Browse Topic: People and personalities

Items (10,198)
In numerous automotive and industrial applications, efficient heat extraction is crucial to prevent system inefficiencies or catastrophic failures. The design of heat exchangers is inherently complex, involving multiple stages defined by the depth of analysis, number of design variables, and the accuracy of physical models. Designers must navigate the trade-offs between highly accurate yet computationally expensive models and less accurate but computationally cheaper alternatives. Multi-fidelity modeling offers a solution by integrating different fidelity models to deliver precise results at a reduced computational cost. In addition to managing these trade-offs, designers often face multi-objective challenges, where optimizing one aspect may lead to compromises in others. Multi-objective optimization, therefore, becomes essential in balancing these competing objectives to achieve the best overall design. In this context, Gaussian Process-based methods have gained prominence as
Chaudhari, PrathameshTovar, Andres
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, XinyingLiang, YunhanMa, XiaoyuanWang, LuyaoChen, GuoyingHu, Hongyu
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, LingboXu, BoyuGreen, Paul
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, MarkSeifert, Heinz
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, MassimilianoBoscaro, LindaDe Guglielmo, VeronicaFossati, AndreaGalbiati, AndreaMastinu, LedaPonti, MarcoMastinu, GianpieroPreviati, GiorgioSabbioni, EdoardoSignorini, Maria GabriellaSomma, AntonellaSubitoni, LucaUccello, Lorenzo
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, LorenzoNobili, AlessandroPasina, LucaNovella, AlessioElli, ChiaraMastinu, Gianpiero
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 PradeepChinnam, Ratna Babu
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, CongWu, GuangqiangYang, Yuchen
Automotive industry is growing rapidly with innovations leading to increase in new features and improving the Quality of vehicles. These new components are developed with the available design standards across global OEMs. This Quality research paper aims to address the need of revision of design standards due to environmental factors prevailing in India. With the increase towards autonomous mobility, the number of electronics is also increasing, and this involves hardware & software evaluation. The hardware testing is a point of concern due to increase in the failure rate from the markets. Environment changes are very much evident with the growing economies and OEMs are developing the components with innovation, but if the basic design standards are not revised in parallel with the changing environment, the issues will continue to trouble the end customers. The failed cases data received from across the country was analyzed and observed that the cases are majorly reported from urban
Marwah, RamnikPyasi, PraveenBindra, RiteshGarg, Vipin
Under extreme driving conditions, such as emergency braking, rapid acceleration, and high-speed cornering, the tire, as the vehicle’s only direct connection to the road, plays a critical role in influencing dynamic performance and driving stability. Accurately predicting and tire longitudinal force under such combined slip conditions is key to improving vehicle control precision and ensuring driving safety. This study proposes a tire longitudinal force estimation strategy based on an intelligent tire system. The core of this system consists of three integrated PVDF (Polyvinylidene Fluoride) sensors embedded in the tire, which, due to their exceptional sensitivity, can precisely capture dynamic deformation information of the tire under varying conditions. This provides real-time, detailed data to better understand the complex interaction forces between the tire and the road. To study and validate the longitudinal force estimation model, the research team employed a high-precision indoor
Zhang, ZipengXu, NanTang, ZepengChen, Hong
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, YanAdhikari, BikramPark, Chung-KyuKan, Cing-DaoWijesekera, Duminda
This paper examines the challenges and mechanisms for ensuring Freedom from Interference in Adaptive AUTOSAR-based platforms, with a focus on managing Memory, Timing, and Execution challenges. It explores the robust safety mechanisms in Classic AUTOSAR that ensure Freedom from Interference and the significant challenges in achieving interference-free operation in Adaptive AUTOSAR environments while adhering to ISO26262 standards. The study emphasizes strategies for managing complexities and outlines the multifaceted landscape of achieving interference-free operation. Additionally, it discusses ASIL-compliant Hypervisor, memory partitioning, and Platform Health Management as mechanisms for ensuring safety execution. The paper also raises open questions regarding real-time problems in live projects that are not solved with existing safety mechanisms. Adaptive AUTOSAR plays a crucial role in the development of autonomous and connected vehicles, where functional safety is of utmost
Jain, Yesha
This study evaluates the performance of alternative powertrains for Class 8 heavy-duty trucks under various real-world driving conditions, cargo loads, and operating ranges. Energy consumption, greenhouse gas emissions, and the Levelized Cost of Driving (LCOD) were assessed for different powertrain technologies in 2024, 2035, and 2050, considering anticipated technological advancements. The analysis employed simulation models that accurately reflect vehicle dynamics, powertrain components, and energy storage systems, leveraging real-world driving data. An integrated simulation workflow was implemented using Argonne National Laboratory's POLARIS, SVTrip, Autonomie, and TechScape software. Additionally, a sensitivity analysis was performed to assess how fluctuations in energy and fuel costs impact the cost-effectiveness of various powertrain options. By 2035, battery electric trucks (BEVs) demonstrate strong cost competitiveness in the 0-250 mile and 250-500 mile ranges, especially when
Mansour, CharbelBou Gebrael, JulienKancharla, AmarendraFreyermuth, VincentIslam, Ehsan SabriVijayagopal, RamSahin, OlcayZuniga, NataliaNieto Prada, DanielaAlhajjar, MichelRousseau, AymericBorhan, HoseinaliEl Ganaoui-Mourlan, Ouafae
Model-based developers are turning to DevOps principles and toolchains to increase engineering efficiency, improve model quality and to facilitate collaboration between large teams. Mature DevOps processes achieve these through automation. This paper demonstrates how integrating modern version control (Git) with collaborative development practices and automated quality enforcement can streamline workflows for large teams using Simulink. The focus is on enhancing model consistency, enabling team collaboration, and development speed.
Mathews, JonTamrawi, AhmedFerrero, SergioSauceda, Jeremias
Vehicle sideslip is a valuable measurement for ground vehicles in both passenger vehicle and racing contexts. At relevant speeds, the total vehicle sideslip, beta, can help drivers and engineers know how close to the limits of yaw stability a vehicle is during the driving maneuver. For production vehicles or racing contexts, this measurement can trigger Electronic Stability Control (ESC). For racing contexts, the method can be used for driver training to compare driver techniques and vehicle cornering performance. In a fleet context with Connected and Autonomous Vehicles (CAVS) any vehicle telemetry reporting large vehicle sideslip can indicate an emergency scenario. Traditionally, sideslip estimation methods involve expensive and complex sensors, often including precise inertial measurement units (IMUs) and dead reckoning, plus complicated sensor fusion techniques. Standard GPS measurements can provide Course Over Ground (COG) with quite high accuracy and, surprisingly, the most
Hannah, AndrewCompere, Marc
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, ChristopherCharles, David
The development of connected and automated vehicles (CAVs) is rapidly increasing in the next generation and the automotive industry is dedicated to enhancing the safety and efficiency of CAVs. A cooperative control strategy helps CAVs to collaborate and share information among the neighboring CAVs, improving efficiency, optimizing traffic flow, and enhancing safety. This research proposes a safe cooperative control framework for CAVs designed for highway merging applications. In the urban transportation system, highway merging scenarios are high-risk collision zone, and the CAVs on the main and merging lanes should collaborate to avoid potential accidents. In the proposed framework, the on-ramp CAVs merge at 40 mph within the same and opposite directions to the main lane CAVs. The proposed framework includes the consensus controller, safety filter, and quadratic programming (QP) optimization method. The consensus controller incorporates the communication between CAVs and designs the
Chang, PeiYuBhatti, SidraJaved, Nur UddinAhmed, Qadeer
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, AdamLeinemann, TomkeBeer, MarkusHaiss, Eric
In 2022, the U.S. transportation sector was the largest source of greenhouse gas emissions in the country, with the combination of passenger and commercial vehicles contributing 80% of these emissions. As adoption of passenger electric vehicles continues to climb, sights are being set on the electrification of heavy-duty commercial vehicle (HDCV) fleets. The sustainability of these shifts relies in part on the addition of significant renewable energy generation resources to both bolster the grid in the face of increased demand, and to prevent a shift in the source of greenhouse gas (GHG) emissions to the grid, as opposed to a true net reduction. Additionally, it is necessary to quantify the variations in economic viability across the country for these technologies as it pertains to their productive capabilities. Doing so will encourage investment and ensure that the transition to electrified HDCV fleets is commercially viable, as well as sustainable. In an effort to meet these goals
Miller, BrandonSun, RuixiaoSujan, Vivek
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, QuanyuRui, ZhangZhang, SongYixuan, Gao
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, ChaoCleary, ThomasJoubaud, Laurentkister, EvanFisher, W Keith
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, HayatoShen, ShuncongHirose, Toshiya
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
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, XiqiaoCui, LeqiYang, LonghaiWang, Gang
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, KahOnnAparow, Vimal RauCheok, Jun Hongde Boer, NielsJamaluddin, Hishamuddin
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, MohamedOuahi, MohamedNaami, GhaliEl Akchioui, Nabil
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, GerardoNapolitano Dell’Annunziata, GuidoBarbaro, MarioFarroni, Flavio
The advancement of autonomous driving perception frequently necessitates the aggregation of data, its subsequent annotation, the implementation of training procedures, and other related activities. In contrast, the utilisation of synthetic data obviates the necessity for data collection, annotation, and the generation of accurate and reliable labels. Its incorporation into the development process is anticipated to streamline the entire algorithmic development process. In this study, we propose a novel approach utilising the Blender software to create a virtual representation of an underground car park and develop an automated parking dataset. The utilisation of virtual simulation technology enables the generation of diverse and high-quality training data, thereby addressing the challenge of acquiring data in the actual scene. The experimental results demonstrate that the model trained based on the synthetic dataset exhibits superior performance in the automatic parking task, thereby
Li, JiakaiLiu, YangleRong, Zheng
Shared autonomous vehicles systems (SAVS) are regarded as a promising mode of carsharing service with the potential for realization in the near future. However, the uncertainty in user demand complicates the system optimization decisions for SAVS, potentially interfering with the achievement of desired performance or objectives, and may even render decisions derived from deterministic solutions infeasible. Therefore, considering the uncertainty in demand, this study proposes a two-stage robust optimization approach to jointly optimize the fleet sizing and relocation strategies in a one-way SAVS. We use the budget polyhedral uncertainty set to describe the volatility, uncertainty, and correlation characteristics of user demand, and construct a two-stage robust optimization model to identify a compromise between the level of robustness and the economic viability of the solution. In the first stage, tactical decisions are made to determine autonomous vehicle (AV) fleet sizing and the
Li, KangjiaoCao, YichiZhou, BojianWang, ShuaiqiYu, Yaofeng
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, KunWang, Jian
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, ChixiongBao, QiongQu, QikaiShen, Yongjun
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, ManjunYu, Xinlian
Through the method of on-site video observation, this study divides the intersection area into three parts according to the road traffic characteristics of the Y-shaped signalized intersections, and at the same time obtains the relevant parameters. These parameters include the left-turn speed and traffic density of motor vehicles within both the internal and exit areas, the frequency of lane-changing and queuing behaviors of non-motorized vehicles in the internal area, and the left-turn speed and traffic density of non-motorized vehicles in both the internal and exit areas. The data extraction and analysis of the parameters provide strong data support for further analysis of the subsequent mixed traffic flow. A cellular automaton model is developed using the intersection’s exit area as the scenario. The exit area is divided into three lanes based on the queuing patterns of mixed traffic. Corresponding traffic rules are established according to the traffic density of motorized and non
Yuan, LiLiu, Xiaowei
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, JunjunZhao, Chihang
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, YalinNiu, PeiGuo, FengYan, WeiLiu, JianKou, Lei
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, ZiyuNi, FujianHuang, JiaqiJiang, Jiwang
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, ErikaOliver, Michele
This research explores the use of salt gradient solar ponds (SGSPs) as an environmentally friendly and efficient method for thermal energy storage. The study focuses on the design, construction, and performance evaluation of SGSP systems integrated with reflectors, comparing their effectiveness against conventional SGSP setups without reflectors. Both experimental and numerical methods are employed to thoroughly assess the thermal behavior and energy efficiency of these systems. The findings reveal that the SGSP with reflectors (SGSP-R) achieves significantly higher temperatures across all three zones—Upper Convective Zone (UCZ), Non-Convective Zone (NCZ), and Lower Convective Zone (LCZ)—with recorded temperatures of 40.56°C, 54.2°C, and 63.1°C, respectively. These values represent an increase of 6.33%, 11.12%, and 14.26% over the temperatures observed in the conventional SGSP (SGSP-C). Furthermore, the energy efficiency improvements in the UCZ, NCZ, and LCZ for the SGSP-R are
J, Vinoth Kumar
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 AliThanapati, AlokDeshmukh, Chandrakant
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, LiyunKui, XuChen, ChenShen, ChongchongLi, BoWei, Yunpeng
This paper presents the strategy design, development, and detailed simulation of an Energy Management System (EMS) for a range extender energy storage microgrid project. Initially, a microgrid system model including photovoltaic (PV) and energy storage devices was established. Secondly, the Latin Hypercube Sampling (LHS) method was employed to generate possible operational scenarios, and an improved K-means clustering algorithm was used for scenario classification. Subsequently, a series of constraints were constructed for the economic viability of the microgrid to minimize its annualized comprehensive cost, while satisfying power balance and equipment operation. Finally, the microgrid system was simulated and solved using the GUROBI solver, covering cost analyses of the energy storage system and diesel generators under different configurations, as well as the State of Charge (SOC) variations of the energy storage system. The simulation results indicate that, after considering the one
Hua, YuweiJin, ZhenhuaHuang, HuilongWang, Zihao
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, YouDu, JingGuo, DonglaiWang, KaiWang, Yupeng
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
This study introduces the Total Cost of Ownership per Unit Operating Time (TCOP) as a novel indicator to assess the economic impact of vehicle durability. A comprehensive analysis is conducted for fuel cell vehicles (FCVs), battery electric vehicles (BEVs), and internal combustion engine vehicles (ICEVs) in light- and heavy-duty scenarios. The results show that in HDVs, the advantages of low prices for hydrogen and electricity are fully demonstrated due to their high durability. In contrast, for LDVs, the purchase cost plays a much larger role, accounting for 68% of the total cost, indicating a significant difference between vehicles. Improving durability can significantly enhance the competitiveness of FCVs. For FCVs, increasing the durability from the current levels of 150,000 km for LDVs and 600,000 km for HDVs to 20,8500 km and 1,122,000 km, respectively, would align their TCOP with that of current ICEVs. A sensitivity analysis shows that for HDVs. The focus should be placed on
Qin, ZhikunYin, YanZhang, FanYao, JunqiGuo, TingWang, Bowen
In this paper, a hybrid model based on deep reinforcement learning (DRL) is proposed for predicting the degradation process of the fuel cell stack. The model integrates the interpretability of mechanism models with the strengths of data-driven approaches in capturing nonlinear dynamics. Voltage is selected as an indicator for predicting the performance degradation of the stack. By utilizing DRL, a dynamic weighting process is achieved, enhancing both the accuracy and robustness of the model. The model is validated by the IEEE 2014 dataset. The results show that the hybrid model achieves high accuracy with the R2 value of 0.875 (30% of the data used as a training set). Moreover, when the training set is 7:3 compared to the test set, the accuracy of the hybrid model is 14.18% higher than that of the long short-term memory network (LSTM) model. The DRL model has the highest accuracy for different percentages of the training set in the total data set, which further verifies the
Qin, ZhikunYin, YanZhang, FanYao, JunqiGuo, TingWang, Bowen
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, ShangHu, Zhishuo
Current work details the preliminary CFD analysis performed on custom-built race car by Team Sakthi Racing team as part of Formula SAE competition using OpenFOAM. The body of the race car is designed in compliance with FSAE regulations, OpenFOAM utilities and solvers are used to generate volumetric mesh and perform CFD analysis. Formula student tracks are typically designed with numerous sharp turns and a few long straights to maintain low speeds for safety. In order to enhance the cars’ performance in sharp turns, the race car should be equipped with aerodynamic devices like nose cone and wings on both the rear and front ends within the confines of the formula student racing rules. Thus, efficient aerodynamic design is highly critical to maximizing tire grip by ensuring consistent contact with the track, reducing the risk of skidding, and maintaining control, especially during high-speed maneuvers. In this work, the performance and behavior of the race car, both with and without the
Rangarajan, KishorePushpananthan, BlesscinAnumolu, LakshmanSelvakumar, KumareshJayakumar, Shyam Sundar
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