.5 and AP@0.75—it shows enhancements of 1.5% and 2.8%, respectively, compared to YOLOv8. The experimental results provide valuable insights into each method’s strengths and weaknesses under low-light conditions. This analysis highlights the importance of a specialized dataset for nighttime pothole detection and shows variations in accuracy and robustness among methods, emphasizing the need for improved nighttime pothole detection techniques. The introduction of the NPD is expected to stimulate further research, encouraging the development of advanced algorithms for nighttime pothole detection, ultimately leading to more flexible and reliable road maintenance and road safety.

On a security scheme against collusive attacks in vehicular ad hoc networks

Vehicular Ad Hoc Networks (VANETs) offer a promising solution to bring drivers comfortable driving experiences and also improve road safety in intelligent transportation systems, but also faces many security issues. Collusive attack is one of the most challenging threats in VANETs because it violates the fundamental assumption made by VANET-based applications that all received information be correct and trustworthy. Collusive attackers can not only generate and send false or forged messages, but also purposely manipulate the reputation value of normal or malicious vehicular nodes. To address these issues, we analyze the behaviors characteristics of collusive attacks and propose a generic, lightweight, and fully distributed detection scheme against collusive attacks in VANETs. This scheme integrates two methods to identify different collusive attacks for fraud reputation and fraud message, respectively, as well as an incentive method to restrain collusive nodes. Simulation-based experiments are conducted and the results illustrate the superiority of the proposed security scheme over state-of-the-art methods.

Improving Driving Style in Connected Vehicles via Predicting Road Surface, Traffic, and Driving Style

This paper investigates the application of ensemble learning in improving the accuracy and reliability of predictions in connected vehicle systems, focusing on driving style, road surface quality, and traffic conditions. Our study’s central methodology is the voting classifier ensemble method, which integrates predictions from multiple machine learning models to improve overall predictive performance. Specifically, the ensemble method combines insights from random forest, decision tree, and K-nearest neighbors models, leveraging their individual strengths while compensating for their weaknesses. This approach resulted in high accuracy rates of 94.67% for driving style, 99.10% for road surface, and 98.80% for traffic predictions, demonstrating the robustness of the ensemble technique. Additionally, our research emphasizes the importance of model explanation ability, employing the tree interpreter tool to provide detailed insights into how different features influence predictions. This paper proposes a model based on the algorithm GLOSA for sharing data between connected vehicles and the algorithm CTCRA for sending road information to navigation application users. Based on prediction results using ensemble learning and similarity in driving styles, road surface conditions, and traffic conditions, an ensemble learning approach is used. This not only contributes to the predictions’ transparency and trustworthiness but also highlights the practical implications of ensemble learning in improving real-time decision-making and vehicle safety in intelligent transportation systems. The findings underscore the significant potential of advanced ensemble methods for addressing complex challenges in vehicular data analysis.

Delineating Sight Occlusions of Head-On Traffic Signboards under Varying Available Sight Distances Using LiDAR Point Clouds

It is essential to delineate visual occlusions of head-on traffic signboards under varying available sight distances (ASDs) to improve road safety and guide maintenance. However, the status of sight occlusions in actual road scenarios is unclear because of the lack of an automatic and quantitative evaluation approach. This study presents a new cluster attention traffic-sign occlusion (CASO) method with a cluster attention traffic-sign network (CASNet) to automatically classify road infrastructures, and an occlusion delineating module to dynamically describe the occlusions of head-on traffic signboards using point clouds. CASNet consists of a cluster module to alleviate the interference of redundant object features and an attention module to focus on learning local features of small samples on road scenes. The occlusion delineating module is investigated to rapidly construct a signboard-related oblique cone and dynamically assess occlusions. The sight occlusions change with varying ASDs and are delineated by two indices: the degree of shaded signboard area, and the degree of occlusion volume from the driver’s dynamic perspective to the head-on signboard. Compared with the state-of-the-art networks, the experimental extraction results achieved for signboards show an overall improved performance. The degree of occlusion volumes toward head-on signboards fluctuates between 26.75% and 36.70%, and the degree of shaded areas on signboards increases from 22.75% to 59.63%, with ASDs varying from 20 to 75 m. This research contributes to evaluating road safety in intelligent transportation systems and accurately guiding the allocation of maintenance budgets to the heavily occluded road sections.

Anomaly Detection in Pedestrian Walkways for Intelligent Transportation System Using Federated Learning and Harris Hawks Optimizer on Remote Sensing Images

Anomaly detection in pedestrian walkways is a vital research area that uses remote sensing, which helps to optimize pedestrian traffic and enhance flow to improve pedestrian safety in intelligent transportation systems (ITS). Engineers and researchers can formulate more potential techniques and tools with the power of computer vision (CV) and machine learning (ML) for mitigating potential safety hazards and identifying anomalies (i.e., vehicles) in pedestrian walkways. The real-world challenges of scenes and dynamics of environmental complexity cannot be handled by the conventional offline learning-based vehicle detection method and shallow approach. With recent advances in deep learning (DL) and ML areas, authors have found that the image detection issue ought to be devised as a two-class classification problem. Therefore, this study presents an Anomaly Detection in Pedestrian Walkways for Intelligent Transportation Systems using Federated Learning and Harris Hawks Optimizer (ADPW-FLHHO) algorithm on remote sensing images. The presented ADPW-FLHHO technique focuses on the identification and classification of anomalies, i.e., vehicles in the pedestrian walkways. To accomplish this, the ADPW-FLHHO technique uses the HybridNet model for feature vector generation. In addition, the HHO approach is implemented for the optimal hyperparameter tuning process. For anomaly detection, the ADPW-FLHHO technique uses a multi deep belief network (MDBN) model. The experimental results illustrated the promising performance of the ADPW-FLHHO technique over existing models with a maximum AUC score of 99.36%, 99.19%, and 98.90% on the University of California San Diego (UCSD) Ped1, UCSD Ped2, and avenue datasets, respectively. Therefore, the proposed model can be employed for accurate and automated anomaly detection in the ITS environment.

Probit Cryptographic Blockchain for Secure Data Transmission in Intelligent Transportation Systems

<p>To address the current security challenges, Digital Twin (DT) models and strategies are to be applied to improve security, privacy, and safety in intelligent transportation systems. However, the existing algorithm was failed to address the security and privacy issues for data between the vehicles. To enhance the security of data transmission, a novel Probit Regressive Davis Mayer Kupyna Cryptographic Hash Blockchain (PRDMKCHB) technique is proposed. Initially, the source vehicle finds the nearest vehicle to transmit the data based on the trust value using the probit regression function. After finding the neighboring nodes, the Blockchain is constructed to improve the security of data transmission from the source to the destination. The Blockchain uses the Kupyna Cryptography to generate the hash value for each data. Davis Mayer compression function is to improve the security of data delivery and to minimize packet loss. The comprehensive simulation is carried out to validate the performance of the proposed PRDMKCHB technique and existing Blockchain technology in terms of packet delivery ratio, packet loss rate, and execution time. Simulation results show the performance improvement of the proposed PRDMKCHB technique compared to the previous Blockchain technology in terms of higher packet delivery ratio, minimum packet loss, and execution time with respect to the number of data packets.</p> <p> </p>

Guest editorial: Decision making and control for connected and automated vehicles

Guest editorial: Decision making and control for connected and automated vehicles

From Less to More: Progressive Generalized Zero-Shot Detection With Curriculum Learning

Object detection, as one of the most important environment perception tasks for traffic safety in intelligent transportation systems, has been widely investigated recently. However, most of the researches focus on the fully supervised scenario, and inevitably lead to model failure. With the continuous development of Zero-Shot Learning (ZSL) models, Generalized Zero-Shot Detection (GZSD) has attracted great attention due to its ability of detecting unseen objects. Many researchers tend to map the detected visual features to semantic attributes and then separate seen and unseen domains during inference. But they have ignore that the generative methods generally have higher performance than these visual-semantic mapping methods, and they have been confirmed from previous GZSL methods. In order to make up for the vacancy of GZSD in the generative methods, we propose an idea of using curriculum learning to generate more precise unseen visual features. And with the excellent performance of WGAN-based method in sample synthesis, we realize the function of using semantics to generate visual features for unseen domains. In addition, we also adopt part of the idea of meta-learning to progressively correct the capability of the generator for better mitigating domain shift problem during the generation process. Through the above ideas, we can detect both seen and unseen bounding boxes and classify them accurately, by combining with the excellent detection ability of Faster-RCNN. Extensive experimental results on two popular datasets, i.e., MSCOCO and KITTI, show that our proposed method can outperform the state-of-the-art methods.

Attention-based deep neural network for driver behavior recognition

Driver behavior recognition is crucial for traffic safety in intelligent transportation systems. To understand the driver distraction behavior, deep learning methods has been used to learn the hierarchical features of receptive field images. However, most of existing studies focus on investing in spatial components based on the convolutional neural network for visual analysis. In this study, an attention-based deep neural network (ADNet) method is proposed for driver behavior recognition. The ADNet framework is first presented, which integrates the lightweight attention block into the deep learning model to strengthen the representative power of the model. Then, the channel attention (CA) block is designed to model inter-channel dependencies in the ADNet. Furthermore, the spatial attention block is combined with the CA for adaptive feature extraction. Data augmentation is used in the data preprocessing stage to improve recognition performance. The effectiveness of the proposed method is verified on the distraction dataset of American University in Cairo (AUC) and the real dataset of Hunan University (HNU). The ADNet method achieves a Top-1 accuracy of 98.48%, which outperforms the state-of-the-art methods.

Interval-Valued Invehicular Latency Characterization for Risk/Fault Management Purposes

Guaranteeing the reliability of Intelligent Transportation Systems (ITSs) is an overlapping issue. Not only the in-road risks must be mastered, but also the internal threats that emerge from the ITS material aspects should be addressed carefully. Most of the safety verification solutions ignore the limitations issued from the ITS onboard embedded structure, such as the invehicular communication delays. In this work, a novel Response Time Analysis (RTA) method is introduced to bridge the gap between the risk/failure management issues and the material aspects (the invehicular communication delays in particular) for ITSs. Aside of its simplicity, the suggested RTA reveals all potential latency scenarios by returning an interval estimation of delays. The interval results enclose certainly the exact delay that may occur in runtime. As a proof of concept of the introduced RTA algorithm, the Hardware-in-the-Loop (HIL) validation technique is adopted. The HIL platform is realized thanks to a model of a modern industrial automotive system. The validation work proves the consistency and efficiency of the proposed RTA solution to ensure ITS safety assurance.

Traffic Sign Recognition Using an Attentive Context Region‐Based Detection Framework

Accurate small traffic sign recognition is more important for the safety of intelligent transportation systems. A recognition framework named attentive context region-based detection framework (AC-RDF) is proposed in this paper. We construct the attentive context feature for the recognition of small traffic signs, which combines the target information and the contextual information by the concatenation operation following a pointwise convolutional layer. The proposed attentive context feature exploits the surrounding information for a given object proposal. Next, we propose a novel attentive loss function to replace the original cross-entropy function. It distinguishes hard negative samples from easy positive ones in the total loss, allows the proposed framework to obtain enough training, and further improve the recognition accuracy. The proposed method is evaluated on the challenging Tsinghua-Tencent 100K dataset. The experimental results indicate that the attentive context region-based detection framework is superior at detecting small traffic signs and achieves state-of-the-art performance compared with other methods.

Detection and Evaluation of Driver Distraction Using Machine Learning and Fuzzy Logic

In addition to vehicle control, drivers often perform secondary tasks that impede driving. Reduction of driver distraction is an important challenge for the safety of intelligent transportation systems. In this paper, a methodology for the detection and evaluation of driver distraction while performing secondary tasks is described and an appropriate hardware and a software environment is offered and studied. The system includes a model of normal driving, a subsystem for measuring the errors from the secondary tasks, and a module for total distraction evaluation. A new machine learning algorithm defines driver performance in lane keeping and speed maintenance on a specific road segment. To recognize the errors, a method is proposed, which compares normal driving parameters with ones obtained while conducting a secondary task. To evaluate distraction, an effective fuzzy logic algorithm is used. To verify the proposed approach, a case study with driver-in-the-loop experiments was carried out, in which participants performed the secondary task, namely chatting on a cell phone. The results presented in this research confirm its capability to detect and to precisely measure a level of abnormal driver performance.

MR-CNN: A Multi-Scale Region-Based Convolutional Neural Network for Small Traffic Sign Recognition

Small traffic sign recognition is a challenging problem in computer vision, and its accuracy is important to the safety of intelligent transportation systems (ITS). In this paper, we propose the multi-scale region-based convolutional neural network (MR-CNN). At the detection stage, MR-CNN uses a multi-scale deconvolution operation to up-sample the features of the deeper convolution layers and concatenates them to those of the shallow layer to construct the fused feature map. The fused feature map has the ability to generate fewer region proposals and achieve higher recall values. At the classification stage, we leverage the multi-scale contextual regions to exploit the information surrounding a given object proposal and construct the fused feature for the fully connected layers. The fused feature map inside the region proposal network (RPN) focuses primarily on improving the image resolution and semantic information for small traffic sign detection, while outside the RPN, the fused feature enhances the feature representation by leveraging the contextual information. Finally, we evaluated MR-CNN on the largest dataset, Tsinghua-Tencent 100K, which is suitable for our problem and more challenging than the GTSDB and GTSRB datasets. The final experimental results indicate that the MR-CNN is superior at detecting small traffic signs, and that it achieves the state-of-the-art performance compared with other methods.

Virus-traffic coupled dynamic model for virus propagation in vehicle-to-vehicle communication networks

With the development of connected vehicle technology, virus propagation that exists in a traditional network environment will gradually penetrate vehicle-to-vehicle (V2V) communication networks, thus posing a serious threat to the security of intelligent transportation systems (ITS). Understanding the characteristics of virus propagation through space and time is the key to ensuring the safety of ITS. However, most existing studies on virus propagation have ignored the dynamic relationship between virus transmission and traffic flow based on the assumption that the probability of virus infection is a constant. In light of this, this study proposes a two-layer model, called the virus-traffic coupled dynamic model, to investigate virus propagation in V2V communication networks. First, the dynamics of traffic flow and vehicular mobility are formulated as many update rules between cellular automata in the lower layer; then, due to the similarity between biological epidemic dissemination and virus propagation, an epidemic model called the susceptible-infected-recovered (SIR) was built to model the virus propagation process in the upper layer; finally, the lower and upper layer are connected by the probability of virus infection. Numerical experiments show that the model can accurately reproduce the process of virus transmission over space and time. The experiments also prove that reducing the probability of virus infection can constrain the spread of the virus effectively for both different communication range limits and various traffic densities.

Cluster‐based data dissemination, cluster head formation under sparse, and dense traffic conditions for vehicular ad hoc networks

SummaryInformation and communication technologies have changed the way of operations in all fields. These technologies also have adopted for wireless communication and provide low cost and convenient solutions. Vehicular ad hoc networks are envisioned with their special and unique intercommunication systems to provide safety in intelligent transportation systems and support large‐size networks. Due to dense and sparse traffic conditions, routing is always a challenging task to establish reliable and effective communication among vehicle nodes in the highly transportable environment. Several types of routing protocols have been proposed to handle high mobility and dynamic topologies including topology‐based routing, position and geocast routing, and cluster‐based routing protocols. Cluster‐based routing is one of the feasible solutions for vehicular networks due to its manageable and more viable nature. In cluster‐based protocols, the network is divided into many clusters and each cluster selects a cluster head for data dissemination. In this study, we investigate the current routing challenges and trend of cluster‐based routing protocols. In addition, we also proposed a Cluster‐based Routing for Sparse and Dense Networks to handle dynamic topologies, the high‐mobility of vehicle nodes. Simulation results show a significant performance improvement of the proposed protocol.

Novel store–carry–forward scheme for message dissemination in vehicular ad-hoc networks

Abstract To improve traffic safety in intelligent transportation systems, vehicles formed by vehicular ad-hoc networks (VANETs) conventionally disseminate warning messages to their nearby vehicles as soon as a dangerous situation occurs. In this paper, we propose a novel scheme employing a novel story–carry–forward (SCF) mechanism to tackle the network partition and broadcast storm problems, which are two major challenges in VANETs. The experimental result in the Veins simulation framework shows that the proposed SCF scheme significantly mitigates broadcast storms relative to other schemes, and maintains good coverage across various traffic densities.

Analysis and Evaluation of the Influence of Selected its Services on Road Traffic Safety and Efficiency

Abstract The implementation of Intelligent Transport Systems (ITS) nowadays is associated with a strong need to solve problems related to the high accident rate, as well as low efficiency of transportation process management. The topic of this paper is the characterisation of selected intelligent transport services and the determination of their influence on road traffic safety and efficiency. Two services implemented within the framework of the National Traffic Management System in Poland were distinguished, namely: transmitting information to drivers and incident detection. A general analysis of the relationship between the functioning of ITS services and safety on national roads, as well as traffic efficiency therein was conducted.

V2X Application-Reliability Analysis of Data-Rate and Message-Rate Congestion Control Algorithms

Intelligent transportation systems (ITS) require vehicle-to-everything communication. In dense traffic, the communication channel may become congested, impairing the reliability of the ITS safety applications. Therefore, the European Telecommunications Standard Institute demands decentralized congestion control (DCC) to control the channel load. Our objective is to investigate whether message-rate or data-rate congestion control provides better application reliability. We compare LIMERIC and PDR-DCC as representatives of the two principles. We analyzed the application reliability of LIMERIC with different data-rates and PDR-DCC with different message-rates for varying traffic densities and application requirements. We observed that, for applications with demanding requirements and in a large variety of vehicular densities, PDR-DCC (data-rate) provides more reliable communication support than LIMERIC (message-rate). Furthermore, this letter hints that a combined message-rate and data-rate congestion control can improve reliability further.

Rigorous Performance Evaluation of Smartphone GNSS/IMU Sensors for ITS Applications.

With the rapid growth in smartphone technologies and improvement in their navigation sensors, an increasing amount of location information is now available, opening the road to the provision of new Intelligent Transportation System (ITS) services. Current smartphone devices embody miniaturized Global Navigation Satellite System (GNSS), Inertial Measurement Unit (IMU) and other sensors capable of providing user position, velocity and attitude. However, it is hard to characterize their actual positioning and navigation performance capabilities due to the disparate sensor and software technologies adopted among manufacturers and the high influence of environmental conditions, and therefore, a unified certification process is missing. This paper presents the analysis results obtained from the assessment of two modern smartphones regarding their positioning accuracy (i.e., precision and trueness) capabilities (i.e., potential and limitations) based on a practical but rigorous methodological approach. Our investigation relies on the results of several vehicle tracking (i.e., cruising and maneuvering) tests realized through comparing smartphone obtained trajectories and kinematic parameters to those derived using a high-end GNSS/IMU system and advanced filtering techniques. Performance testing is undertaken for the HTC One S (Android) and iPhone 5s (iOS). Our findings indicate that the deviation of the smartphone locations from ground truth (trueness) deteriorates by a factor of two in obscured environments compared to those derived in open sky conditions. Moreover, it appears that iPhone 5s produces relatively smaller and less dispersed error values compared to those computed for HTC One S. Also, the navigation solution of the HTC One S appears to adapt faster to changes in environmental conditions, suggesting a somewhat different data filtering approach for the iPhone 5s. Testing the accuracy of the accelerometer and gyroscope sensors for a number of maneuvering (speeding, turning, etc.,) events reveals high consistency between smartphones, whereas the small deviations from ground truth verify their high potential even for critical ITS safety applications.

Cooperative Intelligence of Vehicles for Intelligent Transportation Systems (ITS)

The aim of Intelligent Transportation Systems (ITS) is to automate the interactions among vehicles and infrastructure to accomplish high levels of safety measures, comfort, and competence in vehicular communication. To utilize the future trends of increasing traffic safety and efficiency in ITS, integrating vehicles and infrastructures with the cooperative vehicular technique will be the feasible solution. In order to demonstrate the importance of cooperative communication in vehicular networks, a spectral efficient architecture has been proposed for cooperative centralized and distributed spectrum sensing in vehicular networks. We discuss the possibilities of Cognitive Radio in the cooperative vehicular environment. In order to exhibit cooperative vehicular networks, hardware modules are designed for a vehicle to vehicle, vehicle to infrastructure and infrastructure to infrastructure communications. Furthermore, quantitative analysis is made in order to calculate the energy optimization, connectivity failure probability and traffic management in cooperative vehicular networks. In addition, we test the results of the cooperative vehicular network by simulating it in NS2. In this respect, we have considered three different cases, Emergency vehicles, VIP vehicles, and normal vehicles. It is inferred from the results that end-to-end delay for emergency vehicles in the cooperative environment is considerably less as compared to VIP and normal vehicles.