Road Traffic Environments Research Articles

Enhanced Detection and Recognition of Road Objects in Infrared Imaging Using Multi-Scale Self-Attention.

In infrared detection scenarios, detecting and recognizing low-contrast and small-sized targets has always been a challenge in the field of computer vision, particularly in complex road traffic environments. Traditional target detection methods usually perform poorly when processing infrared small targets, mainly due to their inability to effectively extract key features and the significant feature loss that occurs during feature transmission. To address these issues, this paper proposes a fast detection and recognition model based on a multi-scale self-attention mechanism, specifically for small road targets in infrared detection scenarios. We first introduce and improve the DyHead structure based on the YOLOv8 algorithm, which employs a multi-head self-attention mechanism to capture target features at various scales and enhance the model's perception of small targets. Additionally, to prevent information loss during the feature transmission process via the FPN structure in traditional YOLO algorithms, this paper introduces and enhances the Gather-and-Distribute Mechanism. By computing dependencies between features using self-attention, it reallocates attention weights in the feature maps to highlight important features and suppress irrelevant information. These improvements significantly enhance the model's capability to detect small targets. Moreover, to further increase detection speed, we pruned the network architecture to reduce computational complexity and parameter count, making the model suitable for real-time processing scenarios. Experiments on our self built infrared road traffic dataset (mainly including two types of targets: vehicles and people) show that compared with the baseline, our method achieves a 3.1% improvement in AP and a 2.5% increase in mAP on the VisDrone2019 dataset, showing significant enhancements in both detection accuracy and processing speed for small targets, with improved robustness and adaptability.

On the Detection of Vulnerable Road Users—Simulating Limits of an FMCW Radar

The complexity of urban traffic environments poses unique challenges for automotive radar sensors. Limited range and Doppler resolution hinder the ability to distinguish closely spaced objects and detect vulnerable road users, particularly pedestrians. Therefore, it is important to know the limits of FMCW radars. To assess these limitations, the WinProp channel simulator is used to model and simulate traffic scenarios regarding the detection of the range, velocity, and SNR of targets. To validate WinProp as a simulation tool for traffic scenarios, a real traffic scenario is configured and the simulation results compared to the measurement results in order to demonstrate the suitability of WinProp for simulating traffic scenarios and evaluating radar detection performance. The close agreement between the simulation and measurement results provides validation for WinProp’s accuracy in modeling detections and signal processing within traffic scenarios. To showcase the limitations of FMCW radars, two scenarios with challenging detection conditions are simulated: an occluded pedestrian between parked vehicles and a densely populated road. A monostatic setup with a single patch antenna and a 16 × 16 patch antenna array is evaluated, using chirp bandwidths of 1 GHz and 4 GHz. The simulations have shown that although it is feasible to detect an occluded pedestrian by multipath propagation, there is still a need in improving the detection performance when there is no multipath from an occluded pedestrian. Furthermore, the results indicate that higher bandwidth improves target separation but is insufficient for occluded pedestrian detection without multipath. However, with some targets, even the range resolution at 4 GHz was not sufficient, which required a separation in the Doppler dimension, emphasizing the need for overall resolution improvement of FMCW radars. Future work should focus on developing more complex road user models and simulating a broader range of scenarios to comprehensively evaluate radar performance in road traffic environments.

Personalized driving behavior oriented autonomous vehicle control for typical traffic situations

autonomous driving systems not only provide services for human drivers, but also need to consider the personalized driving requirements of human beings. In current road traffic environments, the driving behaviors of drivers differ significantly. This suggests that the various needs of different drivers cannot be met by a single behavior mode in an autonomous driving decision-making system. This paper looks at the personalized characteristics of various drivers and considers the implications of their differences. First, a Proportional Integral Differential (PID) feedback channel is introduced in a traditional Model Predictive Control (MPC) to improve the performance of the controller, and comprehensively considering the collision risk, motion hysteresis and rule constraints, referring to the MPC idea, a collision avoidance method based on Q-ABSAS optimization is proposed. Then based on the Chance Constrained Programming, the control constraint is combined with driver personalization to reflect a variety of driving personality characteristics. Finally, the proposed method is tested using Hardware-in-the-loop (HIL) experiments. The experiment results demonstrate that the proposed method can successfully implement vehicle tracking control and make the vehicle's state of movement match the driver's expectations, which can increase driver comfort and driving safety.

Modeling urban resident travel satisfaction during the morning and the evening peak hours: A case study in Beijing

Understanding how various factors influence travel satisfaction can assist in traffic policy-making. In the study, we aim to develop innovative urban resident travel satisfaction evaluation models by building a comprehensive travel satisfaction evaluation index system and considering the asymmetric traffic flow and difference in travel time urgency during the morning and evening peak hours. We consider both the internal factors reflecting resident-related characteristics including socio-economic attributes and travel characteristics, and the external factors reflecting road-related characteristics including traffic facilities, road traffic conditions, traffic environments, and service levels. Then, for the morning and the evening peak hours, we respectively build a structural equation model (SEM) to capture the intrinsic interactions between latent factors, and an ordered logit model to describe the direct influencing factors of travel satisfaction considering its ordered nature. Finally, we examine our proposed models with the travel survey data collected in the Yizhuang district of Beijing, China. The numerical results show that both the internal and the external factors have significant impacts on travel satisfaction. The SEM models capture the interactions between the latent variables such as the positive relationship between traffic facilities and traffic environments. The ordered logit modeling results show that most external factors except the satisfaction of the road obstacles have positive influences on travel satisfaction. Our research findings provide a better understanding of the intrinsic interactions between latent variables and direct influencing factors of travel satisfaction and offer the government valuable guidance on how to improve travel satisfaction.

Vehicle Activity Dataset: A Multimodal Dataset to Understand Vehicle Emissions with Road Scenes for Eco-Routing

In the field of smart mobility, Artificial Intelligence (AI) approaches are influential and can make a highly beneficial contribution. Our project aims to develop a real-time ecological map of road traffic. This map will allow electric vehicles (EVs) and thermal vehicles (TVs) to display the cost of energy consumption and CO2 emissions on different road sections. In urban environments, road traffic emissions are a significant contributor to environmental pollution, with vehicle emissions being a major component. Addressing these impacts requires a thorough understanding of the operational behavior of vehicles on different road infrastructures within the region. This paper presents a novel, comprehensive dataset, the Vehicle Activity Dataset (VAD), designed to assess the emissions and fuel consumption characteristics of vehicles about their actual operating environment. Constructed from a large number of real-world driving scenarios, VAD incorporates emission data collected by an industrial Portable Emission Measurement System (PEMS), road scenes captured by an RGB camera, and the detection of different object classes within these images. The primary objective of VAD is to provide a comprehensive understanding of the relationship between vehicle emissions and the diverse range of objects present on the road. Experimental results in real road traffic environments through different studies demonstrate the robustness of the developed dataset.

Safety risk assessment for autonomous vehicle road testing

Objective Road testing can accelerate the development and validation of autonomous vehicles (AVs). AV road testing can come with high safety risks, particularly in a complex road traffic environment, due to the immaturity of AV technology. A priori safety risk assessments of the road traffic environment before AV road testing are of great importance, allow the quantifying of risk levels in different road scenarios, and provide guidelines for AV road testing in low to high-risk environments. Methods This study proposes a framework, namely Safety Risk Assessment for AV road testing (SRAAV), based on the probability and severity of five categories of potential AV accidents. Four groups of influencing factors are considered comprehensively in assessing AV safety risk, and their impacts are quantified using impact coefficients derived from a Bayesian network and empirical AV road testing data. The safety risk is assessed on a road section level, based on which an overall risk level is defined for a corridor and a region. Afterwards, the quantified safety risk is classified into four levels according to expert experience and knowledge, through a questionnaire survey. Results Applications of the proposed SRAAV framework are conducted for urban roads in Shanghai, and expressways in Shanghai and Gothenburg. The assessment results are validated using disengagement data from AV road testing. The results show that the SRAAV framework and its models could estimate the safety risk levels of road traffic environments for AV road testing in a sound way and have the flexibility for further extensions to be made. Conclusions The framework and assessment results can provide technical support for determining where and when to grant permission for public roads to be used for AV road testing, and how to choose public roads from a low to a high risk level, guaranteeing the safety of AV public road testing.

Ruppert's delaunay triangulation refinement scheme for optimal RSUs placement in vehicle to infrastructure communication network

Road Side Units (RSUs) are the essential component of vehicular communication for the objective of improving safety and mobility in the road transportation. RSUs are generally deployed at the roadside and more specifically at the intersections in order to collect traffic information from the vehicles and disseminate alarms and messages in emergency situations to the neighborhood vehicles cooperating with the network. However, the development of a predominant RSUs placement algorithm for ensuring competent communication in VANETs is a challenging issue due to the hindrance of obstacles like water bodies, trees and buildings. In this paper, Ruppert's Delaunay Triangulation Refinement Scheme (RDTRS) for optimal RSUs placement is proposed for accurately estimating the optimal number of RSUs that has the possibility of enhancing the area of coverage during data communication. This RDTRS is proposed by considering the maximum number of factors such as global coverage, intersection popularity, vehicle density and obstacles present in the map for optimal RSUs placement, which is considered as the core improvement over the existing RSUs optimal placement strategies. It is contributed for deploying requisite RSUs with essential transmission range for maximal coverage in the convex map such that each position of the map could be effectively covered by at least one RSU in the presence of obstacles. The simulation experiments of the proposed RDTRS are conducted with complex road traffic environments. The results of this proposed RDTRS confirmed its predominance in reducing the end-to-end delay by 21.32%, packet loss by 9.38% with improved packet delivery rate of 10.68%, compared to the benchmarked schemes.

Wi-CL: Low-Cost WiFi-Based Detection System for Nonmotorized Traffic Travel Mode Classification

Traffic travel mode identification and classification are crucial for the development of intelligent transportation systems (ITSs). At present, scholars have investigated the classification of motorized and nonmotorized traffic travel in various road environments; however, the classification of walking and bicycle modes in nonmotorized travel has been largely ignored. Therefore, in this paper, we investigate nonmotorized traffic travel and propose a new low-cost nonmotorized traffic travel mode classification system, known as the Wi-Fi classification (Wi-CL) system that uses Wi-Fi signal detectors and the refined characteristics of nonmotorized travel modes. The Wi-CL system includes four modules: data acquisition module, data processing module, feature extraction module, and mode classification module. In the data acquisition module, the proposed system detects the Wi-Fi signals of traffic participants in road environments. In addition, we propose a received signal strength indicator (RSSI) filtering algorithm for hybrid traffic networks that effectively addresses surrounding obstacles and environmental noise. In the feature extraction module, we extract relevant traffic features to construct a mode classification model. Finally, a recurrent neural network (RNN) framework based on the long short-term memory (LSTM) algorithm is successfully implemented in the mode classification module for traffic travel mode identification. To validate the effectiveness of the Wi-CL system, extensive experiments were conducted using field data collected by Wi-Fi detectors installed at the South China University of Technology (SCUT). The experimental results show that the proposed RSSI filtering algorithm achieves excellent signal filtering results in real road traffic environments. In addition, the constructed travel speed estimation algorithm outperforms other baseline models in four different scenarios (flat-peak walking, midday peak walking, flat-peak cycling, and midday peak cycling), achieving an overall classification accuracy of 97.92%. In summary, our Wi-CL system is a feasible approach for nonmotorized traffic travel mode classification.

High-resolution reconstruction of non-motorized trajectory in shared space: A new approach integrating the social force model and particle filtering

High-resolution trajectory data from the intersection shared space (ISS) is an ideal resource for testing autonomous driving and studying microscopic traffic flow theory in urban road traffic environments. It is common to use computer vision (CV) technologies to extract trajectory data from camera recordings at the ISS. However, it is challenging to accurately extract non-motorized (NONM) trajectories due to vehicle occlusion and mutual interference, which cause trajectory noise and missing. This study proposed a novel NONM trajectory reconstruction method, which integrates the social force model with the particle filter to reduce trajectory noise. The approach was tested on trajectories extracted from two advanced CV algorithms (ie, Yolov7 and MASK RCNN), and its performance was compared with seven SOTA methods. The results show that the proposed method achieved a much better RMSE (0.6 meters) than both the baseline (unprocessed CV trajectories, the RMSE is 1.16 meters) and selected SOTA methods (the best RMSE is 0.78 meters). The contribution of the study is to enhance the classical noise reduction algorithm by introducing the driving interaction model; ablation experiments and sensitivity analysis further demonstrate the importance of the driving interaction model in reducing trajectory noise and the stability of the method. The approach also includes an LSTM-Hungarian part to make up the missing trajectory. The proposed method is expected to serve as a useful post-processing tool for current CV algorithms.

A Collaborative Monitoring Method for Traffic Situations under Urban Road Emergencies

The complex and diverse urban road traffic environments make it difficult to accurately assess road traffic situations. This paper proposes a collaborative monitoring method for urban road traffic situational assessment during emergency events. This method is applied to a monitoring network mapped by road geographic relations. When an emergency event is captured by a monitoring node in the network, road traffic situational awareness is completed by an activation function. Then, the Incidence matrix of the emergency event is constructed based on the node degree of this monitoring node. The collaborative node set and collaborative monitoring area are formed dynamically from this Incidence matrix. Finally, the AHP and EM combination weight calculation method based on Game Theory (GT-AHP-EM) is used to fuse the data of various information in the collaborative monitoring area to predict the current road traffic trend. The experiments show that the collaborative monitoring method can effectively assess road traffic conditions and enhance the accuracy of road traffic trend prediction.

TAKEN: A Traffic Knowledge-Based Navigation System for Connected and Autonomous Vehicles

Connected and autonomous vehicles (CAVs) have witnessed significant attention from industries, and academia for research and developments towards the on-road realisation of the technology. State-of-the-art CAVs utilise existing navigation systems for mobility and travel path planning. However, reliable connectivity to navigation systems is not guaranteed, particularly in urban road traffic environments with high-rise buildings, nearby roads and multi-level flyovers. In this connection, this paper presents TAKEN-Traffic Knowledge-based Navigation for enabling CAVs in urban road traffic environments. A traffic analysis model is proposed for mining the sensor-oriented traffic data to generate a precise navigation path for the vehicle. A knowledge-sharing method is developed for collecting and generating new traffic knowledge from on-road vehicles. CAVs navigation is executed using the information enabled by traffic knowledge and analysis. The experimental performance evaluation results attest to the benefits of TAKEN in the precise navigation of CAVs in urban traffic environments.

Cognitive Granular-Based Path Planning and Tracking for Intelligent Vehicle with Multi-Segment Bezier Curve Stitching

Unmanned vehicles are currently facing many difficulties and challenges in improving safety performance when running in complex urban road traffic environments, such as low intelligence and poor comfort performance in the driving process. The real-time performance of vehicles and the comfort requirements of passengers in path planning and tracking control of unmanned vehicles have attracted more and more attentions. In this paper, in order to improve the real-time performance of the autonomous vehicle planning module and the comfort requirements of passengers that a local granular-based path planning method and tracking control based on multi-segment Bezier curve splicing and model predictive control theory are proposed. Especially, the maximum trajectory curvature satisfying ride comfort is regarded as an important constraint condition, and the corresponding curvature threshold is utilized to calculate the control points of Bezier curve. By using low-order interpolation curve splicing, the planning computation is reduced, and the real-time performance of planning is improved, compared with one-segment curve fitting method. Furthermore, the comfort performance of the planned path is reflected intuitively by the curvature information of the path. Finally, the effectiveness of the proposed control method is verified by the co-simulation platform built by MATLAB/Simulink and Carsim. The simulation results show that the path tracking effect of multi-segment Bezier curve fitting is better than that of high-order curve planning in terms of real-time performance and comfort.

Path-Planning Strategy for Lane Changing Based on Adaptive-Grid Risk-Fields of Autonomous Vehicles

The quantification and effective representation of safety risks for scenarios in structured road traffic environments of autonomous driving are currently being investigated in an active way. Based on artificial potential fields, a risk-field model for the traffic environment that considers the motion state of an obstacle vehicle is established, and an adaptive-grid risk-field method is proposed for autonomous vehicles. In this method, the traffic environment is meshed initially, and adaptive-grid division is performed using a quadtree grid-dividing strategy for root grids where the grid risk values are within the division interval, which allows for a more accurate quantification of traffic environment risk values. Adding adaptive-grid risk-field parameters to the cost function of the path-planning algorithm improves the accuracy of path safety risk assessment and completes the evaluation and selection of the optimal lane-change path. Simulation results show that the adaptive-grid risk-field established in this paper can effectively express the safety risks of the traffic environment, and the path-planning algorithm incorporating the adaptive-grid risk-field can obtain better paths for lane change compared with the traditional path-planning algorithm, while ensuring the safety of lane change.

Traffic Risk Environment Impact Analysis and Complexity Assessment of Autonomous Vehicles Based on the Potential Field Method.

Although autonomous vehicles have introduced a promising potential for improving traffic safety and efficiency, ensuring the safety of autonomous vehicles in complex road traffic environments is still a huge challenge to be tackled. To quickly quantify the potential risk factors of autonomous vehicles in traffic environments, this paper focuses mainly on the influence of the depth and breadth of the environment elements on the autonomous driving system, uses the potential field theory to establish a model of the impact of the environmental elements on the autonomous driving system, and combines AHP to quantify equivalent virtual electric quantity of each environment element, so as to realize the quantitative evaluation of the traffic environment complexity. The proposed method comprehensively considers the physical attributes and state parameters of the environmental elements, which compensates for the fact that the shortage of the factors considered in the traffic environment complexity assessment is not comprehensive. Finally, a series of experiments was carried out to verify the reliability of our proposed method. The results show that the complexity of the static elements is determined only by the physical attributes and shape of the obstacle; the complexity of the dynamic elements is determined by the movement of the obstacle and the movement of the autonomous vehicle, and the comprehensive complexity mainly depends on the complexity of their dynamic elements. Compared with other methods, the complexity evaluation values are generally consistent, the absolute percentage error of the majority of samples was within ±5%, and the degree of deviation was −1.143%, which provides theoretical support for autonomous vehicles on safety and the risk assessment in future.

Dual-V-Sense-Net (DVN): Multisensor Recommendation Engine for Distraction Analysis and Chaotic Driving Conditions

The Advanced Driver Assistance System (ADAS) features provide better safe driving dynamics reducing road accidents and collisions. Typically, these include lane assist, automatic night vision, adaptive cruise control, driver risk analysis primarily seen in high-end vehicles. Furthermore, such a proactive mechanism alerts the drivers in unsafe/ dangerous situations and enable drivers to respond with immediate actions. However, such ADAS-based solutions do not work very well in heterogeneous, less-disciplined, and chaotic road traffic environments, as seen in Asian countries such as India, Bangladesh, Burma, Nepal, etc. In this regard, this paper aims to propose an advanced integrated deep learning framework <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dual-V-sense-Net (DVN)</i> to provide recommendations for safe driving. A new dataset - Behavioral Understanding of Driver’s-Distraction Dataset for India (BUDDI), is proposed for the behavioral understanding of driver’s distraction. This proposed recommendation framework is designed in a two-step manner. The first step involves an in-vehicle driver distraction analysis with real-time frontal and cross-view naturalistic driver data for driver distraction analysis. Second step is to perform an exhaustive experimental evaluation is carried out for the tasks such as road traffic scene perception, road navigable area prediction with vehicular temporal dynamics, lane assist, safe drivable area navigation and driver distraction alert. A decision block modeling helps in safe/unsafe driving predictions considering the feature importance. We present comprehensive experimental results for road segmentation, vehicular road lane categorization, obstacle recognition, and driver behavioral pattern prediction using the BUDDI and Indian Driving Dataset (IDD) datasets.

Association of adolescents’ independent mobility with road traffic injuries in Karachi, Pakistan: a cross-sectional study

ObjectiveParticipation in walking, cycling and taking public transportation without adult supervision is defined as independent mobility of children and adolescents. The association between adolescents’ independent mobility and road traffic injury...

Service-Centric Heterogeneous Vehicular Network Modeling for Connected Traffic Environments.

Heterogeneous vehicular communication on the Internet of connected vehicle (IoV) environment is an emerging research theme toward achieving smart transportation. It is an evolution of the existing vehicular ad hoc network architecture due to the increasingly heterogeneous nature of the various existing networks in road traffic environments that need to be integrated. The existing literature on vehicular communication is lacking in the area of network optimization for heterogeneous network environments. In this context, this paper proposes a heterogeneous network model for IoV and service-oriented network optimization. The network model focuses on three key networking entities: vehicular cloud, heterogeneous communication, and smart use cases as clients. Most traffic-related data–oriented computations are performed at cloud servers for making intelligent decisions. The connection component enables handoff-centric network communication in heterogeneous vehicular environments. The use-case-oriented smart traffic services are implemented as clients for the network model. The model is tested for various service-oriented metrics in heterogeneous vehicular communication environments with the aim of affirming several service benefits. Future challenges and issues in heterogeneous IoV environments are also highlighted.

Connection between lung deposited surface area (LDSA) and black carbon (BC) concentrations in road traffic and harbour environments

Black carbon (BC) is one of the main components of ambient particulate matter. Previous studies have suggested that BC is more toxic than PM2.5 (mass concentration of all sub-2.5 μm particles). One possible reason for the greater toxicity is that BC is typically in a size range which penetrates easily into lung alveoli and BC particles have a large surface area due to their fractal structure. Due to these properties, toxic gaseous compounds can condensate on the surface of BC particles and then be transported effectively into human lungs, causing a large lung-depositing surface area (LDSA) of particles. In this study, we investigated the relationship between BC and LDSA concentrations in street canyon, highway, and harbour environments in the Helsinki Metropolitan area. In all the studied environments, BC and LDSA concentrations were strongly correlated. In the harbour, cases where marine traffic was considered as the main emission source, the average LDSA per BC mass was 2.4–2.7 times higher than in the road traffic environments. This result was linked to a larger lung depositing size of BC, suggesting that condensation and coagulation of other co-emitted compounds can have a major role in the lung deposition of BC. Thus, BC emissions from marine traffic can cause higher exposure of other co-emitted toxic compounds in the human lungs than the road traffic. The fraction of LDSA linked to BC emissions in the street canyon, the highway, and the harbour were 33%, 30%, and 47%, respectively, whereas the fractions of BC mass in PM1 concentration were 14%, 14%, and 7%, respectively. The results show that BC emissions contribute much more to LDSA than to mass concentration, which indicates that the possible strong negative health effects linked to ambient BC mass concentration could be related to the high LDSA concentration.

Evaluation of Vegetation Configuration Models for Managing Particulate Matter along the Urban Street Environment

As a green infrastructure component, urban street vegetation is increasingly being utilized to mitigate air pollution, control microclimates, and provide aesthetic and ecological benefits. This study investigated the effect of vegetation configurations on particulate matter (PM) flows for pedestrians in road traffic environments via a computation fluid dynamics analysis based on the road width (four and eight-lane) and vegetation configuration (single-, multi-layer planting, and vegetation barrier). Airflow changes due to vegetation influenced PM inflow into the sidewalk. Vegetation between roadways and sidewalks were effective at reducing PM concentrations. Compared to single-layer planting (trees only), planting structures capable of separating sidewalk and roadway airflows, such as a multi-layer planting vegetation barrier (trees and shrubs), were more effective at minimizing PM on the sidewalk; for wider roads, a multi-layer structure was the most effective. Furthermore, along a four-lane road, the appropriate vegetation volume and width for reducing PM based on the breathing height (1.5 m) were 0.6 m3 and 0.4 m, respectively. The appropriate vegetation volume and width around eight-lane roads, were 1.2–1.4 m3 and 0.8–0.93 m, respectively. The results of this study can provide appropriate standards for street vegetation design to reduce PM concentrations along sidewalks.

Auto-Alert: A Spatial and Temporal Architecture for Driving Assistance in Road Traffic Environments

Auto-Alert: A Spatial and Temporal Architecture for Driving Assistance in Road Traffic Environments