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In the automotive sector, the use of multi-body software for modeling of existing vehicles has become essential due to its advantages in understanding vehicle dynamics in different situations and improving vehicle performances. This paper aims to model an off-road truck (4 × 4) by using ADAMS software 2020. Several steps must be achieved, including experimentally identifying some truck characteristics such as the mass, center of gravity coordinates, and tire vertical stiffness. The truck features leaf springs in both the front and rear suspensions, which must be validated before their integration into the full model due to their modeling complexity. This validation is performed by comparing the force–displacement characteristics obtained experimentally with simulation results from ADAMS, showing a good agreement. Then, the full truck is modeled in ADAMS software and validated through an experimental test using a repeated double-lane change scenario with two tests for the validation of the truck's lateral dynamics. The comparison between the results shows a good correlation, validating the multi-body truck model. [ABSTRACT FROM AUTHOR]

Road vehicles play an important role in the UK's energy systems and are a critical component in reducing the reliance on fossil fuels and mitigating emissions. A dynamic model of light-duty vehicle fleet, based on predator-prey concepts, is presented. This model is designed to be comprehensive but captures the important features of the competition between types of vehicles on the car market. It allows to predict the evolution of the hydrogen based vehicle's role in the UK's vehicle fleet. The model allows to forecast effects of policies, hence to inform policy makers. In particular, it is shown that the transition happens only if the hydrogen supply can absorb at least 350,000 new vehicles per year. In addition to this, the model is used to predict the demand for hydrogen for the passenger vehicle fleet for various scenarios. A key finding of the policy-oriented model is that a successful transition to a clean fleet before 2050 is unlikely without policies designed to fully support the supply chain development. It also shows that the amount of hydrogen required to support a full hydrogen based vehicle fleet is currently not economically viable; the needed infrastructure requires yearly investment larger than £2.5 billions. In order to mitigate these costs, the policy focus should shift from hydrogen based vehicles to hybrid vehicles and range extenders in the transport energy system. [ABSTRACT FROM AUTHOR]

Relocating the electric motor to a position inside the wheel offers a variety of benefits, such as reduced overall vehicle weight, more efficient production of vehicles, more flexible design and better road handling because of the accurate torque control on each wheel. However, the motor also increases the unsprung mass, which can impair the ride comfort of the vehicle. In this paper, we propose and investigate a control strategy using the vertical component of the driving force provided by an in-wheel motor for better ride comfort and handling dynamics. In the next stage, a mathematical model of the vehicle with coupled longitudinal, lateral, and vertical dynamics was introduced. The parameters of the vehicle were measured and collected from an actual car equipped with in-wheel motors. To analyze the performance of the controller, a model of the vehicle was tested in a series of simulations. The straight-line acceleration test indicated an improvement to the ride comfort of 31.8% in terms of the RMS vertical vibrations of the sprung mass. The control is achieved without any deterioration of the tire load and the vehicle's longitudinal dynamics. The stable lateral dynamics of the controlled vehicle was confirmed using a sine-sweep steering excitation, conducted on a perfectly smooth road surface with a constant longitudinal acceleration. [ABSTRACT FROM AUTHOR]

Automotive control functions are becoming increasingly complex and their development is becoming more and more elaborate, leading to a strong need for automated solutions within the development process. Here, reinforcement learning offers a significant potential for function development to generate optimized control functions in an automated manner. Despite its successful deployment in a variety of control tasks, there is still a lack of standard tooling solutions for function development based on reinforcement learning in the automotive industry. To address this gap, we present a flexible framework that couples the conventional development process with an open-source reinforcement learning library. It features modular, physical models for relevant vehicle components, a co-simulation with a microscopic traffic simulation to generate realistic scenarios, and enables distributed and parallelized training. We demonstrate the effectiveness of our proposed method in a feasibility study to learn a control function for automated longitudinal control of an electric vehicle in an urban traffic scenario. The evolved control strategy produces a smooth trajectory with energy savings of up to 14%. The results highlight the great potential of reinforcement learning for automated control function development and prove the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

Minimum lap-time planning (MLTP) is a well-established problem in the race car industry to provide guidelines for drivers and optimize the vehicle's setup. In this paper, we tackle the 3D nature of the problem in its full extension, making no simplifying assumptions on the mechanics of the system. We propose a multibody vehicle model, described by rigorous dynamical equations. To effectively handle the resulting complexity, we devised an efficient direct dynamics computational method based on Featherstone's articulated-body algorithm (ABA). To solve the MLTP, we employed a direct-collocation technique, discretizing the problem so that all information of the 3D track is pre-processed and directly embedded into the discrete problem. This discretization approach turns out to be perfectly compatible with our vehicle model, leading to a solution in accessible computational time frames. The high level of detail of the model makes the proposed approach most useful for in-depth vehicle dynamics analyses on complex tracks. To substantiate the analysis, we provide a comparison with the results obtained by a double-track model on the Nürburgring Nordschleife circuit. Consistently with the average trend defined by the double track, the proposed model features a more dynamically rich behavior, realistically capturing the higher-order effects elicited by the sharp corners and the highly variable slope of the track. [ABSTRACT FROM AUTHOR]

Automated assessment of car damage is a major challenge in the auto repair and damage assessment industries. The domain has several application areas, ranging from car assessment companies, such as car rentals and body shops, to accidental damage assessment for car insurance companies. In vehicle assessment, the damage can take many forms, from scratches, minor dents, and major dents to missing parts. Often, the assessment area has a significant level of noise, such as dirt, grease, oil, or rush, which makes accurate identification challenging. Moreover, in the repair industry, identifying a particular part is the first step in obtaining an accurate labor and part assessment, where the presence of different car models, shapes, and sizes makes the task even more challenging for a machine-learning model to perform well. To address these challenges, this study explores and applies various instance segmentation methodologies to determine the best-performing models. This study focuses on two genres of real-time instance segmentation models, namely, SipMask and YOLACT, owing to their industrial significance. These methodologies were evaluated against a previously reported car parts dataset (DSMLR) as well as an internally curated dataset extracted from local car repair workshops. The YOLACT-based part localization and segmentation method outperformed other real-time instance mechanisms with an mAP of 66.5. For the workshop repair dataset, SipMask++ reported better accuracy for object detection with a mAP of 57.0, with outcomes for A P I o U =. 50 and A P I o U =. 75 reporting 72.0 and 67.0, respectively, whereas YOLACT was observed to be a better performer for A P s with 44.0 and 2.6 for object detection and segmentation categories, respectively. [ABSTRACT FROM AUTHOR]

The vehicle market at the European level (and also elsewhere) has registered a high dynamic for the adoption of electric vehicles as the future means of transport. Government policies and decisions fully support this move, but there are still massive barriers to entry into the EV market due to consumer attitudes and perceptions. Consumer attitudes and perceptions will be decisive in the market success of future electric vehicle models, given that consumers are particularly interested in the vehicles' technical and dynamic performance. The pressure from customers' demands for performance leaves its mark not only on the increase of performance and technical parameters, but directly contributes to the generation of interdependence between these parameters. This article presents a comparative statistical analysis of 203 electric vehicle models (from various construction categories), present in the European vehicle market (between the years 2019 and 2022), to highlight the direct and indirect links (interdependencies) between technical and performance parameters depending on the constructive class (type). The goal of this article is to determine whether there is a relationship between the three key performance metrics for electric vehicles—autonomy, top speed, and acceleration—and five significant technical/constructive metrics for these vehicles—battery capacity, energy efficiency, electric motor power, fast charging speed, and vehicle weight (brand and year of availability in the market). Based on the analyzed data, it can be stated that the design and construction of an electric vehicle model currently takes into account both performance and technical parameters, with a strong link between the weight of the vehicle and the energy capacity of the battery (+0.687) being highlighted and also the relationship between autonomy and vehicle weight (+0.355). The conclusions of this study can be used in the future by manufacturers for the development of new models of electric vehicles (new generic platforms and chassis) by classifying/standardizing these vehicles into specific classes, corresponding to the requirements of different classes of consumers or identifying constructive solutions specific to each type of consumer. [ABSTRACT FROM AUTHOR]

The rapid development of the automobile industry has made life easier for people, but traffic accidents have increased in frequency in recent years, making vehicle safety particularly important. This paper proposes an improved YOLOv5s algorithm for vehicle identification and detection to reduce vehicle driving safety issues based on this problem. In order to solve the problems of a disappearing model training gradient in the YOLOv5s algorithm, difficulty in recognizing small objects and poor recognition accuracy caused by the boundary frame regression function, it is necessary to implement a new function. These aspects have been enhanced in this article. On the basis of the traditional YOLOv5s algorithm, the ELU activation function is used to replace the original activation function. The attention mechanism module is then added to the YOLOv5s algorithm's backbone network to improve the feature extraction of small and medium-sized objects. The CIoU Loss function replaces the original regression function of YOLOv5s, thereby enhancing the convergence rate and measurement precision of the loss function. In this paper, the constructed dataset is utilized to conduct pertinent experiments. The experimental results demonstrate that, compared to the previous algorithm, the mAP of the enhanced YOLOv5s is 3.1% higher, the convergence rate is 0.8% higher, and the loss is 2.5% lower. [ABSTRACT FROM AUTHOR]

The automotive industry has to deal with wide-reaching challenges and changes. A continuous gain of significant competitive advantages in a highly competitive market requires automotive manufacturers to produce new products with a large diversity of variants at ever-shorter time intervals. As a result, the frequency of series launches has increased and the need for a sophisticated vehicle project management and an efficient handling of vehicle changes and model updates along the product process have gained significant importance. However, current project management practices and frameworks utilized by automotive manufacturers do not effectively address this demand, e.g., the goals of increasing operational efficiency and productivity as well as establishing continuous improvement are not sufficiently achieved. While these goals are traditionally common-place in certain fields of the automotive manufacturing, e.g., in production, logistics, or warehousing, their dedicated consideration in the vehicle project management or one of its neighboring areas like the vehicle change management has not been examined thoroughly in the required extend. Understanding the determinants of the needed change in the vehicle project management and its related fields is a central objective of the research presented in this paper. In this context, this paper proposes a novel approach on how to manage the transition and why this is a challenge to common project management practices utilized traditionally in the automotive industry. [ABSTRACT FROM AUTHOR]

The rapid development of autonomous vehicle technology has brought a new experience to people's daily travel. However, if a traffic accident involving autonomous vehicles occurs, it will face difficulties in vehicle accident forensic‐preservation, leakage of vehicle owner's privacy, and identifying legal liabilities. This article proposes an accident responsibility identification model for the Internet of Vehicles based on lightweight blockchain to solve the above problems. This model uses Car Forensics Master to collect evidence from the accident vehicle, and at the same time collects evidence from maintenance service providers, automobile manufacturers, transportation management departments, insurance companies, and other vehicle accident related parties and stores them in the preservation chain. We also use VPKI to protect the autonomous vehicle identity privacy. In order to improve the efficiency of the model and set up authorized access to related entities, the identification of accident liability is jointly completed by the preservation chain and the accident identification chain. In addition, we prove that the protocol proposed in the model has ideal security properties. Finally, we implement the smart contracts in the model through the Solidity language, and evaluate its performance. [ABSTRACT FROM AUTHOR]

Riding quality is considered a key element in automotive industry which imposes a challenge on car manufacturers to develop new alternative control strategies for the classical suspension system. To this extent, many efforts have been carried out on developing several active or semi-active suspension systems. In the past few years, the decreasing cost of electromechanical actuators has, however, opened new trends to face this challenge. Active aerodynamic surfaces (spoilers) represent an alternative and effective solution to the issue. Two contradicting criteria of good vehicle suspension performance are typically their ability to provide good road handling and increase passengers comfort. The main disturbance affecting these two criteria is terrain irregularities. Active suspension control systems reduce these undesirable effects by isolating car body motion from vibrations at the wheels. In this article, we are trying to investigate the use of active aerodynamic surfaces to enhance ride comfort in sport vehicles for half car model. The article describes also the model and controller used in the study and discusses the vehicle response results obtained from a typical road sinusoidal signal input. The effect of active aerodynamic surfaces could enhance the ride comfort by (15%) without affecting road holding. [ABSTRACT FROM AUTHOR]