Abstract With the development of intelligent transportation systems and the increasing demand for transportation, traffic congestion on highways has become more prominent. So accurate short-term traffic flow prediction on these highways is exceedingly crucial. However, because of the complexity, nonlinearity, and randomness of highway traffic flows, short-term prediction of its flows can be difficult to achieve the desired accuracy and robustness. This article presents a novel architectural model that harmoniously fuses bidirectional long–short-term memory (BiLSTM), bidirectional gated recurrent unit (BiGRU), and multi-head attention (MHA) components. Bayesian optimization (BO) is also used to determine the optimal set of hyperparameters. Based on the PeMS04 dataset from California, USA, we evaluated the performance of the proposed model across various prediction intervals and found that it performs best within a 5-min prediction interval. In addition, we have conducted comparison and

Chen, Peng, Wang, Tao, Ma, Changxi, Chen, Jun

Traffic Flow Prediction Based on the Optuna–CNN–BiLSTM–GBRT Model

12-09-01-0001

04/24/2025

Abstract Traffic flow prediction is very important in traffic-related fields, and increasing prediction accuracy is the primary goal of traffic prediction research. This study proposes a new traffic flow prediction method, which uses the CNN–BiLSTM model to extract features from traffic data, further models these features through GBRT, and uses Optuna to tune important hyperparameters of the overall model. The main contribution of this study is to propose a new combination model with better performance. The model integrates two deep learning models that are widely used in this field and creatively uses GBRT to process the output features of the front-end model. On this basis, the optimal hyperparameters and the robustness of the model are deeply explored, providing an effective and feasible solution to the difficult problems in traffic flow prediction. This model is experimentally studied using three different data transformation methods (original data, wavelet transform, Fourier

Ma, Changxi, Jin, Renzhe

Cross-Scene Traffic Target Detection and Recognition Based on Improver YOLOv5s

12-09-01-0003

04/24/2025

Abstract Aiming at the problem of insufficient cross-scene detection performance of current traffic target detection and recognition algorithms in automatic driving, we proposed an improved cross-scene traffic target detection and recognition algorithm based on YOLOv5s. First, the loss function CIoU of insufficient penalty term in the YOLOv5s algorithm is adjusted to the more effective EIoU. Then, the context enhancement module (CAM) replaces the original SPPF module to improve feature detection and extraction. Finally, the global attention mechanism GCB is integrated with the traditional C3 module to become a new C3GCB module, which works cooperatively with the CAM module. The improved YOLOv5s algorithm was verified in KITTI, BDD100K, and self-built datasets. The results show that the average accuracy of mAP@0.5 is divided into 95.1%, 72.2%, and 97.5%, respectively, which are 0.6%, 2.1%, and 0.6% higher than that of YOLOv5s. Therefore, it shows that the improved algorithm has better

Ning, Qianjia, Zhang, Huanhuan, Cheng, Kehan

Fatigue Analysis of Motorcycle Rear Swing Arm on Different Road Surfaces

2024-32-0046

04/18/2025

The rear swing arm, a crucial motorcycle component, connects the frame and wheel, absorbing the vehicle’s load and various road impacts. Over time, these forces can damage the swing arm, highlighting the need for robust design to ensure safety. Identifying potential vulnerabilities through simulation reduces the risk of failure during the design phase. This study performs a detailed fatigue analysis of the swing arm across different road conditions. Data for this research were collected from real-vehicle experiments and simulation analyses, ensuring accuracy by comparing against actual performance. Following CNS 15819-5 standards, road surfaces such as poorly maintained, bumpy, and uneven roads were tested. Using Motion View, a comprehensive multi-body dynamic model was created for thorough fatigue analysis. The results identified the most stress-prone areas on the swing arm, with maximum stress recorded at 109.6N on poorly maintained roads, 218.3N on bumpy surfaces, and 104.8N on

Chiou, Yi-Hau, Hwang, Hsiu-Ying, Huang, Liang-Yu

Local and Global Entropy Generation of Topographically Optimized Porous Reactors in Reaction-Diffusion Systems Considering Coupling Effects between Heat and Mass Transfer

2024-32-0069

04/18/2025

As the automotive sector shifts towards cleaner and more sustainable technologies, fuel cells and batteries have emerged as promising technologies with revolutionary potential. Hydrogen fuel cell vehicles offer faster refueling times, extended driving ranges, and reduced weight and space requirements compared to battery electric vehicles, making them highly appealing for future transportation applications. Despite these advantages, optimizing electrode structures and balancing various transport mechanisms are crucial for improving PEFCs’ performance for widespread commercial viability. Previous research has utilized topology optimization (TO) to identify optimal electrode structures and attempted to establish a connection between entropy generation and topographically optimized structures, aiming to strengthen TO numerical findings with a robust theoretical basis. However, existing studies have often neglected the coupling of transport phenomena. Typically, it is assumed that a single

Tep, Rotanak Visal Sok, Long, Mengly, Alizadeh, Mehrzad, Charoen-amornkitt, Patcharawat, Suzuki, Takahiro, Tsushima, Shohji

Reducing the Waste of Plating Solution for Magnetostrictive Torque Sensors

2024-32-0006

04/18/2025

The power assist system of an electric bicycle uses a magnetostrictive torque sensor to detect the pedal force based on the magnetic properties of the crankshaft, which change according to stress. Fe–Ni alloy plating is used to coat the surface of the crankshaft with a magnetic film to enhance the magnetostrictive effect. However, the sensor performance decreases as the plating solution degrades, which necessitates replacement of the plating solution. In this study, experiments were performed to investigate how to prevent or mitigate degradation of the plating solution to reduce waste. The amounts of carbon and sulfur in the magnetic film were found to increase with degradation of the plating solution. The carbon derived from organic reducing agents and their decomposition products, and the sulfur derived from stress relievers and their decomposition products. A method was developed for reducing the amounts of carbon and sulfur in the magnetic film, which would help maintain the sensor

Ohnishi, Hiromichi

Analysis of Lane Departure Caused by Inadequate Motorcycle Driving Maneuvers Due to Road Alignment

2024-32-0034

04/18/2025

There are many riders who drive motorcycles on winding mountain roads and caused single motorcycle traffic accidents on curved roads by lane departure. Driving a motorcycle requires subtle balancing and maneuvering. In this study, in order to clarify the influence of lane departure caused by inadequate driving maneuvers against road alignment, the authors analyzed the required curve initial operation and driving maneuvers in curves depending on the traveling speed using a kinematics simulation for motorcycle dynamics. In addition, it was analyzed how inadequate driving maneuvers for curved roads can easily cause lane departure. As a result, it shows that the steering maneuvers and the lean of motorcycle body during the curves are highly affected by the vehicle speed, and the required maneuvers increases rapidly with increasing speed. The inadequate maneuver in the curves, especially for the lean of motorcycle body and steering torque, even by 10%, may cause failure to follow the

Kuniyuki, Hiroshi, Takechi, So

A Power Split eCVT Hybrid for Sustainable Urban Mobility

2024-32-0029

04/18/2025

The main drivers for powertrain electrification of two-wheelers, motorcycles and ATVs are increasingly stringent emission and noise limitations as well as the upcoming demand for carbon neutrality. Two-wheeler applications face significantly different constraints, such as packaging and mass targets, limited charging infrastructure in urban areas and demanding cost targets. Battery electric two wheelers are the optimal choice for transient city driving with limited range requirements. Hybridization provides considerable advantages and extended operation limits. Beside efficiency improvement, silent and zero emission modes with solutions allowing fully electric driving, combined boosting enhances performance and transient response. In general, there are two different two-wheeler base categories for hybrid powertrains: motorcycles featuring frame-integrated internal combustion engine (ICE) and transmission units, coupled with secondary drives via chain or belt; and scooters equipped with

Schoeffmann, W., Fuckar, G., Hubmann, C., Gruber, M.

SAE TOMORROW TODAY UNPLUGGED - The Fragmentation of the Robotaxi Market

1351004/10/2025

The robotaxi market is experiencing significant change, with the once-dominant Waymo now facing competition from new entrants like Nuro and Wayve who are licensing their autonomous driving technology -- a strategy that could reshape the industry. In addition, Tesla's Cybercab is aiming to launch a commercial service by the end of 2025 that allows owners to generate revenue through the Tesla network. Lyft and Uber are also adapting through the power of partnership, emphasizing the importance of fleet management in the operation and maintenance of robotaxis. In this Unplugged episode of SAE Tomorrow Today, host Grayson Brulte breaks it all down and discusses how the impact of a fragmented market will likely result in a convergence of personally owned AVs, traditional robotaxis, and niche players. Have your own thoughts on this topic? We'd love to hear from you! Share your comments, questions or ideas for future topics with Grayson on Twitter or send them to podcast@sae.org. Follow SAE on

Hineman, Marcie

SAE TOMORROW TODAY BRIEFS - NEVI and America's EV Charging Future

1350904/03/2025

The National Electric Vehicle Infrastructure Program (NEVI) is a $5 billion initiative aimed at developing EV charging infrastructure across all 50 states and Puerto Rico. However, recent executive orders have put the program and its funding on hold. To dig into what this all means, Roberto Baldwin, Sustainability Editor, SAE Sustainable Mobility Solutions, recently sat down with Andrew Wishnia, Senior Vice President at Boundary Stone Partners, to discuss what NEVI is all about and what the future holds for this EV charging infrastructure initiative. For more information on the evolution of sustainability, head on over to sustainablecareers.sae.org. There, you can check out our drive of the new 2025 Ioniq 5, the first non-Telsa EV to hit the road with SAE J3400 (a.k.a., NACS). 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

Hineman, Marcie

An Investigation of the Inter-Vehicle Distances and Lateral Offsets on Aerodynamic Forces and Wake Structures of Vehicles Platooning

2025-01-8769

04/01/2025

In traffic scenarios, the spacing between vehicles plays a key role, as the actions of one vehicle can significantly impact others, particularly with regards to energy conservation. Accordingly, modern vehicles are equipped with inter-vehicle communication systems to maintain specific distances between vehicles. The aerodynamic forces experienced by both leading vehicles (leaders) and following vehicles (followers) are connected to the flow patterns in the wake region of the leaders. Therefore, improving our understanding of the turbulent characteristics associated with vehicles platooning is important. This paper investigates the effects of inter-vehicle distances on the flow structure of two vehicles: a small SUV as the leader and a larger light commercial van as the follower, using a Delayed Detached Eddy Simulation (DDES) CFD technique. The study focuses on three specific inter-vehicle distances: S = 0.28 L, 0.4L, and 0.5L, where S represents the spacing between the two vehicles

Mosavati, Maziar, Guzman, Arturo, Lounsberry, Todd, Fadler, Gregory

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

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

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.

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

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

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

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

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

Robust V2X Cruise Control for Class 8 Trucks in the Presence of Traffic Lights

2025-01-8033

04/01/2025

An implementation of a robust predictive cruise control method for class 8 trucks utilizing V2X communication with connected traffic lights is presented in this work. This method accounts for traffic signal phases with the goal of reducing energy consumption when possible while respecting safety concerns. Tightened constraints are created using a robust model predictive control (RMPC) framework in which constraints are modified so that the safety critical requirements are satisfied even in the presence of disturbances, while requiring only the expected bounds of the disturbances to be provided. In particular, variation in the actuator performance under different conditions presents a unique challenge for this application, which the approach applied in this work is well-suited to handle. The errors resulting from lower-level control and actuator performance are accounted for by treating them as bounded and additive disturbances on the states of the model used in the higher level MPC

Ellison, Evan, Ward, Jacob, Brown, Lowell, Bevly, David M.

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

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

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

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

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

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.

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

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 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

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

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

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

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

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

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

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

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

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

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