Vehicle Safety Research Articles

Evaluation of Autonomous Driving Safety by Operational Design Domains (ODD) in Mixed Traffic

This study derived effective driving behavior indicators to assess the driving safety of autonomous vehicles (AV). A variety of operation design domains (ODD) in urban road networks, which include intersections, illegal parking, bus stop, bicycle lanes, and pedestrian crossings, were taken into consideration in traffic simulation analyses. Both longitudinal and interaction driving indicators were investigated to identify the driving performance of AVs in terms of traffic safety in mixed traffic stream based on simulation experiments. As a result of identifying the appropriate evaluation indicator, time-varying stochastic volatility (VF) headway time was selected as a representative evaluation indicator for left turn and straight through signalized intersections among ODDs related to intersection types. VF headway time is suitable for evaluating driving ability by measuring the variation in driving safety in terms of interaction with the leading vehicle. In addition to ODDs associated with intersection type, U-turns, additional lane segments, illegal parking, bus stops, and merging lane have common characteristics that increase the likelihood of interactions with neighboring vehicles. The VF headway time for these ODDs was derived as driving safety in terms of interaction between vehicles. The results of this study would be valuable in establishing a guideline for driving performance evaluation of AVs. The study found that unsignalized left turns, signalized right turns, and roundabouts had the highest risk scores of 0.554, 0.525, and 0.501, respectively, indicating these as the most vulnerable ODDs for AVs. Additionally, intersection and mid-block crosswalks, as well as bicycle lanes, showed high risk scores due to frequent interactions with pedestrians and cyclists. These areas are particularly risky because they involve unpredictable movements from non-vehicular road users, which require AVs to make rapid adjustments in speed and trajectory. These findings provide a foundation for improving AV algorithms to enhance safety and establishing objective criteria for AV policy-making.

Analysis of Children's Road Crashes in Hungary

In the EU, more than 6,000 children died in road accidents between 2011 and 2020. Children are particularly vulnerable road users, and they need to be protected. This underlines the importance of the Safe System approach. The Safe System approach is a holistic view of road safety, which integrates the different elements of the traffic system and takes human vulnerability and fallibility into account. Children are still in the phase of developing the cognitive and physical skills necessary to travel safely in traffic. Because of their small size, children are less visible than other road users and less experienced; they can easily become innocent victims in collisions. Despite significant improvements in vehicle safety in recent years, almost half of all child road deaths occur while traveling in cars. Limited data is available on the correct use of child seats in cars across the EU, but studies have shown that misuse remains a significant problem. Several measures have been taken in recent years to make it safer for children to travel on the roads, but many more interventions are needed to further improve their safety. Our research aimed to examine the characteristics of child accidents in Hungary and to highlight the main road safety problems affecting children in Hungary.

Torsional damper design for diesel engine: theory and application

Abstract Torsional vibration is the primary cause of engine crankshaft failure. Consequently, the reduction of engine vibration is of paramount importance for the enhancement of automotive safety and comfort. However, the lack of comprehensive insight into the damping mechanism of the torsional damper has impeded the effective control of engine torsional vibration. In order to gain insight into the vibration characteristics of the shaft system in an inline six-cylinder diesel engine, an analysis of the system’s behaviour during both free and forced vibration was conducted. A mathematical relationship was deduced between the dimensions, material, operational temperature and damping properties of the silicone oil damper. The objective was to determine the optimal damping and moment of inertia of the damper, with the aim of minimizing the torsional amplitude of the sixth harmonic. The results demonstrate a reduction in the six-harmonic torsional amplitude from 0.32° to 0.14° following the installation of the damper. The mean and relative deviations between the calculated and experimental results are 0.0018° and 0.83%, respectively. To facilitate the design of the silicone oil damper, a software program, Vibsim, was developed based on Visual Studio for the design and torsional vibration analysis of the silicone oil damper. This software is reliable in calculating results and is user-friendly.

Computer simulation of the truck cabin tests to find ways to meet the requirements of the UNECE Regulation 29

BACKGROUND: Ensuring the required level of passive safety of trucks is a relevant task. To solve this problem, it is necessary to use computer simulation methods. AIM: Search for the ways to ensure the passive safety of trucks by choosing the appropriate cabin design. MATERIALS AND METHODS: During the study, the method of virtual simulation of the truck cabin tests was used in accordance with the requirements of the UNECE Regulation 29. RESULTS: A simulation model of a truck cabin has been developed, which makes it possible to conduct the computer simulation of the tests according to the UNECE Regulation 29. The main requirement of the UNECE Regulation 29 is retaining sufficient space in the cabin to accommodate the driver’s dummy inside it after its testing. As a part of the study, the properties of the material (steel) used for manufacturing of prototype cabins of the truck under development were determined, and the tested specimens were obtained by dissecting the prototype of the cabin, so the obtained results take into account the technological history of the material. The obtained data on the properties of the materials were used in the development of the simulation model of the cabin. The developed and used simulation model takes into account all the design features of the cabin under study, including the location and properties of welding points. Various options of cabin design changes and their impact on the ability to meet the requirements of the UNECE Regulations 29 are investigated. It has been found that it is not possible to fully comply with the requirements of the UNECE Regulation 29 only by increasing (within reasonable limits) the thickness of individual elements of the cabin frame and/or using high-strength steel for their manufacturing. Based on the analysis of the deformation of the prototype cabin frame during the Test C according to the UNECE Regulation 29 Revision 2, proposals for changes to the cabin design to meet the requirement of the UNECE Regulation 29 are formulated. CONCLUSION: The results of the study indicate the prospects of the proposed changes to the cabin design.

Predictive Analytics in ADAS Development: Leveraging CRM Data for Customer-Centric Innovations in Car Manufacturing

The rapid evolution of Advanced Driver Assistance Systems (ADAS) has transformed the automotive landscape, necessitating a shift towards more customer-centric development strategies. This paper explores the integration of predictive analytics with Customer Relationship Management (CRM) data to foster innovations in ADAS development. By harnessing insights from customer interactions, preferences, and feedback, manufacturers can anticipate market demands and tailor ADAS features to enhance user experience. Through a comprehensive analysis of case studies and industry practices, we demonstrate how predictive analytics can improve decision-making processes, facilitate the identification of emerging trends, and optimize resource allocation. The findings underscore the potential of leveraging CRM data to drive customer- focused innovations, ultimately resulting in enhanced vehicle safety, satisfaction, and competitive advantage in the automotive sector.

Foresight Study of IoT Technology in Preventing Road Crash: A Case of Car Alcohol Detector

Alcohol detector in car is the application of IoT technology that senses alcohol level consumed by the driver’s breath and prevents them from driving. Road crash that caused by drunk driver recorded the number of deaths increase and it is uncontrollable and continuously happened. The aim of this study is to identify the key drivers and determine the futures implication wheel of road crash prevention by the implementation of alcohol detector installation in car. The STEEPV analysis is used to identify key drivers and for questionnaires development. A total of 223 respondents from Klang Valley participated in the survey with a response rate of 58.07%. Based on impact-uncertainty analysis, the top two drivers are “save human lives” and “safety feature increase car safety.” The implications of saving human lives are reducing the number of deaths, improving market needs, avoiding traffic congestion, and increasing the value of human life. Whereas the implications of safety feature that increasing car safety are that it increases production activity of new car, preventing road crash, increasing car cost as well as business capabilities. Overall, this study is important for future researchers and the automotive industry by contributing a significant idea for future needs technology in car.

How Covid-19 Has Changed Safety in the Car Transportation Sector: A Corpus-Assisted Analysis of Non-Financial Reports

The importance of people’s safety during Covid-19 has resulted in greater attention to this aspect in CSR and ESG disclosures, especially in the field of transport. The relevance of this topic is also reflected in the recent publication of linguistic studies that investigate how companies in some sub-sectors communicate their commitment to passengers’ safety (Rossato & Nocella, 2022; Bondi & Nocella, 2023). This paper explores how safety is discursively constructed and communicated in the car rental and ride sharing sectors, both before and during the pandemic. Working along the lines of corpus-assisted discourse studies (Partington et al., 2013), the research analyses a corpus of English CSR and ESG reports published by international American companies in the above-mentioned fields of transport. Findings suggest a great concern for safety issues and a more inclusive approach to both customers’ and employees’ safety during the pandemic. Additionally, car rental companies show more attention to vehicle safety, while ride sharing operators put more emphasis on road safety. Evidence also suggests that the companies largely employ commissive statements and vague linguistic choices that signal a general lack of transparency on the practices and initiatives enacted to ensure customers’ and workers’ safety.

Trust calibration through perceptual and predictive information of the external context in autonomous vehicle

Maintaining an appropriate level of trust is critical for driving safety in autonomous vehicles. While enhancing the driver’s situation awareness (SA) of system information in autonomous driving is known to significantly promote trust calibration, it remains unclear whether enhancing the driver’s SA of the external context during driving contributes to this calibration. This study addresses this gap by improving SA of the external context during Level 3 (L3) driving automation across various driving environments. Driving contexts were manipulated using distinct road conditions containing low, medium, or high contextual risks. To enhance driver’s SA of the driving context, we redesigned the in-vehicle central control panel to display real-time perceptual and predictive information about the external driving context. We hypothesized that SA of driving contexts would facilitate trust calibration rather than merely enhancing trust, allowing trust to adjust to appropriate levels under different driving conditions. Experiment 1 examined the impact of perceptual information about the road, traffic infrastructure, and surrounding vehicles on drivers’ trust. We found that driver’s trust decreased with increased contextual risk only when the reconfigured panel was used, while the number of accidents was not affected. Experiment 2 investigated the effect of predictive information about the external context on drivers’ trust by marking safe and dangerous zones around driver’s vehicle with green and red areas, respectively. We revealed that the predictive information calibrated the trust according to road conditions and increased overall trust levels, while the number of accidents was not affected. Together, these findings suggest that enhancing perception and prediction of external contexts helps drivers align their trust with contextual risk levels in L3 driving automation without compromising driving safety.

Intelligent control algorithms to ensure the flight safety of aerospace vehicles.

Intelligent control algorithms to ensure the flight safety of aerospace vehicles.

자율주행 차량 사고 원인 분석을 통한 안전성 제고 방안

Although the convenience of drivers is maximizing due to the full-fledged self-driving service, complex problems are still emerging in various areas, including legal, ethical, and social aspects. As a result, social awareness and acceptability of self-driving cars have not been sufficiently formed, and efforts to improve stability are essential at this point. Therefore, in this study, only records of accidents while driving autonomously were selected among the accident data of autonomous vehicles provided by California DMV and the causes of accidents were analyzed for about 400 cases. As a result, main causes were verified as low recognition rate of parked vehicles, inability to grasp changes in vehicle size due to attachment of convenience goods, insufficient defense driving system, low recognition speed in special road conditions, etc. To mitigate these problems, it is expected to increase vehicle recognition rate, reconstruct safety area range, devise additional vehicle functionalities for defensive driving, and emphasize the importance of communication between self-driving cars and smart roads. These measures aim to enhance the safety of autonomous vehicles and contribute to increasing positive social perception and trust in autonomous driving.

Preparation, Characterization and Conductivity of SiO2 doped rGO-WO3 Composite for Battery Application

High-performance solid polymer electrolytes are regarded as one of the most promising materials to be used in the development of lithium ion batteries with improved extended life, increased recyclability and increased safety for electric vehicles and energy storage. In this work, a microwave-assisted hydrothermal technique was used to produce SiO2-doped rGO-WO3 composite, which possesses outstanding supercapacitor capabilities. This method is simple, low-cost method without using any other capping and reducing agents. The obtained SiO2 doped rGO-WO3 composite structural, morphology and components have been examined by XRD, FT-IR, FE-SEM, TEM, EDX, UV-visible techniques. It was found that the SiO2 doped rGO-WO3 composite well crystalline and the size range is about 20-30 nm. The SEM morphology results showed that the nanoparticles were stabilized by rGO, having cavity like structure and randomly distributed on the rGO-WO3 matrix. The solid state electrolyte revealed superior lithium ion transference number and cyclic stability. The SiO2 doped rGO-WO3 composite shows improved ionic conductivity of 2.74 × 10-5 S cm-1 at room temperature.

Optimization of autonomous vehicle control system reliability on a commercial scale through LIF dombi methodologies.

The resilient framework of Linguistic Intuitionistic Fuzzy Sets (LIFSs) allows for the representation and management of uncertainties related to intuitionistic judgments and linguistic expressions. Recent advances in passive and active safety systems have reduced highway fatalities. Autonomous vehicles can improve safety, efficiency, and mobility by navigating traffic without a driver. One of the primary benefits associated with this technology is that it reduces the number of traffic collisions that result in millions of fatalities and numerous injuries. In this research work, we devise two novel aggregation operators: the linguistic intuitionistic fuzzy Dombi ordered weighted averaging (LIFDOWA) operator and the linguistic intuitionistic fuzzy Dombi ordered weighted geometric (LIFDOWG) operator, and explore their fundamental structural properties. We provide innovative score and accuracy functions for multiple attribute decision-making (MADM) problems within the framework of LIF knowledge. Moreover, we use these techniques to develop a specialized algorithm for MADM issues that addresses the complexities arising from ambiguous data during the selection process. We also demonstrate the effectiveness of our proposed methods by applying them to solve the MADM scenario of selecting an optimal approach to improve the credibility of autonomous vehicle control systems on a commercial scale. In addition, we also compare and evaluate the authenticity and practicability of the newly designed techniques in comparison to existing methodologies.

Methodology for determining the heat distribution in disc brakes of mine hoisting machines

Purpose. To study the course of heat phenomena in disc brakes using modern computing systems in order to determine and substantiate the operating parameters for the hoisting machine components. Methodology. The research uses software packages, with the help of which a computational-theoretical apparatus is developed for heat mode modeling processes. In particular, the mentioned function is used in the SolidWorks Simulation software with the ability to evaluate the errors of the calculation results. Findings. In the course of the research, the dependence of the hoisting machine disc brake performance on the operating parameters of its components was determined. The research has led to a better understanding of the heat transfer processes in disc braking devices, as well as the ability to study the friction response of various materials and determine optimal parameters that help improve the performance of braking systems. The effectiveness has been proven of the proposed method for analyzing the heat distribution processes of the mine hoisting machine drum components under the influence of operating and emergency braking modes. Originality. For the first time, a methodology for calculating the heating temperature distribution along the brake rim plane during a safety stop has been developed and substantiated. A method has also been developed for determining the temperature field arising under steady-state heating conditions, which occur after repeated operating braking and cooling of the device. In this case, when using the formula for determining the temperature on the brake rim surface, the sample length, the relative velocity between the friction components and the heat flow distribution coefficient are taken into account. The brake disc geometric model developed in the SolidWorks software package makes it possible to study the temperature change on the device rim in real time. Practical value. The proposed design improvement based on the research results of heating processes should improve vehicle safety. The cost of braking systems is expected to be reduced through the use of optimal materials and production technologies. The software methods for modeling and analyzing the temperature influence on brake discs of the Mine Hoisting Machine (MHM) have been improved. Based on the research results, recommendations have been developed for the optimum braking process of machines in various operating conditions.

MEASUREMENT AND CHARACTERISTIC ANALYSIS OF SKELETAL GEOMETRIC PARAMETERS IN CHINESE HUMANS

Problem: Significant differences exist between the body dimensions of Chinese and Western individuals, and these differences affect the injury characteristics of passengers in traffic accidents. Most current parametric finite element human models are based on Western body data, which does not adequately assess the injury risks for Chinese individuals. Therefore, it is imperative to establish a digital model that reflects the anatomical features of Chinese humans. This requires comprehensive anthropometric data specific to the Chinese population. Aim: This study aims to collect and analyze skeletal geometric parameters from various representative regions in China across different age groups to fill the gap in Chinese anthropometric data. Additionally, it seeks to explore regional and age-related differences in these parameters, providing a scientific basis for developing finite element models that reflect the characteristics of the Chinese population. Methods: Clinical CT data of skeletal geometric parameters were collected from 224 individuals across seven representative regions of China, including parameters of the head, chest, and lower limbs. Descriptive statistics, K-W tests, and U-tests were used to analyze regional differences. Spearman correlation and linear regression analyses were employed to assess the impact of age. Furthermore, weighted averages and weighted variances of skeletal geometric parameters were calculated to reflect the characteristics of individuals from various regions across the country, which will be applied in the development of Chinese human models. Results: The results indicate significant regional differences in the skeletal geometric parameters of Chinese individuals, exhibiting regional patterns between the north–south and east–west areas. While skeletal geometric parameters showed significant differences with age, no clear statistical pattern was observed. Conclusion: This study systematically extracts and analyzes the skeletal geometric parameters of the Chinese population from regional and age perspectives for the first time. These data will be applied in the development of Chinese human models, providing crucial support for improving passenger safety in traffic accidents, enhancing vehicle safety design, and establishing anthropometric standards suitable for the Chinese context.

Application of Artificial Intelligence Technology in New Energy Vehicles

Due to environmental pollution caused by excessive exploitation of world resources, countries are now advocating for green travel, which has led to the rapid development of the new energy vehicle industry. Compared to traditional cars, the safety, maneuverability, and convenience of new energy vehicles have made the application of artificial intelligence technology increasingly widespread in this field. Therefore, this article provides a systematic review and analysis of the application of artificial intelligence technology in new energy vehicles. Firstly, the article outlined the main application scenarios of artificial intelligence in the field of new energy vehicles, including autonomous driving, energy management optimization, vehicle intelligent interconnection, and predictive maintenance. On this basis, this paper analyzed the role of artificial intelligence technology in improving the performance, safety, and reliability of new energy vehicles, as well as the technological challenges and development trends it faces. For example, issues such as data privacy protection and real-time performance need to be further addressed. In the future, artificial intelligence and new energy vehicles will further integrate, making important contributions to achieving intelligent, green, and safe future travel.

Estimation of Lithium-Ion Battery SOC Based on IFFRLS-IMMUKF

The state of charge (SOC) is a characteristic parameter that indicates the remaining capacity of electric vehicle batteries. It plays a significant role in determining driving range, ensuring operational safety, and extending the service life of battery energy storage systems. Accurate SOC estimation can ensure the safety and reliability of vehicles. To tackle the challenge of precise SOC estimation in complex environments, this study introduces an improved forgetting factor recursive least squares (IFFRLS) method, which integrates the Golden Jackal optimization (GJO) algorithm with the traditional FFRLS method. This integration is grounded in the formulation of a lithium battery state equation derived from a second-order RC equivalent circuit model. Additionally, the research utilizes the interactive multiple model unscented Kalman filter (IMMUKF) algorithm for SOC estimation, with experimental validation conducted under various conditions, including hybrid pulse power characterization (HPPC), urban dynamometer driving schedule (UDDS), and real underwater scenarios. The experimental results demonstrate that the SOC estimation method of lithium batteries based on IFFRLS-IMMUKF exhibits high accuracy and a favorable temperature applicability range.

Response of safety belt webbing used for formula student race car in a frontal collision

A particularly important element for ensuring the passive safety of a vehicle is the seat belt. In automotive engineering, the study of this subassembly is given special attention, because it plays a key role among the multiple safety systems, active and passive, that currently equip a vehicle. The paper studies several ways in which belt webbing can be damaged during use and how these defects can influence the behavior of the webbing. For this purpose, using the Finite Element Method (FEM) based on a mechanical model that considers the vehicle-driver-seat belt assembly, the forces that appear in the seat belt in the event of a frontal collision are determined. Experimental determinations carried out in the laboratory by the authors provide the mechanical properties modified following the failures. By introducing these mechanical properties into the developed model, it will be determined how the safety belt webbing responds in the event of a collision with a wall. A comparison is made between the behavior of the “new state” belt and one with defects in order to estimate the damage that can be caused by the appearance of the defects considered in the work.

A Nonlinear Suspension Road Roughness Recognition Method Based on NARX-PASCKF

Road roughness significantly impacts vehicle safety and dynamic responses. For nonlinear suspension systems, the nonlinear characteristics often make it challenging for estimators to identify the actual road roughness accurately. This paper proposes a hybrid road roughness identification algorithm based on nonlinear auto-regressive with exogenous inputs (NARX) and a process noise adaptive square root cubature Kalman filter (PASCKF) to address this issue. Driven by vehicle acceleration data, an NARX-based road roughness identification system is constructed to mitigate the model uncertainties. Furthermore, a hybrid strategy is proposed. On the one hand, the accurate road roughness estimated by the NARX is converted into process noise covariance, enhancing the estimator’s accuracy and convergence rate. Another switching strategy is proposed to optimize the non-convergence issues of the PASCKF. Finally, simulation and actual vehicle experiment data demonstrate that this approach offers superior identification accuracy and adaptability compared to the standalone SCKF algorithm.

Application of safety and stability optimization algorithms for charging connection devices in high-power charging systems

In response to the safety and stability issues of current electric vehicle charging connection devices, this study proposes a charging system planning for electric vehicles with different capacity charging piles based on the user behavior characteristics of electric vehicles and Monte Carlo methods. It is found that the predicted results under the set management strategy are most consistent with the trend of actual load changes. Moreover, in the prediction of weekly load, the research strategy has better performance than traditional unmanaged strategies. Under the research scheme, the average charging speed of charging piles with capacity of A and B in the peak period was 41.4 min/ and 18.8 min/, respectively, which increased by 29.3% and 11.7% respectively compared with 58.6 min/ and 21.3 min/ in the normal period. The total economic cost of the research plan was 4.871 million yuan, which was 67.0 million yuan and 3.833 million yuan lower than the control methods 1 and 2, respectively. The total number of charging stations of types a and b that need to be purchased for the research method decreased by 18.47% and 63.24% compared to the comparative method 3. The results indicate that the research method significantly improves the utilization rate of charging stations in the electric vehicle charging system. This study has important application value in the intelligent management of electric vehicle charging systems.

Risk and Complexity Assessment of Autonomous Vehicle Testing Scenarios

Autonomous vehicles (AVs) must fulfill adequate safety requirements before formal application, and performing an effective functional evaluation to verify vehicle safety requires extensive testing in different scenarios. However, it is crucial to rationalize the application of different scenarios to support different testing needs; thus, one of the current challenges limiting the development of AVs is the critical evaluation of scenarios, i.e., the lack of quantitative criteria for scenario design. This study introduces a method using the Spherical Fuzzy-Analytical Network Process (SF-ANP) to evaluate these scenarios, addressing their inherent risks and complexities. The method involves constructing a five-layer model to decompose scenario elements and using SF-ANP to calculate weights based on element interactions. The study evaluates 700 scenarios from the China In-depth Traffic Safety Study–Traffic Accident (CIMSS-TA) database, incorporating fuzzy factors and element weights. Virtual simulation of vehicles in the scenarios was performed using Baidu Apollo, and the performance of the scenarios was assessed by collecting the vehicle test results. The correlation between the obtained alternative safety indicators and the quantitative values confirms the validity and scientific validity of this approach. This will provide valuable guidance for categorizing audiovisual test scenarios and selecting corresponding scenarios to challenge different levels of vehicle functionality. At the same time, it can be used as a design basis to generate a large number of effective scenarios to accelerate the construction of scenario libraries and promote commercialization of AVs.