Traffic Vehicle Research Articles

Vehicle and Pedestrian Traffic Signal Performance Measures Using LiDAR-Derived Trajectory Data.

Light Detection and Ranging (LiDAR) sensors at signalized intersections can accurately track the movement of virtually all objects passing through at high sampling rates. This study presents methodologies to estimate vehicle and pedestrian traffic signal performance measures using LiDAR trajectory data. Over 15,000,000 vehicle and 170,000 pedestrian waypoints detected during a 24 h period at an intersection in Utah are analyzed to describe the proposed techniques. Sampled trajectories are linear referenced to generate Purdue Probe Diagrams (PPDs). Vehicle-based PPDs are used to estimate movement level turning counts, 85th percentile queue lengths (85QL), arrivals on green (AOG), highway capacity manual (HCM) level of service (LOS), split failures (SF), and downstream blockage (DSB) by time of day (TOD). Pedestrian-based PPDs are used to estimate wait times and the proportion of people that traverse multiple crosswalks. Although vehicle signal performance can be estimated from several days of aggregated connected vehicle (CV) data, LiDAR data provides the ability to measure performance in real time. Furthermore, LiDAR can measure pedestrian speeds. At the studied location, the 15th percentile pedestrian walking speed was estimated to be 3.9 ft/s. The ability to directly measure these pedestrian speeds allows agencies to consider alternative crossing times than those suggested by the Manual on Uniform Traffic Control Devices (MUTCD).

Unmanned vehicle off-road path-planning method with comprehensive constraints on multiple environmental factors

ABSTRACT In complex off-road environments, different slopes, slope directions, and land types differently affect unmanned vehicle trafficability. Thus, we propose an unmanned vehicle off-road path-planning method with comprehensive constraints on multiple environmental factors. First, the influence of slope, aspect, and surface-coverage type on unmanned vehicle drivability was quantitatively evaluated, and the slope and land type influence layers were formed. Concurrently, a goal guidance layer was formed using the artificial potential field method. Second, the slope influence, land type influence, and goal guidance layers were used to quantitatively evaluate each grid’s pass cost value. Finally, the traditional A* algorithm, A* algorithm considering the impassable area, A* algorithm with comprehensive constraints, and Dijkstra algorithm with comprehensive constraints were used for path planning. The results show that the path slopes planned by A* algorithm with comprehensive constraints and Dijkstra algorithm with comprehensive constraints were smaller than those planned by A* algorithm and A* algorithm considering the impassable area; particularly, the length of the uphill path was shorter and the stability was better. The relevant research results provide methods and technical guidance for obtaining the shortest length, shortest time, and most stable and passable path in off-road environments.

Experimental study on bioaerosols behavior and purification measures in a subway compartment.

Bioaerosols in public transportation systems raise critical environmental concerns, seriously threatening passenger health and safety. In this study, we investigate the spread characteristics of bioaerosols in a standard type-B subway compartment using both air sampling and sediment sampling methods. Additionally, without compromising indoor passenger comfort, two self-designed air purification devices, based on intense field dielectric (IFD) and dielectric barrier discharge (DBD) technologies, respectively, are successfully applied for the improvement of the subway air quality. The results show that bioaerosols can propagate rapidly throughout the entire compartment in 5min via airborne transmission. Under the effect of the symmetric air ducts and compartment structure, the difference in bioaerosol concentration in the air is less than 10% between both ends of the compartment. Concurrent substantial bioaerosol deposition on the ground, seats, and windows underscores the risk of contact transmission. Furthermore, the real-time purification rates of the two devices integrated into the air conditioning system reach 59.40% and 44.98%, respectively. With their demonstrated high efficiency in purifying bioaerosols and modular design featuring low energy consumption, easy cleaning, and reusability, these devices stand out as viable long-term solutions for large traffic vehicles. These research findings provide practical equipment recommendations and installation strategies for optimizing indoor air quality in subways and are applicable to other similar transportation systems.

Behaviour of wet clay due to off-road traffic loading

Abstract The presence of water in the soil can greatly affect the trafficability of off-roading vehicles, making it difficult to operate. As the water content increases, it can eventually lead to sinkage and immobility. Despite the relevance of this topic in many applications, such as military, agricultural, and earthmoving vehicles, the studies available on tire and moist soil interaction are limited. The numerical modeling of such a system is very challenging because it requires accounting for hydro-mechanical effects, large deformations, non-linear behavior, and complex interactions between the tire and the soil. This work contributes to the understanding of the behavior of moist clay under the influence of traffic loading. In particular, the preliminary results of numeric simulations to study the interaction between moist clay and a passing tire using physics-based techniques are presented. A great emphasis is given to the development of an accurate soil model that can capture the fundamental characteristics of moist clays in saturated conditions.

Stretchable and Self-Powered Mechanoluminescent Triboelectric Nanogenerator Fibers toward Wearable Amphibious Electro-Optical Sensor Textiles.

Flexible electro-optical dual-mode sensor fibers with capability of the perceiving and converting mechanical stimuli into digital-visual signals show good prospects in smart human-machine interaction interfaces. However, heavy mass, low stretchability, and lack of non-contact sensing function seriously impede their practical application in wearable electronics. To address these challenges, a stretchable and self-powered mechanoluminescent triboelectric nanogenerator fiber (MLTENGF) based on lightweight carbon nanotube fiber is successfully constructed. Taking advantage of their mechanoluminescent-triboelectric synergistic effect, the well-designed MLTENGF delivers an excellent enhancement electrical signal of 200% and an evident optical signal whether on land or underwater. More encouragingly, the MLTENGF device possesses outstanding stability with almost unchanged sensitivity after stretching for 200%. Furthermore, an extraordinary non-contact sensing capability with a detection distance of up to 35cm is achieved for the MLTENGF. As application demonstrations, MLTENGFs can be used for home security monitoring, intelligent zither, traffic vehicle collision avoidance, and underwater communication. Thus, this work accelerates the development of wearable electro-optical textile electronics for smart human-machine interaction interfaces.

DBI-Attack: Dynamic Bi-Level Integrated Attack for Intensive Multi-Scale UAV Object Detection

Benefiting from the robust feature representation capability of convolutional neural networks (CNNs), the object detection technology of intelligent high-altitude UAV remote sensing has been developed rapidly. In this field, the adversarial examples (AEs) pose serious security risks and vulnerabilities to deep learning-based systems. Due to the limitation of object size, image degradation, and scene brightness, adding adversarial disturbances to small and dense objects is extremely challenging. To study the threat of AE for UAV object detection, a dynamic bi-level integrated attack (DBI-Attack) is proposed for intensive multi-scale UAV object detection. Firstly, we use the dynamic iterative attack (DIA) method to generate perturbation on the classification level by improving the momentum iterative fast gradient sign method (MIM). Secondly, the bi-level adversarial attack method (BAAM) is constructed to add global perturbation on the decision level for completing the white-box attack. Finally, the integrated black-box attack method (IBAM) is combined to realize the black-box mislabeling and fabrication attacks. We experiment on the real drone traffic vehicle detection datasets to better evaluate the attack effectiveness. The experimental results show that the proposed method can achieve mislabeling and fabrication attacks on the UAV object detectors in black-box conditions. Furthermore, the adversarial training is applied to improve the model robustness. This work aims to call more attention to the adversarial and defensive aspects of UAV target detection models.

Stabilization of Stop-and-Go Waves in Vehicle Traffic Flow

Stabilization of Stop-and-Go Waves in Vehicle Traffic Flow

Synthetic Optimization of Trafficability and Roll Stability for Off-Road Vehicles Based on Wheel-Hub Drive Motors and Semi-Active Suspension

Considering the requirements pertaining to the trafficability of off-road vehicles on rough roads, and since their roll stability deteriorates rapidly when turning violently or passing slant roads due to a high center of gravity (CG), an efficient anti-slip control (ASC) method with superior instantaneity and robustness, in conjunction with a rollover prevention algorithm, was proposed in this study. A nonlinear 14 DOF vehicle model was initially constructed in order to explain the dynamic coupling mechanism among the lateral motion, yaw motion and roll motion of vehicles. To acquire physical state changes and friction forces of the tires in real time, corrected LuGre tire models were utilized with the aid of resolvers and inertial sensors, and an adaptive sliding mode controller (ASMC) was designed to suppress each wheel’s slip ratio. In addition, a model predictive controller (MPC) was established to forecast rollover risk and roll moment in reaction to the change in the lateral forces as well as the different ground heights of the opposite wheels. During experimentation, the mutations of tire adhesion capacity were quickly discerned and the wheel-hub drive motors (WHDM) and ASC maintained the drive efficiency under different adhesion conditions. Finally, a hardware-in-the-loop (HIL) platform made up of the vehicle dynamic model in the dSPACE software, semi-active suspension (SAS), a vehicle control unit (VCU) and driver simulator was constructed, where the prediction and moving optimization of MPC was found to enhance roll stability effectively by reducing the length of roll arm when necessary.

Multi-agent Iot-based System for Intelligent Vehicle Traffic Management Using Wireless Sensor Networks

Aims: Integrated computing technologies such as the Internet of Things (IoT), Multi- Agent Systems (MAS), and automatic networking to deliver Internet of Vehicles (IoV) applications. Methodology: The main objective of this paper is to combine MAS with IoT or IoV a new paradigm within its Cypher Physical System (CPS) for intelligent car applications. We proposed the MAS algorithm and applied it to control traffic lights at multiple intersections. When using MAS together with scattered computing architectures, IoV can achieve higher efficiency. The proposed combination is based on the independent knowledge, adaptability, assertiveness, and responsiveness that can be used in wireless sensor paradigms to bring new remedies. Smart products will explore further advancements and diverse mobility capabilities. Results: IoT provides an appropriate atmosphere for connecting with MAS concepts and programs in addition to providing reliable, adaptable, efficient, and intelligent solutions in the automotive network. In addition, the combination of MAS with IoT and cognitive conditions could result in scalable, automated, and smart wireless sensor solutions. Conclusion: We conduct experiments on three different datasets, and the results demonstrate that MAS outperforms several state-of-the-art algorithms in alleviating traffic congestion with shorter training time.

A Survey on Emergency Vehicle Route Optimization and Traffic Management Application

Abstract: Ambulance delays in India result in approximately 10,000 lives lost annually, highlighting the critical need for efficient emergency response systems. To address this issue, we present the "Emergency Vehicle Priority with Route Optimization and Traffic Management Application." Leveraging advanced technology, including an Android application, Firebase, and IoT integration with traffic signals, our system dynamically calculates and optimizes ambulance routes using Google API, minimizing delays and improving overall efficiency. The application facilitates seamless coordination between ambulance drivers and traffic police officers, triggering real-time route updates and traffic signal adjustments to expedite ambulance passage. By streamlining traffic flow and enhancing emergency response coordination, our solution aims to significantly reduce ambulance delays and save lives in urban areas.

Evaluating vehicle trafficability on soft ground using wheel force information

In the areas of aerospace and military industry, wheeled vehicles are expected to have the ability of passing various ground surfaces, including lunar soil, sand, marsh, mud flat, etc. This makes vehicle trafficability on soft ground become a very hot research topic. There are very a few difficulties in the present research of vehicle trafficability on soft ground, such as obtaining wheel-ground interaction information, inaccurate identification of soil mechanical characteristics parameters, and single evaluation index. In this paper, a novel approach of evaluating the vehicle trafficability on soft ground using wheel force information is proposed. As parts of the proposed approach, the methods of obtaining wheel force information, identification of soil mechanical characteristics parameters and integated method of trafficability evaluation, are discussed in detail. The proposed approach was validated through a practical test.

ANALYSIS OF PEDESTRIAN PATH WIDTH REQUIREMENTS IN THE MANUKAN LOR ROAD AREA, SURABAYA CITY

The commercial area on Manukan Lor street is one of the places in the city of Surabaya that currently has quite heavy traffic. The problem in this area is that there is no special pedestrian/pedestrian route available so it can endanger pedestrians, apart from that it can also disrupt the activity of traffic vehicles. The aim of this thesis refers to the formulation of the problem and the existing background, including: Determining the characteristics of pedestrians in the Manukan Lor Street area of Surabaya, determining the width requirements for pedestrian/pedestrian lanes in the Manukan Lor Street area, and to determine the performance results in Manukan Lor road section after and before the sidewalk. Guidelines used in planning pedestrian paths in the area "Sidewalk Planning Guidebook Number.007/T/BNKT/1990 Directorate General of Highways", "Guidebook for Building Construction Materials and Civil Engineering Ministry of PUPR Number.02/SE/M/2018 regarding technical planning of pedestrian facilities", and "Indonesian Road Capacity Guidebook 2023 (PKJI 2023)". The results of the analysis of the required width of the pedestrian path on the Manukan Lor road are 1.5 m wide with the level of service obtained is category A, and includes low-level side obstacles, with an average increase in the degree of saturation (Dj) of 0.026 but still in service level category C with a stable flow the speed can be controlled by traffic. Keywords: Pedestrian Width; Characteristics; Road Performance.

Emergency Department Visits for Pedestrians Injured in Motor Vehicle Traffic Crashes - United States, January 2021-December 2023.

Traffic-related pedestrian deaths in the United States reached a 40-year high in 2021. Each year, pedestrians also suffer nonfatal traffic-related injuries requiring medical treatment. Near real-time emergency department visit data from CDC's National Syndromic Surveillance Program during January 2021-December 2023 indicated that among approximately 301 million visits identified, 137,325 involved a pedestrian injury (overall visit proportion=45.62 per 100,000 visits). The proportions of visits for pedestrian injury were 1.53-2.47 times as high among six racial and ethnic minority groups as that among non-Hispanic White persons. Compared with persons aged ≥65 years, proportions among those aged 15-24 and 25-34 years were 2.83 and 2.61 times as high, respectively. The visit proportion was 1.93 times as high among males as among females, and 1.21 times as high during September-November as during June-August. Timely pedestrian injury data can help collaborating federal, state, and local partners rapidly monitor trends, identify disparities, and implement strategies supporting the Safe System approach, a framework for preventing traffic injuries among all road users.

SAFEPARK: Vehicle Detection and Traffic Violation Parking Management System

Abstract: The escalating motorcycle accident rates highlight the pressing need for improved safety measures. Helmets, a crucial safety gear, are often neglected, contributing significantly to fatalities. This paper addresses the pervasive issue of noncompliance with motorcycle safety rules, focusing on helmet usage and triple riding. Existing systems for monitoring lack precision, prompting our proposed Bike Traffic Violation System. Leveraging Haar Cascade and YOLOv3 models, it identifies motorcycles, detects riders without helmets, instances of triple riding, and even empty parking spots with unprecedented accuracy. The Machine Learning component employs a Support Vector Classification model, bolstered by 4-fold cross-validation, ensuring robustness. This innovative system provides real-time insights into traffic violations, enabling prompt interventions by law enforcement agencies. It overcomes manual identification shortcomings, offering a comprehensive solution for enforcement and awareness campaigns. In summary, our Bike Traffic Violation System not only advances automated traffic rule monitoring but introduces a novel methodology for precise detection, significantly contributing to enhanced road safety and accident prevention.

Emergency Vehicle Detection and Traffic Prevention Using Open CV

The proposed project focuses on the development of an innovative system for "Emergency Vehicle Detection and Traffic Prevention using OpenCV." The project aims to enhance road safety and expedite emergency response by leveraging computer vision techniques implemented through the OpenCV framework. The primary objective is to design a robust algorithm that can accurately identify emergency vehicles in real-time based on distinct visual features such as color, shape, and motion patterns. OpenCV, a powerful open-source computer vision library, will serve as the foundation for image processing and analysis. Upon successful detection of an emergency vehicle, the system will employ dynamic traffic prevention measures. This includes the activation of intelligent traffic signal control to ensure the smooth and rapid passage of the emergency vehicle through intersections. The system may also interface with connected infrastructure, such as smart traffic lights, to optimize overall traffic flow and minimize delays. The project aims to contribute to public safety and emergency response efficiency by providing a reliable and automated solution for prioritizing emergency vehicles on the road. Through the integration of OpenCV, the system will offer a versatile and scalable approach to emergency vehicle detection and traffic prevention, making it a valuable contribution to the field of intelligent transportation systems. Index terms Emergency Vehicle Detection, Traffic Prevention, OpenCV, Computer Vision Techniques, Real-time Vehicle Identification, Intelligent Traffic Signal Control, Dynamic Traffic Management, Road Safety Enhancement, Emergency Response Efficiency, Public Safety, Automated Traffic Prioritization, Intelligent Transportation Systems, Visual Feature Analysis, Smart Traffic Lights Integration, Automated Traffic Flow Optimization

MEDAVET: Traffic Vehicle Anomaly Detection Mechanism based on spatial and temporal structures in vehicle traffic

Road traffic anomaly detection is vital for reducing the number of accidents and ensuring a more efficient and safer transportation system. In highways, where traffic volume and speed limits are high, anomaly detection is not only essential but also considerably more challenging, given the multitude of fast-moving vehicles, often observed from extended distances and diverse angles, occluded by other objects, and subjected to variations in illumination and adverse weather conditions. This complexity has meant that human error often limits anomaly detection, making the role of computer vision systems integral to its success. In light of these challenges, this paper introduces MEDAVET - a sophisticated computer vision system engineered with an innovative mechanism that leverages spatial and temporal structures for high-precision traffic anomaly detection on highways. MEDAVET is assessed in its object tracking and anomaly detection efficacy using the UA-DETRAC and Track 4 benchmarks and has its performance compared with that of an array of state-of-the-art systems. The results have shown that, when MEDAVET’s ability to delimit relevant areas of the highway, through a bipartite graph and the Convex Hull algorithm, is paired with its QuadTree-based spatial and temporal approaches for detecting occluded and stationary vehicles, it emerges as superior in precision, compared to its counterparts, and with a competitive computational efficiency.

Autonomous Vehicle Traffic Accident Prevention using Mobile-Integrated Deep Reinforcement Learning Technique

When it concerns autonomous traffic management, the most effective decision-making reinforcement learning methods are often utilized for vehicle control. Surprisingly demanding circumstances, however, aggravate the collisions and, as a consequence, the chain collisions. In order to potentially offer guidance on eliminating and decreasing the danger of chain collision malfunctions, we first evaluate the main types of chain collisions and the chain events typically proceed. In an emergency, this study proposes mobile-integrated deep reinforcement learning (DRL) for autonomous vehicles to control collisions. Three essential influencing substances are completely taken into consideration and ultimately achieved by the offered strategy: accuracy, efficiency, and passenger comfort. Following this, we investigate the safety performance currently employed in security-driving solutions by interpreting the chain collision avoidance problem as a Markov Decision Process problem and offering a decision-making strategy based on mobile-integrated reinforcement learning. All of the analysis's findings have the objective of aid academics and policymakers to appreciate the positive aspects of a more reliable autonomous traffic infrastructure and to smooth out the way for the actual adoption of a driverless traffic scenario.

Evaluation of Ground Pressure, Bearing Capacity, and Sinkage in Rigid-Flexible Tracked Vehicles on Characterized Terrain in Laboratory Conditions.

Trafficability gives tracked vehicles adaptability, stability, and propulsion for various purposes, including deep-sea research in rough terrain. Terrain characteristics affect tracked vehicle mobility. This paper investigates the soil mechanical interaction dynamics between rubber-tracked vehicles and sedimental soils through controlled laboratory-simulated experiments. Focusing on Bentonite and Diatom sedimental soils, which possess distinct shear properties from typical land soils, the study employs innovative user-written subroutines to characterize mechanical models linked to the RecurDyn simulation environment. The experiment is centered around a dual-tracked crawler, which in itself represents a fully independent vehicle. A new three-dimensional multi-body dynamic simulation model of the tracked vehicle is developed, integrating the moist terrain's mechanical model. Simulations assess the vehicle's trafficability and performance, revealing optimal slip ratios for maximum traction force. Additionally, a mathematical model evaluates the vehicle's tractive trafficability based on slip ratio and primary design parameters. The study offers valuable insights and a practical simulation modeling approach for assessing trafficability, predicting locomotion, optimizing design, and controlling the motion of tracked vehicles across diverse moist terrain conditions. The focus is on the critical factors influencing the mobility of tracked vehicles, precisely the sinkage speed and its relationship with pressure. The study introduces a rubber-tracked vehicle, pressure, and moisture sensors to monitor pressure sinkage and moisture, evaluating cohesive soils (Bentonite/Diatom) in combination with sand and gravel mixtures. Findings reveal that higher moisture content in Bentonite correlates with increased track slippage and sinkage, contrasting with Diatom's notable compaction and sinkage characteristics. This research enhances precision in terrain assessment, improves tracked vehicle design, and advances terrain mechanics comprehension for off-road exploration, offering valuable insights for vehicle design practices and exploration endeavors.

Analysis of Safety Assessment and Testing of Heavy Traffic Vehicles on Old Bridges Without Data

This article presents a real-life project that aimed to evaluate the safety of traffic vehicles on old bridges without any prior data. The project involved various safety inspections, including conventional, static, and dynamic load inspections and safety assessments. After conducting these tests, it was concluded that the structure of the old bridge is relatively safe, with only a few bumps. The bridge could function normally following appropriate treatment. The analysis provides valuable insights into the assessment of the quality and safety of such bridges to ensure the safe driving of heavy vehicles.

An analysis of various deep learning-based target detection algorithms in the field of autonomous driving

Target detection is a crucial research objective within the domain of computer vision, finding extensive applications in areas such as robotics, autonomous driving, industrial inspections, and various other fields. Based on the foundation of deep learning theory, this paper systematically summarizes the application and prospect of each type of target detection algorithm (based on regression and based on candidate region) on automatic driving, compares the advantages and disadvantages of the two types of algorithms, as well as the results of detecting traffic signals, traffic vehicles, and pedestrians, and focuses on the application scenarios as well as the comparison of advantages and disadvantages of each method. A systematic summary of the current development results is made. Among them, the most prominent target detection in the field of transportation is undoubtedly the algorithms of various branches of the YOLO series.