In this study, an initial approach using deep reinforcement learning to replicate the complex behaviors of motorcycle riders was presented. Three learning examples were demonstrated: following a target velocity, maintaining stability at low speeds, and following a target trajectory. These examples serve as a starting point for further research. Additionally, the proficiency of the constructed models was examined using rider proficiency evaluation methods developed in previous studies. Initial results indicated that the models have the potential to mimic real rider behaviors; however, challenges such as differences between the model’s output and what humans can produce were also identified for future work.

Mitsuhashi, Yasuhiro, Momiyama, Yoshitaka, Yabe, Noboru

An incremental coordinated control method for distributed drive electric vehicle through the anti-squat/lift/dive suspension

2025-01-8267

04/01/2025

In this paper, an incremental coordinated control method through anti-squat/lift/dive suspension is proposed based on and suited to a distributed drive electric vehicle with front and rear dual motors. The precise relationship between the suspension reaction force and the driving force of the wheel is derived as the control model through an in-depth analysis of the wheel motion and force. Through imposing the first-order dynamics, the proposed method not only provides the longitudinal speed control of the vehicle but also suppresses the longitudinal, vertical and pitch vibration of the vehicle. Simulation results show that the suspension reaction force formula derived in this paper is more suitable for dynamic conditions, and compared with the control method based on the simplified suspension anti-squat/lift/dive control model, the proposed method using the accurate control model has superior comprehensive control performance.

Feng, Cong, Wu, Guangqiang, Yang, Yuchen

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

A Novel Hybrid Approach for Segmenting Drivers Based on Their Driving Style

2025-01-8205

04/01/2025

Drivers present diverse landscapes with their distinct personalities, preferences, and driving habits influenced by many factors. Though drivers' behavior is highly variable, they can exhibit clear patterns that make sorting them into one category or another possible. Discrete segmentation provides an effective way to categorize and address the differences in driving style. The segmentation approach offers many benefits, including simplification, measurement, proven methodology, customization, and safety. Numerous studies have investigated driving style classification using real-world vehicle data. These studies employed various methods to identify and categorize distinct driving patterns, including naturalist differences in driving and field operational tests. This paper presents a novel hybrid approach for segmenting driver behavior based on their driving patterns. We leverage vehicle acceleration data to create granular driver segments by combining event and trip-based methodologies

Chavan, Shakti Pradeep, Chinnam, Ratna Babu

Research on Pedal Operation Characteristics for Detecting Pedal Misapplication

2025-01-8675

04/01/2025

Drivers sometimes operate the accelerator pedal instead of the brake pedal due to driver error, which can potentially result in serious accidents. To address this, the Acceleration Control for Pedal Error (ACPE) system has been developed. This system detects such errors and controls vehicle acceleration to prevent these incidents. The United Nations is already considering regulations for this technology. This ACPE system is designed to operate at low speeds, from vehicle standstill to creep driving. However, if the system can detect errors based on the driver's operation of the accelerator pedal at various driving speeds, the system will be even more effective in terms of safety. The activation threshold of ACPE is designed to detect operational errors, and it is necessary to prevent the system from being activated during operational operations other than operational errors, i.e., false activation. This study focuses on the pedal operation characteristics of pedal stroke speed and

Natsume, Hayato, Shen, Shuncong, Hirose, Toshiya

Objective and Subjective Evaluation of the Interaction of Automated Vehicles and Human Drivers in Take-Over Scenarios

2025-01-8048

04/01/2025

The research activity aims at defining specific Operational Design Domains (ODDs) representative of Italian traffic environments. The paper focuses on the human-machine interaction in Automated Driving (AD), with a focus on take-over scenarios. The study, part of the European/Italian project “Interaction of Humans with Level 4 AVs in an Italian Environment - HL4IT”, describes suitable methods to investigate the effect of the Take-Over Request (TOR) on the human driver’s psychophysiological response. The DriSMI dynamic driving simulator at Politecnico di Milano has been used to analyse three different take-over situations. Participants are required to regain control of the vehicle, after a take-over request, and to navigate through a urban, suburban and highway scenario. The psychophysiological characterization of the drivers, through psychological questionnaires and physiological measures, allows for analyzing human factors in automated vehicles interactions and for contributing to

Gobbi, Massimiliano, Boscaro, Linda, De Guglielmo, Veronica, Fossati, Andrea, Galbiati, Andrea, Mastinu, Leda, Ponti, Marco, Mastinu, Gianpiero, Previati, Giorgio, Sabbioni, Edoardo, Signorini, Maria Gabriella, Somma, Antonella, Subitoni, Luca, Uccello, Lorenzo

Regression Model based Method for Stiffness Design of Laminated Glasses

2025-01-8320

04/01/2025

This paper presents a new regression model-based method for accurate predictions of stiffness of different glass laminate constructions with a point-load bending test setup. Numerical FEA models have been developed and validated with experimental data, then used to provide training data required for the statistical model. The multi-variable regression method considered six input variables of total glass thickness, thickness ratio of glass plies as well as high-order terms. Highly asymmetrical, hybrid laminates combining a relatively thick soda-lime glass (SLG) ply joined with a relatively thin Corning® Gorilla® Glass (GG) ply were analyzed and compared to standard symmetrical SLG-SLG constructions or a monolithic SLG with the same total glass thickness. Both stiffness of the asymmetrical laminates and the improvement percentage over the standard symmetrical design can be predicted through the model with high precision.

Yu, Chao, Cleary, Thomas, Joubaud, Laurent, kister, Evan, Fisher, W Keith

Reproduction of Real-World Scenarios in CARLA: An Extension of CARLA Functionality

2025-01-8059

04/01/2025

Reproducing driving scenarios involving near-collisions and collisions in a simulator can be useful in the development and testing of autonomous vehicles, as it provides a safe environment to explore detailed vehicular behavior during these critical events. CARLA, an open-source driving simulator, has been widely used for reproducing driving scenarios. CARLA allows for both manual control and traffic manager control (the module that controls vehicles in autopilot manner in the simulation). However, current versions of CARLA are limited to setting the start and destination points for vehicles that are controlled by traffic manager, and are unable to replay precise waypoint paths that are collected from real-world collision and near-collision scenarios, due to the fact that the collision-free pathfinding modules are built into the system. This paper presents an extension to CARLA’s source code, enabling the replay of exact vehicle trajectories, irrespective of safety implications

Ai, Yan, Adhikari, Bikram, Park, Chung-Kyu, Kan, Cing-Dao, Wijesekera, Duminda

Using CAN Electrical Signals for Cybersecurity and Harness Diagnostics

2025-01-8080

04/01/2025

This paper describes a novel invention which is an Intrusion Detection System based on fingerprints of the CAN bus analogue features. Clusters of CAN message analogue signatures can be associated with each ECU on the network. During a learning mode of operation, fingerprints can be learnt with the prior knowledge of which CAN identifier should be transmitted by each ECU. During normal operation, if the fingerprint of analogue features of a particular CAN identifier does not match the one that was learnt then there is a strong possibility that this particular CAN identifier’s message is symptomatic of a problem. It could be that the message has been sent by either an intruder ECU or an existing ECU has been hacked to send the message. In this case an intruder can be defined as a device that has been added to the CAN bus OR a device that has been hacked/manipulated to send CAN messages that it was not designed to (i.e. could be originally transmitted by another device). It could also be

Quigley, Christopher, Charles, David

The Effect of Directional Tactile Guidance on Takeover Performance in Level 3 Automated Driving

2025-01-8049

04/01/2025

Automated driving is an important development direction of the current automotive industry. Level 3 automated driving allows the driver to perform non-driving related tasks (NDRTs) during automated driving, however, once the operating conditions exceed the designed operating domain, the driver is still required to take over. Therefore, it is important to rationally design takeover requests (TORs) in Level 3 conditional automated driving. This paper investigates the effect of directional tactile guidance on driver takeover performance in emergency obstacle avoidance scenarios during the transfer of control from automated driving mode to manual driving. 18 participants drove a Level 3 conditional automated driving vehicle in a driving simulator on a two-way four-lane urban road, performed a takeover, and avoided obstacles while performing non-driving related tasks. The driver's takeover performance during the takeover process was measured and subjective driver evaluation data was

Liang, Xinying, Liang, Yunhan, Ma, Xiaoyuan, Wang, Luyao, Chen, Guoying, Hu, Hongyu

Driving under the Influence: A Review of Fitness to Drive, Drug-Induced Impairments, and Simulation Testing

2025-01-8666

04/01/2025

This literature review examines the concept of Fitness to Drive (FTD) and its impairment due to drug consumption. Using a Systematic Literature Review (SLR) methodology, the paper analyzes literature from mechanical engineering and related fields to develop a multidisciplinary understanding of FTD. Firstly, the literature is analysed to provide a definition of FTD and collect methods to assess it. Secondly, the impact of drug use on driving performance is emphasized. Finally, driving simulators are presented as a valid possibility for analysing such effects in a safe, controlled and replicable environment. Key findings reveal a lack of a comprehensive taxonomy for FTD, with various assessment protocols in use. Only static simulators are employed for drug evaluation, limiting realism and result reliability. Standard Deviation of Lane Position (SDLP) emerges as a gold-standard measure for assessing driver performance. Future research should focus on developing standard definitions for

Uccello, Lorenzo, Nobili, Alessandro, Pasina, Luca, Novella, Alessio, Elli, Chiara, Mastinu, Gianpiero

Use of Vehicle Cyber Engineering (VCE) Testbeds to Develop AI Safety Enhancements Based on ISO TR 5469

2025-01-8090

04/01/2025

The ISO TR 5469 Technical Report provides a framework to classify the AI/ML technology based on usage level and the properties and requirements to mitigate cyber and functional safety risks for the technology. This paper provides an overview of the approach used by ISO TR 5469 as well as an example of how one of the six ISO TR 5469 desirable properties (resilience to adversarial and intentional malicious input) can be analyzed for adversarial attacks. This paper will also show how a vehicle testbed can be used to provide a student with an AI model that can be used to simulate a non-targeted cyber security attack. The testbed can be used to simulate a poisoning attack where the student can manipulate a training data set to deceive the AI model during a simulated deployment.1 The University of Detroit Mercy (UDM) has developed Cyber-security Labs as a Service (CLaaS) to support teaching students how to understand and mitigate cyber security attacks. The UDM Vehicle Cyber Engineering (VCE

Zachos, Mark, Seifert, Heinz

Highly Accurate Machine Learning Models for Automotive Crash Applications Using CAE Centric AI/ML Platform

2025-01-8719

04/01/2025

In the automotive industry, there have been many efforts of late in using Machine Learning tools to aid crash virtual simulations and further decrease product development time and cost. As the simulation world grapples with how best to incorporate ML techniques, two main challenges are evident. There is the risk of giving flawed recommendations to the design engineer if the training data has some suspect data. In addition, the complexity of porting simulation data back and forth to a Machine Learning software can make the process cumbersome for the average CAE engineer to set up and execute a ML project. We would like to put forth a ML workflow/platform that a typical CAE engineer can use to create training data, train a PINN (Physics Informed Neural Network) ML model and use it to predict, optimize and even synthesize for any given crash problem. The key enabler is the use of an industry first data structure named mwplot that can store diverse types of training data - scalars, vectors

Krishnan, Radha

Objective System Level Sustainability Evaluation of Competing Materials for Demanding Automotive Applications

2025-01-8607

04/01/2025

The authors will present findings from their cradle-to-cradle Product Carbon Footprint (PCF) study which captures an objective and comprehensive system level evaluation of the greenhouse gas (GHG) footprint of four different material types used in the same automotive application: Unsaturated Polyester Resin (UPR) SMC, steel, aluminum and glass fiber reinforced polypropylene (PP-GF). This study includes the simulation driven design of four mid-sized pickup boxes which were designed according to automotive requirements and relevant design guidelines for each material. OEM experts were consulted to validate the relevant specifications and boundary conditions. The technical paper includes details on the geometric design, simulation, production processes, life cycle and environmental impact assessment all in compliance with ISO standards (14040/14044) for the Cradle-to-Cradle PCF. This paper provides guidance and insights to help engineers develop effective strategies for material selection

Halsband, Adam, Leinemann, Tomke, Beer, Markus, Haiss, Eric

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

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

System Configuration and Validation of Hybrid-Based Six Degrees of Freedom Motion Platform for Vehicle Dynamic Testing

2025-01-5021

03/27/2025

Scenario-based testing has become one of the important elements to evaluate the performance of automated vehicle systems before deploying on actual road. There are several approaches that can be used to conduct scenario-based testing via simulation approach. One of the important aspects in scenario-based safety testing is the driver-in-the-loop (DiL) simulation where it involves integration of hardware and human interaction. Therefore, motion platform-based vehicle driving simulators are commonly used for the DiL simulation for scenario-based testing. Generally, a high degree of freedom driving simulator is used for scenario-based testing such as 6 degrees of freedom (DoF) to achieve high accuracy to represent an actual vehicle response. Moreover, most of the motion platforms are designed using hexapod configuration, which also contributes to 6-DoF. However, this type of design requires large space to conduct the testing because the field of motion (FoM) is high in three axes and high

Kleolee, KahOnn, Aparow, Vimal Rau, Cheok, Jun Hong, de Boer, Niels, Jamaluddin, Hishamuddin

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

Experimental Methodology for Tire Ride Analysis Based on Outdoor Cleat Testing

10-09-03-0023

03/21/2025

Handling and ride comfort optimization are key vehicle design challenges. To analyze vehicle performance and investigate the dynamics of the vehicle and its subcomponents, we rely heavily on robust experimental data. The current article proposes an outdoor cleat test methodology to characterize tire dynamics. Compared to indoor procedures, it provides an effective tire operating environment, including the suspensions and the vehicle chassis motion influence. In addition, it overcomes the main limitation of existing outdoor procedures, the need for dedicated cleat test tracks, by using a set of removable cleats of different sizes. A passenger vehicle was equipped with sensors including an inertial measurement unit, a noncontact vehicle speed sensor, and a wheel force transducer, providing a setup suitable to perform both a handling test routine and the designed cleat procedure, aimed at ride testing and analysis. Thus, the outdoor cleat test data were compared with indoor test

Gravante, Gerardo, Napolitano Dell’Annunziata, Guido, Barbaro, Mario, Farroni, Flavio

Tunnel Lining Crack Intelligent Recognition Based on YOLOv11 Algorithm

2025-01-7137

02/21/2025

Tunnel linings are an important safeguard for the integrity and stability of tunnels. However, cracks in the tunnel lining may have extremely unfavourable consequences. With the acceleration of urbanisation and the increasing construction of tunnels, the problem of cracks in the concrete lining is becoming more and more prominent. These cracks not only seriously affect the stability of the structure, but also pose a serious threat to the safety of tunnel operation. If left unchecked, the cracks may expand further and cause various safety hazards, such as water leakage and falling blocks. This in turn will undermine the normal function of the tunnel and endanger the lives of tunnel users. It has been proved that the traditional manual method of detecting cracks in tunnels has problems such as low accuracy and low efficiency. In order to solve this problem, it is very necessary for this study to pioneer an intelligent method for identifying tunnel lining cracks using the YOLOv11

Zhang, Yalin, Niu, Pei, Guo, Feng, Yan, Wei, Liu, Jian, Kou, Lei

Uncertainty Quantification for Predicting Low-Temperature Crack Resistance of Asphalt Mixture Base on Bayesian Neural Network

2025-01-7143

02/21/2025

Developing models for predicting the low-temperature cracking resistance of asphalt mixtures is a complex process with a wide variety and complex influence mechanisms of variables, leading to higher uncertainty in the prediction results. Several models have been developed in this regard. This study developed a Bayesian neural network (BNN) model for predicting the fracture energy of low-temperature semi-circular bending (SCB) tests based on pavement condition measurements, traffic, climate, and basic parameters of the material. The model was trained and evaluated using low-temperature SCB test data from in-situ pavement core samples, and the results showed that the coefficient of determination (R2) of the BNN model was greater than 0.8 for both the training and testing sets. The variable importance scores showed that the decrease of transverse crack rating index (TCEI) and gradation were the most important factor affecting low-temperature fracture energy and that the ambient

Song, Ziyu, Ni, Fujian, Huang, Jiaqi, Jiang, Jiwang

Intelligent Courier Delivery Scheduling Based on XGBoost Feature Selection and Reinforcement Learning: A Case Study Using Chongqing Cainiao Express Data

2025-01-7176

02/21/2025

This study tackles the issue of order delays in logistics using XGBoost for feature analysis and reinforcement learning for intelligent courier scheduling. Pickup order data from May 1 to October 31, 2023, in Chongqing is analyzed using spatio-temporal statistical methods. Key findings include that order placement peaks at 9:00 a.m., delays peak at 10:00 a.m., and the delay rate is 8.6%. A significant imbalance exists between the regional daily average of dispatchable couriers and order volumes.XGBoost is employed to predict order delays, revealing that pickup location is the most influential factor (27%), followed by courier pickup location (22%). These factors and their relationships are identified as key drivers of delays.To address these issues, a reinforcement learning-based courier scheduling optimization model is developed. The model defines courier location, current time, and pending orders as state variables and adopts an epsilon-greedy strategy for action selection

Wang, Manjun, Yu, Xinlian

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

2025-01-7136

02/21/2025

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

Wang, Junjun, Zhao, Chihang

Semantic Segmentation of Lane Lines for Pix2Pix Network Introducing Ghost Module

2025-01-7208

02/21/2025

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

Yang, Kun, Wang, Jian

Driver Cognitive Distraction Recognition Based on Multi-Source Data from Simulated Driving Experiments

2025-01-7140

02/21/2025

Nowadays, cognitive distraction in the process of driving has become a frequent phenomenon, which has led to a certain proportion of traffic accidents, causing a lot of property losses and casualties. Since the fact that cognitive distraction is mostly reflected in the driver's reception and thinking of information unrelated to driving, it is difficult to recognize it from the driver's facial features. As a result, the accuracy of prediction is usually lower relying solely on facial performance to detect cognitive distraction. In this research, fifty participants took part in our simulated driving experiment. And each participant conducted the experiment in four different traffic scenarios using a high-fidelity driving simulator, including three cognitive distraction scenarios and one normal driving scenarios. Firstly, we identified the facial performance indicators and vehicle performance indicators that had a significant effect on cognitive distraction through one-way ANOVA. Then we

Qu, Chixiong, Bao, Qiong, Qu, Qikai, Shen, Yongjun

Validating Human Factors-Based Classification Models for Safe and Efficient Mixed-Autonomy Interactions at Intersections Using a Virtual Simulator

12-08-02-0019

02/12/2025

The introduction of autonomous vehicles (AVs) promises significant improvements to road safety and traffic congestion. However, mixed-autonomy traffic remains a major challenge as AVs are ill-suited to cooperate with human drivers in complex scenarios like intersection navigation. Specifically, human drivers use social cooperation and cues to navigate intersections while AVs rely on conservative driving behaviors that can lead to rear-end collisions, frustration from other road users, and inefficient travel. Using a virtual driving simulator, this study investigates the use of a human factors-informed cooperation model to reduce AV reliance on conservative driving behaviors. Four intersection scenarios, each involving a left-turning AV and a human driver proceeding straight, were designed to obfuscate the right-of-way. The classification models were trained to predict the future priority-taking behavior of the human driver. Results indicate that AVs employing the human factors-informed

Ziraldo, Erika, Oliver, Michele

A reduced-order beam modelling approach of BIW for torsional and bending stiffness optimization

2025-28-0187

02/07/2025

Noise, Vibration, and Harshness (NVH) simulations of vehicle bodies are crucial for assessing performance during the design phase. However, these simulations typically require detailed computer-aided design (CAD) models and are time-consuming. In the early stages of vehicle development, when only high-level vehicle sections are available, designing the body-in-white (BIW) structure to meet target values for bending and torsional stiffness is challenging and often requires multiple iterations. To address these challenges, this study deploys a reduced-order beam modelling approach. This method involves identifying the beam-like sections and major joints within the BIW and calculating their sectional properties (area, area moments of inertia along the plane’s independent axes, and torsion constant). These components form a simplified skeleton model of the BIW. Load and boundary conditions are applied to the suspension mount locations at the front and rear of the vehicle, and torsional and

Khan, Mohd Zishan Ali, Thanapati, Alok, Deshmukh, Chandrakant

Aerospace & Defense Technology: February 2025

25AERP0202/06/2025

The Art of Reverse Engineering Yesteryear for Aerospace and Defense Data Storage Drives Manufacturing Spotlight: Machining Complex Parts and Materials for Space Flight and Exploration Notre Dame's New Boundary Breaking Mach 10 Quiet Wind Tunnel Shape-Shifting Antenna Poised to Transform Communications British Army Completes First Test of Drone Killing Radio Frequency Weapon A Swarm of Sensors, Rovers and Astronauts Explore the 'Moon' Researchers at the German Aerospace Center have developed a new approach to networked communications that could theoretically occur on the Moon or in environments on Earth where conventional communications are unavailable. Army Researchers Examine Nanotechnology for Climate Solutions Army scientists are joining forces with experts from across the nation to tackle the climate crisis using the power of nanotechnology Airman Brings Fuel Sampling Innovation to Eglin, Possibly Air Force The U.S. Air Force is testing a new prototype single point nozzle adapter

Thermal Parameters Identification Method for Induction Machines Based on a Parallel Branches Network

2025-01-7054

01/31/2025

Monitoring the rotor temperature of drive machines is crucial for the safety and performance of electric vehicles. However, due to the complex operating conditions of electric vehicles, the thermal parameters of vehicular induction machines (IMs) vary significantly and are difficult to identify accurately. This article first establishes a concise but effective thermal network for IMs and analyzes the influencing factors of thermal parameters. Then, a parameter identification network (PIN) with multiple parallel branches is constructed to learn the mapping relationship between electromechanical variables and thermal parameters. Afterward, temperature datasets for network training are built through bench testing. Finally, the effectiveness of identified parameters for rotor temperature estimation application is verified, demonstrating improved interpretability, generalization ability, and accuracy compared to an end-to-end neural network.

Jiang, Shang, Hu, Zhishuo

Multi-Criteria Evaluation and Operating Parameter Optimization of the SOFC-GT System under Variable Loads

2025-01-7077

01/31/2025

At present, due to the complexity and nonlinearity, the thermal safety and economic feasibility assessment and optimization of the Solid Oxide Fuel Cell-Gas Turbine (SOFC-GT) system under variable loads is important to extend the service life and reduce the cost. To solve these problems, this paper proposes a top-level cyclic SOFC-GT system, which considers the design of two-stage preheaters, as well as the impact of material reaction kinetics and thermoelectric coupling characteristics on system performance. Furthermore, the multi-criteria evaluation of the SOFC-GT system under variable loads has been studied, with evaluation indicators primarily including thermodynamic and economic indicators. Afterwards, a Spearman-based parametric sensitivity analysis is used to explore the response trends of performance indicators within the SOFC-GT system. Additionally, an intelligent learning method based on convolutional neural network is designed to determine the dynamic behavior between

Fan, Liyun, Kui, Xu, Chen, Chen, Shen, Chongchong, Li, Bo, Wei, Yunpeng

Research on Fuel Cell Vehicle Performance Based on PEMFC Stack Degradation

2025-01-7072

01/31/2025

The degradation of vehicle performance resulting from powertrain degradation throughout the lifecycle of alternative energy vehicles (AEVs) has consistently been a focal issue among scholars and consumers. The purpose of this paper is to utilize a one-dimensional vehicle simulation model to analyze the changes in power performance and economy of fuel cell vehicles as the Proton Exchange Membrane Fuel Cell (PEMFC) stack degrades. In this study, a simulation model was developed based on the design parameters and vehicle architecture of a 45kW fuel cell vehicle. The 1D model was validated for accuracy using experimental data. The results indicate that as the stack performance degrades, the attenuation rate of the fuel cell engine is further amplified, with a degradation of up to 13.6% in the system's peak output power at the End of Life (EOL) state after 5000 hours. Furthermore, the level of economic performance degradation of the complete vehicle in the EOL state is dependent on the

Li, You, Du, Jing, Guo, Donglai, Wang, Kai, Wang, Yupeng

Driving Cycle Simulation and Performance Comparison of Hydrogen Engine Hybrid Electric Vehicle

2025-01-7001

01/31/2025

As a clean energy, low carbon and pollution-free, hydrogen is the preferred alternative fuel for traditional internal combustion engines. However, how to use hydrogen internal combustion engine to achieve satisfactory performance under vehicle conditions is still a challenge.In this paper, a vehicle simulation model is established based on a modified 25-ton hydrogen internal combustion engine truck, and the model is designed as a hybrid model by selecting a suitable motor. The two models are used to simulate the CHTC (China Heavy-duty Commercial Vehicle Test Cycle) cycle conditions. According to the simulation results, compared with the original vehicle's power performance and economy, the results show that the power performance is increased by 100%, and the economy is increased by 20%. Hybrid technology can effectively improve the performance of the vehicle.

Bai, Xueyan

SAE TOMORROW TODAY: Launching Human Space Travel with a Luxury Balloon

1349801/23/2025

Imagine a six-hour journey to outer space that includes a world-class meal, cocktail service, Wi-Fi, and unprecedented views of Earth. This experience could soon become reality. Space Perspective, the world's first carbon-neutral spaceflight experience company, is commercializing space travel with Spaceship Neptune, a pressurized capsule propelled by a giant SpaceBalloon(TM). The experience is designed to be gentle and accessible with no rockets, weightlessness, heavy g-forces, or training required. Alongside world-class meal and cocktail service, space travelers will also enjoy views through the largest windows ever flown to space, Wi-Fi, and a proper restroom. To learn more, we sat down with Taber MacCallum, Founder and Chief Technology Officer, Space Perspective, to discuss the technical development of the SpaceBalloon(TM) and how the company is unlocking the market for space travel in a new and innovative way. We'd love to hear from you. Share your comments, questions and ideas for

Hineman, Marcie

Simulation Analysis of Multi-Objective Functions in Mobile Robot Navigation Based on Enhanced Deep Q-Network Algorithm

2024-01-5110

12/24/2024

This research, path planning optimization of the deep Q-network (DQN) algorithm is enhanced through integration with the enhanced deep Q-network (EDQN) for mobile robot (MR) navigation in specific scenarios. This approach involves multiple objectives, such as minimizing path distance, energy consumption, and obstacle avoidance. The proposed algorithm has been adapted to operate MRs in both 10 × 10 and 15 × 15 grid-mapped environments, accommodating both static and dynamic settings. The main objective of the algorithm is to determine the most efficient, optimized path to the target destination. A learning-based MR was utilized to experimentally validate the EDQN methodology, confirming its effectiveness. For robot trajectory tasks, this research demonstrates that the EDQN approach enables collision avoidance, optimizes path efficiency, and achieves practical applicability. Training episodes were implemented over 3000 iterations. In comparison to traditional algorithms such as A*, GA

Arumugam, Vengatesan, Alagumalai, Vasudevan, Rajendran, Sundarakannan

Augmenting Airworthiness Education via Concept Maps: An Exploratory Study among University Students

01-18-01-0001

12/24/2024

Aerospace engineering programmes typically cover airworthiness philosophies, principles, structures, processes, and procedures. The industry has recently recognized the need to enhance the graduate engineers’ skills around airworthiness. This has led to introduction of standards acting as guides for developing curricula and content for university airworthiness courses. Concept maps, a visual mapping of concepts in a hierarchical way, enjoy wide use in engineering education (teaching and assessment). Airworthiness courses are both technical and legalistic, presenting challenges to students when it comes to understanding complex and intertwined regulations. Schematic representations of concepts can foster the cognitive processes of learning. Concept maps can assess efficiently and comprehensively a multitude of airworthiness topics. This study examines the feasibility of applying concept maps in airworthiness education. Fill-in-a-map concept maps were developed as assessment tools for an

Kourousis, Kyriakos, Chatzi, Anna

Numerical Simulation of Drive Cycles for Formula Electric Vehicle Comparative Powerflow Analysis

2024-36-0097

12/20/2024

This paper proposes a theoretical drive cycle for the competition, considering the battery pack project under design. The vehicle has a non-reversible, double-stage gear train, created without a dynamic investigation. To evaluate the effect on performance, several ratios were analyzed. Dynamic model uses Eksergian’s Equation of Motion to evaluate car equivalent mass (generalized inertia), and external forces acting on the vehicle. The circuit is divided into key locations where the driver is likely to accelerate or brake, based on a predicted behavior. MATLAB ODE Solver executed the numerical integration, evaluating time forward coordinates, creating the drive cycle. Linear gear train results provided data as boundary conditions for a second round of simulations performed with epicyclic gear trains. Model is updated to include their nonlinearity by differential algebraic equation employment with Lagrange multipliers. All data undergoes evaluation to ascertain the mechanical and

Rodrigues, Patrícia Mainardi Tortorelli, Silveira, Henrique Leandro

Brake Pedal Sizing and Preliminary Design of Balance Bar in the Brake of a SAE Formula Type Vehicle

2024-36-0054

12/20/2024

The SAE Formula prototypes are developed by students, where in the competition, various aspects of project definitions are evaluated. Among the factors evaluated for scoring is the braking system, in which the present work aims to present the development and design of the braking system of a vehicle, prototype of Formula SAE student competition. As it is a project manufactured mostly by students, where the chassis, suspension system, electrical, transmission and powertrain are developed, it is important to first pass the static and safety tests, where the brakes of the four wheels are tested during deceleration at a certain distance from the track. To enable such approval and also to demonstrate, for the competition judges, the veracity of the system’s sizing, all the parameters and assumptions of the choice of the vehicle’s braking system are presented, thus ensuring their reliability, efficiency and safety. Using drawing and simulation software such as SolidWorks and Excel for

Gomes, Lucas Olenski, Grandinetti, Francisco José, Martins, Marcelo Sampaio, Souza Soares, Alvaro Manoel, Reis de Faria Neto, Antônio, Castro, Thais Santos, Almeida, Luís Fernando

Development of the Steering System for a Formula SAE Prototype

2024-36-0099

12/20/2024

The SAE Formula, a national stage of the international competition, consists of a student project at universities in Brazil that seeks to encourage engineering students to apply the theoretical knowledge obtained in the classroom to practice, dealing with real problems and difficulties in order to prepare them for the job market. The SAE Formula prototype is developed with the intention of competing in the SAE national competition, where teams from various universities in Brazil meet to compete and demonstrate the projects developed during the year. Focusing on the vehicle dynamics subsystem, which can be divided into the braking, suspension, and steering systems of a prototype, the steering system includes main mechanical components such as the front axle sleeves, wheel hub, steering arm, steering column, rack, wheel, and tire. All these components work together with the suspension systems, including suspension arms, “bell crank,” and spring/shock absorber assembly. These components

Rigo, Cristiano Shuji Shimada, Neto, Antonio Dos Reis De Faria, Grandinetti, Francisco Jose, Castro, Thais Santos, Dias, Erica Ximenes, Martins, Marcelo Sampaio

653P2-5 Avionics Application Software Standard Interface, Part 2, Extended Services

ARINC653P2-5 (Current)12/16/2024

As avionics software continues to evolve, so does ARINC Specification 653. ARINC 653 Part 2 specifies extensions (i.e., optional services) to the required Application Program Interfaces (APIs) described in ARINC 653 Part 1.

Airlines Electronic Engineering Committee

Campus Bus Station Layout Planning in Mountainous Universities Based on User Needs

13-06-01-0006

12/13/2024

The planning of mountain campus bus routes needs to take into account user demand, convenience, and other factors. This study adopts a comprehensive research method that combines quantitative and qualitative viewpoints. From the perspective of university students, this article studies the demand of campus public transportation and proposes the layout of campus bus routes in mountainous universities to meet the needs of users. The psychological needs questionnaire was used to investigate college students’ expectation of bus station service function. Taking three mountain universities as examples, the integration and selectivity of campus road networks are evaluated by using space syntax analysis, which provides valuable insights into the quality of bus stop areas. This article discusses the correlation between psychological needs assessment of college students and objective conditions of campus road network. The study concludes with the following findings: (1) The pedestrian environment

Duan, Ran, Tang, Rui, Wang, Zhigang, Zhao, Yixue, Wang, Qida, Yang, Jiyi, Su, Jiafu

Human-like Behavior Strategy for Autonomous Vehicles Considering Driving Styles

2024-01-7046

12/13/2024

Autonomous driving technology plays a crucial role in enhancing driving safety and efficiency, with the decision-making module being at its core. To achieve more human-like decision-making and accommodate drivers with diverse styles, we propose a method based on deep reinforcement learning. A driving simulator is utilized to collect driver data, which is then classified into three driving styles—aggressive, moderate, and conservative—using the K-means algorithm. A driving style recognition model is developed using the labeled data. We then design distinct reward functions for the Deep Q-Network (DQN), Proximal Policy Optimization (PPO), and Soft Actor-Critic (SAC) algorithms based on the driving data of the three styles. Through comparative analysis, the SAC algorithm is selected for its superior performance in balancing comfort and driving efficiency. The decision-making models for different styles are trained and evaluated in the SUMO simulation environment. The results indicate that

Shen, Chuanliang, Zhang, Longxu, Shi, Bowen, Ma, Xiaoyuan, Li, Yi, Hu, Hongyu

Beyond Aviation: Embedded Gaming, Artificial Intelligence, Training, and Recruitment for the Advanced Air Mobility Industry

EPR2024028

12/12/2024

Recent advancements in electric vertical take-off and landing (eVTOL) aircraft and the broader advanced air mobility (AAM) movement have generated significant interest within and beyond the traditional aviation industry. Many new applications have been identified and are under development, with considerable potential for market growth and exciting potential. However, talent resources are the most critical parameters to make or break the AAM vision, and significantly more talent is needed than the traditional aviation industry is able to currently generate. One possible solution—leverage rapid advancements of artificial intelligence (AI) technology and the gaming industry to help attract, identify, educate, and encourage current and future generations to engage in various aspects of the AAM industry. Beyond Aviation: Embedded Gaming, Artificial Intelligence, Training, and Recruitment for the Advanced Air Mobility Industry discusses how the modern gaming population of 3.3 million

Doo, Johnny

Validation Methodology of Different Driver Monitoring Systems Using a Modular Motion Drive Simulator

2024-28-0263

12/05/2024

In India, Driver Drowsiness and Attention Warning (DDAW) system-based technologies are rising due to anticipation on mandatory regulation for DDAW. However, readiness of the system to introduce to Indian market requires validations to meet standard (Automotive Industry Standard 184) for the system are complex and sometimes subjective in nature. Furthermore, the evaluation procedure to map the system accuracy with the Karolinska sleepiness scale (KSS) requirement involves manual interpretation which can lead to false reading. In certain scenarios, KSS validation may entail to fatal risks also. Currently, there is no effective mechanism so far available to compare the performance of different DDAW systems which are coming up in Indian market. This lack of comparative investigation channel can be a concerning factor for the automotive manufactures as well as for the end-customers. In this paper, a robust validation setup using motion drive simulator with 3 degree of freedom (DOF) is

Raj, Prem raj Anand, Selvam, Dinesh Kumar, Thanikachalam, Ganesh, Sivakumar, Vishnu

Items per page:

1 – 50 of 6264