Browse Topic: Roads and highways

Items (1,049)

Object Detection for City and Highway Driving Scenario with YOLOX and Mask RCNN

2025-01-8015

04/01/2025

This paper explores the integration of two deep learning models that are currently being used for object detection, specifically Mask R-CNN and YOLOX, for two distinct driving environments: urban cityscapes and highway settings. The hypothesis underlying this work is that different methods of object detection will work best in different driving environments, due to the differences in their unique strengths as well as the key differences in those driving environments. Some of these differences in the driving environment include varying traffic densities, diverse object classes, and differing scene complexities, including specific differences such as the types of signs present, the presence or absence of stoplights, and the limited-access nature of highways as compared to city streets. As part of this work, a scene classifier has also been developed to categorize the driving context into the two categories of highway and urban driving, in order to allow the overall object detection

Patel, Krunal, Peters, Diane

Safe Cooperative Control Framework for Highway On-Ramp Merging

2025-01-8024

04/01/2025

The development of connected and automated vehicles (CAVs) is rapidly increasing in the next generation and the automotive industry is dedicated to enhancing the safety and efficiency of CAVs. A cooperative control strategy helps CAVs to collaborate and share information among the neighboring CAVs, improving efficiency, optimizing traffic flow, and enhancing safety. This research proposes a safe cooperative control framework for CAVs designed for highway merging applications. In the urban transportation system, highway merging scenarios are high-risk collision zone, and the CAVs on the main and merging lanes should collaborate to avoid potential accidents. In the proposed framework, the on-ramp CAVs merge at 40 mph within the same and opposite directions to the main lane CAVs. The proposed framework includes the consensus controller, safety filter, and quadratic programming (QP) optimization method. The consensus controller incorporates the communication between CAVs and designs the

Chang, PeiYu, Bhatti, Sidra, Javed, Nur Uddin, Ahmed, Qadeer

Spectral Estimation to Quantify Road/Track Surface Degradation

2025-01-8252

04/01/2025

Track testing methods are utilized in the automotive industry for emissions and fuel economy certification. These track tests are performed on smooth road surfaces which deteriorate over time due to wear and weather effects, hence warranting regular track repaves. The study focuses on the impact of repaving on track quality and surface degradation due to weather effects. 1D surface profiles and 2D surface images at different spatial frequencies were measured at different times over a span of two years using various devices to study the repave and degradation effects. Data from coastdown tests was also collected over a span of two years and is used to demonstrate the impact of track degradation and repaving on road load characterization parameters that are used for vehicle certification tests. Kernel density estimation and non-parametric spectral estimation methods are used to visualize the characteristic features of the track at different times. In the pre-processing stage, outliers

Singh, Yuvraj, Jayakumar, Adithya, Rizzoni, Giorgio

Aggressive Autonomous Control on Snow and Ice

2025-01-8040

04/01/2025

In cold and snowy areas, low-friction and non-uniform road surfaces make vehicle control complex. Manually driving a car becomes a labor-intensive process with higher risks. To explore the upper limits of vehicle motion on snow and ice, we use an existing aggressive autonomous algorithm as a testing tool. We built our 1:5 scaled test platform and proposed an RGBA-based cost map generation method to generate cost maps from either recorded GPS waypoints or manually designed waypoints. From the test results, the AutoRally software can be used on our test platform, which has the same wheelbase but different weights and actuators. Due to the different platforms, the maximum speed that the vehicle can reach is reduced by 1.38% and 2.26% at 6.0 m/s and 8.5 m/s target speeds. When tested on snow and ice surfaces, compared to the max speed on dirt (7.51 m/s), the maximum speed decreased by 48% and 53.9%, respectively. In addition to the significant performance degradation on snow and ice, the

Yang, Yiming, Bos, Jeremy P.

Terrain Environment Estimating Method for off-Road Vehicle Anticipated Driving Area Based on Stereo Vision

2025-01-8279

04/01/2025

Off-road vehicles are required to traverse a variety of pavement environments, including asphalt roads, dirt roads, sandy terrains, snowy landscapes, rocky paths, brick roads, and gravel roads, over extended periods while maintaining stable motion. Consequently, the precise identification of pavement types, road unevenness, and other environmental information is crucial for intelligent decision-making and planning, as well as for assessing traversability risks in the autonomous driving functions of off-road vehicles. Compared to traditional perception solutions such as LiDAR and monocular cameras, stereo vision offers advantages like a simple structure, wide field of view, and robust spatial perception. However, its accuracy and computational cost in estimating complex off-road terrain environments still require further optimization. To address this challenge, this paper proposes a terrain environment estimating method for off-road vehicle anticipated driving area based on stereo

Zhao, Jian, Zhang, Xutong, Hou, Jie, Chen, Zhigang, Zheng, Wenbo, Gao, Shang, Zhu, Bing, Chen, Zhicheng

Research on Road-Sense Simulation Algorithm and Analysis of Its Effect on Different Steering Wheels

2025-01-8753

04/01/2025

With the continuous development of automotive intelligence, there is an increasing demand for vehicle chassis systems to become more intelligent, electronically controlled, integrated, and lightweight. In this context, the steer-by-wire system, which is electronically controlled, offers high precision and fast response. It provides greater flexibility, stability, and comfort for the vehicle, thus meeting the above requirements and has garnered widespread attention. Unlike traditional systems, the steer-by-wire system eliminates mechanical components, meaning the road feel cannot be directly transmitted to the steering wheel. To address this, the road feel, which is derived from the vehicle's state or integrated with environmental driving data, must be simulated and transmitted to the steering wheel through a road feel motor. This motor generates feedback that mimics the road feel, similar to that experienced in a conventional steering system. This simulation enhances the driver's

Li, Shang, Kaku, Chuyo, Zheng, Hongyu, Zhang, Yuzhou

Enhanced Secondary Crash Classification through Functional Class-Based Weighting and Hybrid Machine Learning Models

2025-01-8207

04/01/2025

Road safety and traffic management face significant challenges due to secondary crashes, which frequently cause increased traffic, delays, and collisions. Traditional methods for anticipating secondary crashes often overlook the importance of different road types, resulting in suboptimal predictions and response plans. This research presents a novel method that combines a hybrid machine-learning model with a functional class-based weighting strategy to classify secondary crashes. The functional classes in the dataset are categorized as interstates, arterial roads, collector roads, and local roads. The dataset also includes comprehensive crash narratives and various road attributes. Each functional class is assigned a weight reflecting its proportional importance in the likelihood of a subsequent crash, based on historical data and road usage patterns. This weighting technique is integrated into a hybrid model architecture that trains a Random Forest (RF) model on structured data to

Patil, Mayur, Marik PE, Stephanie

Tube-MPC Based Path Tracking for I Vehicle Considering Data-Driven Stable Region and External Disturbances

2025-01-8262

04/01/2025

Path tracking is a key function of intelligent vehicles, which is the basis for the development and realization of advanced autonomous driving. However, the imprecision of the control model and external disturbances such as wind and sudden road conditions will affect the path tracking effect and even lead to accidents. This paper proposes an intelligent vehicle path tracking strategy based on Tube-MPC and data-driven stable region to enhance vehicle stability and path tracking performance in the presence of external interference. Using BP-NN combined with the state-of-the-art energy valley optimization algorithm, the five eigenvalues of the stable region of the vehicle β−β̇ phase plane are obtained, which are used as constraints for the Tube-MPC controller and converted into quadratic forms for easy calculation. In the calculation of Tube invariant sets, reachable sets are used instead of robust positive invariant sets to reduce the calculation. Simulation results demonstrates that the

Zhang, Haosen, Li, Yihang, Wu, Guangqiang

Research on Composite Braking Strategy of Pure Electric Commercial Vehicle on Downhill Low-Adhesion Road Considering Rainfall Intensity

2025-01-8813

04/01/2025

As a distributed wire control brake system, the electro-mechanical brake (EMB) may face challenges due to the need to integrate the actuator in the limited space beside the wheel. During extended downhill braking, especially on wet roads with reduced adhesion, the EMB must operate at high intensity. The significant heat generated by friction can lead to thermal deformation of components, such as the lead screw, compromising braking stability. This paper focuses on pure electric light trucks and proposes a tandem composite braking method. This approach uses an eddy current retarder (ECR) or motor to provide basic braking torque, while the EMB supplies the dynamic portion of the braking torque, thereby alleviating the braking pressure on the EMB. First, a driver model, tire model, motor model, and braking models are developed based on the vehicle's longitudinal dynamics. In addition, the impact of various factors, such as rainfall intensity, road slope, ramp length and vehicle speed, on

Liu, Wang, Zhang, Yu, Xiao, Hongbiao, Shen, Leiming

Modal Characteristics of Oil-Gas Interconnected Suspension Systems and their Influence on Vehicle Ride Quality

2025-01-8271

04/01/2025

This study focuses on the dynamic behavior and ride quality of three different modes of oil-gas interconnected suspension systems: fully interconnected mode, left-right interconnected mode, and independent mode. A multi-body dynamics model and a hydraulic model of the oil-gas suspension were established to evaluate the system's performance under various operating conditions. The research includes simulations of pitch and roll excitations, as well as ride comfort tests on different road surfaces, such as Class B roads and gravel roads. The analysis compares the effectiveness of the modes in suppressing pitch and roll movements and their impact on overall ride comfort. Results show that the independent mode outperforms the other two in minimizing roll, while the fully interconnected mode offers better pitch control but at the cost of reduced comfort. These findings provide valuable insights for the future design and optimization of oil-gas interconnected suspension systems, especially in

Xinrui, Wang, Chen, Zixuan, Zhang, Yunqing, Wu, Jinglai

Development of Prediction Model for Vehicle Road Load Using Machine Learning

2025-01-8258

04/01/2025

In the modern automotive industry, improving fuel efficiency while reducing carbon emissions is a critical challenge. To address this challenge, accurately measuring a vehicle’s road load is essential. The current methodology, widely adopted by national guidelines, follows the coastdown test procedure. However, coastdown tests are highly sensitive to environmental conditions, which can lead to inconsistencies across test runs. Previous studies have mainly focused on the impact of independent variables on coastdown results, with less emphasis on a data-driven approach due to the difficulty of obtaining large volumes of test data in a short period, both in terms of time and cost. This paper presents a road load energy prediction model for vehicles using the XGBoost machine learning technique, demonstrating its ability to predict road load coefficients. The model features 27 factors, including rolling, aerodynamic, inertial resistance, and various atmospheric conditions, gathered from a

Song, Hyunseung, Lee, Dong Hyuk, Chung, Hyun

The Model Predictive Control of Dual-Chamber Active Air Suspension Based on Road Preview

2025-01-8789

04/01/2025

Adverse weather conditions such as rain and snow, as well as heavy load transportation, can cause varying degrees of damage to road surfaces, and untimely road maintenance often results in potholes. Perception sensors equipped on intelligent vehicles can identify road surface conditions in advance, allowing each wheel’s suspension to actively adjust based on the road information. This paper presents an active suspension control strategy based on road preview information, utilizing a newly designed dual-chamber active air suspension system. It addresses the issue of point cloud stratification caused by vehicle body vibrations in onboard LiDAR data. The point cloud is processed through segmentation, filtering, and registration to extract real-time road roughness information, which serves as preview information for the suspension control system. The MPC algorithm is applied to actively adjust the nonlinear stiffness and damping of the suspension’s dual-chamber air springs, enhancing

Dong, Fuxin, Shen, Yanhua, Wang, Kaidi, Liu, Zuyang, Qian, Shuo

CL-infoRRT Collision-Avoidance Trajectory Planning for Autonomous Vehicle

2025-01-8027

04/01/2025

To ensure the safety and stability of road traffic, autonomous vehicles must proactively avoid collisions with traffic participants when driving on public roads. Collision avoidance refers to the process by which autonomous vehicles detect and avoid static and dynamic obstacles on the road, ensuring safe navigation in complex traffic environments. To achieve effective obstacle avoidance, this paper proposes a CL-infoRRT planning algorithm. CL-infoRRT consists of two parts. The first part is the informed RRT algorithm for structured roads, which is used to plan the reference path for obstacle avoidance. The second part is a closed-loop simulation module that incorporates vehicle kinematics to smooth the planned obstacle avoidance reference path, resulting in an executable obstacle avoidance trajectory. To verify the effectiveness of the proposed method, four static obstacle test scenarios and four RRT comparison algorithms were designed. The implementation results show that all five

Wu, Wei, Lu, Jun, Zeng, Dequan, Yang, Jinwen, Hu, Yiming, Yu, Qin, Wang, Xiaoliang

Research on Dangerous Driving Behavior Recognition Algorithm Based on LSTM

2025-01-8204

04/01/2025

To address the issue of high accident rates in road traffic due to dangerous driving behaviors, this paper proposes a recognition algorithm for dangerous driving behaviors based on Long Short-Term Memory (LSTM) networks. Compared with traditional methods, this algorithm innovatively integrates high-frequency trajectory data, historical accident data, weather data, and features of the road network to accurately extract key temporal features that influence driving behavior. By modeling the behavioral data of high-accident-prone road sections, a comprehensive risk factor is consistent with historical accident-related driving conditions, and assess risks of current driving state. The study indicates that the model, in the conditions of movement track, weather, road network and conditions with other features, can accurately predict the consistent driving states in current and historical with accidents, to achieve an accuracy rate of 85% and F1 score of 0.82. It means the model can

Huang, Yinuo, Zhang, Miaomiao, Xue, Ming, Jin, Xin

Design of A Differential Braking Controller Based on Road Reaction Force Estimation to Respond to Steering System Failure in Autonomous Driving Situations

2025-01-8797

04/01/2025

As the electrification of chassis systems accelerates, the demand for fail-safety strategies is increasing. In the past, the steering system was mechanically connected, so the driver could respond directly to some extent. However, the Steer-by-Wire (SbW) system is composed of the column and rack bar as electrical signals, so the importance of response strategies for steering system failure is gradually increasing. When a steering system failure occurs, a differential braking control using the difference in braking force between the left and right wheels was studied. Recently, some studies have been conducted to model the wheel reaction force generated during a differential braking. Since actual tires and road surfaces are nonlinear and cause large model errors, model-based control methods have limited performance. Also, in previous studies assumed that the driver normally operates the steering wheel in a failure situation. However, if limited to a situation such as autonomous driving

Kim, Sukwon, Kim, Young Gwang, Kim, SungDo, Moon, Sung Jin

Real-time Terrain Analysis for Off-road Autonomous Vehicles

2025-01-8343

04/01/2025

One challenge for autonomous vehicle (AV) control is the variation in road roughness which can lead to deviations from the intended course or loss of road contact while steering. The aim of this work is to develop a real-time road roughness estimation system using a Bayesian-based calibration routine that takes in axle accelerations from the vehicle and predicts the current road roughness of the terrain. The Bayesian-based calibration method has the advantage of providing posterior distributions and thus giving a quantifiable estimate of the confidence in the prediction that can be used to adjust the control algorithm based on desired risk posture. Within the calibration routine, a Gaussian process model is first used as a surrogate for a simulated half-vehicle model which takes vehicle velocity and road surface roughness (GD) to output the axle acceleration. Then the calibration step takes in the observed axle acceleration and vehicle velocity and calibrates the Gaussian process model

Lewis, Edwina, Parameshwaran, Aditya, Redmond, Laura, Wang, Yue

Adding and Assessing Vehicle Sound and Steering Feedback: Application to an Unreal Engine Driving Simulator

2025-01-8668

04/01/2025

To provide an affordable and practical platform for evaluating driving safety, this project developed and assessed 2 enhancements to an Unreal-based driving simulator to improve realism. The current setup uses a 6x6 military truck from the Epic Games store, driving through a pre-designed virtual world. To improve auditory realism, sound cues such as engine RPM, braking, and collision sounds were implemented through Unreal Engine's Blueprint system. Engine sounds were dynamically created by blending 3 distinct RPM-based sound clips, which increased in volume and complexity as vehicle speed rose. For haptic feedback, the road surface beneath each tire was detected, and Unreal Engine Blueprints generated steering wheel feedback signals proportional to road roughness. These modifications were straightforward to implement. They are described in detail so that others can implement them readily. A pilot study was conducted with 3 subjects, each driving a specific route composed of a straight

Duan, Lingbo, Xu, Boyu, Green, Paul

Development and Validation of Hands-off Detection Function for Steering Wheel for the CEPS System with a Torque Sensor

2025-01-8047

04/01/2025

Traditional Hands-Off Detection (HOD) is realized by analyzing the torque applied to the steering wheel by the driver (driver torque), which is less accurate. In order to solve this problem, this paper takes the Column Electric Power Steering (CEPS) system as an object, analyzes the influence of the inertia effect and damping effect of the steering wheel and steering column on the HOD, establishes two kinds of state observers to obtain the accurate driver torque, proposes the estimation method of the road condition level, and can determine the torque threshold according to the information of the road condition level and the vehicle speed, and finally compares the driver torque and the torque threshold to obtain the HOD results. Experimentally, it is proved that this method can effectively reduce the interference of road surface interference on HOD. In addition, a fault-tolerant detection mechanism is proposed and validated to calculate the HOD result based on the frequency-domain

Huang, ZhaoLin, Li, Min, Shangguan, Wenbin, Duan, XiaoCheng, Xia, ZhiJun

Combine HD Map Information to Enhance Road Topology Reasoning by Offline Supervised Training and on-Line Ensemble Learning

2025-01-8021

04/01/2025

Topology reasoning plays a crucial role in understanding complex driving scenarios and facilitating downstream planning, yet the process of perception is inevitably affected by weather, traffic obstacles and worn lane markings on road surface. Combine pre-produced High-definition maps (HDMaps), and other type of map information to the perception network can effectively enhance perception robustness, but this on-line fused information often requires a real-time connection to website servers. We are exploring the possibility to compress the information of offline maps into a network model and integrate it with the existing perception model. We designed a topology prediction module based on graph attention neural network and an information fusion module based on ensemble learning. The module, which was pre-trained on offline high-precision map data, when used online, inputs the structured road element information output by the existing perception module to output the road topology, and

Kuang, Quanyu, Rui, Zhang, Zhang, Song, Yixuan, Gao

Reducing Power Consumption through Effective Data Sharing in Platooning Scenarios

2025-01-8051

04/01/2025

Platooning occurs when vehicles travel closely together to benefit from multi-vehicle movement, increased road capacity, and reduced fuel consumption. This study focused on reducing energy consumption under different driving scenarios and road conditions. To quantify the energy consumption, we first consider dynamic events that can affect driving, such as braking and sudden acceleration. In our experiments, we focused on modeling and analyzing the power consumption of autonomous platoons in a simulated environment, the main goal of which was to develop a clear understanding of the different driving and road factors influencing power consumption and to highlight key parameters. The key elements that influence the energy consumption can be identified by simulating multiple driving scenarios under different road conditions. The initial findings from the simulations suggest that by efficiently utilizing the inter-vehicle distances and keeping the vehicle movements concurrent, the power

Khalid, Muhammad Zaeem, Azim, Akramul, Rahman, Taufiq

A Motion-Aware Continuous Time LiDAR-Inertial SLAM Framework

2025-01-8044

04/01/2025

Towards the goal of real-time navigation of autonomous robots, the Iterative Closest Point (ICP) based LiDAR odometry methods are a favorable class of Simultaneous Localization and Mapping (SLAM) algorithms for their robustness under any light conditions. However, even with the recent methods, the traditional SLAM challenges persist, where odometry drifts under adversarial conditions such as featureless or dynamic environments, as well as high motion of the robots. In this paper, we present a motion-aware continuous-time LiDAR-inertial SLAM framework. We introduce an efficient EKF-ICP sensor fusion solution by loosely coupling poses from the continuous time ICP and IMU data, designed to improve convergence speed and robustness over existing methods while incorporating a sophisticated motion constraint to maintain accurate localization during rapid motion changes. Our framework is evaluated on the KITTI datasets and artificially motion-induced dataset sequences, demonstrating

Kokenoz, Cigdem, Shaik, Toukheer, Sharma, Abhishek, Pisu, Pierluigi, Li, Bing

Modeling Vehicle Behavior in Highway On-Ramps: A Game Theory and Deep Reinforcement Learning Method

12-08-04-0037

03/28/2025

With the improvement of autonomous driving technology, the testing methods for traditional vehicles can no longer meet autonomous driving needs. The simulation methods based on virtual scenario have become a current research hotpot. However, the background vehicles are often pre-set in most existing scenarios, making it difficult to interact with the tested autonomous vehicles and generate dynamic test scenarios that meet the characteristics of different drivers. Therefore, this study proposes a method combining game theory and deep reinforcement learning, and uses a data-driven approach to realistically simulate personalized driving behavior in highway on-ramps. The experimental results show that the proposed method can realistically simulate the speed change and lane-change actions during vehicle interaction. This study can provide a dynamic interaction test scenario with different driver style for autonomous vehicle virtual test in highway on-ramps and a more realistic environment

Qiu, Fanke, Wang, Kan, Li, Wenli

Editorial

09-13-01-0008

03/24/2025

Driving Change: NHTSA’s Role in Advancing Road Safety

Hardy, Warren N.

Advancing Road Sensing Technology: Functional Observer for Vehicle Road Profile Estimation

12-08-04-0038

03/24/2025

Having an in-depth comprehension of the variables that impact traffic is essential for guaranteeing the safety of all drivers and their automobiles. This means avoiding multiple types of accidents, particularly rollover accidents, that may have the capacity of causing terrible repercussions. The non-measured factors in the system state can be estimated employing a vehicle model incorporating an unknown input functional observer, this gives an accurate estimation of the unknown inputs such as the road profile. The goal of the proposed functional observer design constraints is to reduce the error of estimation converging to a value of zero, which results in an improved calculation of the observer parameters. This is accomplished by resolving linear matrix inequalities (LMIs) and employing Lyapunov–Krasovskii stability theory with convergence conditions. A simulator that enables a precise evaluation of environmental factors and fluctuating road conditions was additionally utilized. This

Saber, Mohamed, Ouahi, Mohamed, Naami, Ghali, El Akchioui, Nabil

Traffic Event Extraction and Geographization from Natural Language Web Text

2025-01-7213

03/19/2025

Real-time traffic event information is essential for various applications, including travel service improvement, vehicle map updating, and road management decision optimization. With the rapid advancement of Internet, text published from network platforms has become a crucial data source for urban road traffic events due to its strong real-time performance and wide space-time coverage and low acquisition cost. Due to the complexity of massive, multi-source web text and the diversity of spatial scenes in traffic events, current methods are insufficient for accurately and comprehensively extracting and geographizing traffic events in a multi-dimensional, fine-grained manner, resulting in this information cannot be fully and efficiently utilized. Therefore, in this study, we proposed a “data preparation - event extraction - event geographization” framework focused on traffic events, integrating geospatial information to achieve efficient text extraction and spatial representation. First

Hu, Chenyu, Wu, Hangbin, Wei, Chaoxu, Chen, Qianqian, Yue, Han, Huang, Wei, Liu, Chun, Fu, Ting, Wang, Junhua

Freeway Traffic Conflict Forecasting: A Machine Learning Approach with RF-LSTM Integration

2025-01-7138

03/19/2025

This paper aims to forecast and examine traffic conflicts by integrating Random Forest (RF) alongside Long Short-Term Memory Network (LSTM). The paper begins with the Random Forest method, pinpointing essential elements affecting traffic conflicts, revealing that the speed difference between interacting vehicles and their leaders, as well as the average headway and distance have significant effects on the occurrence of traffic conflicts. The forecasted Time to Collision (TTC) metric demonstrates extraordinary accuracy, confirming the creation of a precise traffic conflict forecast model. The model expertly predicts the vehicle's trajectory. This model skillfully anticipates vehicle paths and potential traffic conflict, demonstrating strong alignment with actual traffic patterns and offering support for traffic management by highlighting imminent risks. Merging RF with feature selection and LSTM for temporal dynamics enhances the forecasting capability. Furthermore, it also illuminates

Cui, XinYuan, Shi, Xiaomeng, Shao, Yichang

Optimizing Electric Vehicle Charging Station Locations Based on Urban Road Network Congestion Tracing

2025-01-7216

03/19/2025

Exhaust emissions from congested road segments constitute a significant source of urban air pollution. Resolving traffic congestion throughout the road network presents considerable challenges. However, alleviating tailpipe emissions on congested roads can be achieved by increasing the proportion of electric vehicles (EVs) in the traffic flow. Therefore, we propose a method for optimizing the layout of EV charging stations based on urban road networks congestion tracing. This method traces congestion sources through similarity between road networks, and evaluates the installation potential value of adjacent candidate installation points using the congestion contribution degree of the road segment as an indicator. The analysis is conducted on 100 routes within the Qinhuai district of Nanjing city, using spatiotemporal similarity metrics. The utilization of point-of-interest and traffic data from online mapping sources overcomes the complexity of road network structure and the sparsity

Zeng, Wenyi, Jian, Lu, Hu, Xiaojian

Monitoring Compaction Of cement Stabilized Macadam Using Smart Aggregate Sensor

2025-01-7151

03/19/2025

Monitoring changes in pavement material compaction degree and analyzing the interaction mechanism between particles are essential for improving compaction quality. In this paper, an on-site intelligent compaction test was carried out using intelligent sensor, the correlation between the in-situ test results and the intelligent compaction measurement value (ICMV) was written, and the influences of moisture content on the correlations were discussed. Further, the gyratory compaction tests were carried out using smart aggregate (SA) sensors to investigate the characteristics of the sensing results during the gyratory compaction of mixtures with different moisture contents, revealing the interaction mechanism between particles. Finally, the compaction characteristic indexes CEI, CDI and CSI were proposed using the SA sensing results, which were used to characterize the flow, compaction degree and stability characteristics of the mixtures, respectively. The conclusions of the study are of

Wang, Ning, Li, Qiang, Wang, Jiaqing

Driving Force Control for Water-to-Land Transition of Distributed Drive Amphibious Vehicles

2025-01-5011

03/04/2025

In response to the complex shore slope road conditions and the switching of water–land environments during the amphibious vehicle’s landing process, a landing drive force control strategy for amphibious vehicles is proposed. First, based on the shore slope gradient, buoyancy effect, and amphibious vehicle acceleration, the drive force of the front and rear wheels of the amphibious vehicle is pre-allocated. Then, referring to the road parameters of common road types, the road adhesion coefficient and optimal slip ratio of the current road surface where the amphibious vehicle is located are identified based on the principle of fuzzy control. Subsequently, with the slip ratio difference as the control target, the drive motor is controlled based on the sliding mode control algorithm to achieve tracking of the optimal slip ratio. A joint simulation is carried out using CarSim and Simulink, and the results are compared with those without control. The simulation results show that the drive

Huang, Bin, Yuan, Zineng, Yu, Wenbin

Analysis of Speeding and Crashes on Bridges in Mugling to Narayanghat Highway Segment in Nepal

09-13-01-0002

03/03/2025

Driving speed affects road safety, impacting crash severity and the likelihood of involvement in accidents on highway bridges. However, their impacts remain unclear due to inconsistent topography and consideration of crash types. This study aimed to identify the status of accidents and factors associated with accidents occurring on bridges along the Mugling to Narayanghat highway segment in Nepal. The study area involves the selected highway segment stretching from Aptari junction (CH: 2+42) to Mugling junction (CH: 35+677). Spanning 33.25 km, the road traverses through both hilly and Terai regions. The study employs descriptive and correlation statistics to analyze crash data from 2018 to 2023, aiming to achieve its research objectives. The study reveals overspeeding as the primary cause of crashes, notably head-on and rear-end collisions. Two-wheelers frequently exceed the speed limit of 40 km/h limit (29–88 km/h), and four-wheelers do similarly (18–81 km/h), leading to overspeeding

Giri, Om Prakash, Shahi, Padma Bahadur, Kunwar, Deepak Bahadur

Short-Term Prediction of Highway Visibility Level Based on Attention Mechanism LSTM

2025-01-7212

02/21/2025

To improve the accuracy and reliability of short-term prediction of highway visibility level in key scenarios such as short duration and fast changing speed, this paper proposes a short-term prediction method for highway visibility level based on attention mechanism LSTM. Firstly, XGBoost and SHAP methods are used to analyze the factors affecting highway visibility, determine the importance ranking of different influencing factors, and select the factors that have a greater impact on visibility as inputs for the visibility level prediction model. Secondly, based on LSTM as the model foundation network and innovative coupling attention mechanism, a visibility level prediction model based on attention mechanism LSTM is constructed, which can dynamically update the correlation between meteorological feature information at each historical time point and the visibility level at the current prediction time, thereby dividing the importance of information and flexibly capturing important

Ding, Shanshan, Xiong, Zhuozhi, Huang, Xu, Li, Yurong

Automatic Detection of Dead-End Roads in High-Resolution Satellite Image

2025-01-7165

02/21/2025

There are dead-end roads in the road network, and many of them have the function of indicating specific target clues, which is of great significance in the fields of military, urban construction, and disaster relief and rescue. However, many of the important cut-offs are in mountainous or wilderness areas, and surveying them is difficult and costly. The research objective of this project is to extract the breakpoints in the road network using high-resolution Google satellite imagery, so as to provide clues and indications for the subsequent relevant work. Firstly, the image is corrected and pre-processed to highlight road edge information.Then the phase grouping method is improved by setting a double-angle threshold, filtering the edge operator to reduce the calculation error of the gradient angle, and the road network is extracted by the improved phase grouping method. And finally screens out the dead-end road points with the eight-neighbourhood method, and marks them on the

Liu, Ruohan, Haoping, Qi, Jingjie, Kang, Yanyan, Wu, Feifei, Li

Cooperative Control Strategy for Variable Speed Limit and Dynamic Hard Shoulder Running of Highway On-Ramp Merging Area

2025-01-7207

02/21/2025

To meet the traffic control demands of highway merging areas and address the accuracy error of traffic flow prediction models, a cooperative control strategy based on adaptive prediction horizon Model Predictive Control (MPC) has been proposed for variable speed limits (VSL) and dynamic hard shoulder running (HSR). Firstly, the METANET model was improved based on the characteristics of merging areas and the impact of cooperative control strategy. Secondly, to mitigate the negative impact of the METANET prediction errors on control effectiveness, a fuzzy rule-based adaptive prediction horizon controller is designed. Thirdly, a cooperative control strategy for VSL and dynamic HSR is formulated under the MPC framework, aiming to optimize Total Time Spent(TTS)and Total Travel Distance (TTD), using genetic algorithms equipped with sliding time windows for resolution. Finally, using actual traffic flow data from Changtai Highway, simulation experiments are conducted, involving four scenarios

Li, Jiahui, Zhang, Jian, Wang, Bo

Analysis of Left-Turn Mixed Traffic Flow Dynamics at Y-Shaped Signalized Intersections Using Cellular Automata

2025-01-7204

02/21/2025

Through the method of on-site video observation, this study divides the intersection area into three parts according to the road traffic characteristics of the Y-shaped signalized intersections, and at the same time obtains the relevant parameters. These parameters include the left-turn speed and traffic density of motor vehicles within both the internal and exit areas, the frequency of lane-changing and queuing behaviors of non-motorized vehicles in the internal area, and the left-turn speed and traffic density of non-motorized vehicles in both the internal and exit areas. The data extraction and analysis of the parameters provide strong data support for further analysis of the subsequent mixed traffic flow. A cellular automaton model is developed using the intersection’s exit area as the scenario. The exit area is divided into three lanes based on the queuing patterns of mixed traffic. Corresponding traffic rules are established according to the traffic density of motorized and non

Yuan, Li, Liu, Xiaowei

Spatio-Temporal Traffic Forecasting with Uncertainty Quantification via Bayesian Graph Convolution

2025-01-7142

02/21/2025

Traffic prediction plays an important role in urban traffic management and signal control optimization. As research in this area advances, traffic prediction has become increasingly accurate. However, the complexity of the traffic system makes the quantification of uncertainty particularly important, as it is influenced by various factors such as weather changes, emergencies and road construction, which lead to the fluctuation and uncertainty of the traffic state. Although some progress has been made in traffic uncertainty quantification, most methods remain primarily focused on individual traffic observation points, with little exploration of the complex spatiotemporal dependencies at the road network level. In light of this situation, this paper proposes a spatiotemporal traffic prediction model based on Bayesian graph convolutional network, which can effectively capture the spatiotemporal dependence in traffic data, facilitating accurate predictions and comprehensive uncertainty

Li, Linfeng, Lin, Limeng

Research on Digital Twin Model Construction Method for Smart Highway

2025-01-7175

02/21/2025

To facilitate the construction of a robust transport infrastructure, it is essential to implement a digital transformation of the current highway system. The concept of digital twins, which are virtual replicas of physical assets, offers a novel approach to enhancing the operational efficiency and predictive maintenance capabilities of highway networks. The present study begins with an exhaustive examination of the demand for the smart highway digital twin model, underscoring the necessity for a comprehensive framework that addresses the multifaceted aspects of digital transformation. The framework, as proposed, is composed of six integral components: spatiotemporal data acquisition and processing, multidimensional model development, model integration, application layer construction, model iteration, and model governance. Each element is critical in ensuring the fidelity and utility of the digital twin, which must accurately reflect the dynamic nature of highway systems. The

Zhang, Yawen, Cai, Xianhua

Risk Assessment Model for Highway Accidents Using Bayesian Random-Parameters Regression Model

2025-01-7121

02/21/2025

This paper presents a highway accident risk assessment model based on a Bayesian random-parameters logit model, aiming to evaluate the effects of real-time traffic conditions on crash risks on freeways. By incorporating random parameters to account for variations in the impacts of traffic variables across different freeway segments, the model offers greater flexibility and adaptability compared to traditional fixed-parameters logit models. The study utilizes traffic flow data collected from the Hangzhou-Shanghai-Ningbo expressway over a 14-month period, analyzing factors such as traffic density, average vehicle speed, and lane-changing frequency. The estimation process employs Markov Chain Monte Carlo (MCMC) methods, including Gibbs sampling and Metropolis-Hasting algorithms, to ensure model convergence and stability. Empirical results demonstrate significant impacts of these traffic variables on crash risks and successfully identify key variables with random effects, enhancing the

Feng, Shi, Wang, Zichen, Liu, Shaoweihua, Wang, Feng, Zhang, Yujie, Luo, Xi

VMD-CNN-BiGRU Model for Vehicle Fuel Consumption Prediction Considering Spatial-Functional Road Characteristics

2025-01-7211

02/21/2025

To accurately predict the fuel consumption of vehicles, this study proposes a vehicle fuel consumption prediction model based on the VMD-CNN-BiGRU algorithm by considering six road spatial features such as road grades, one-way road attributes and intersection attributes. First, the VMD algorithm is employed to reduce the nonlinearity and nonsmoothness of the raw data by determining the optimal number of VMD decomposition modes. Then, the CNN-BiGRU algorithm is used to predict each modal component after decomposition, and the obtained prediction results are compared and analyzed with the prediction results of existing CNN-BiGRU, EMD-CNN-BiGRU and EEMD-CNN-BiGRU models. The results show that the VMD-CNN-BiGRU model significantly outperforms other models in terms of prediction performance and can accurately capture the trend of vehicle fuel consumption, thus effectively verifying the superiority and feasibility of the model. In addition, this study provides an in-depth analysis of the

Gao, Yating, Yan, Lixin, Deng, Guangyang, Chen, Siyuan

Deep Learning-Based Road Spill Detection Technology

2025-01-7130

02/21/2025

Rapid identification and cleanup of road debris are essential for enhancing traffic safety and ensuring unobstructed road conditions. Traditional detection methods often face challenges in accurately identifying debris in complex environments with varying light and weather conditions. To address these limitations, this study proposes a deep learning-based road debris detection method designed for improved accuracy and robustness. First, road images are processed using a semantic segmentation approach to remove background information, isolating only the drivable areas. This segmented region is then subjected to further object detection to filter out typical non-debris objects, such as vehicles, pedestrians, and non-motorized vehicles, thereby retaining a focused image that only contains potential debris or spill objects. Lastly, the processed image is compared to a baseline image to detect differences and identify road debris with high precision. Through these steps, the proposed method

Gao, Xiaofei

A Traffic Conflict Risk Assessment Model for Highway Construction Zones Based on Trajectory Data

2025-01-7217

02/21/2025

Highway construction zones present substantial safety challenges due to their dynamic and unpredictable traffic conditions. With the rising number of highway projects, limited accident data during brief construction phases underscores the need for alternative safety evaluation methods, such as traffic conflict analysis. This study addresses vehicular safety issues within the Kunshan section of the Shanghai-Nanjing Expressway, focusing on conflict risk assessment through a spatio-temporal analysis of a construction zone. Using drone-captured video, vehicle trajectories were extracted to derive key operational indicators, including speed and acceleration, providing a spatio-temporal foundation for analyzing traffic flow and conflict dynamics. A novel **Comprehensive Collision Risk Index (CCRI)** was introduced, integrating Time-to-Distance-to-Collision (TDTC) and Enhanced Time-to-Collision (ETTC) metrics to enable a multidimensional assessment of conflict risk. The CCRI captures both

Zhang, Yuwen, Guo, Xiucheng, Ma, Yuheng

Study on the Minimum Diversion Decision Distance of Autonomous Vehicle on Freeway Based on Lane-Changing Trajectory Modelling

2025-01-7170

02/21/2025

Most autonomous vehicles employ a relatively conservative lane-changing strategy in freeway system. In the diversion areas, autonomous vehicles typically initiate lane-changing to curb lanes at lower speeds at a considerable distance from the diversion point, resulting in a decrease in the overall traffic efficiency within the diversion areas. However, lane-changing decision points excessively close to exit ramps can exacerbate the urgency of the lane-changing process, prompting irrationally forced lane-changing and increasing the collision risk. To provide decision-making references for the safe and rapid diverging of autonomous vehicles in freeway diversion areas, this study proposes a minimum diversion decision distance (MDDD) model for autonomous vehicles through microscopic lane-changing trajectory data. Specifically, the lane-changing process was divided into waiting for the acceptable gap stage and executing the lane-changing stage in this model. Subsequently, UAV aerial

Li, ZhenFa, Luo, BaoGuo, Yang, Qi, Chen, Xu, Pan, BingHong

Research on Map Matching Method Based on Road Geometric Features

2025-01-7159

02/21/2025

For the mismatching defects of vertical projection method, this paper proposes an improved map matching algorithm based on road geometric features. For GNSS data, static repeated data is eliminated, dynamic high frequency data is compressed by light bar method. For network map data, extract motorized road segment, break road segment curve at the turning point, and establish network topology relationship. During map matching, determine the candidate road segment through the circular error area, and determine the matching path through the heading angle, connectivity and projection distance, and determine the projection points through the historical trajectory and driving speed. The effectiveness of the proposed algorithm is verified by case study.

Zhang, Hongbin, Zhang, Xu

Vehicle Trajectory and Traffic Conflict Prediction at Urban Expressway Confluence

2025-01-7128

02/21/2025

The urban expressway ramp entrance has always been one of the traffic accident prone areas. At present, most of the traffic safety research focuses on the intersection traffic conflict prediction analysis, so it is necessary to conduct related research on the urban expressway ramp entrance traffic safety. In this paper, we focus on trajectory prediction and traffic conflict analysis between straight vehicles and ramp vehicles on main roads for small data sets. Firstly, the transformer model and LSTM model were used to predict the trajectories of the straight vehicle on the main road and the vehicle at the entrance of the ramp respectively. TTC is calculated from the predicted trajectories of straight vehicles on the main road and confluence vehicles on the ramp. Then, the historical features are used to predict the future TTC by using LSTM plus self-attention mechanism, and the two conflict prediction methods are compared. The results show that in the case of small data sets, the

Liu, Tiansheng, Xiang, QiaoJun

Remaining Useful Life Prediction Method for Highway Electromechanical Equipment Based on Bayesian Algorithm-Optimized CNN-LSTM Model

2025-01-7149

02/21/2025

Efficient maintenance of highway electromechanical equipment is crucial for ensuring reliability within intelligent highway infrastructure and optimizing the allocation of limited maintenance resources. Traditional Remaining Useful Life (RUL) prediction models frequently face limitations due to the complex and dynamic operating conditions of such systems, which often hinder their predictive accuracy and adaptability. To overcome these persistent challenges, this study introduces an advanced RUL prediction model that integrates a Bayesian-optimized Convolutional Neural Network (CNN) with a Long Short-Term Memory (LSTM) network. Initially, the study identifies key health indicators that effectively represent the degradation of equipment performance over time. These indicators undergo Spearman correlation analysis to determine their relevance to equipment capacity, ensuring that only the most pertinent features are used for model input. The CNN-LSTM model leverages CNN’s spatial pattern

Wang, Leyan, Zhang, Jian, Yao, Xuejian, Ping, Hao

Semantic Segmentation of Lane Lines for Pix2Pix Network Introducing Ghost Module

2025-01-7208

02/21/2025

Technology for lane line semantic segmentation is crucial for ensuring the safe operation of intelligent cars. Intelligent cars can now comprehend the distribution and meaning of scenes in an image more precisely thanks to semantic segmentation, which calls for a certain degree of accuracy and real-time network performance. A lightweight module is selected, and two previous models are improved and fused to create the lane line detection model. Finally, experiments are conducted to confirm the model's efficacy. This paper proposes a lightweight replacement program with the aim of addressing the issue of large parameterization in the generative adversarial network (GAN) model and difficult training convergence. The overall network structure is selected from the Pix2Pix network in the conditional generative adversarial network, and the U-net network of the generator is cut and replaced by the Ghost Module, which consists of a modified downsampling module that enhances the global fusion

Yang, Kun, Wang, Jian

Matching and Fusing Multi-Source Vehicle Information in Highway Gantry Scenarios Using Convolutional Neural Networks

2025-01-7136

02/21/2025

The performance differences of multiple sensors lead to inconsistencies, incompleteness, and distortion in the perception data of multi-source vehicle information in highway scenarios. Optimizing data fusion methods is important for intelligent toll collection systems on highways. First, this paper constructs a dataset for matching and fusing multi-source vehicle information in highway gantry scenarios. Second, it develops convolutional neural network models, Match-Pyramid-MVIMF-EGS and CDSSM-MVIMF-EGS, for this purpose. Finally, comparative experiments are conducted based on the constructed dataset to assess the performance of the Match-Pyramid-MVIMF-EGS and CDSSM-MVIMF-EGS models. The experimental results indicate that the Match-Pyramid-MVIMF-EGS model performs better than the CDSSM-MVIMF-EGS model, achieving matching and fusion accuracy of 93.07%, precision of 95.71%, recall of 89.17%, F1 scores of 92.32%, and 186 of training throughput respectively.

Wang, Junjun, Zhao, Chihang

A Car-following Model Considering Acceleration of Multiple Vehicles for Mixed Traffic Flow

2025-01-7195

02/21/2025

The practice of vehicle platooning for managing mixed traffic can greatly enhance safety on the roads, augment overall traffic flow, and boost fuel efficiency, garnering considerable focus in transportation. Existing research on vehicle platoon control of mixed traffic has primarily focused on using the state information of the leading or head vehicle as control input for following vehicles without accounting for the driving variability of Human-driven Vehicles (HDVs), which does not conform to the driving conditions of vehicles in reality. Inspired by this, this paper presents a car-following model for Connected and Automated Vehicles (CAVs) that utilizes communication with multiple preceding vehicles in mixed traffic. The study further investigates the impact of parameters such as the speed and acceleration of preceding vehicles on the car-following behavior of CAVs, as well as the overall effect of different CAV penetration rates on mixed traffic flow. Firstly, a mixed-vehicle

Peng, Fuke, Huang, Xin

Classification of Pavement Distress Images Using Fusion Convolutional Neural Network of Dual Branch

2025-01-7131

02/21/2025

In recent years, the issue of highway maintenance has become increasingly prominent. How to precisely detect and classify fine cracks and various types of pavement defects on highways through technical means is an essential foundation for achieving intelligent road maintenance. This paper first constructs the DenseNet201-PDC and MobileNetV2-PDC sub-classification networks that incorporate the three-channel attention judgment mechanism MCA. Secondly, based on the principle of parallel connection, a brand-new dual-branch fusion convolutional neural network DBF-PDC capable of classifying pavement defects in highway scenarios is proposed. Finally, this paper builds the Pavement Distress Datasets of Southeast University and conducts relevant ablation experiments. The experimental results demonstrate that both the attention mechanism module and the feature fusion strategy can significantly enhance the network's ability to classify pavement defects in highway scenarios. The average

Zhang, Ziyi, Zhao, Chihang, Shao, Yongjun, Wang, Junjun

A Distributed Control Strategy for Highway Merge Zone Based on Virtual Rotation

2025-01-7200

02/21/2025

The merging problem in the highway merge zone has been a research focus in the field of transportation for a long time. The rise of Connected and Automated Vehicles (CAVs) provides the potential to improve traffic flow efficiency, alleviate congestion and handle safety issues. However, existing two-dimensional merging strategies are facing challenges such as high computational complexity and the inevitable traffic oscillations during merging, which hinder the stability of traffic flow and fail to meet the dynamic requirements of merging control. To address these issues, this study proposes a distributed control strategy for CAVs in highway merge scenarios. Firstly, a virtual rotation method is designed to transform the merging problem of two different lanes into a car-following problem of a virtual platoon, and a virtual leader vehicle is introduced, to reduce computational complexity and determine vehicle sequencing. Based on this method, a Spring Cooperative Merging System (SCMS) is

Liu, Yandan, Qu, Xu

Time-Varying Gain-Scheduled Path-Tracking Controller with Delay Compensation (TGDC) for Autonomous Vehicles

10-09-02-0012

02/19/2025

Path-tracking control occupies a critical role within autonomous driving systems, directly reflecting vehicle motion and impacting both safety and user experience. However, the ever-changing vehicle states, road conditions, and delay characteristics of control systems present new challenges to the path tracking of autonomous vehicles, thereby limiting further enhancements in performance. This article introduces a path-tracking controller, time-varying gain-scheduled path-tracking controller with delay compensation (TGDC), which utilizes a linear parameter-varying system and optimal control theory to account for time-varying vehicle states, road conditions, and steering control system delays. Subsequently, a polytopic-based path-tracking model is applied to design the control law, reducing the computational complexity of TGDC. To evaluate the effectiveness and real-time capability of TGDC, it was tested under a series of complex conditions using a hardware-in-the-loop platform. The

Hu, XuePeng, Zhang, Yu, Hu, Yuxuan, Wang, Zhenfeng, Qin, Yechen

Items per page:

1 – 50 of 1049