Vehicles, Volume 6, Issue 3 (September 2024) – 32 articles

Aerial vehicles are increasingly relying on connectivity to cellular networks, with 5G new radio (NR) and 6G technologies deemed critical for the next generation of indoor and outdoor positioning systems. Conventional time of arrival approaches require time synchronisation between base stations and vehicles, and a clock bias greater than 30 ns can result in a positioning inaccuracy above 10 m. This work, thereby, proposes an integrated positioning technique based on RF fingerprinting using ray-tracing data and reinforced with machine learning. The system leverages advanced sensing technologies, NR communications, and AI-driven random forests to enhance the precision and reliability of air vehicle positioning, contributing to safer and more efficient air travel and autonomous flight operations. The developed solution is evaluated in a representative urban canyon environment, in which the performance of conventional radio-based positioning systems is often degraded. Notably, a supervised learning algorithm based on the received signal strength and time of arrival is shown to exhibit an accuracy of under 3 m in 75% of the areas studied. Full article

Autonomous vehicles frequently encounter performance degradation during high-speed cornering due to excessive speed and lateral acceleration, potentially leading to collisions or rollovers. This paper proposes a novel curve-passing control approach that first estimates the curve radius and then controls the steer and speed for smooth and comfortable handling. In particular, the curve radius is innovatively estimated using a combination of a camera-based lane detection model and a steering wheel dynamic model. The curve-passing control approach is validated on high-speed ramps and curves, demonstrating its robustness and intelligence to adapt to dynamic changes in curve curvature. The model effectively prevents vehicles from entering curves at dangerously high speeds from straight roads and mitigates sudden accelerations or decelerations when entering curves. Experimental results indicate that the vehicle speed is reduced to around 50 km/h and the corresponding acceleration is −0.6 m/s² upon entering curves with a minimum radius of 150 m. This demonstrates that the proposed control model can ensure a comfortable and safe driving experience by autonomously decelerating the vehicle before entering various types of curves. Full article

The accurate prediction of tire and brake thermomechanical behavior is crucial for various applications in the automotive industry, including vehicle dynamics analysis, racing performance optimization, and driver assistance system development. The temperature of the brakes plays a crucial role in determining the performance of rubber by altering its temperature. This change impacts the rim and the air within the tire, leading to variations in temperature and tire pressure, which consequently affect the vehicle’s overall performance. Traditionally, these components have been modeled separately, neglecting the crucial thermal interaction between them, thereby losing a lot of important information from the outside that influences the tire. This paper presents a novel method that overcomes this limitation by coupling the thermomechanical models of the tire and brake, enabling a more comprehensive understanding of their combined behavior. Therefore, the present work could be an interesting starting point to understand how a control system can be influenced by the thermodynamic of the wheel–brake system. Full article

Semi-autonomous vehicles (AVs) enable drivers to engage in non-driving tasks but require them to be ready to take control during critical situations. This “out-of-the-loop” problem demands a quick transition to active information processing, raising safety concerns and anxiety. Multimodal signals in AVs aim to deliver take-over requests and facilitate driver–vehicle cooperation. However, the effectiveness of auditory, visual, or combined signals in improving situational awareness and reaction time for safe maneuvering remains unclear. This study investigates how signal modalities affect drivers’ behavior using virtual reality (VR). We measured drivers’ reaction times from signal onset to take-over response and gaze dwell time for situational awareness across twelve critical events. Furthermore, we assessed self-reported anxiety and trust levels using the Autonomous Vehicle Acceptance Model questionnaire. The results showed that visual signals significantly reduced reaction times, whereas auditory signals did not. Additionally, any warning signal, together with seeing driving hazards, increased successful maneuvering. The analysis of gaze dwell time on driving hazards revealed that audio and visual signals improved situational awareness. Lastly, warning signals reduced anxiety and increased trust. These results highlight the distinct effectiveness of signal modalities in improving driver reaction times, situational awareness, and perceived safety, mitigating the “out-of-the-loop” problem and fostering human–vehicle cooperation. Full article

This paper details the conceptualization, design, topology optimization, manufacturing, and validation of a hydraulic brake caliper for Formula SAE race cars made of Scalmalloy^®, an innovative Al-Mg-Sc alloy which was never adopted before to manufacture a brake caliper. A monoblock fixed caliper with opposing pistons was developed, focusing on reducing mass for a fixed braking force. The design process began with a theoretical analysis to establish braking force and pressure requirements, followed by preliminary design and topology optimization. The caliper was then manufactured using laser powder bed fusion (LPBF). Comprehensive experimental validation, including testing with static and rotating brake discs on an inertial dynamometer, confirmed the expected caliper’s performance, with the results showing strong alignment with finite element analysis estimations. In particular, strain and displacement measurements showed excellent correlation with numerical estimates, validating the design’s accuracy and effectiveness. Full article

Traditional approaches to safety event analysis in autonomous systems have relied on complex machine and deep learning models and extensive datasets for high accuracy and reliability. However, the emerge of multimodal large language models (MLLMs) offers a novel approach by integrating textual, visual, and audio modalities. Our framework leverages the logical and visual reasoning power of MLLMs, directing their output through object-level question–answer (QA) prompts to ensure accurate, reliable, and actionable insights for investigating safety-critical event detection and analysis. By incorporating models like Gemini-Pro-Vision 1.5, we aim to automate safety-critical event detection and analysis along with mitigating common issues such as hallucinations in MLLM outputs. The results demonstrate the framework’s potential in different in-context learning (ICT) settings such as zero-shot and few-shot learning methods. Furthermore, we investigate other settings such as self-ensemble learning and a varying number of frames. The results show that a few-shot learning model consistently outperformed other learning models, achieving the highest overall accuracy of about 79%. The comparative analysis with previous studies on visual reasoning revealed that previous models showed moderate performance in driving safety tasks, while our proposed model significantly outperformed them. To the best of our knowledge, our proposed MLLM model stands out as the first of its kind, capable of handling multiple tasks for each safety-critical event. It can identify risky scenarios, classify diverse scenes, determine car directions, categorize agents, and recommend the appropriate actions, setting a new standard in safety-critical event management. This study shows the significance of MLLMs in advancing the analysis of naturalistic driving videos to improve safety-critical event detection and understanding the interactions in complex environments. Full article

The Xfinity Racing Series is an American stock car racing series organized by NASCAR. For the 2017 racing season, NASCAR introduced new regulations with the objective of creating a level playing field by reducing aerodynamic influence on vehicle performance. In this context, the primary objective of this work is to explore the differences in the aerodynamic performance between the 2016 and 2017 Toyota Camry Xfinity racecars using only open-source Computational Fluid Dynamics (CFD) and CAE tools. During the CFD validation process, it was observed that none of the standard turbulence models, with default turbulence model closure coefficients, were able to provide racecar aerodynamic characteristics predictions with acceptable accuracy compared to experiments. This necessitated a fine-tuning of the closure coefficient numeric values. This work also demonstrates that it is possible to generate CFD predictions that are highly correlated with experimental measurements by modifying the closure coefficients of the standard $k-\omega$ SST turbulence model. Full article

Recently, with climate deterioration and environmental pollution, consumers are becoming more and more aware of the use of sustainable energy. In particular, the demand for electric vehicles that use sustainable energy is also increasing. In addition, due to the simple driving principle of pure electric vehicles, many electric vehicles developed by electronics companies are continuously being launched. Electric vehicles not only use renewable energy to protect the environment but also save on various usage expenses, so they are expected to become the main products in the mobile travel equipment market in the future. This study aims to explore the impact of product design dimensions on electric vehicle (EV) purchase intentions, provide a theoretical basis for companies’ differentiation strategies, and reflect the impact of product design on purchase intention. This study uses Davis’s TAM combined with environmental awareness (EA) for analysis; an online survey was conducted on Chinese (n = 468) and Korean (n = 409) consumers, both male and female, aged 20–60 years and above. We found that, for Chinese consumers, the aesthetic and symbolic dimensions do not affect perceived usefulness and perceived ease of use, but they do affect environmental awareness, while the functional dimension affects not only perceived ease of use and usefulness but also environmental awareness. For Korean consumers, the aesthetic, functional, and symbolic dimensions all affect perceived ease of use and environmental awareness, but perceived usefulness is only affected by aesthetics and environmental awareness. Through simulation analysis, the results show that perceived ease of use, usefulness, and environmental awareness all directly affect purchase intentions. Perceived ease of use and environmental awareness are particularly important for Chinese consumers, while Korean consumers pay more attention to the test drive experience and environmental awareness. The results show that electric vehicle manufacturers should develop new technologies for the Chinese market to attract consumers, while in the Korean market, they should improve perceived usefulness through test drives and pay attention to environmental awareness. Specific statistical data show that both Chinese and Korean consumers assign importance to the impact of environmental awareness on purchase intention, proving the importance of environmental awareness. The results of this study will be of great reference value to electric vehicle manufacturers, policymakers, and consumer behavior researchers, helping them to better understand the role of product design in improving the market acceptance of electric vehicles. Full article

Regenerative braking technology is essential for reducing energy consumption in electric vehicles (EVs). This study introduces a method for optimizing the distribution of deceleration forces in front-wheel-drive electric vehicles that complies with the distribution range outlined by ECE-R13 braking regulations and aligns with an ideal braking distribution curve. In addition, using a fuzzy control strategy to manage the complex variables of the regenerative braking process, a robust and adaptable system is developed on the Simulink platform. Tested across various driving cycles are NEDC (New European Driving Cycle), WLTC (World Light Duty Vehicle Test Cycle), FTP-72 (Federal Test Procedure 72), and FTP-75 (Federal Test Procedure 75). The method significantly improves energy efficiency: 13% for WLTC, 16% for NEDC, and 30% for both FTP-72 and FTP-75. The simulation results were compared to regenerative braking control techniques A and B, showing that the proposed control method achieves a higher brake energy recovery rate. This leads to a considerable improvement in the vehicle’s energy recovery efficiency. These findings confirm the efficacy of the proposed regenerative brake control system, highlighting its potential to significantly enhance the energy efficiency of electric vehicles. Full article

In the realm of supply chain logistics, the Multi-Depot Multi-Supplier Vehicle Routing Problem (MDMSVRP) poses a significant challenge in optimizing the transportation process to minimize costs and enhance operational efficiency. This problem involves determining the most cost-effective routes for a fleet of vehicles to deliver goods from multiple suppliers to multiple depots, considering various constraints and non-linear relationships. The routing problem (RP) is a critical element of many logistics systems that involve the routing and scheduling of vehicles from a depot to a set of customer nodes. One of the most studied versions of the RP is the Vehicle Routing Problem with Time Windows (VRPTW), in which each customer must be visited at certain time intervals, called time windows. In this paper, it is considered that there are multiple depots (supply centers) and multiple suppliers, along with a fleet of vehicles. The goal is to efficiently plan routes for these vehicles to deliver goods from the suppliers to various customers while considering relaxed time windows. This research is intended to establish a new relaxation scheme that relaxes the time window constraints in order to lead to feasible and good solutions. In addition, this study develops a discrete optimization model as an alternative model for the time-dependent VRPTW involving multi-suppliers. This research also develops a metaheuristic algorithm with an initial solution that is determined through time window relaxation. Full article

Nearly 3700 people are killed in broadside collisions in the U.S. every year. To reduce broadside collisions, we created and tested the CornerGuard, a prototype system that senses around a corner to alert a car driver of an impending collision with a pedestrian or automobile that is not in the line of sight (LOS). The CornerGuard leverages a microwave-transceiving radar sensor mounted on the car and a curved radio wave reflector installed at the corner to sense around the corner and detect a broadside collision threat. The car’s speed is constantly read by an onboard diagnostics (OBD) system to allow the sensor to differentiate between static objects and objects approaching around the corner. Field testing demonstrated that the CornerGuard can effectively and consistently detect threats at a consistent range without blind spots under broad weather conditions. Our proof of concept study shows that the CornerGuard can be enhanced to be readily integrated into automobile construction and street infrastructure. Full article

Reducing CO₂ emissions is becoming a particularly important goal for motorcycle manufacturers. A fully electric transition still seems far away, given the difficulties in creating an electric motorcycle with an acceptable range and mass. This opens up opportunities for the application of hybrid powertrains in motorcycles. Managing mass, cost, and volume is a challenging issue for motorcycles; therefore, an MHEV architecture represents an interesting opportunity, as it is a low-complexity and low-cost solution. Firstly, in this work, an adequate sizing of the powertrain components is studied for the maximum reduction in fuel consumption. This is performed by analyzing many different system configurations with different hybridization ratios. A 1D simulation of the motorcycle traveling along the homologation cycle (WMTC) is performed, and the powerunit use strategy is optimized for each configuration using the Dynamic Programming technique. The results are analyzed in order to highlight the impact of kinetic energy recovery and engine load-point shifting on fuel consumption reduction. The results show the applicability of MHEV technology to road motorcycles, thus providing a useful tool to analyze the cost/benefit ratio of this technology. The developed methodology is also suitable for different vehicles once a specific test cycle is known. Full article

High-speed electric drive units promise improved power density and, theoretically, driving range of battery electric vehicles. An essential step of the development process is extensive testing of the drive unit on a test rig. In particular, at a high rotational speed level, experimental testing can be challenging. This paper describes a test rig for investigating the overall function of a high-speed drive unit and the transmission’s efficiency and dynamics. The high-speed drive unit developed in the Speed4E research project was the reference drive unit. The test rig is based on the concept of electrical power circulation. Thus, the test rig can be used universally for different drive unit designs and operating modes. A reaction torque measurement unit was developed to enable measurements at high rotational speeds. Simultaneously, this unit allows robust measurements at low costs. The expected measurement uncertainties of torque, rotational speed, transmission efficiency, and power losses were calculated using the Monte Carlo method. The results demonstrate that the developed torque measurement unit combines precise torque measurement with a robust design and low costs, making it competitive with state-of-the-art solutions for torque measurement at high speeds. Full article

Urban air mobility (UAM) is a revolutionary approach to transportation in densely populated cities. UAM involves using small, highly automated aircraft to transport passengers and goods at lower altitudes within urban and suburban areas, aiming to transform how people and parcels move within these environments. On average, UAM can reduce travel times by 30% to 40% for point-to-point journeys, with even greater reductions of 40% to 50% in major cities in the United States and China, compared to land transport. UAM includes advanced airborne transportation options like electric vertical takeoff and landing (eVTOL) aircraft and unmanned aerial vehicles (UAVs or drones). These technologies offer the potential to ease traffic congestion, decrease greenhouse gas emissions, and substantially cut travel times in urban areas. Studying the applications of eVTOLs and UAVs in parcel delivery and passenger transportation poses intricate challenges when examined through the lens of operations research (OR). By OR approaches, we mean mathematical programming, models, and solution methods addressing eVTOL- and UAV-aided parcel/people transportation problems. Despite the academic and practical importance, there is no review paper on eVTOL- and UAV-based optimization problems in the UAM sector. The present paper, applying a systematic literature review, develops a classification scheme for these problems, dividing them into routing and scheduling of eVTOLs and UAVs, infrastructure planning, safety and security, and the trade-off between efficiency and sustainability. The OR methodologies and the characteristics of the solution methods proposed for each problem are discussed. Finally, the study gaps and future research directions are presented alongside the concluding remarks. Full article

Accurate vehicle detection is crucial for the advancement of intelligent transportation systems, including autonomous driving and traffic monitoring. This paper presents a comparative analysis of two advanced deep learning models—YOLOv8 and YOLOv10—focusing on their efficacy in vehicle detection across multiple classes such as bicycles, buses, cars, motorcycles, and trucks. Using a range of performance metrics, including precision, recall, F1 score, and detailed confusion matrices, we evaluate the performance characteristics of each model.The findings reveal that YOLOv10 generally outperformed YOLOv8, particularly in detecting smaller and more complex vehicles like bicycles and trucks, which can be attributed to its architectural enhancements. Conversely, YOLOv8 showed a slight advantage in car detection, underscoring subtle differences in feature processing between the models. The performance for detecting buses and motorcycles was comparable, indicating robust features in both YOLO versions. This research contributes to the field by delineating the strengths and limitations of these models and providing insights into their practical applications in real-world scenarios. It enhances understanding of how different YOLO architectures can be optimized for specific vehicle detection tasks, thus supporting the development of more efficient and precise detection systems. Full article

In industrial vehicle design processes, most companies have implemented model-based systems engineering (MBSE). As a consequence, design processes are nowadays not driven by documents, but by digital models of the vehicle to be developed and its components. These models exist on different levels of abstraction. The models on the requirements level are already well defined as well as the models of the defined product behavior and product properties. In recent years, the specification of models on the level of product functions was largely clarified, and elaborate frameworks already exist. However, this is not yet true for the level between functions and definite properties; this level can be referred to as "abstract physics". The enormous importance of this level, which, amongst others, can represent the physical effect chains which allow a vehicle component to function, is expressed by several researchers. Several research works aim at specifying models on this level, but, until now, no general consensus can be identified, and the existing model specifications are less appropriate for the early stages of vehicle design. This paper explains an Integrated Framework for Abstract Physics Modeling (IF4APM), which incorporates different perspectives of abstract physics and is suited for the early phases. The explanation is based on typical components of several kinds of vehicles. The main advantages of the proposed approach are the consistent interconnection of abstract product models, the clearness and understandability of the resulting matrices, and the aptitude to be used in the early phases of a vehicle design process. Full article

Computational fluid dynamic (CFD) models and workflows are often developed in an ad hoc manner, leading to a limited understanding of interaction effects and model behavior under various conditions. Machine learning (ML) and explainability tools can help CFD process development by providing a means to investigate the interactions in CFD models and pipelines. ML tools in CFD can facilitate the efficient development of new processes, the optimization of current models, and enhance the understanding of existing CFD methods. In this study, the turbulent closure coefficient tuning of the SST $k-\omega$ Reynolds-averaged Navier–Stokes (RANS) turbulence model was selected as a case study. The objective was to demonstrate the efficacy of ML and explainability tools in enhancing CFD applications, particularly focusing on external aerodynamic workflows. Two variants of the Ahmed body model, with 25-degree and 40-degree slant angles, were chosen due to their availability and relevance as standard geometries for aerodynamic process validation. Shapley values, a concept derived from game theory, were used to elucidate the impact of varying the values of the closure coefficients on CFD predictions, chosen for their robustness in providing clear and interpretable insights into model behavior. Various ML algorithms, along with the SHAP method, were employed to efficiently explain the relationships between the closure coefficients and the flow profiles sampled around the models. The results indicated that model coefficient ${\beta }^{*}$ had the greatest overall effect on the lift and drag predictions. The ML explainer model and the generated explanations were used to create optimized closure coefficients, achieving an optimal set that reduced the error in lift and drag predictions to less than 7% and 0.5% for the 25-degree and 40-degree models, respectively. Full article

In this paper, we introduce a control method for the linear quadratic tracking (LQT) problem with zero steady-state error. This is achieved by augmenting the original system with an additional state representing the integrated error between the reference and actual outputs. One of the main contributions of this paper is the integration of a linear quadratic integral component into a general LQT framework. In this framework, the reference trajectories are generated using a linear exogenous system. During a simulative implementation for the specific real-world system of a car-in-the-loop (CiL) test bench, we assumed that the ‘real’ system was completely known. Therefore, for model-based control, we could have a perfect model identical to the ‘real’ system. It became clear that for CiL, stable solutions cannot be achieved with a controller designed with a perfect model of the ‘real’ system. On the contrary, we show that a model trained via Bayesian optimization (BO) can facilitate a much larger set of stable controllers. It exhibited an improved control performance for CiL. To the best of the authors’ knowledge, this discovery is the first in the LQT-related literature, which is a further distinctive feature of this work. Full article

External human–machine interfaces (eHMIs) may be useful for communicating the intention of an autonomous vehicle (AV) to road users, but it is questionable whether an eHMI is effective in guiding the actual behavior of road users, as intended by the eHMI. To address this question, we developed a Unity game in which the player drove a conventional car and the AVs were operating with eHMIs. We examined the effects of different eHMI designs—namely, textual, graphical, and anthropomorphic—on the driving behavior of a player in a gaming environment, and compared it to one with no eHMI. Participants (N = 18) had to follow a specified route, using the typical keys for PC games. They encountered AVs with an eHMI placed on the rear window. Five scenarios were simulated for the specified routes: school safety zone; traffic island; yellow traffic light; waiting for passengers; and an approaching e-scooter. All scenarios were repeated three times (a total of 15 sessions per participant), and the eHMI was randomly generated among the four options. The behavior was determined by observing the number of violations in combination with keystrokes, fixations, and saccades. Their subjective evaluations of the helpfulness of the eHMI and their feelings about future AVs revealed their attitudes. Results showed that a total of 45 violations occurred, the most frequent one being exceeding the speed limit in the school safety zones (37.8%) when the eHMI was textual, anthropomorphic, graphical, and when there was no eHMI, in decreasing order; the next was collisions (33.3%), when the eHMI was anthropomorphic, none, or graphical. The rest were ignoring the red light (13.3%), crossing the stop line (13.3%), and violation of the central line (2.2%). More violations occurred when the eHMI was set to anthropomorphic, followed by no eHMI, graphical, and textual eHMI. The helpfulness of the five scenarios scored high (5.611 to 6.389) on a seven-point Likert scale, and there was no significant difference for the scenarios. Participants felt more positive about the future of AVs after their gaming experience (p = 0.049). We conclude that gazing at unfamiliar and ambiguous information on eHMIs may cause a loss of driver attention and control. We propose an adaptive approach in terms of timing and distance depending on the behavior of other road users. Full article

This study presents an effective control strategy for improving the dynamic performance index of a two degrees-of-freedom (DOF) quarter-car model equipped with an active suspension system that collaborates with an active aerodynamic surface, using optimal control theory. The model takes several road excitations as input and applies an optimal control law to improve the ride comfort and road-holding capability, which are otherwise in conflict. MATLAB^® (R2024a) simulations are carried out to evaluate the time and frequency domain characteristics of the quarter-car active suspension system. Individual performance indices in the presence of an active aerodynamic surface are calculated based on mean squared values for different sets of weighting factors and compared with those of passive and active suspension systems. From the viewpoint of total performance, the overall results show that the proposed control strategy enhances the performance index by approximately 70–80% compared to the active suspension system. Full article

This study assessed the impacts of a toll information sign with different toll lane configurations on queue length and collision risk at a toll plaza with an estimated high percentage of heavy vehicles (HVs). The toll information sign displays information about different toll payment methods for cars and HVs upstream of the toll booth. The impacts were assessed for the toll plaza of the Gordie Howe International Bridge under construction at the Windsor–Detroit international border crossing using a traffic simulation model. Results show that the toll information sign upstream of the toll plaza and converting the toll lanes with multiple toll payment methods to electronic toll collection (ETC)-only lanes reduced queue length and collision risk. However, increasing the number of HV-only lanes for a higher percentage of HVs increased lane-change collision risk. Thus, it is recommended that toll lane configurations be changed based on the percentage of HVs to reduce collision risk at a toll plaza. Full article

The automotive industry faces development challenges due to emerging technologies, regulatory demands, societal trends, and evolving customer mobility needs. These factors contribute to a wide range of vehicle variants and increasingly complex powertrains. The layout of a vehicle is usually based on standardized driving cycles such as WLTC, gradeability, acceleration test cases, and many more. In real-world driving cycles, however, this can lead to limitations under certain boundary conditions. To ensure that all customer requirements are met, vehicle testing is conducted under extreme environmental conditions, e.g., in Sweden or Spain. One way to reduce the development time while ensuring high product quality and cost-effectiveness is to use model-based methods for the comprehensive design of powertrains. This study presents a layout methodology using a top-down approach. Initially, powertrain-relevant requirements for an exemplary target customer are translated into a specification sheet with specific test cases. An overall vehicle model with detailed thermal sub-models is developed to evaluate the different requirements. A baseline design for a C-segment plug-in hybrid vehicle was developed as part of the FVV research project HyFlex-ICE using standardized test cases, highlighting the influence of customer profiles on the design outcome through varying weighting factors. The target customer’s design is analyzed in four real driving scenarios, considering variations in parameters such as the ambient temperature, traffic, driver type, trailer pulling, and battery state-of-charge, to assess their influence on the target variables. In the next step, the potential of hardware technologies and predictive driving functions is examined in selected driving scenarios based on the identified constraints of the baseline design. As a result, four application-specific technology packages (Cost neutral, Cold country, Hot country, and Premium) for different customer requirements and sales markets are defined, which, finally, demonstrates the applicability of the holistic methodology. Full article

The design of an efficient thermal management system for a lithium-ion battery pack hinges on a deep understanding of the cells’ thermal behavior. This understanding can be gained through theoretical or experimental methods. While the theoretical study of the cells using electrochemical and numerical methods requires expensive computing facilities and time, the Equivalent Circuit Model (ECM) offers a more direct approach. However, upfront experimental cell characterization is needed to determine the ECM parameters. In this study, the behavior of a cell is characterized experimentally, and the results are used to build a second-order equivalent electrical circuit model of the cell. This model is then integrated with the cooling system of the battery pack for effective thermal management. The Equivalent Circuit Model estimates the internal heat generation inside the cell using instantaneous load current, terminal voltage, and temperature data. By extrapolating the heat generation data of a single cell, we can determine the heat generation of the cells in the pack. With the implementation of the ECM in the cooling system, the coolant flow rate can be adjusted to ensure the attainment of a safe operating cell temperature. Our study confirms that 14% of pumping power can be reduced when compared to the conventional constant flow rate cooling system, while still maintaining the temperature of the cells within safe limits. Full article

The introduction of advanced driver assistance systems has significantly reduced vehicle accidents by providing crucial support for high-speed driving and alerting drivers to imminent dangers. Despite these advancements, current systems still depend on the driver’s ability to respond to warnings effectively. To address this limitation, this research focused on developing a neural network model for the automatic detection and classification of objects in front of a vehicle, including pedestrians and other vehicles, using radar technology. Radar sensors were employed to detect objects by measuring the distance to the object and analyzing the power of the reflected signals to determine the type of object detected. Experimental tests were conducted to evaluate the performance of the radar-based system under various driving conditions, assessing its accuracy in detecting and classifying different objects. The proposed neural network model achieved a high accuracy rate, correctly identifying approximately 91% of objects in the test scenarios. The results demonstrate that this model can be used to inform drivers of potential hazards or to initiate autonomous braking and steering maneuvers to prevent collisions. This research contributes to the development of more effective safety features for vehicles, enhancing the overall effectiveness of driver assistance systems and paving the way for future advancements in autonomous driving technology. Full article

System perception of the environment becomes more important as the level of automation increases, especially at the higher levels of automation (L3+) of Automated Driving Systems. As a consequence, scenario-based validation becomes more important in the overall validation process of a vehicle. Testing all scenarios with potential triggering conditions that may lead to hazardous vehicle behaviour is not a realistic approach, as the number of such scenarios tends to be unmanageable. Therefore, another approach has to be provided to deal with this problem. In this paper, we present our approach, which uses the injection of perception performance insufficiencies instead of directly testing the potential triggering conditions. Finally, a use case is described that illustrates the implementation of the proposed approach. Full article

In modern automobiles, the infotainment system is crucial for enhancing driver and passenger capabilities, offering advanced features such as music, navigation, communication, and entertainment. Leveraging Wi-Fi, cellular networks, NFC, and Bluetooth, the system ensures continuous internet connectivity, providing seamless access to information. However, the increasing complexity of IT connectivity in vehicles raises significant cybersecurity concerns, including potential data breaches and exposure of sensitive information. To enhance security in infotainment systems, this study applied component-level threat modeling to a proposed infotainment system using the Microsoft STRIDE model. This approach illustrates potential component-level security issues impacting privacy and security concerns. The study also assessed these impacts using SAHARA and DREAD risk assessment methodologies. The threat modeling process identified 34 potential security threats, each accompanied by detailed information. Moreover, a comparative analysis is performed to compute risk values for prioritizing treatment, followed by recommending mitigation strategies for each identified threat. These identified threats and associated risks require careful consideration to prevent potential cyberattacks before deploying the infotainment system in automotive vehicles. Full article

The rise in autonomous vehicles further impacts road networks and driving conditions over the road networks. Cameras and sensors allow these vehicles to gather the characteristics of their surrounding traffic. One crucial factor in this environment is the appearance of emergency vehicles, which require special rules and priorities. Machine learning and deep learning techniques are used to develop intelligent models for detecting emergency vehicles from images. Vehicles use this model to analyze regularly captured road environment photos, requiring swift actions for safety on road networks. In this work, we mainly developed a Generative Adversarial Network (GAN) model that generates new emergency vehicles. This is to introduce a comprehensive expanded dataset that assists emergency vehicles detection and classification processes. Then, using Convolutional Neural Networks (CNNs), we constructed a vehicle detection model demonstrating satisfactory performance in identifying emergency vehicles. The detection model yielded an accuracy of 90.9% using the newly generated dataset. To ensure the reliability of the dataset, we employed 10-fold cross-validation, achieving accuracy exceeding 87%. Our work highlights the significance of accurate datasets in developing intelligent models for emergency vehicle detection. Finally, we validated the accuracy of our model using an external dataset. We compared our proposed model’s performance against four other online models, all evaluated using the same external dataset. Our proposed model achieved an accuracy of 85% on the external dataset. Full article

Electric vehicles (EVs) encounter substantial obstacles in effectively managing energy, particularly when faced with varied driving circumstances and surrounding factors. This study aims to evaluate the performance of three different control systems in a fully operational hybrid energy storage system (HESS) installed in the Nissan Leaf. The objective is to improve the performance of EVs by focusing on optimising energy management in response to different global environmental and driving circumstances. This study utilises an analytical strategy by developing a distinct energy management system model using MATLAB/Simulink. This model is specifically designed for optimising the integration and control of batteries and supercapacitors (SCs) in a fully active HESS. This model mimics the performance of the controllers under three different driving cycles—Artemis rural, Artemis motorway, and US06. The findings demonstrate notable progress in managing the battery state of charge (SOC) and the system’s responsiveness, especially when employing the radial basis function (RBF) controller. This study emphasises the capacity of HESSs to enhance the effectiveness and durability of EVs, therefore promoting wider acceptance and progress in electric transportation technology. Full article

Novel vehicle concepts are needed to meet the requirements of resource-conserving and efficient mobility in the future, especially in urban areas. In the automated, driverless electric vehicle concept U-Shift, a new form of mobility is created by separating a vehicle into a drive module and a transport capsule. The autonomous driving module, the so-called Driveboard, is able to change the transport capsules independently and is therefore used to transport both people and goods. The wide range of possible capsules poses major challenges for the development of the Driveboard and the chassis in particular. A lifting actuator integrated into the chassis concept enables levelling and, thus, the raising and lowering of the Driveboard and the capsules to ground level. This means that no additional lifting devices are required for changing the capsules or for lowering them to the ground, e.g., for loading and unloading the capsules. To realise this mechanism simply and efficiently, a fully electromechanical actuator is designed and constructed. The actuator consists primarily of a profile rail guide, a steel cable winch, an electric motor, a housing that connects the subsystems and a locking mechanism. The electric motor is used to lift the vehicle and regulate the weight force-driven lowering of the vehicle. This paper describes the design of the actuator and shows the dimensioning of all main components according to the boundary conditions. Finally, the prototype model of the realised concept is presented. Full article