Vehicle Failure Research Articles

Artificial Intelligence Failures in Autonomous Vehicles: Causes, Implications, and Prevention

Artificial Intelligence Failures in Autonomous Vehicles: Causes, Implications, and Prevention

Comprehensive Assessment of Deep-Water Vessel Implosion Mechanisms: OceanGate's Titan Submersible Failure Sequence Explained

This study outlines the physical mechanisms involved in a submersible implosion and analyzes the loss of the Titan submersible (‘sub’) that occurred on 18 June 2023 during a mission to visit the wreck of the Titanic. Titan’s collapse mechanisms at the moment of implosion are described in detail and outer hull fracturing rate, subsequent implosion rate, accompanying heat release and other key processes are quantified. Plausible causes of the hull’s leak leading up to critical loss of the sub's hermetic closure are reviewed using test results made publicly available by the U.S. Marine Board of Investigation. Their data indicated that the bond interfaces between the individual layers of the carbon fiber hull (Hull V2) were critically compromised due to manufacturing defects, voids, porosity, and inadequate adhesive integrity, resulting in significant delamination. Analysis of data from the real-time hull health monitoring system, revealed acoustic anomalies and strain shifts, pointing toward increasing structural fatigue, which went unaddressed prior to the fatal dive. The implosion process can be characterized by an instantaneous collapse of the air volume within the hull under extreme external pressure: even the tiniest leak would lead to destruction of the vessel's structural integrity. The destruction was the more devastating, because it was accompanied by a secondary explosion due to the heat exchange between the collapsing air volume and the ambient sea water. While the collapsing air was phase-changed into a supercritical state, the generated heat caused the adjacent seawater to evaporate and expand. Hull pieces fragmented by the initial implosion were strewn around during the secondary explosion phase, which ceased rapidly as the steam condensed back into seawater once again. The Titan incident underscores the urgent need for improved design standards, rigorous quality control in manufacturing, and enhanced real-time monitoring to prevent similar failures of future deep-sea exploration vehicles.

Research on Network Security and Privacy Protection Technology of Autonomous Vehicle

With the rapid development of information technology and the increasing maturity of intelligent transportation systems, autonomous vehicles, as an important carrier of future travel, are gradually moving from concept to commercial application. However, the highly connected and intelligent nature of autonomous vehicles also makes them face unprecedented cybersecurity and privacy protection challenges. This paper deeply discusses the security threats and privacy disclosure risks of autonomous vehicles in network architecture, data transmission, data processing and other links, and systematically reviews the current technical means and research progress to address these challenges. This paper Outlines the data collection and transmission mechanism of autonomous vehicles, including the collection and processing of sensor data as well as the communication process between vehicles and the cloud, other vehicles and transportation infrastructure, and analyzes in detail the network security threats faced by autonomous vehicles, including remote control attacks, malware attacks, communication hijacking, etc. These threats can lead to serious consequences such as vehicle failure, data breach, or system crash. At the same time, the paper also points out the challenges faced by autonomous vehicles in terms of privacy protection, such as the risk of leakage of sensitive data such as personal location information and driving habits, as well as the risk of data abuse and sharing. In order to address the above challenges, this paper focuses on the technical research progress of cybersecurity and privacy protection of autonomous vehicles. In the aspect of network security, this paper discusses the key technical means such as data encryption, access control, intrusion detection and response system, and emphasizes the importance of security hardware and software in improving the overall security of the system. In terms of privacy protection, this paper proposes strategies such as anonymization and desensitization technology, privacy protection protocols and standards to protect users' privacy from invasion.

Detecting APS failures using LSTM-AE and anomaly transformer enhanced with human expert analysis

This study develops a novel semi-supervised approach for detecting Air Pressure System (APS) failures in Heavy-Duty Vehicles (HDVs) by exploiting two modern Machine Learning (ML) models: Long Short-Term Memory Autoencoder (LSTM-AE) and Transformer for Anomaly Detection (TranAD), and enhancing their performance with human expertise. To tackle the failure detection problem, a dataset comprising 30 days of operational time-series data from 110 healthy vehicles with no recorded APS issues and 30 vehicles that experienced APS failures requiring road assistance was acquired. Several preprocessing steps are proposed and three key features are extracted as APS health indicators. These features are then utilized both in human expert analysis (HEA) and training of ML models. When compared to HEA, both LSTM-AE and TranAD models exhibit superior performance individually in APS failure detection, achieving F1 scores of 0.75 and 0.79 respectively, and the same accuracy of 91.4%. Further, the integration of HEA with those ML models enhances model effectiveness in all experimental results, especially in reducing false alarms that cause customer dissatisfaction. The TranAD model combined with human expert analysis achieved the best performance with an unprecedented 0.82 F1 score and 92.8% accuracy. In addition to presenting a new methodology for failure detection, this paper suggests a way for more efficient and reliable predictive maintenance practices for HDVs.

Concept of Early Prediction and Identification of Truck Vehicle Failures Supported by In-Vehicle Telematics Platform Based on Abnormality Detection Algorithm

Concept of Early Prediction and Identification of Truck Vehicle Failures Supported by In-Vehicle Telematics Platform Based on Abnormality Detection Algorithm

Fault-Tolerant Optimal Consensus Control for Heterogeneous Multi-Agent Systems

This study explores fault-tolerant consensus in leader–following heterogeneous multi-agent systems, focusing on actuator failures in uncrewed aerial vehicles (UAVs) and uncrewed ground vehicles (UGVs). An optimization-based fault-tolerant consensus algorithm is proposed. The algorithm utilizes the Euler–Lagrange formula to ensure system consistency under actuator failures, with the Lyapunov stability theory proving the asymptotic stability of the consistency error. The algorithm is applied to heterogeneous multi-agent systems of UAVs and UGVs to derive optimal fault-tolerant consensus control laws for each vehicle type. Simulation experiments give evidence for the feasibility of the proposed control strategy.

A Survey on Sensor Failures in Autonomous Vehicles: Challenges and Solutions.

Autonomous vehicles (AVs) rely heavily on sensors to perceive their surrounding environment and then make decisions and act on them. However, these sensors have weaknesses, and are prone to failure, resulting in decision errors by vehicle controllers that pose significant challenges to their safe operation. To mitigate sensor failures, it is necessary to understand how they occur and how they affect the vehicle's behavior so that fault-tolerant and fault-masking strategies can be applied. This survey covers 108 publications and presents an overview of the sensors used in AVs today, categorizes the sensor's failures that can occur, such as radar interferences, ambiguities detection, or camera image failures, and provides an overview of mitigation strategies such as sensor fusion, redundancy, and sensor calibration. It also provides insights into research areas critical to improving safety in the autonomous vehicle industry, so that new or more in-depth research may emerge.

Safety risk assessment for automotive battery pack based on deviation and outlier analysis of voltage inconsistency

Safety risk assessment is essential for evaluating the health status and averting sudden battery failures in electric vehicles. This study introduces a novel safety risk assessment approach for battery systems, addressing both cell and pack levels with three key indexes. The core of the assessment lies in representing the relative deviation of cell voltages through scatter diagrams across various stages of service life. Specifically, the study quantifies voltage deviations and deviation angles within different state of charge intervals to gauge safety risks at the cell level. Leveraging clustering algorithms aids in identifying outlier values. Furthermore, the dispersion of scatter points is utilized to assess safety risks at the pack level. Validation of this proposed model is conducted through cycling tests on battery modules with a deformed cell, demonstrating its efficacy in capturing inconsistent features of mechanical deformation abuse across the three indexes, thereby triggering alarms during operation. Moreover, the method is applied to assess the safety risks of five hazardous and accident-prone vehicles, enabling comprehensive evaluation of potential faulty cells and safety risks at the pack level. This proposed approach offers a fresh perspective on the comprehensive safety risk assessment of battery systems.

Bioinspired Control Architecture for Adaptive and Resilient Navigation of Unmanned Underwater Vehicle in Monitoring Missions of Submerged Aquatic Vegetation Meadows.

Submerged aquatic vegetation plays a fundamental role as a habitat for the biodiversity of marine species. To carry out the research and monitoring of submerged aquatic vegetation more efficiently and accurately, it is important to use advanced technologies such as underwater robots. However, when conducting underwater missions to capture photographs and videos near submerged aquatic vegetation meadows, algae can become entangled in the propellers and cause vehicle failure. In this context, a neurobiologically inspired control architecture is proposed for the control of unmanned underwater vehicles with redundant thrusters. The proposed control architecture learns to control the underwater robot in a non-stationary environment and combines the associative learning method and vector associative map learning to generate transformations between the spatial and velocity coordinates in the robot actuator. The experimental results obtained show that the proposed control architecture exhibits notable resilience capabilities while maintaining its operation in the face of thruster failures. In the discussion of the results obtained, the importance of the proposed control architecture is highlighted in the context of the monitoring and conservation of underwater vegetation meadows. Its resilience, robustness, and adaptability capabilities make it an effective tool to face challenges and meet mission objectives in such critical environments.

An integrated data-driven framework for vehicle quality analysis based on maintenance record mining and Bayesian network

PurposeThe purpose of this paper is to present an integrated data-driven framework for processing and analyzing large-scale vehicle maintenance records to get more comprehensive understanding on vehicle quality.Design/methodology/approachWe propose a framework for vehicle quality analysis based on maintenance record mining and Bayesian Network. It includes the development of a comprehensive dictionary for efficient classification of maintenance items, and the establishment of a Bayesian Network model for vehicle quality evaluation. The vehicle design parameters, price and performance of functional systems are modeled as node variables in the Bayesian Network. Bayesian Network reasoning is then used to analyze the influence of these nodes on vehicle quality and their respective importance.FindingsA case study using the maintenance records of 74 sport utility vehicle (SUV) models is presented to demonstrate the validity of the proposed framework. Our results reveal that factors such as vehicle size, chassis issues and engine displacement, can affect the chance of vehicle failures and accidents. The influence of factors such as price and performance of engine and chassis show explicit regional differences.Originality/valuePrevious research usually focuses on limited maintenance records from a single vehicle producer, while our proposed framework enables efficient and systematic processing of larger-scale maintenance records for vehicle quality analysis, which can support auto companies, consumers and regulators to make better decisions in purchase choice-making, vehicle design and market regulation.

ПОВЫШЕНИЕ ЭКСПЛУАТАЦИОННЫХ ПОКАЗАТЕЛЕЙ ДИЗЕЛЬНОГО ДВИГАТЕЛЯ ВНУТРЕННЕГО СГОРАНИЯ

The current problem of operating automotive equipment at low temperatures associated with the loss of diesel fuel’s properties (reducing the cloud point and crystallization of the fuel) is being considered, resulting in equipment failures due to failure of the power system and direct freezing of the fuel in the tubes, paraffin deposition leading to clogging of fuel filters. Domestic automotive vehicles do not have any effective means of heating diesel fuel, which forces drivers operating equipment in harsh climatic conditions to use their own individual developments. For these purposes, a device has been developed that allows you to heat all the diesel fuel poured into the fuel tank. The drive of the device (fuel priming pump) is proposed to be electric (from a linear electric motor), from the vehicle's on-board network, the type of pump used is piston, double-acting. A comprehensive calculation of the fuel priming pump is presented in order to determine its main characteristics and design dimensions. A schematic diagram of the installation of the developed device and all elements in the diesel engine power system is presented. The implementation of the proposed technical solution was carried out in the power system of the YaMZ-238 diesel engine installed on a load stand under conditions of its operation in low temperatures (the Far North, etc.). The obtained results of tests on a bench engine confirm the relevance of the research and showed that the use of the developed technical solution will allow heating all the diesel fuel poured into the car tank to a temperature of plus 50 °C, thereby eliminating the possibility of its crystallization and cloudiness and, in general, reducing the number of vehicle failures. techniques related to this problem.

Mixed-model sequencing with reinsertion of failed vehicles: a case study for automobile industry

In the automotive industry, some vehicles, failed vehicles, cannot be produced according to the planned schedule due to some reasons such as material shortage, paint failure, etc. These vehicles are pulled out of the sequence, potentially resulting in an increased work overload. On the other hand, the reinsertion of failed vehicles is executed dynamically as suitable positions occur. In case such positions do not occur enough, either the vehicles waiting for reinsertion accumulate or reinsertions are made to worse positions by sacrificing production efficiency. This study proposes a bi-objective two-stage stochastic program and formulation improvements for a mixed-model sequencing problem with stochastic product failures and integrated reinsertion process. Moreover, an evolutionary optimisation algorithm, a two-stage local search algorithm, and a hybrid approach are developed. Numerical experiments over a case study show that while the hybrid algorithm better explores the Pareto front representation, the local search algorithm provides more reliable solutions regarding work overload objective. Finally, the results of the dynamic reinsertion simulations show that we can decrease the work overload by 20% while significantly decreasing the waiting time of the failed vehicles by considering vehicle failures and integrating the reinsertion process into the mixed-model sequencing problem.

Advanced Data Science Applications in Vehicles: A Comprehensive Review

Purpose: This research paper aims to explore the transformative impact of data science applications within the automotive industry, with a focus on the evolution of vehicles into intelligent, connected, and autonomous entities. Through an extensive literature review and analysis of recent developments, the paper seeks to provide a comprehensive understanding of the current state and future prospects of data science in vehicles.
 Methodology: Advancements in sensor technologies, such as LiDAR, HD maps, radars, location tracking and cameras, are discussed, highlighting their role in data collection for applications like advanced driver assistance systems (ADAS) and autonomous driving. Relevant citations are provided. This subsection covers the collection and transmission of real-time data through telematics systems, showcasing their importance for predictive maintenance, fleet management, and personalized insurance programs. Citations support the presented information. The deployment of edge computing for real-time data processing in vehicles is discussed, emphasizing its significance for safety-critical applications like collision avoidance. Citations are provided to support the information. This section explores the application of machine learning algorithms to predict vehicle failures, optimize fuel efficiency, and analyze driver behavior. The importance of leveraging historical data to create accurate models is highlighted with supporting citations.
 Findings: The pivotal role of data science in enabling autonomous vehicles to navigate complex environments is discussed, emphasizing the use of machine learning models for real-time decision-making. Citations support the presented information. This subsection explores the integration of data science in traffic management systems, covering dynamic traffic signal control, congestion prediction, and route optimization. Citations support the findings related to traffic management applications. The paper discusses challenges associated with widespread implementation, including data privacy concerns, cybersecurity risks, and the need for standardized communication protocols. Additionally, potential future directions are outlined, such as the integration of blockchain for secure data sharing and the development of advanced human-machine interfaces.
 Unique Contribution to Theory, Practice and Policy: This research paper provides a well-rounded contribution by seamlessly integrating theoretical concepts, practical applications, and policy considerations in the realm of advanced data science applications in vehicles.

Adaptive fault‐tolerant platooning control for nonlinear homogeneous multiple autonomous vehicles under unreliable communication networks

AbstractThis article presents a fault‐tolerant platooning control algorithm for a class of nonlinear homogeneous multi‐vehicle systems under unreliable network communication environment. The considered types of actuator failures in individual vehicle are loss‐in‐effectiveness, and bias faults. Suppose the communication network among vehicles is here described as a switching graph subject to intermittent communications. Unlike the existing results dealing with vehicle platoon under continuous communications, this article develops a hierarchical fault‐tolerant platooning control approach, under which, the multiple vehicle coordination platooning objective is ensured even in the cases of discontinuous communication faults and actuator failures. First, each following vehicle can estimate the desire reference information via cooperative adaptive virtual estimator even in intermittent communications. Second, the local fault‐tolerant controller with adaptive parameter update mechanism can guarantee the practical position and velocity of following vehicles asymptotically track the desire estimations. Finally, a simulation example of a team of autonomous vehicles is given to verify the effectiveness of the proposed method.

Building causal models for finding actual causes of unmanned aerial vehicle failures.

Finding actual causes of unmanned aerial vehicle (UAV) failures can be split into two main tasks: building causal models and performing actual causality analysis (ACA) over them. While there are available solutions in the literature to perform ACA, building comprehensive causal models is still an open problem. The expensive and time-consuming process of building such models, typically performed manually by domain experts, has hindered the widespread application of causality-based diagnosis solutions in practice. This study proposes a methodology based on natural language processing for automating causal model generation for UAVs. After collecting textual data from online resources, causal keywords are identified in sentences. Next, cause-effect phrases are extracted from sentences based on predefined dependency rules between tokens. Finally, the extracted cause-effect pairs are merged to form a causal graph, which we then use for ACA. To demonstrate the applicability of our framework, we scrape online text resources of Ardupilot, an open-source UAV controller software. Our evaluations using real flight logs show that the generated graphs can successfully be used to find the actual causes of unwanted events. Moreover, our hybrid cause-effect extraction module performs better than a purely deep-learning based tool (i.e., CiRA) by 32% in precision and 25% in recall in our Ardupilot use case.

Modernization of Railway Wagons for Customer Satisfaction and Safety

This article focuses on the assessment of the efficiency of the modernization of railway wagons intended for passenger transport. The modernization of railway wagons is an important step towards ensuring the safer and more efficient operation of railways. However, this modernization is not without risks and, in addition to its many positive effects, it can also bring new challenges in the form of breakdowns and technical problems. Failures in passenger rail vehicles are a challenge facing manufacturers and transport operators, and identifying the relationships between these failures can help to improve the design, manufacture, and maintenance of these vehicles. Passenger rail vehicle failures can vary in nature, whether mechanical, electrical, or inside the vehicle. This paper attempts to provide a comprehensive view of the effectiveness of passenger rail car retrofitting based on the data collected and statistical analyses. The article further focuses on the description of selected rail passenger wagons, describes the analyses of the statistical data using the correlation between the selected rail wagon failures and their significance, in addition to whether the impact of modernization reduces the number of rail wagon failures. The results of this statistical analysis can be used to better understand the impact of modernization on different aspects of passenger transport and will help in making future investment and policy decisions in this area. © 2017 Elsevier Inc. All rights reserved.

Classification of Factors Used to Predict Vehicle Break down in Commercial Vehicles

Prediction of vehicle breakdowns in commercial vehicles is of utmost importance to ensure the safety, reliability, and cost-effectivenessof road transport. In today's fast-paced and competitive business landscape, the smooth operation of commercialvehicles is crucial for ensuring efficient logistics and supply chain management. Unplanned breakdowns of commercial vehiclescan lead to significant financial losses, delays in delivery schedules, and potential damage to the reputation of the transportation companies. Commercial vehicle breakdowns pose significant challenges to both fleet operators and the broader transportation industry. The unexpected failure of a commercial vehicle can result in substantial costs, operational disruptions, and safetyhazards. In light of these considerations, the development of effective breakdown prediction models hasbecome an essentialendeavor.Predicting when andwhy abreakdownmight occurnotonly allows for proactive maintenance but also minimizes the economic and safety risksassociated withbreakdowns.©2024 STAIQC.Allrightsreserved

Collective Navigation of Aerial Vehicle Swarms: A Flocking Inspired Approach

Collective navigation is inspired by natural migratory species traveling long distances and conserving their group behavior in the whole journey. With the technological development of autonomous intelligent systems, collective navigation has been an important application in the field of intelligent vehicles. However, many of the existing collective behavior approaches rely on communication exchanges between individuals and focus on the emergence of collective motion, which limits the application in specific mission scenarios and lacks robustness against interference. To address these challenges, this paper proposes a novel flocking-inspired approach of collective navigation for aerial vehicle swarms flying synchronously to the desired region. Firstly, flocking without communication is achieved based on bionic visual projection field (VPF), ensuring the normal operation of the swarm in communication-denied unknown environments. Secondly, each aerial entity is assigned a hidden identity as informed or uninformed, but only the informed agents are allocated information about the migration destination. Under such an architecture, the trade-off between informed and uninformed individuals in the group enables few-to-many control and fast navigation. Finally, simulation and experiments are both conducted within multiple scenarios, in terms of adaptability to different environments, swarm scalability, and robustness to vehicle failures. Results validate the effectiveness of the developed bio-inspired collective navigation algorithm.

Methodology for collecting information in the study of vehicle safety

The article proposes a methodology for collecting information about the reliability and safety of the car during its operation. The proposed method allows manufacturers and motor transport enterprises to predict vehicle failures, determines the time period for its approach to current repairs and other types of technical impacts. Recommendations are given for determining reliability indicators, as well as part of the mathematical apparatus for calculating intervals and the probability of failures.

Road Design on Human Driver Accidents Versus Automated Vehicle Failures: Comparison With Real-World Field Data

Road Design on Human Driver Accidents Versus Automated Vehicle Failures: Comparison With Real-World Field Data