Vehicles

Considering the nonlinear dynamics, this paper devises an advanced position trajectory tracking controller with a model-free filter for two-wheeled vehicle (TWV) applications. The proposed technique preserves a simple structure in the form of the proportional–integral (PI) controller involving the model-free filter and nonlinearly structured feedback gains, which make the following contributions: (a) the proposed filter smooths the position and yaw angle measurements according to the first-order convergence rate without any model information; and (b) the PI control with the nonlinearly structured feedback gains robustly stabilizes the position and yaw angle errors along the desired first-order system to accomplish the trajectory tracking mission, which is obtained by the pole-zero cancellation (PZC) in the presence of modeling errors. MATLAB/Simulink was used to emulate the resulting feedback system and validate the effectiveness of the proposed technique. Full article

The presence of contaminants influences braking efficiency in the railway system because it alters the adhesion at the wheel–rail interface. It is essential to study this phenomenon, as contaminants reduce the friction between wheels and rails, which impacts braking and transport safety. In addition, these contaminants increase the risk of derailments. The objective of the research was to determine the impact of different contaminants and operating speeds on the critical braking system’s responses. Using the 3^k full factorial experimental design methodology, with analysis of variance (ANOVA) and linear and quadratic regressions, visualized using surface graphs, the effects of three operating conditions were studied: clean rails, with sand and sawdust, and driving the train at three operating speeds. These conditions gave rise to variations in braking distances, maximum creep, wheel slip times, and maximum peaks of electric current when braking in each experiment. The tests were carried out on the straight section of a β-shaped track and a railway vehicle, designed at a scale of 1:20. The analysis reveals that the braking distance increases significantly with surface roughness (clean track < sawdust < sand). At 0.75 m/s, the sawdust track reduces braking distance by 21% compared with the clean track; at 1.00 m/s, the reduction is 19%; and at 1.30 m/s, it is 35%. Full article

A main circuit ground fault (MCGF) is a typical system fault in an electrical traction drive system (ETDS). When two or more MCGFs occur, it will cause serious accidents. Therefore, it is particularly important to detect and handle MCGFs in a timely manner. To improve the efficiency of train operation and ensure driving safety, this paper proposes a hybrid data-driven MCGF diagnosis method. First, the voltage signals related to the fault are selected according to the mechanism analysis of the MCGF, and then the initial feature variables are constructed according to these voltage signals. Secondly, the initial feature variables of different types of MCGF are analyzed in the time and frequency domains by wavelet transform, and four feature indicators are calculated. Finally, the fault feature indicators are trained by random forest to obtain a model for subsequent fault diagnosis. After comparative experiments using various machine learning methods, it was found that the RF used in the proposed method has a better diagnostic effect, and the correct isolation rate exceeds 99%. Full article

Connected vehicle data have the potential to warn motorists of impending slowdowns and congestion in real time. Multiple data providers have recently begun providing in-cab alerts to commercial vehicle drivers. This study reports on one such deployment of in-cab alerts on 44 corridors in Indiana from April–June 2024. Approximately 20,000 alerts were analyzed, with 92% being Congestion alerts and 8% being Dangerous Slowdown alerts. Observations showed that 15% of trucks lowered their speeds by at least 5 mph 30 s after receiving a Congestion alert, while 21% of trucks reduced their speeds by at least 5 mph 30 s after receiving a Dangerous Slowdown alert. The analysis also showed that a majority of Congestion alerted trucks encountered slow-speed traffic about 3 min after receiving an alert, while a majority of Dangerous Slowdown alerted drivers had traveled through the zone of slow speeds 2 min after receiving the alert. Although these results are encouraging, the study also found that 8.1% of Congestion alerts and 8.3% of Dangerous Slowdown alerts were received by trucks when they were operating at speeds of less than or equal to 45 mph, indicating they were already in congested conditions. The study reports that 43% of trucks that received Dangerous Slowdown alerts never reduced their speed below 45 mph. The paper concludes that it is important to converge on a shared vision for these performance measures so that public agencies, in-cab alert providers, and trucking companies can agilely improve these systems and increase driver confidence in the alerts. Full article

Nowadays, it is essential to find increasingly rapid and efficient design strategies. This approach becomes crucial in the railway industry, where components must be verified according to multiple reference standards, both structurally and dynamically. In this context, the present research activity aims to develop a fast and effective desin procedure based on European reference standards. The goal was to develop the geometry of a motor bogie frame for a tram vehicle, integrating three fundamental tools for development: finite element simulations and topological structural optimization, a Write Computer Aided Design (CAD) environment, and a multibody environment. Their integration could enhance design accuracy, streamline the traditional design workflow, and support innovation. The optimization process involved the introduction of complex technological constraints, directing the geometry toward production by casting. A tool was developed to automate running dynamics simulations and the output of results for immediate verification of the entire vehicle performance. Finally, the new geometry was tested both structurally and dynamically. The mass was reduced by approximately 7% while ensuring satisfactory mechanical performance. The maximum value of stress was reduced by about 16%. The dynamic performance showed negligible variation, confirming the encouraging outcomes to make this procedure increasingly effective and reliable. Full article

This paper presents an electric vehicle-drone (EV–drone) collaborative-delivery routing optimization model that leverages the time-varying characteristics of electric vehicles and drones across multiple distribution centers (i.e., central depots) to address the logistics industry’s low-carbon transformation in the last-mile delivery. The model aims to minimize total delivery costs by formulating a mixed-integer programming (MIP) model that accounts for essential constraints such as nonlinear charging time, time-varying EV travel time, delivery time window, payload capacity, and maximum range. An improved adaptive large-neighborhood search (ALNS) algorithm is developed to solve the model. Experimental results validate the effectiveness of the proposed algorithm and highlight the impact of EV and drone technology parameters, along with the time-varying EV travel times, on the economic efficiency of delivery distribution and route planning. Full article

Modern automotive systems must satisfy strict reliability requirements. Most real vehicle systems and safety-critical networks have complex interconnections. The sensitivities and probabilistic uncertainties of the reliability of systems with complex interconnections (SwCIs) can be investigated by Monte-Carlo Simulation (MCS). This paper focuses on the sensitivities and parametrical uncertainties of SwCIs’ reliability. The proposed method can be implemented in the investigation of the uncertainties of SwCI reliability, i.e., in the determination of critical system elements and the estimation of the required number of spare parts (RNSP) of the system, which depends on the probability of allowable spare equipment shortage. Full article

Unmanned aerial vehicles (UAVs) have emerged as a promising technology to enhance the performance and functionality of mobile networks. UAVs can act as flying base stations, relays, or users to provide wireless services to ground users or devices. However, the optimal placement and trajectory design of UAVs in mobile networks is a challenging problem, as it involves multiple objectives, constraints, and uncertainties. In this paper, we provide a comprehensive survey of the state-of-the-art research on UAV placement and trajectory optimization in cellular networks. We first introduce the main objectives and challenges of UAV placement and trajectory optimization, such as maximizing coverage, throughput, energy efficiency, or reliability, while minimizing interference, delay, or cost. We also examine the primary models and assumptions employed for UAV placement and trajectory optimization, including channel models, mobility models, network architectures, and constraints. Additionally, we discuss the main methods and algorithms employed for UAV placement and trajectory optimization. These include optimization techniques, heuristic algorithms, machine learning approaches, and distributed solutions. Analytical results, numerical simulations, or experimental tests are further discussed as the main performance metrics and evaluation methods used for UAV placement and trajectory optimization. We also highlight the main applications and scenarios of UAV placement and trajectory optimization, such as cellular offloading, emergency communications, or aerial base stations. Finally, we identify some open problems and future research directions on UAV placement and trajectory optimization in cellular networks. Full article

In the heavy-duty vehicle industry, unbalance in the armature is one of the most common problems affecting starters’ performance and durability. This research presents a comprehensive study to improve the balancing process for starting rotors in heavy-duty vehicles. The complete manufacturing process of armatures was analyzed to understand the contribution of assembly processes to unbalancing. The analysis revealed that the primary factor leading to high unbalance in these parts is the misalignment of conductors within the armature winding. During assembly, these conductors experience axial movements, resulting in non-uniform mass distribution and causing unbalanced values ranging from 150 to 350 g·mm. These values surpass the permissible limit, making rectification during the balancing process at the end of the assembly impossible. Consequently, a novel alignment tool was designed to address this issue, significantly reducing the effect and achieving the maximum allowable unbalance of 100 g·mm. This allowed the balancing machine used in the process to correct the initial unbalance of the reinforcements in a single work cycle, improving operation efficiency by about 15%. Full article

In Vehicular Ad Hoc Networks (VANETs), high mobility of vehicles issues a huge challenge to the reliability and security of transmitting packets. Therefore, a Secure and Reliable Opportunistic Routing (SROR) is proposed in this paper. During construction of Candidate Forwarding Nodes (CFNs) set, the relative velocity, connectivity probability, and packet forwarding ratio are taken into consideration. The aim of SROR is to maximally improve the packet delivery ratio as well as reduce the end-to-end delay. The selection of a relay node from CFNs is formalized as a Markov Decision Process (MDP) optimization. The SROR algorithm extracts useful knowledge from historical behavior of nodes by interacting with the environment. This useful knowledge are utilized to select the relay node as well as to prevent the malicious nodes from forwarding packets. In addition, the influence of different learning rate and exploratory factor policy on rewards of agents are analyzed. The experimental results show that the performance of SROR outperforms the benchmarks in terms of the packet delivery ratio, end-to-end delay, and attack success ratio. As vehicle density ranges from 10 to 50 and percentage of malicious vehicles is fixed at 10%, the average of packet delivery ratio, end-to-end delay, and attack success ratio are 0.82, 0.26s, and 0.37, respectively, outperforming benchmark protocols. Full article

The emergence of automotive-grade LiDARs has given rise to new potential methods to develop novel advanced driver assistance systems (ADAS). However, accurate and reliable parking slot detection (PSD) remains a challenge, especially in the low-light conditions typical of indoor car parks. Existing camera-based approaches struggle with these conditions and require sensor fusion to determine parking slot occupancy. This paper proposes a parking slot detection (PSD) algorithm which utilizes the intensity of a LiDAR point cloud to detect the markings of perpendicular parking slots. LiDAR-based approaches offer robustness in low-light environments and can directly determine occupancy status using 3D information. The proposed PSD algorithm first segments the ground plane from the LiDAR point cloud and detects the main axis along the driving direction using a random sample consensus algorithm (RANSAC). The remaining ground point cloud is filtered by a dynamic Otsu’s threshold, and the markings of parking slots are detected in multiple windows along the driving direction separately. Hypotheses of parking slots are generated between the markings, which are cross-checked with a non-ground point cloud to determine the occupancy status. Test results showed that the proposed algorithm is robust in detecting perpendicular parking slots in well-marked car parks with high precision, low width error, and low variance. The proposed algorithm is designed in such a way that future adoption for parallel parking slots and combination with free-space-based detection approaches is possible. This solution addresses the limitations of camera-based systems and enhances PSD accuracy and reliability in challenging lighting conditions. Full article

The assessment of driver assistance system effectiveness in crash scenarios can be conducted through simulations. This approach allows for system improvements and contributes to overall road safety. However, it is important to recognize that simulations are an approximation of reality. Specifically, simulation models can significantly impact the results. In our work, we focus on sensor models that provide environmental information to driver assistance systems. Real-world sensor data differ from idealized simulation data due to measurement errors and environmental influences. We evaluate various sensor models for their applicability in simulating effectiveness assessments. Our research is implemented using the open-source platform openPASS. We investigate the impact of inaccurate sensor data on the effectiveness of an autonomous emergency braking system based on longitudinal traffic crashes from the German In-Depth Accident Study database. To achieve this, we compare stochastic phenomenological models with ideal sensor models. In the evaluation of the modeled autonomous emergency braking, we observed a major influence on the effectiveness due to the less accurate, more realistic sensor data. This evaluation approach enables early optimization of the considered driver assistance system’s robustness against inaccuracies. Full article

Understanding the behavior of tires on uneven and varied road surfaces poses a substantial challenge for vehicle ride engineers. To accurately predict road load forces on the axle, various numerical ride models must be utilized to incorporate a realistic road enveloping algorithm. This algorithm filters the geometries of uneven surfaces and must be seamlessly integrated with a rigid ring model. The complexity of predicting and calculating dynamic tire response increases with varying obstacle dimensions. A two-dimensional, five-degree-of-freedom rigid ring ride model based on Short Wavelength Intermediate Frequency (SWIFT) has been developed, employing a tandem cam enveloping algorithm to filter short wavelength road obstacles. Selecting generalized cam parameters to ensure high accuracy and an enhanced runtime performance poses a challenge in specific ride simulations. A design of experiments (DOE) approach is used to identify key control factors related to the quasi-static tandem cam enveloping model and dynamic rigid ring model, which significantly affect the enveloping response. DOE findings suggest optimization strategies for selecting tire parameters to achieve a high test-to-simulation correlation with improved computational efficiency. Additionally, the study confirms the robustness of these predictions against external noise factors, including variations in tires and road conditions. Full article

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