The transportation industry is transforming with the integration of advanced data technologies, edge devices, and artificial intelligence (AI). Intelligent transportation systems (ITS) are pivotal in optimizing traffic flow and safety. Central to this are transportation management centers, which manage transportation systems, traffic flow, and incident responses. Leveraging Advanced Data Technologies for Smart Traffic Management explores emerging trends in transportation data, focusing on data collection, aggregation, and sharing. Effective data management, AI application, and secure data sharing are crucial for optimizing operations. Integrating edge devices with existing systems presents challenges impacting security, cost, and efficiency. Ultimately, AI in transportation offers significant opportunities to predict and manage traffic conditions. AI-driven tools analyze historical data and current conditions to forecast future events. The importance of multidisciplinary approaches and

Ercisli, Safak

Edge Artificial Intelligence in Cooperative, Connected, and Automated Mobility

EPR2025009

05/20/2025

Regarding the development of automated driving, manufacturers, technology startups, and systems developers have taken some different approaches. Some are on the path toward stand-alone vehicles, mostly relying on onboard sensors and intelligence. On the other hand, the connected, cooperative, and automated mobility (CCAM) approach relies on additional communication and information exchange to ensure safe and secure operation. CCAM holds great potential to improve traffic management, road safety, equity, and convenience. In both approaches, there are increasingly large amounts of data generated and used functions in perception, situational awareness, path prediction, and decision-making. The use of artificial intelligence is instrumental in processing such data; and in that context, “edge AI” is a more recent type of implementation. Edge Artificial Intelligence in Cooperative, Connected, and Automated Mobility explores perspectives on edge AI for CCAM, explores primary applications, and

Van Schijndel-de Nooij, Margriet, Beiker, Sven

Methods to Reduce Transmission of Airborne Viruses in a Bus Cabin

02-18-04-0021

05/19/2025

Public buses can be high-risk environments for the transmission of airborne viruses due to the confined space and high passenger density. However, advanced cabin air control systems and other measures can mitigate this risk. This research was conducted to explore various strategies aimed at reducing airborne particle transmission in bus cabins by using retrofit accessories and a redesigned parallel ventilation system. Public transit buses were used for stationary and on-road testing. Air exchange rates (ACH) were calculated using CO2 gas decay rates measured by low-cost sensors throughout each cabin. An aerosol generator (AG) was placed at various locations inside the bus and particle concentrations were measured for various experiments and ventilation configurations. The use of two standalone HEPA air filters lowered overall concentrations of particles inside the bus cabin by a factor of three. The effect of using plastic “barriers” independently showed faster particle arrival times

Lopez, Brenda, Swanson, Jacob, Dover, Kevin, Renck, Evan, Chang, M.-C. Oliver, Jung, Heejung

V2P-Based Speed Guidance Method for Avoiding Vehicle–Pedestrian Collisions at Intersections

12-09-01-0006

05/14/2025

Conflicts between vehicles and pedestrians at unsignalized intersections occur frequently and often result in serious consequences. In order to alleviate traffic flow congestion at unsignalized intersections caused by accidents, reduce vehicle congestion time and waiting time, and improve intersection safety as well as intersection access efficiency, a speed guidance algorithm based on pedestrian-to-vehicle (P2V) and vehicle-to-pedestrian (V2P) communication technologies is proposed. The method considers the heading angle (direction of motion) of vehicles and pedestrians and combines the post encroachment time (PET) and time to collision (TTC) to determine whether there is a risk of collision, so as to guide the speed of vehicles. Network simulator NS3 and traffic flow simulation software SUMO are used to verify the effectiveness of the speed guidance strategy proposed in this article. The experimental findings demonstrate that the speed guidance strategy introduced in this article

Sun, Yuanyuan, Wang, Kan, Liu, Weizhen, Li, Wenli

Variable Speed Limit Control Based on Deep Deterministic Policy Gradient under Mixed Traffic Environment

12-09-01-0004

05/06/2025

The existing variable speed limit (VSL) control strategies rely on variable message signs, leading to slow response times and sensitivity to driver compliance. These methods struggle to adapt to environments where both connected automated vehicles (CAVs) and manual vehicles coexist. This article proposes a VSL control strategy using the deep deterministic policy gradient (DDPG) algorithm to optimize travel time, reduce collision risks, and minimize energy consumption. The algorithm leverages real-time traffic data and prior speed limits to generate new control actions. A reward function is designed within a DDPG-based actor-critic framework to determine optimal speed limits. The proposed strategy was tested in two scenarios and compared against no-control, rule-based control, and DDQN-based control methods. The simulation results indicate that the proposed control strategy outperforms existing approaches in terms of improving TTS (total time spent), enhancing the throughput efficiency

Ding, Xibin, Zhang, Zhaolei, Liu, Zhizhen, Tang, Feng

Letter from the Guest Editors

12-08-02-0011

05/06/2025

Letter from the Guest Editors

Liang, Ci, Törngren, Martin

Road Noise Analysis using Component TPA and FBS with Vehicle Component Modifications

2025-01-0055

05/05/2025

Road noise caused by road excitation is a critical factor for vehicle NVH (Noise, Vibration, and Harshness) performance. However, assessing the individual contribution of components, particularly bushings, to NVH performance is generally challenging, as automobiles are composed of numerous interconnected parts. This study describes the application of Component Transfer Path Analysis (CTPA) on a full vehicle to provide insights into improving NVH performance. With the aid of Virtual Point Transformation (VPT), blocked forces are determined at the wheel hubs; afterward, a TPA is carried out. As blocked forces at the wheel hub are independent of the vehicle dynamics, these forces can be used in simulations of modified vehicle components. These results allow for the estimation of vehicle road noise. To simulate changes in vehicle components, including wheel/tire and rubber bushings, Frequency-Based Substructuring (FBS) is used to modify the vehicle setup in a simulation model. In this

Kim, Jungu, Reichart, Ron, de Klerk, Dennis, Schütler, Willem, Malic, Mario, Kim, Hyeongjun, Kim, Uije

Investigation and Application of Tire Model to Optimize Road Noise

2025-01-0111

05/05/2025

Tires have a significant impact on vehicle road noise. The noise in 80~160Hz is easily felt when driving on rough roads and has a great relationship with the tire structural design. How to improve the problem through tire simulation has become an important issue. Therefore, this paper puts forward the concept of virtual tire tuning to optimize the noise. An appropriate tire model is crucial for road noise performance, and the CDtire (Comfort and Durability Tire) model was used in the article. After conducting experimental validation to get an accurate tire model, adjust the parameters and structure of the tire model to generate alternative model scenarios. The transfer function of the tire center was analyzed and set as the evaluation condition for tire NVH (Noise, vibration, and harshness) performance. This enabled a comparison among various model scenarios to identify the best-performing tire scenario in focused frequency whose transfer function needed to be lowest. Manufacture the

Zhang, Ben, Yu Sr, Jing, Chen, Qimiao, Liu, Xianchen, Gu, Perry

Objectifying Visual Perception for Automotive Screens through Vibration Targets

2025-01-0043

05/05/2025

New mobility concepts with smart infrastructure have led to enhanced customer driving experience. The potential to develop safe cars with minimal driver intervention is a great need of the future. The cusp for fully autonomous driving has produced much technical talk, which has led to faster transition and adoption. One of the features that global OEMs have tried to focus on, is Human Machine Interface (HMI) solutions, popularly called display screens. The touchscreen HMIs are common in all mid-range budget cars. They offer driver support beyond just streaming music, including inputs for navigation, parking assistance, in-car technologies, Advanced Driver Assistance Systems (ADAS), and infotainment. Poor display screen visibility is a phenomenon observed when a vehicle is driven over different road surfaces. This paper presents a user-centric approach for the right design & development of the HMI for a vibration free driving experience. The mounting strategies for the display screens

Adil, MD Shahzad, C M, Mithun, Mohammed, Riyazuddin, R, Prasath

SAE TOMORROW TODAY - Scalable AV Deployment Starts with Fewer Close Calls

1351305/01/2025

The hallmark of exceptional autonomous driving technology isn't just how it reacts in a crisis but how it avoids one altogether. That's the vision behind May Mobility: a world where self-driving cars confidently navigate busy intersections, unexpected detours, and pedestrian-filled crosswalks with the instincts of a seasoned human driver. At the core of May Mobility's technology platform is its patented Multi-Policy Decision Making (MPDM) system. This breakthrough technology uses real-time, in-situ AI to interpret data, continuously learning and adapting to new, complex, and unpredictable driving conditions. By learning on the fly--much like a human driver--May Mobility's AVs can be deployed faster and more cost-effectively than traditional systems. To explore how May Mobility is scaling its AV technology, we spoke with Ed Olson, CEO and Founder, about the company's city-wide AV deployments, strategic partnerships with Toyota and NTT, and its entrance into the rideshare market through

Hineman, Marcie

A Comprehensive Review of Vehicle and Road Condition Estimation Techniques

10-09-02-0014

04/30/2025

This article reviews the key physical parameters that need to be estimated and identified during vehicle operation, focusing on two key areas: vehicle state estimation and road condition identification. In the vehicle state estimation section, parameters such as longitudinal vehicle speed, sideslip angle, and roll angle are discussed, which are critical for accurately monitoring road conditions and implementing advanced vehicle control systems. On the other hand, the road condition identification section focuses on methods for estimating the tire–road friction coefficient (TRFC), road roughness, and road gradient. The article first reviews a variety of methods for estimating TRFC, ranging from direct sensor measurements to complex models based on vehicle dynamics. Regarding road roughness estimation, the article analyzes traditional methods and emerging data-driven approaches, focusing on their impact on vehicle performance and passenger comfort. In the section on road gradient

Chen, Zixuan, Duan, Yupeng, Wu, Jinglai, Zhang, Yunqing

Advanced Air Mobility and eVTOL: Vol. 3

EPRCOMPV102024

04/30/2025

Qian, Yuping, Zhang, Yangjun, ZHUGE, WEILIN, Doo, Johnny

Infrastructure Enablers for Automation: Vol. 3

EPRCOMPV042024

04/30/2025

Coyner, Kelley, Bittner, Jason

Additive Manufacturing in the Mobility Industry: Vol. 4

EPRCOMPV012024

04/30/2025

Phillips, Paul, Slattery, Kevin, Coyne, Jennifer, Hayes, Michael

SAE TOMORROW TODAY BRIEFS - Scaling Software-Defined EV Fleets

1351204/25/2025

When it comes to electrifying commercial vehicle fleets, one company offers a transformative modular platform that can drastically reduce the time to market. REE Automotive is a publicly traded company that designs and manufactures advanced software-defined vehicles (SDVs) that are fully electric, capable of autonomy, and compatible with existing fleet management software. The company recently began production on its first medium-duty truck for the US market and has since seen a 230% growth in reservations, indicating strong customer interest and positive feedback. To learn more, Roberto Baldwin, Sustainability Editor, SAE Sustainable Mobility Solutions, recently sat down with Daniel Barel, CEO, and Pete Dow, VP of Engineering, to discuss REE Automotive's revolutionary approach to scaling software-defined commercial EV fleets. For more information on the evolution of sustainability, head on over to sustainablecareers.sae.org. There, you can check out our podcast on the state of NEVI

Hineman, Marcie

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

12-09-01-0003

04/24/2025

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 detection

Ning, Qianjia, Zhang, Huanhuan, Cheng, Kehan

A Novel Model for Short-Term Traffic Flow Prediction on Highways Utilizing Multi-Head Attention–BiLSTM–BiGRU

12-09-01-0002

04/24/2025

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 ablation

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

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 transform

Ma, Changxi, Jin, Renzhe

Automated Vehicles and Infrastructure Enablers: Dedicated Lanes

EPR2025007

04/24/2025

Dedicated lanes provide a simpler operating environment for ADS-equipped vehicles than those shared with other roadway users including human drivers, pedestrians, and bicycles. This final report in the Automation and Infrastructure series discusses how and when various types of lanes whether general purpose, managed, or specialty lanes might be temporarily or permanently reserved for ADS-equipped vehicles. Though simulations and economic analysis suggest that widespread use of dedicated lanes will not be warranted until market penetration is much higher, some US states and cities are developing such dedicated lanes now for limited use cases and other countries are planning more extensive deployment of dedicated lanes. Automated Vehicles and Infrastructure: Dedicated Lanes includes a review of practices across the US as well as case studies from the EU and UK, the Near East, Japan, Singapore, and Canada. Click here to access the full SAE EDGETM Research Report portfolio.

Coyner, Kelley, Bittner, Jason

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.

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

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

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

Transforming Respiratory Imaging: Mapping Airway Cilia in Real Time

TBMG-52851

04/01/2025

Cilia, small, slender, hair-like structures present on the surface of all mammalian cells, play a major role in locomotion and are involved in mechanoreception. Ciliary motion in the upper airway is the primary mechanism by which the body transports foreign particulates out of the respiratory system to maintain proper respiratory function.

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

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

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

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

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

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

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

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

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

2025-01-8271

04/01/2025

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

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

Development of Prediction Model for Vehicle Road Load Using Machine Learning

2025-01-8258

04/01/2025

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

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

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

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

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