Real Traffic Environment Research Articles

Virtual Reality for Traffic Safety Education in Elementary Schools

Introduction: Traffic safety education needs to be taught to children at an early age. Traffic safety training for children in a real-world environment has several challenges and difficulties. Letting children practice in the real traffic environment will expose them to potential hazards. The purpose of this study was to develop media promotion for traffic safety education using a virtual reality (VR) for elementary school students. Methods: This research design used the 4-D model (define, design, develop, and disseminate). Data were collected qualitatively by conducting FGDs and interviews, while quantitative data were collected by distributing questionnaires. Results: The traffic safety VR media tested 4 scenarios, namely: driving equipment, traffic lights, how to cross the road, and walking etiquette. The validity test results show that VR traffic safety is feasible to be used as learning media for elementary school students. Students and teachers responded positively to this media. The advantage of this media is that it is fun and interactive for children. Conclusion: Media promotion for traffic safety education using virtual reality can be applied in a wider scope in other elementary schools. Future research can develop more complex scenarios such as cycling, crossing railroad, and many more. The use of virtual reality (VR) in traffic safety training provides an immersive and interactive learning experience, which is more engaging compared to traditional methods.

SS-DETR: a strong sensing DETR road obstacle detection model based on camera sensors for autonomous driving

Abstract As a key step in obstacle avoidance and path planning, obstacle detection via camera sensors is crucial for autonomous driving. The real traffic road environment is complex and variable, and the existing obstacle detection algorithms still have the problem of insufficient sensing ability. Therefore, this work suggests a camera sensors-based Strong Sensing DEtection TRansformer (SS-DETR) obstacle detection model for autonomous driving. Firstly, receptive-field attention ResNet is designed to improve feature analysis and extraction performance by considering the importance of receptive field spatial features and channels. Then, an intra-scale feature interaction module based on multiple information fusion attention is created to strengthen the representation of advanced feature maps. Furthermore, the cross-scale feature-fusion module is optimized to extract more detailed information from multi-scale feature maps. Finally, a localization loss function based on L1 and Powerful Intersection over Union v2 is implemented to further boost the detection performance. To verify the efficacy of the suggested model, the KITTI dataset containing camera sensors-based road obstacle images is adopted. The experimental results reveal that compared to real-time DETR, SS-DETR improves mean average precision (mAP)@50:95 and mAP@50 by 2.4% and 1.9%, respectively, and has a real-time inference speed of 33.7 frames per second. To further confirm the generalization ability of the approach, experiments are conducted on the camera sensors-based Cityscapes dataset. The results divulge that the suggested strategy can effectively raise the detection accuracy of obstacles, and offer a fresh perspective on obstacle identification.

AI-Driven Traffic Simulation using Unity: Implementing Finite State Machines for Adaptive NPC Behaviour

This research develops an AI-powered traffic simulation using the Unity Engine, leveraging finite state machines (FSM) to enable adaptive and responsive non-player characters (NPCs). The integration of FSM with advanced pathfinding algorithms, such as A*, allows NPCs to dynamically adjust their behavior based on traffic conditions, obstacles, and environmental changes. The experimental results indicate a 25% improvement in route optimization and a 30% reduction in path conflicts compared to conventional static models, demonstrating the robustness of the proposed approach. Optimized navmesh deployment further enhances navigation fluidity, ensuring efficient agent movement in high-density scenarios without compromising system performance. The findings establish the effectiveness of the FSM-driven NPC behavior in simulating realistic traffic environments, contributing both to the advancement of AI applications in game development and urban planning. By providing an interactive platform for traffic management, this simulation offers a practical tool to study congestion patterns and test intervention strategies. In addition, it improves player engagement by fostering emergent gameplay through dynamic NPC interactions. Future work could explore the integration of real-time procedural generation or multiplayer functionality to enrich simulation depth and scalability. This study provides a comprehensive framework that bridges AI-based mechanics with simulation technology, providing significant insights for researchers and practitioners in game design, artificial intelligence, and urban planning.

Distributed MPC for Multi-Vehicle Cooperative Control Considering the Surrounding Vehicle Personality

In real traffic environment, a single control mode of traditional autonomous vehicles cannot meet various driving requirements for different drivers, which will decrease the acceptance of autonomous vehicles, and even may further cause traffic risks. This paper studies the cooperative strategies between ego vehicle and surrounding vehicles with the naturalistic experiment data, and then designs an autonomous vehicle control method based on the distributed Model Predictive Control (MPC) in order to consider the interaction relationship of ego vehicles and surrounding vehicles. Finally, the proposed method is verified by software simulation and Hardware in the Loop (HIL) simulation experiments, and the experiment results demonstrate that the control method proposed in this paper not only can control the vehicle to complete the typical driving tasks smoothly, in terms of car-following and lane-changing, but also can reflect the different cooperative strategies among different driving behavior characteristics, which can improve safety and acceptance of autonomous vehicles to promote the practical application of autonomous vehicle technology.

Modified Model Predictive Control for Coordinated Signals along an Arterial under Relaxing Assumptions

This paper proposes modified model predictive control (MMPC) for coordinated signals, aiming to enhance a model’s fidelity to the realistic traffic environment by relaxing typical assumptions. We focus on the arterial, where every intersection is equipped with a dual-ring-barrier signal controller that complies with the standards of the National Electric Manufacturers Association. MMPC employs the store-and-forward model to describe traffic flow, thereby transforming the signal control problem into a model-based rolling-horizon optimization problem, in which the prediction horizon is composed of several future sample intervals, commonly equal to the cycle length. A radar detector is used to collect vehicle data upstream of the stop line at every sampling instant. The optimization problem is solved to minimize the number of vehicles within the prediction horizon, and the next timing plan is determined based on the optimization results. Constraints are added and modified in order to incorporate the typical relaxed assumptions in the optimization process. For this purpose, MMPC introduces a transition-free ring-barrier structure, vehicle distribution ratio, and percent arrival before the end of green. Simulation results indicate that coordination can be maintained by MMPC without the need for transitions, and the estimation of current and future traffic states can be improved with the assistance of modified constraints. Compared with benchmark techniques, MMPC offers superior vehicle progression for coordinated movement and significant improvements in delays, number of stops, and total travel time from a system-wide perspective, with an acceptable small increase in runtime.

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.

Data Mining-Based Collision Scenarios of Vehicles and Two Wheelers for the Safety Assessment of Intelligent Driving Functions

The safety performance test of intelligent driving vehicles needs to rely on the collision scenarios in a real road traffic environment. In order to study the collision scenarios and accident characteristics of vehicles and two wheelers (TWs) in line with the complex traffic conditions in China, this paper proposes using clustering analysis to initially cluster traffic accident data to obtain the base scenarios and then applying the association rule algorithm to each base scenario to obtain the potential connection of its accident attributes and describe the collision scenarios in more detail. This study is based on data from 335 vehicle and two-wheeler crashes in the National Automobile Accident In-Depth Investigation System (NAIS). It used clustering analysis to cluster the crash data into different partitions to obtain eight clusters of vehicle and two-wheeler base scenarios and applied association rules to analyze the rest of the accident attributes, revealing common crash characteristics to describe the base scenarios in more detail. In the end, it constructed eleven types of detailed vehicle and two-wheeler collision scenarios covering straight roads, intersections, and T-junctions. The results provide richer and more suitable crash scenarios of vehicles and two wheelers in China’s complex traffic and is an important reference for the development of intelligent driving testing scenarios in the future.

A Novel Traffic Obfuscation Technology for Smart Home

With the widespread popularity of smart home devices and the emergence of smart home integration platforms such as Google, Amazon, and Xiaomi, the smart home industry is in a stage of vigorous development. While smart homes provide users with convenient and intelligent living, the problem of smart home devices leaking user privacy has become increasingly prominent. Smart home devices give users the ability to remotely control home devices, but they also reflect user home activities in traffic data, which brings the risk of privacy leaks. Potential attackers can use traffic classification technology to analyze traffic characteristics during traffic transmission (e.g., at the traffic exit of a smart home gateway) and infer users’ private information, such as their home activities, causing serious consequences of privacy leaks. To address the above problems, this paper focuses on research on privacy protection technology based on traffic obfuscation. By using traffic obfuscation technology to obscure the true traffic of smart home devices, it can prevent malicious traffic listeners from analyzing user privacy information based on traffic characteristics. We propose an enhanced smart home traffic obfuscation method called SHTObfuscator (Smart Home Traffic Obfuscator) based on the virtual user technology concept and a virtual user behavior construction method based on logical integrity. By injecting traffic fingerprints of different device activities into the real traffic environment of smart homes as obfuscating traffic, attackers cannot distinguish between the real device working status and user behavior privacy in the current home, effectively reducing the effect of traffic classification attack models. The protection level can be manually or automatically adjusted, achieving a balance between privacy protection and bandwidth overhead. The experimental results show that under the highest obfuscation level, the obfuscation method proposed in this paper can effectively reduce the classification effect of the attack model from 95% to 25%.

A Multi-Objective Roadside Unit Deployment Model for an Urban Vehicular Ad Hoc Network

Vehicular ad hoc networks (VANETs) are a type of mobile ad hoc network that forms a unified wireless communication network between vehicles and roadside nodes. Roadside units (RSUs), as the infrastructure and key component of VANETs, play a critical role in improving the performance of VANETs. Their deployment can effectively improve the communication performance of the network. The goal of the RSU deployment (RSUD) problem is to install as few RSUs as possible on both sides of roads or intersections so that they can cover most areas and achieve better communication performance. This paper proposes a multi-objective optimization model to solve the RSUD problem using the characteristics of RSUDs on urban roads. Our optimized target includes three performance indicators, which are the deployment cost of RSUs, their coverage area, and communications. Since these three indicators cannot achieve consensus, this paper proposes a multi-objective evolutionary algorithm, the NSGA-II Pareto optimal solution, to solve the RSUD problem. The simulation results show the effectiveness of the multi-objective model and method. Finally, we present an experiment featuring RSU deployment that is based on a real traffic environment and uses OpenStreetMap; this experiment proves that our proposed method is practical.

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.

Development of a Pavement-Embedded Piezoelectric Harvester in a Real Traffic Environment.

Road pavements are spread over large areas and convey various possibilities for energy sources such as high thermal gradients due to their materials and colors, wind corridors, large flat areas for solar harvesting, and heavy loading from traffic. The latest advances in road energy generation have been discretely implemented and have mainly focused on photovoltaic surface applications; other studies have explored the use of piezoelectric transducers with high stresses for better energy-production performance but limited life span. This study explores the stresses on pavement surfaces from traffic loading shockwaves that yield to the natural frequency vibration a piezoelectric harvester using a cantilever array. The passing vehicles triggered 16 piezoelectric sensors divided into four embedded steel profiles. The peak electrical power obtained in the experiment was 55.6 µW with a single transducer using a tip mass of 16 g. The proposed harvester demonstrated potential for applications in micro-generation of energy with limited infrastructure modification and high endurance under traffic loading over time. Its generation capacity is around 50 mWh a month with 16 piezoelectric cantilevers installed (for a commercial traffic volume of 1500 vehicles a day), enough to power a 200 m flashing LED raised marker strip to guide drivers for lane alignment during night shifts.

Modelo Diffserv (Qor S) in a real traffic environment to evaluate the behavior of QoS parameters on cisco and mikrotik equipment

This document presents the evalua tion d the behavior of the parameters of bandwidth, latency, packet loss, jitter and cpu, by implementing a real traffic environment on two scenarios of Mikrotik and Cisco equipment. The generation of real traffic was implemented using the TRex tool creating an environment of traffic of clients and servers focused on the Streaming and Web service, through a virtual machine, with two network cards. Routing between client-servers was established using static routes. The implementation of the DiffServ Quality of Service model was carried out through the identification stages using the Omnipeek tool, marking in the DSCP field of the IP header and establishing policies for traffic treatment. For the analysis of results, the T-Student test was used comparing the parametersbetween both brands before and after applying the DiffServ Quality of Service model. Based on the results obtained, there is a difference in bandwidth, latency and packet loss between the average values 0.1502 Mbps, 39.56 ms and 14.4352% respectively before applying the model and 0.0844 Mbps, 0.162 ms after applying it.

Causal inference multi-agent reinforcement learning for traffic signal control

A primary challenge in multi-agent reinforcement learning for traffic signal control is to produce effective cooperative traffic-signal policies in non-stationary multi-agent traffic environments. However, each agent suffers from its local non-stationary traffic environment caused by the time-varying traffic-signal policies of adjacent agents; At the same time, different agents also produce time-varying traffic-signal policies, which further results in the non-stationarity of the whole traffic environment, so these produced traffic-signal policies may be ineffective. In this work, we propose a Causal Inference Multi-Agent reinforcement learning (CI-MA) algorithm, which can alleviate the non-stationarity of multi-agent traffic environments from both feature representation and optimization, eventually helps to produce effective cooperative traffic-signal policies. Specifically, a Causal-Inference (CI) model is first designed to reason about and tackle the non-stationarity of multi-agent traffic environments by both acquiring feature representation distributions and deriving variational lower bounds (i.e., objective functions); And then, based on the designed CI model, we propose a CI-MA algorithm, in which the feature representations are acquired from the non-stationarity of multi-agent traffic environments at both task level and timestep level, the acquired feature representations are used to produce cooperative traffic-signal policies and Q-values for multiple agents; Finally the corresponding objective functions optimize the whole algorithm from both causal inference and multi-agent reinforcement learning. Experiments are conducted in different non-stationary multi-agent traffic environments. Results show that CI-MA algorithm outperforms other state-of-the-art algorithms, and demonstrate that the proposed algorithm trained in synthetic-traffic environments can be effectively transferred to both synthetic- and real-traffic environments with non-stationarity.

The influence of the optimal velocity deviation on the traffic evolution process under lane change in two-lane lattice model

In a real traffic environment, there is always a certain deviation between the real traffic information and the expected traffic information, which definitely has an important impact on traffic flow. Consequently, the optimal velocity deviation effect (OVDE) is taken into account to build a novel lattice model on two lanes. Moreover, the influence of OVDE on the linear stable condition is investigated, which shows that the OVDE plays a positive stable effect in two-lane traffic flow. Also, the OVDE can relieve more traffic congestion on two lanes via numerical simulations concerning the density and the hysteresis loop.

Differences in Pedestrian Behavior at Crosswalk between Communicating with Conventional Vehicle and Automated Vehicle in Real Traffic Environment

In this study, we examine the differences in pedestrian behavior at crosswalks between communicating with conventional vehicles (CVs) and automated vehicles (AVs). To analyze pedestrian behavior statistically, we record the pedestrian’s position (x- and y-coordinates) every 0.5 s and perform a hot spot analysis. A Toyota Prius (ZVW30) is used as the CV and AV, and the vehicle behavior is controlled using the Wizard of Oz method. An experiment is conducted on a public road in Odaiba, Tokyo, Japan, where 38 participants are recruited for each experiment involving a CV and an AV. The participants cross the road after communicating with the CV or AV. The results show that the pedestrians can cross earlier when communicating with the CV as compared with the AV. The hot spot analysis shows that pedestrians who communicate with the CV decide to cross the road before the CV stops; however, pedestrians who communicate with the AVs decide to cross the road after the AV stops. It is discovered that perceived safety does not significantly affect pedestrian behavior; therefore, earlier perceived safety by drivers’ communication and external human–machine interface is more important than higher perceived safety for achieving efficient communication.

Deep Reinforcement Learning Based Traffic Signal Control: A Comparative Analysis

More recently, the advancement of deep learning techniques enables reinforcement learning (RL) to handle high-dimensional decision-making problems. It brings increasing attention in transport areas. Some work has applied them to solve the intractable traffic signal control (TSC) problem and achieved promising results. However, very few research comprehensively investigates the impacts of key design elements, including state definitions, reward functions and deep reinforcement learning (DRL) methods, on TSC performance. To fill this research gap, this paper first selects commonly used design elements from existing literature. Then, we compare their learning stability and control performance at an isolated intersection under different scenarios via simulation experiments. The experimental results show that the quantitative state (e.g., the number of vehicles on lanes) and image-like state (e.g., vehicle position and speed) have no significant impact on the performance under different traffic demands. However, the impact of various reward functions on performance is vivid, especially under high traffic demand. Also, high-resolution vehicular network data may not be possible to improve control performance versus ordinary camera data. In addition, the value-based DRL algorithms outperform the policy-based algorithm and traditional TSC control methods. The findings of this research would provide insights and guidance for transport engineers to design an efficient DRL-based TSC system in a real traffic environment.

A Less-Disturbed Ecological Driving Strategy for Connected and Automated Vehicles

This paper proposes a less-disturbed ecological driving strategy for connected and automated vehicles (CAVs). The proposed strategy integrates the offline planning and the online tracking. In offline planning, an energy efficient reference speed is created based on traffic information (such as the average traffic speed) and characteristics of the vehicle (such as the engine efficiency map) via dynamic programming. The consideration of average traffic speed in speed planning avoids selfish optimisations. In online tracking, model predictive control is employed to update the vehicle speed in real-time to track the reference speed. A key challenge in applying ecological driving strategies in real driving is that the vehicle has to consider other traffic participants when tracking the reference speed. Therefore, this paper combines both longitudinal control and lateral control to achieve better speed tracking by overtaking the preceding vehicle when necessary. The proposed less-disturbed ecological driving strategy has been evaluated in simulations in both single road segment scenario and real traffic environment. Comparisons of the proposed method with benchmark strategies and human drivers are made. The results demonstrate that the proposed less-disturbed ecological driving strategy is more effective in energy saving. Compared to human drivers, the less-disturbed eco-driving strategy improves the fuel efficiency of CAVs by 4.53%.

Hybrid Vehicle-to-X Communication Network by Using ITS-G5 and LTE-V2X

Vehicle-to-X communication enables vehicles and other road users to exchange information in order to increase traffic efficiency and safety. All participants build a decentralized ad hoc network that can operate without the need for additional communication infrastructure. Two competing transmission technologies are available for this purpose. The WLAN-based approach ITS-G5 and the cellular-mobile-based LTE-V2X both enable the direct exchange of information between participants. Each of these technologies has individual advantages and disadvantages that recommend a specific technology for specific use cases. The hybrid usage of the two technologies in a common network can help to improve the reliability of transmissions for safety-critical applications by providing suitable transmission features for every use case, including dissemination range, latency, and channel throughput. The proposed hybrid V2X network approach provides participants with various options for selecting the transmission path to better adapt to the requirements of the specific use cases. The investigation is based on measurements of the transmission properties of ITS-G5 and LTE-V2X in real traffic environments as well as on simulations of hybrid V2X networks in a combined simulation environment for traffic and communication behavior. An important part of this investigation is the development of suitable dissemination models that describe the transmission behavior of both technologies. Initial results show that both technologies can be constructively combined to improve the reliability of communication for safety-critical applications in decentralized V2X networks.

An Application-Oriented Method Based on Cooperative Map Matching for Improving Vehicular Positioning Accuracy

Accurate vehicular positioning is important for intelligent and connected vehicles (ICVs). However, in urban canyons, vehicles that rely solely on global navigation satellite system (GNSS) are susceptible to factors such as signal blocking and multi-path, reducing the positioning performance. In this paper, an application-oriented cooperative map matching (CMM) method is proposed, and a low-cost Global Positioning System (GPS)/BeiDou navigation satellite system (BDS) integrated positioning system is designed. The road constraints of a real traffic environment, which simplifies the computational complexity and facilitates practical applications, are modeled. The positioning system is designed to collect and store the positioning data for experimental analysis. Static and dynamic experiments are conducted to verify the effectiveness of the CMM method. From the experimental results, the mean absolute error (MAE) and root mean square error (RMSE) of the positioning with CMM correction in the static experiment are reduced by 9.0% and 4.9%, respectively. In the dynamic experiment, compared with the original positioning error, the MAE is reduced by 44.2% while the RMSE is reduced by 24.3%. The results show that the proposed method can improve vehicular positioning accuracy effectively in both static and dynamic environments.

Enabling internet of things in road traffic forecasting with deep learning models

Integration of the latest technological advancements such as Internet of Things (IoT) and Computational Intelligence (CI) techniques is an active research area for various industrial applications. The rapid urbanization and exponential growth of vehicles has led to crowded traffic in cities. The deployment of IoT infrastructures for building smart and intelligent traffic management system greatly improves the quality and comfort of city dwellers. This work aims at building a cost effective IoT enabled traffic forecasting system using deep learning techniques. The case study experimentation is done in a real time traffic environment. The main contributions of this work include: (i) deploying road side sensor station built with ultrasonic sensor and Arduino Uno controller for obtaining traffic flow data (ii) building an IoT cloud system based on open source Thingspeak cloud platform for monitoring real time traffic (iii) performing short term traffic forecast using Recurrent Neural Network (RNN) models such as Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). The performance of the prediction model is compared with the traditional statistical methods such as Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA) and Convolutional Neural Network (CNN). The results show good performance metrics with RMSE of 5.8, 7.9, 10.2 for LSTM model and 6.7, 8.6, 10.9 for GRU model for three different scenarios such as whole day, morning congested hour and evening congested hour datasets.