Browse Topic: Transportation Systems

Items (4,424)

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

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

FIT File Processing for Accident Reconstruction

2025-01-8696

04/01/2025

It is becoming increasingly common for bicyclists to record their rides using specialized bicycle computers and watches, the majority of which save the data they collect using the Flexible and Interoperable Data Transfer (.fit) Protocol. The contents of .fit files are stored in binary and thus not readily accessible to users, so the purpose of this paper is to demonstrate the differences induced by several common methods of analyzing .fit files. We used a Garmin Edge 830 bicycle computer with and without a wireless wheel speed sensor to record naturalistic ride data at 1 Hz. The .fit files were downloaded directly from the computer, uploaded to the chosen test platforms - Strava, Garmin Connect, and GoldenCheetah - and then exported to .gpx, .tcx and .csv formats. Those same .fit files were also parsed directly to .csv using the Garmin FIT Software Developer Kit (SDK) FitCSVTool utility. The data in those .csv files (henceforth referred to as “SDK data”) were then either directly

Sweet, David, Bretting, Gerald

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

Next-Gen Italian Urban Mobility: Emissions LCA and TCO Prospective for Innovative Transportation Solutions

2025-01-8593

04/01/2025

Reducing vehicle numbers and enhancing public transport can significantly cut emissions in the transport sector. Hydrogen-fueled and battery electric buses show the potential for decarbonization, but a Life Cycle Assessment (LCA) is essential to evaluate carbon emissions from energy production and manufacturing. In addition, even associated pollutant emissions, together with components’ wear, must be taken into account to evaluate the overall environmental impact. Total Cost of Ownership (TCO) analysis complements this by assessing long-term expenses, enabling stakeholders to balance environmental and economic considerations. This study examines carbon and pollutant emissions alongside TCO for innovative urban mobility powertrains (compared with diesel), focusing on Italian current and future hydrogen and electricity mix scenarios, even considering 100 % green hydrogen (100GH), the goal being to support sustainable decision-making and to promote eco-friendly transport solutions. The

Brancaleoni, Pier Paolo, Damiani Ferretti, Andrea Nicolò, Corti, Enrico, Ravaglioli, Vittorio, Moro, Davide

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

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

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

Effect of Aftermarket Modifications on ADAS Functionality – 2021 Ford F-150 Light Vehicle

2025-01-8057

04/01/2025

Both automotive aftermarket vehicle modifications and Advanced Driver Assistance Systems (ADAS) are growing. However, there is very little information available in the public domain about the effect of aftermarket modifications on ADAS functionality. To address this deficiency, a research study was previously performed in which a 2022 Chevrolet Silverado 1500 light truck was tested in four different hardware configurations. These included stock as well as three typical aftermarket configurations comprised of increased tire diameters, a suspension level kit, and two different suspension lift kits. Physical tests were carried out to investigate ADAS performance of lane keeping, crash imminent braking, traffic jam assist, blind spot detection, and rear cross traffic alert systems. The results of the Silverado study showed that the ADAS functionality of that vehicle was not significantly altered by aftermarket modifications. To determine if the results of the Silverado study were

Bastiaan, Jennifer, Muller, Mike, Morales, Luis

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

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

Research on Segmented Latency Testing Method for in-Vehicle Real-Time Systems Based on DDS Middleware

2025-01-8083

04/01/2025

With the rapid development of intelligent connected vehicles, their open and interconnected communication characteristics necessitate the use of in-vehicle Ethernet with high bandwidth, real-time performance, and reliability. DDS is expected to become the middleware of choice for in-vehicle Ethernet communication. The Data Distribution Service (DDS), provided by the Object Management Group (OMG), is an efficient message middleware based on the publish/subscribe model. It offers high real-time performance, flexibility, reliability, and scalability, showing great potential in service-oriented in-vehicle Ethernet communication. The performance of DDS directly impacts the stable operation of vehicle systems, making accurate evaluation of DDS performance in automotive systems crucial for optimizing system design. This paper proposes a latency decomposition method based on DDS middleware, aiming to break down the overall end-to-end latency into specific delays at each processing stage

Yu, Yanhua, Luo, Feng, Ren, Yi, Hou, Yongping

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

Efficient Sidewalk Route Planning on Aerial Images with Cost-Based A* and Mini-Max Objective Function

2025-01-8032

04/01/2025

With the growing diversification of modern urban transportation options, such as delivery robots, patrol robots, service robots, E-bikes, and E-scooters, sidewalks have gained newfound importance as critical features of High-Definition (HD) Maps. Since these emerging modes of transportation are designed to operate on sidewalks to ensure public safety, there is an urgent need for efficient and optimal sidewalk routing plans for autonomous driving systems. This paper proposed a sidewalk route planning method using a cost-based A* algorithm and a mini-max-based objective function for optimal routes. The proposed cost-based A* route planning algorithm can generate different routes based on the costs of different terrains (sidewalks and crosswalks), and the objective function can produce an efficient route for different routing scenarios or preferences while considering both travelling distance and safety levels. This paper’s work is meant to fill the gap in efficient route planning for

Bao, Zhibin, Lang, Haoxiang, Lin, Xianke

Bicycle Pitch-over Reconstruction Analysis

2025-01-8684

04/01/2025

During a pitch-over event, the forward momentum of the combined bicycle and rider is suddenly arrested causing the rider and bicycle to rotate about the front wheel and also possibly propelling the rider forward. This paper examines the pitch-over of a bicycle and rider using two methods different from previous approaches. One method uses Newton’s 2nd Law directly and the other method uses the principle of impulse and momentum, the integrated form of Newton’s 2nd Law. The two methods provide useful equations, contributing to current literature on the topic of reconstructing and analyzing bicycle pitch-over incidents. The analysis is supplemented with Madymo simulations to evaluate the kinematics and kinetics of the bicycle and rider interacting with front wheel obstructions of different heights. The effect of variables such as rider weight, rider coupling to the bicycle, bicycle speed, and obstruction height on resulting kinematics were evaluated. The analysis shows that a larger

Brach, R. Matthew, Kelley, Mireille, Van Poppel, Jon

Quantifying the Uncertainty in Bicycle-Computer Speed Measurements

2025-01-8701

04/01/2025

Bicycle computers record and store kinematic and physiologic data that can be useful for forensic investigations of crashes. The utility of speed data from bicycle computers depends on the accurate synchronization of the speed data with either the recorded time or position, and the accuracy of the reported speed. The primary goals of this study were to quantify the temporal asynchrony and the error amplitudes in speed measurements recorded by a common bicycle computer over a wide area and over a long period. We acquired 96 hours of data at 1-second intervals simultaneously from three Garmin Edge 530 computers mounted to the same bicycle during road cycling in rural and urban environments. Each computer recorded speed data using a different method: two units were paired to two different external speed sensors and a third unit was not paired to any remote sensors and calculated its speed based on GPS data. We synchronized the units based on the speed signals and used one of the paired

Booth, Gabrielle R., Siegmund, Gunter P.

Portable Track-Based Connected Intersection Testing System for Connected and Automated Vehicles

2025-01-8062

04/01/2025

Connected and automated vehicle (CAV) technology is a rapidly growing area of research as more automakers strive towards safer and greener roads through its adoption. The addition of sensor suites and vehicle-to-everything (V2X) connectivity gives CAVs an edge on predicting lead vehicle and connected intersection states, allowing them to adjust trajectory and make more fuel-efficient decisions. Optimizing the energy consumption of longitudinal control strategies is a key area of research in the CAV field as a mechanism to reduce the overall energy consumption of vehicles on the road. One such CAV feature is autonomous intersection navigation (AIN) with eco-approach and departure through signalized intersections using vehicle-to-infrastructure (V2I) connectivity. Much existing work on AIN has been tested using model-in-loop (MIL) simulation due to being safer and more accessible than on-vehicle options. To fully validate the functionality and performance of the feature, additional

Hamilton, Kayla, Misra, Priyashraba, Ord, David, Goberville, Nick, Crain, Trevor, Marwadi, Shreekant

Heavy Tracked Vehicles: Preliminary Design for Mobility

2025-01-8337

04/01/2025

Modern military operations prove that increased terrain mobility is critical for heavy tracked vehicles’ (HTVs) survivability and lethality. HTV major system packaging as a component of preliminary design with many physical constraints and assumptions poses great challenges for mobility. This paper develops an approach and a method that accounts for such constraints/assumptions and optimizes the packaging of the HTV system assembly, including vehicle armor, armament and munition, powertrain, and fuel tanks. The optimization purpose is to accommodate the center of gravity for improving ground pressure distribution and then reducing the sinkage. This work is based on a literature review and combines numerous techniques rooted in Western literature and Eastern Soviet- and post-Soviet-era literature. The optimization process is developed using a genetic algorithm. The Mean Relative Design (MRD) parameter is proposed to study the average system rearrangement (i.e., re-packing) that is

Vardi, Haggay, Vantsevich, Vladimir, Gorsich, David

Fuel Consumption Estimation Using Spatio-Temporal Modeling and Traffic Flow Predictions: A Comparative Analysis

2025-01-8101

04/01/2025

Effective traffic management and energy-saving techniques are increasingly needed as metropolitan areas grow and traffic volumes rise. This work estimates fuel consumption over three selected routes in an urban context using spatio-temporal modeling essentially building on a previously developed approach in traffic prediction and forecasting. A weighted adjacency matrix for a Graph Neural Network (GNN) is constructed in the original approach which combines graph theory frameworks with travel times obtained from average speeds and distances between traffic count stations. Next, the traffic flow estimate uncertainty is measured using Adaptive Conformal Prediction (ACP) to provide a more reliable forecast. This work predicts fuel consumption under different scenarios by utilizing Monte Carlo simulations based on the expected traffic flows providing insights into energy efficiency and the best routes to take. The study compares passenger vehicles' and heavy-duty trucks' mean fuel

Patil, Mayur, Moon, Joon, Hanif, Athar, Ahmed, Qadeer

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

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

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

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

Comparative Kinematic Performance Testing to SAE J3230 on a Dynamometer and Test Track

2025-01-8792

04/01/2025

SAE J3230 provides Kinematic Performance Metrics for Powered Standing Scooters. These performance metrics include many tests which require specific conditions including flat pavement with a near zero slope, drivers of specific height and weights, and data acquisition equipment. In order to determine the efficacy of replicating SAE J3230 tests in a laboratory setting, a device called the Micromobility Device Thermo-Electric Dynamometer was used alongside outdoor tests to provide a comparison of scooter performance in these two testing applications. Based on the testing outcomes, it can be determined whether SAE J3230 and similar standards for other micromobility devices can be replicated in a lab-based setting, saving time, operator hazard, and providing more thorough data outputs.

Bartholomew, Meredith, Andreatta, Dale, Zagorski, Scott, Heydinger, Gary

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

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

Impact of Connected and Automated Vehicles on Longitudinal and Lateral Performance of Heterogeneous Traffic Flow in Shared Autonomy on Two-Lane Highways

2025-01-8098

04/01/2025

Intelligent transportation systems and connected and automated vehicles (CAVs) are advancing rapidly, though not yet fully widespread. Consequently, traditional human-driven vehicles (HDVs), CAVs, and human-driven connected and automated vehicles (HD-CAVs) will coexist on roads for the foreseeable future. Simultaneously, car-following behaviors in equilibrium and discretionary lane-changing behaviors make up the most common highway operations, which seriously affect traffic stability, efficiency and safety. Therefore, it’s necessary to analyze the impact of CAV technologies on both longitudinal and lateral performance of heterogeneous traffic flow. This paper extends longitudinal car-following models based on the intelligent driver model and lateral lane-changing models using the quintic polynomial curve to account for different vehicle types, considering human factors and cooperative adaptive cruise control. Then, this paper incorporates CAV penetration rates, shared autonomy rates

Wang, Tianyi, Guo, Qiyuan, He, Chong, Li, Hao, Xu, Yiming, Wang, Yangyang, Jiao, Junfeng

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

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

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.

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

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

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

Vehicle–Vehicle Collaborative Control Strategy for Dedicated Lanes of Connected Vehicles

12-08-02-0012

03/28/2025

To alleviate the problem of reduced traffic efficiency caused by the mixed flow of heterogeneous vehicles, including autonomous and human-driven vehicles, this article proposes a vehicle-to-vehicle collaborative control strategy for a dedicated lane in a connected and automated vehicle system. First, the dedicated lane’s operating efficiency and formation performance are described. Then, the characteristics of connected vehicle formations are determined, and a control strategy for heterogeneous vehicle formations was developed. Subsequently, an interactive strategy was established for queueing under the coordination of connected human-driven and autonomous vehicles, and the queue formation, merging, and splitting processes are divided according to the cooperative interaction strategy. Finally, the proposed lane management and formation strategies are verified using the SUMO+Veins simulation software. The simulation results show that the dedicated lane for connected vehicles can

Zhang, Xiqiao, Cui, Leqi, Yang, Longhai, Wang, Gang

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

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

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

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

A Unified-BEV Network for Joint 3D Object Detection and Map Segmentation in Complex Traffic Scenario

2025-01-7202

03/19/2025

Recently, the multi-view image-based Bird’s Eye View (BEV) perception for autonomous driving has gained considerable attention due to its cost-effectiveness and capacity for rich semantic information. However, the majority of existing studies focus primarily on improving the performance of single task, neglect to utilize the dense and robust BEV representation that is beneficial for various downstream tasks such as 3D object detection, semantic map segmentation. These approaches inherently add extra computational burden due to repeated feature extraction and propagation for different tasks. To this end, we develop a network that simultaneously performs 3D object detection and map segmentation in a unified BEV representation space with multi-camera perspective view (PV) image inputs. Firstly, a shared network includes image feature extractor and PV-BEV transformation is employed to generate a unified BEV feature. The BEV feature serves as the input for the decoders of various tasks

Li, Mohan, Song, Tao, Xu, Yanhai, Zhou, Zhisong, Zhou, Guofeng, Liu, Xulei

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

SAE TOMORROW TODAY BRIEFS - How Are Automakers Reacting to Tariffs?

1350302/26/2025

There is no doubt that businesses, especially automakers, do not like uncertainty. So how are they reacting to potential tariffs and the impact on cross-border manufacturing? Roberto Baldwin, Sustainability Editor, SAE Sustainable Mobility Solutions, recently sat down with Transportation and Business Journalist, Abigail Bassett, to the discuss the Trump administration's flurry of executive orders and how automakers are reacting. For more information on the evolution of sustainability, head on over to sustainablecareers.sae.org. There, you can check out the latest video of the Honda 0 Series. We'd love to hear from you. Share your comments, questions and ideas for future topics and guests to podcast@sae.org. Don't forget to take a moment to follow SAE Tomorrow Today--a podcast where we discuss emerging technology and trends in mobility with the leaders, innovators and strategists making it all happen--and give us a review on your preferred podcasting platform. Follow SAE on LinkedIn

Hineman, Marcie

ARP7094 (Current)

02/25/2025

The scope of this ARP is as follows: Use of M&S for type certification of the Advanced Air Mobility (AAM) aircraft, product, or system. However, this does not preclude this ARP being used for certification of other aircraft types and associated products and systems. This ARP is not applicable to flight simulation training device (FSTD) qualifications or pilot certification. If a qualified FSTD is proposed for aircraft, product, or system certification, it must demonstrate sufficient M&S substantiation to meet the related requirement. Structural design and modeling are not addressed by this document. EMI/EMC certification is not addressed by this document.

G-35, Modeling, Simulation, Training for Emerging AV Tech

Taxonomy and Classification of Powered Micromobility Vehicles

J3194_202502 (Current)

02/25/2025

This technical report provides a taxonomy and classification of powered micromobility vehicles. These vehicles may be privately owned or be available via shared- or rental-fleet operations. This technical report does not provide specifications or otherwise impose minimum safety design requirements for powered micromobility vehicles.

Powered Micromobility Vehicles Committee

Powered Micromobility Vehicle Identification Number

J3272_202502 (Current)

02/25/2025

This standard will apply primarily to the vehicle classes identified in SAE J3194. It provides a schema for utilizing alphanumeric values to represent identifying information such as the manufacturer or vehicle provider, year of manufacture, model, vehicle type, weight, width, speed, and power source. Although conceptually similar to a Vehicle Identification Number (VIN), this standard does not classify or intend to suggest classification of these vehicles as motor vehicles for regulatory or safety data purposes. The location for placement of these identifiers on the vehicle, type of label, permanence, and visibility are out of scope for this document.

Powered Micromobility Vehicles Committee

Toward a Methodology for Safety—Performance Trade-Off Analysis for Connected Automated Vehicles Supported by a Smart Infrastructure

12-08-02-0020

02/24/2025

Introducing connectivity and collaboration promises to address some of the safety challenges for automated vehicles (AVs), especially in scenarios where occlusions and rule-violating road users pose safety risks and challenges in reconciling performance and safety. This requires establishing new collaborative systems with connected vehicles, off-board perception systems, and a communication network. However, adding connectivity and information sharing not only requires infrastructure investments but also an improved understanding of the design space, the involved trade-offs and new failure modes. We set out to improve the understanding of the relationships between the constituents of a collaborative system to investigate design parameters influencing safety properties and their performance trade-offs. To this end we propose a methodology comprising models, analysis methods, and a software tool for design space exploration regarding the potential for safety enhancements and requirements

Fornaro, Gianfilippo, Törngren, Martin, Gaspar Sánchez, José Manuel

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

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