Advanced Detection of Failed LEDs in a Short Circuit for Automotive Lighting Applications.

This paper addresses the issue of LED short-circuit fault detection in signaling and lighting systems in the automotive industry. The conventional diagnostic method commonly implemented in newer vehicles relies on measuring the voltage drop across different LED branches and comparing it with threshold values indicating faults caused by open circuits or LED short circuits. With this algorithm, detecting cases of a few LEDs short-circuited within a branch, particularly a single malfunctioning LED, is particularly challenging. In this work, two easily implementable algorithms are proposed to address this issue within the vehicle's control unit. One is based on a mathematical prediction model, while the other utilizes a neural network. The results obtained offer a 100% LED short-circuit fault detection rate in the majority of analyzed cases, representing a significant improvement over the conventional method, even in scenarios involving a single malfunctioning LED within a branch. Additionally, the neural network-based model can accurately predict the number of failed LEDs.

An Experimental Performance Assessment of Temporal Convolutional Networks for Microphone Virtualization in a Car Cabin.

In this paper, the experimental results on microphone virtualization in realistic automotive scenarios are presented. A Temporal Convolutional Network (TCN) was designed in order to estimate the acoustic signal at the driver's ear positions based on the knowledge of monitoring microphone signals at different positions-a technique known as virtual microphone. An experimental setup was implemented on a popular B-segment car to acquire the acoustic field within the cabin while running on smooth asphalt at variable speeds. In order to test the potentiality of the TCN, microphone signals were recorded in two different scenarios, either with or without the front passenger. Our experimental results show that, when training is performed in both scenarios, the adopted TCN is able to robustly adapt to different conditions and guarantee a good average performance. Furthermore, an investigation on the parameters of the Neural Network (NN) that guarantee the sufficient accuracy of the estimation of the virtual microphone signals while maintaining a low computational complexity is presented.

On-Road Evaluation of Unobtrusive In-Car Respiration Monitoring.

This paper introduces and evaluates an innovative sensor for unobtrusive in-car respiration monitoring, mounted on the backrest of the driver's seat. The sensor seamlessly integrates into the vehicle, measuring breathing rates continuously without requiring active participation from the driver. The paper proves the feasibility of unobtrusive in-car measurements over long periods of time. Operation of the sensor was investigated over 12 participants sitting in the driver seat. A total of 107 min of driving in diverse conditions with overall coverage rate of 84.45% underscores the sensor potential to reliably capture physiological changes in breathing rate for fatigue and stress detection.

Overview of Radar Alignment Methods and Analysis of Radar Misalignment's Impact on Active Safety and Autonomous Systems.

The rapid development of active safety systems in the automotive industry and research in autonomous driving requires reliable, high-precision sensors that provide rich information about the surrounding environment and the behaviour of other road users. In practice, there is always some non-zero mounting misalignment, i.e., angular inaccuracy in a sensor's mounting on a vehicle. It is essential to accurately estimate and compensate for this misalignment further programmatically (in software). In the case of radars, imprecise mounting may result in incorrect/inaccurate target information, problems with the tracking algorithm, or a decrease in the power reflected from the target. Sensor misalignment should be mitigated in two ways: through the correction of an inaccurate alignment angle via the estimated value of the misalignment angle or alerting other components of the system of potential sensor degradation if the misalignment is beyond the operational range. This work analyses misalignment's influences on radar sensors and other system components. In the mathematically proven example of a vertically misaligned radar, pedestrian detectability dropped to one-third of the maximum range. In addition, mathematically derived heading estimation errors demonstrate the impact on data association in data fusion. The simulation results presented show that the angle of misalignment exponentially increases the risk of false track splitting. Additionally, the paper presents a comprehensive review of radar alignment techniques, mostly found in the patent literature, and implements a baseline algorithm, along with suggested key performance indicators (KPIs) to facilitate comparisons for other researchers.

RadarTCN: Lightweight Online Classification Network for Automotive Radar Targets Based on TCN.

Automotive radar is one of the key sensors for intelligent driving. Radar image sequences contain abundant spatial and temporal information, enabling target classification. For existing radar spatiotemporal classifiers, multi-view radar images are usually employed to enhance the information of the target and 3D convolution is employed for spatiotemporal feature extraction. These models consume significant hardware resources and are not applicable to real-time applications. In this paper, RadarTCN, a novel lightweight network, is proposed that achieves high-accuracy online target classification using single-view radar image sequences only. In RadarTCN, 2D convolution and 3D-TCN are employed to extract spatiotemporal features sequentially. To reduce data dimensionality and computational complexity, a multi-layer max pooling down-sampling method is designed in a 2D convolution module. Meanwhile, the 3D-TCN module is improved through residual pruning and causal convolution is introduced for leveraging the performance of online target classification. The experimental results demonstrate that RadarTCN can achieve high-precision online target recognition for both range-angle and range-Doppler map sequences. Compared to the reference models on the CARRADA dataset, RadarTCN exhibits better classification performance, with fewer parameters and lower computational complexity.

Automotive Cybersecurity: A Survey on Frameworks, Standards, and Testing and Monitoring Technologies.

Modern vehicles are increasingly interconnected through various communication channels, which requires secure access for authorized users, the protection of driver assistance and autonomous driving system data, and the assurance of data integrity against misuse or manipulation. While these advancements offer numerous benefits, recent years have exposed many intrusion incidents, revealing vulnerabilities and weaknesses in current systems. To sustain and enhance the performance, quality, and reliability of vehicle systems, software engineers face significant challenges, including in diverse communication channels, software integration, complex testing, compatibility, core reusability, safety and reliability assurance, data privacy, and software security. Addressing cybersecurity risks presents a substantial challenge in finding practical solutions to these issues. This study aims to analyze the current state of research regarding automotive cybersecurity, with a particular focus on four main themes: frameworks and technologies, standards and regulations, monitoring and vulnerability management, and testing and validation. This paper highlights key findings, identifies existing research gaps, and proposes directions for future research that will be useful for both researchers and practitioners.

Novel Water Probe for High-Frequency Focused Transducer Applied to Scanning Acoustic Microscopy System: Simulation and Experimental Investigation.

A scanning acoustic microscopy (SAM) system is a common non-destructive instrument which is used to evaluate the material quality in scientific and industrial applications. Technically, the tested sample is immersed in water during the scanning process. Therefore, a robot arm is incorporated into the SAM system to transfer the sample for in-line inspection, which makes the system complex and increases time consumption. The main aim of this study is to develop a novel water probe for the SAM system, that is, a waterstream. During the scanning process, water was supplied using a waterstream instead of immersing the sample in the water, which leads to a simple design of an automotive SAM system and a reduction in time consumption. In addition, using a waterstream in the SAM system can avoid contamination of the sample due to immersion in water for long-time scanning. Waterstream was designed based on the measured focal length calculation of the transducer and simulated to investigate the internal flow characteristics. To validate the simulation results, the waterstream was prototyped and applied to the TSAM-400 and W-FSAM traditional and fast SAM systems to successfully image some samples such as carbon fiber-reinforced polymers, a printed circuit board, and a 6-inch wafer. These results demonstrate the design method of the water probe applied to the SAM system.

Realistic 3D Simulators for Automotive: A Review of Main Applications and Features.

Recent advancements in vehicle technology have stimulated innovation across the automotive sector, from Advanced Driver Assistance Systems (ADAS) to autonomous driving and motorsport applications. Modern vehicles, equipped with sensors for perception, localization, navigation, and actuators for autonomous driving, generate vast amounts of data used for training and evaluating autonomous systems. Real-world testing is essential for validation but is complex, expensive, and time-intensive, requiring multiple vehicles and reference systems. To address these challenges, computer graphics-based simulators offer a compelling solution by providing high-fidelity 3D environments to simulate vehicles and road users. These simulators are crucial for developing, validating, and testing ADAS, autonomous driving systems, and cooperative driving systems, and enhancing vehicle performance and driver training in motorsport. This paper reviews computer graphics-based simulators tailored for automotive applications. It begins with an overview of their applications and analyzes their key features. Additionally, this paper compares five open-source (CARLA, AirSim, LGSVL, AWSIM, and DeepDrive) and ten commercial simulators. Our findings indicate that open-source simulators are best for the research community, offering realistic 3D environments, multiple sensor support, APIs, co-simulation, and community support. Conversely, commercial simulators, while less extensible, provide a broader set of features and solutions.

A Virtual Testing Framework for Real-Time Validation of Automotive Software Systems Based on Hardware in the Loop and Fault Injection.

To validate safety-related automotive software systems, experimental tests are conducted at different stages of the V-model, which are referred as "X-in-the-loop (XIL) methods". However, these methods have significant drawbacks in terms of cost, time, effort and effectiveness. In this study, based on hardware-in-the-loop (HIL) simulation and real-time fault injection (FI), a novel testing framework has been developed to validate system performance under critical abnormal situations during the development process. The developed framework provides an approach for the real-time analysis of system behavior under single and simultaneous sensor/actuator-related faults during virtual test drives without modeling effort for fault mode simulations. Unlike traditional methods, the faults are injected programmatically and the system architecture is ensured without modification to meet the real-time constraints. Moreover, a virtual environment is modeled with various environmental conditions, such as weather, traffic and roads. The validation results demonstrate the effectiveness of the proposed framework in a variety of driving scenarios. The evaluation results demonstrate that the system behavior via HIL simulation has a high accuracy compared to the non-real-time simulation method with an average relative error of 2.52. The comparative study with the state-of-the-art methods indicates that the proposed approach exhibits superior accuracy and capability. This, in turn, provides a safe, reliable and realistic environment for the real-time validation of complex automotive systems at a low cost, with minimal time and effort.

A Predictive Quality Inspection Framework for the Manufacturing Process in the Context of Industry 4.0.

The purpose of this research is to develop an innovative software framework with AI capabilities to predict the quality of automobiles at the end of the production line. By utilizing machine learning techniques, this framework aims to prevent defective vehicles from reaching customers, thus enhancing production efficiency, reducing costs, and shortening the manufacturing time of automobiles. The principal results demonstrate that the predictive quality inspection framework significantly improves defect detection and supports personalized road tests. The major conclusions indicate that integrating AI into quality control processes offers a sustainable, long-term solution for continuous improvement in automotive manufacturing, ultimately increasing overall production efficiency. The economic benefit of our solution is significant. Currently, a final test drive takes 10-30 min, depending on the car model. If 200,000-300,000 cars are produced annually and our data prediction of quality saves 10 percent of test drives with test drivers, this represents a minimum annual saving of 200,000 production minutes.

A Feedback Active Control Approach to Road Noise Based on a Single Microphone Sensor to Improve Automotive Cabin Sound Comfort.

Tire-road noise deteriorates the sound quality of a vehicle's interior and affects the driving safety and comfort. Obtaining low interior noise is a challenge for passenger car manufacturers. Traditional passive noise control (PNC) is efficient for canceling high frequency noise but not useful for low frequency noise, while active noise control (ANC), according to the residual error signal, can generate an anti-noise signal to reduce the original noise. Most research has focused on improving the control effect for a feedforward ANC system. However, this paper emphasizes a feedback ANC system based on a signal microphone sensor. There are two main contributions in this study to improve automotive cabin sound comfort. One is that the algorithm of the feedback ANC system using a single microphone sensor without a reference noise signal is proposed based on the Filtered-x Least Mean Square method. The other is that the algorithm applies additive random noise online to estimate the secondary path model. A simulation was implemented based on measured real road noise data, and the simulation results indicate that the proposed feedback ANC system with the single microphone sensor can effectively attenuate road noise. This study shows the feasibility of applying a feedback ANC system in automobiles to increase the cabin sound quality.

Automating Fault Test Cases Generation and Execution for Automotive Safety Validation via NLP and HIL Simulation.

The complexity and the criticality of automotive electronic implanted systems are steadily advancing and that is especially the case for automotive software development. ISO 26262 describes requirements for the development process to confirm the safety of such complex systems. Among these requirements, fault injection is a reliable technique to assess the effectiveness of safety mechanisms and verify the correct implementation of the safety requirements. However, the method of injecting the fault in the system under test in many cases is still manual and depends on an expert, requiring a high level of knowledge of the system. In complex systems, it consumes time, is difficult to execute, and takes effort, because the testers limit the fault injection experiments and inject the minimum number of possible test cases. Fault injection enables testers to identify and address potential issues with a system under test before they become actual problems. In the automotive industry, failures can have serious hazards. In these systems, it is essential to ensure that the system can operate safely even in the presence of faults. We propose an approach using natural language processing (NLP) technologies to automatically derive the fault test cases from the functional safety requirements (FSRs) and execute them automatically by hardware-in-the-loop (HIL) in real time according to the black-box concept and the ISO 26262 standard. The approach demonstrates effectiveness in automatically identifying fault injection locations and conditions, simplifying the testing process, and providing a scalable solution for various safety-critical systems.

Studies on the Application of PGM Nanocatalysts from Spent Automotive Converters for Degradation of Ibuprofen in Aqueous Solutions.

There is an increasing concern about the presence of various types of pharmaceuticals in drinking water, as long-term exposure of people to even low concentrations of drugs can lead to many problems, such as endocrine disorders or drug resistance. As the removal in sewage treatment plants is not effective enough, as indicated, among others, by the EC and OECD reports, it is justified to search for new materials that will allow for an effective and rapid reduction of these pollutants in water. Therefore, in our work, catalytically active nanomaterials containing platinum group metals (PGMs) were synthesized from model and real multicomponent solutions and examined in reactions of organic compounds. The nanoparticles (NPs) were obtained from real solutions from the hydrometallurgical processing of spent automotive converters (SACs), and to the best of our knowledge, the novelty of the proposed paper is the application of solutions from SAC processing as precursors for PGM-NPs. The synthesized PGM-NPs were deposited on a support (TiO2), characterized and, finally, examined as nanocatalysts in a degradation reaction of ibuprofen (IB) from model aqueous solutions. The degree of IB degradation reached more than 90%. The main products of IB degradation were p-isobutylphenol and CO2.

Recovery of Residual Lead from Automotive Battery Recycling Slag Using Deep Eutectic Solvents.

In this study, we address the ecological challenges posed by automotive battery recycling, a process notorious for its environmental impact due to the buildup of hazardous waste like foundry slag. We propose a relatively cheap and safe solution for lead removal and recovery from samples of this type of slag. The analysis of TCLP extracts revealed non-compliance with international regulations, showing lead concentrations of up to 5.4% primarily in the form of anglesite (PbSO4), as detected by XRF/XRD. We employed deep eutectic solvents (DES) as leaching agents known for their biodegradability and safety in hydrometallurgical processing. Five operational variables were systematically evaluated: sample type, solvent, concentration, temperature, and time. Using a solvent composed of choline chloride and glycerin in a 2:1 molar ratio, we achieved 95% lead dissolution from acidic samples at 90 °C, with agitation at 470 rpm, a pulp concentration of 5%, and a 5 h duration. Furthermore, we successfully recovered 55% of the lead in an optimized solution using an electrowinning cell. This research demonstrates the ability of DES to decontaminate slag, enabling compliance with regulations, the recovery of valuable metals, and new possibilities for the remaining material.

Evaluation of genotoxicity, oxidative stress and immune parameters of auto-paint workers.

The automotive industry is a very wide area from the manufacturing of the pieces of the engine, the body, plastics to the assembly of the car. There is a chemical risk at different stages of production because of the requirement of the use of many corrosive and irritant chemicals such as paints, adhesives, acids, and bases. The aim of the study was to determine the genotoxicity, oxidative stress and immune parameters of automotive paint workers in Ankara, Türkiye. DNA damage of workers mainly responsible from the painting of the automotives were evaluated using the alkaline comet assay and the levels of some oxidative stress and immune biomarkers were also investigated. Increased lymphocyte DNA damage and also higher 8-hydroxy-2'-deoxyguanosine (8-OHdG) and malondialdehyde (MDA) levels were observed while decreased glutathione (GSH), glutathione peroxidase (GPx), and glutathione reductase (GR) levels were found in the workers compared to their controls There were no significant differences between the study groups in the levels of interleukin (IL)- 1beta, IL-17, IL-23, Clara cell secretory protein (CC16), tumor necrosis factor-alpha (TNF-alpha), catalase (CAT), and superoxide dismutase (SOD). The results show that occupational exposure to chemicals in automotive industry may cause DNA damage in workers due to oxidative stress.

Efficient HCl leaching of platinum group metals from waste three-way catalysts: A study on kinetics and mechanisms.

Waste three-way catalysts (TWCs) have attracted much attention due to the presence of platinum group metals (PGMs) and hazardous substances such as heavy metals and organic matter. The extraction of PGMs from waste TWCs using hydrochloric acid (HCl) has been extensively researched. However, the addition of oxidizing agents like H2O2 and aqua regia is necessary to facilitate PGMs dissolution, which poses significant environmental and operational hazards. Hence, developing a green PGMs recovery process without oxidants is imperative. Previously, we investigated the process of Li2CO3 calcination pretreatment to enhance the leaching of PGMs from waste TWCs by HCl, focusing on the process and mechanism of Li2CO3 calcination pretreatment. In this study, we focused on the leaching process of HCl after pretreatment. Our investigation includes a detailed examination of leaching kinetics and mechanisms. The optimal leaching conditions were: leaching temperature of 150 °C, leaching time of 2 h, HCl concentration of 12 M, and liquid-solid ratio of 10 mL/g. The experiments resulted in maximum leaching rates of about 96%, 97%, and 97% for Pt, Pd, and Rh, respectively. However, given the presence of heavy metals, attention needs to be paid to the harmless treatment of waste acids and leaching residues. The Pt and Pd leaching process is controlled by a mixture of interfacial chemical reactions and internal diffusion, and dominated by internal diffusion, while the leaching process of Rh is controlled by interfacial chemical reactions. Li+ in Li2PtO3, Li2PdO2, and Li2RhO3 preferentially leached and underwent ion-exchange reactions with H+, promoting the dissolution of Pt, Pd, and Rh in HCl.

Four mesothelioma cases from a single automotive dealership: A case series.

BACKGROUND: Four cases of mesothelioma were noted in a workplace of some 110 persons at a tractor dealership between 2006 and 2023. Each worker had a different job title. METHODS: Medical-legal case material was reviewed and abstracted from four cases from the same dealership, all supplied via one law firm. RESULTS: Four mesotheliomas are reported from this single facility that used chrysotile asbestos automotive products. Two of the four cases had no other known exposures to asbestos. DISCUSSION: Automotive products containing chrysotile do appear capable of causing mesothelioma. Job category is not a good surrogate for exposure.

Automotive 2.1 µm Full-Depth Deep Trench Isolation CMOS Image Sensor with a 120 dB Single-Exposure Dynamic Range.

An automotive 2.1 µm CMOS image sensor has been developed with a full-depth deep trench isolation and an advanced readout circuit technology. To achieve a high dynamic range, we employ a sub-pixel structure featuring a high conversion gain of a large photodiode and a lateral overflow of a small photodiode connected to an in-pixel storage capacitor. With the sensitivity ratio of 10, the expanded dynamic range could reach 120 dB at 85 °C by realizing a low random noise of 0.83 e- and a high overflow capacity of 210 ke-. An over 25 dB signal-to-noise ratio is achieved during HDR image synthesis by increasing the full-well capacity of the small photodiode up to 10,000 e- and suppressing the floating diffusion leakage current at 105 °C.

A Real-Time Inspection System for Industrial Helical Gears.

Manufacturing is an imperfect process that requires frequent checks and verifications to ensure products are being produced properly. In many cases, such as visual inspection, these checks can be automated to a certain degree. Incorporating advanced inspection techniques (i.e., via deep learning) into real-world inspection pipelines requires different mechanical, machine vision, and process-level considerations. In this work, we present an approach that builds upon prior work at an automotive gear facility located in Guelph, Ontario, which is looking to expand its defect detection capabilities. We outline a set of inspection-cell changes, which has led to full-gear surface scanning and inspection at a rate of every 7.5 s, and which is currently able to detect three common types of surface-level defects.

Single-Wire Control and Fault Detection for Automotive Exterior Lighting Systems.

The design of exterior lighting is crucial for automotive manufacturers to ensure the visibility and safety of the driver. This article proposes a new strategy to control and diagnose one or more exterior lighting functions in electric vehicles by maximising the electrical faults that are detected and their transfer over a single-wire. The outcome is a decreased system cost and an additional method for vehicle lighting infrastructure control and diagnosis. Virtual simulation tools are used to explore the correlation between master-slave architecture and the effectiveness of the single-wire approach to comply with safety and regulatory demands. Safety-related and non-safety-related needs are explored to properly assess lighting functions, internal logic, and fault-case scenarios. Furthermore, assessing the viability of minimizing wire harness utilization while retaining the diagnostic capabilities for the controlled lighting sources, thereby simultaneously reducing the vehicle's overall weight. This approach aims to concurrently decrease the overall weight of the vehicle. This work has three main contributions: (1) the development of efficient and reliable lighting systems in electric vehicles, a critical factor for achieving optimal performance, ensuring customer satisfaction, meeting regulatory compliance, and enhancing cost-effectiveness in automotive lighting systems. (2) Framework for future development and implementation of lighting systems in electric vehicles. (3) Simulation of the hardware architecture associated with the system strategy to achieve the desired system strategy for effectively applying the single-wire approach.