Safety Assessment Of Vehicles Research Articles

Advancing autonomous vehicle safety assessment: A novel methodology for moving from functional to concrete scenarios using kinetic 3D-LiDAR and SHAP

The commercialization of autonomous driving systems is gaining momentum, yet ensuring safety remains an ongoing challenge. To address these safety concerns, autonomous vehicle safety assessment employs a scenario-based approach, which can be categorized into knowledge-based and data-driven methods. In this study, we propose a framework that extends existing data-driven approaches by addressing three critical limitations: data, method, and scenario development. Using actual driving data, including 3D LiDAR Point Cloud Data (3D-LiDAR PCD), we extracted kinetic properties such as vehicle speed, acceleration, and detected critical accident triggered vehicle (ATV). Subsequently, we employed SHAP (SHapley Additive exPlanations) to assess the importance of kinetic properties and set criteria for selecting the configuration value into scenario. Specifically, we used SHAP value to determine the optimal configuration values for each variable in concrete scenario. This study presents a comprehensive scenario development framework that not only overcomes data limitations but also provides a methodological foundation for developing scenarios that accurately reflect real world. It offers an innovative approach to addressing safety concerns in the commercialization of autonomous driving systems.

Safety Trends in the Automobile Industry in India

The automobile industry in India has experienced significant advancements in recent years, driven by rapid technological innovations and evolving consumer expectations. Safety has emerged as a critical focus area, with stakeholders ranging from government bodies to manufacturers and consumers increasingly emphasizing the need for stringent safety measures. This paper explores the key safety trends shaping the Indian automotive sector, including the adoption of advanced driver-assistance systems (ADAS), the implementation of new crash test standards, and the increased integration of passive and active safety features in vehicles. Additionally, it highlights the regulatory changes brought forth by the Indian government, such as the Bharat New Vehicle Safety Assessment Program (BNVSAP) and mandatory safety norms like airbags and anti-lock braking systems (ABS). The study also examines the role of technological innovations, including connected vehicle technologies and the potential of autonomous driving in enhancing road safety. By analyzing these trends, the paper aims to provide insights into how safety measures in the Indian automobile industry are evolving to meet global standards and reduce the rising number of road accidents in the country.

Benchmarks for retrospective automated driving system crash rate analysis using police-reported crash data

Objectives With fully automated driving systems (ADS; SAE level 4) ride-hailing services expanding in the U.S., we are now approaching an inflection point in the history of vehicle safety assessment. The process of retrospectively evaluating ADS safety impact (as seen with seatbelts, airbags, electronic stability control, etc.) can start to yield statistically credible conclusions. An ADS safety impact measurement requires a comparison to a “benchmark” crash rate. Most benchmarks generated to-date have focused on the current human-driven fleet, which enable researchers to understand the impact of the introduced ADS technology on the current crash record status quo. This study aims to address, update, and extend the existing literature by leveraging police-reported crashes to generate human crash rates for multiple geographic areas with current ADS deployments. Methods: All of the data leveraged is publicly accessible, and the benchmark determination methodology is intended to be repeatable and transparent. Generating a benchmark that is comparable to ADS crash data is associated with certain challenges, including data selection, handling underreporting and reporting thresholds, identifying the population of drivers and vehicles to compare against, choosing an appropriate severity level to assess, and matching crash and mileage exposure data. Results Consequently, we identify essential steps when generating benchmarks, and present our analyses amongst a backdrop of existing ADS benchmark literature. One analysis presented is the usage of established underreporting correction methodology to publicly available human driver police-reported data to improve comparability to publicly available ADS crash data. We also identified several important crash rate dependencies (geographic region, road type, and vehicle type), and show how failing to account for these features in ADS comparisons can bias results. Conclusions Working with police-reported crash data to create crash rate benchmarks is fraught with challenges. Researchers should be cautious in their selection of crash rate benchmarks. We present these challenges, discuss their consequences, and provide analytical guidance for addressing them. This body of work aims to contribute to the ability of the community - researchers, regulators, industry, and experts - to reach consensus on how to estimate accurate benchmarks.

An injury risk-based comprehensive framework for testing and assessing ADAS functions in critical road scenarios

Assisted driving is currently considered a key aspect for improving road safety, and automakers and OEMs are working to achieve higher levels of vehicle automation by introducing new technologies and Advanced Driver Assistance Systems (ADAS) in the circulating fleet. This trend requires test protocols for vehicle safety assessment to be frequently reviewed and updated, considering the latest advances in the state of the art regarding ADAS functions and systems. As of today, performance assessment programs (such as NCAP) mainly evaluate how an ADAS behaves in terms of crash avoidance in specific critical scenarios, which represent the most frequent crash constellations among real-world impacts. However, enhanced safety can be also obtained in case the impact is not avoided if a decrease in Injury Risk (IR) for the involved road users is achieved by ADAS intervention, compared to the case of no intervention.The purpose of this work is to propose an overall framework to draft or update test protocols for ADAS performance assessment based on real car-to-car impact observations, representative of impact scenarios in terms of both occurrence frequency and IR. First, the in-depth accident database IGLAD is analyzed to identify the most relevant car-to-car accident scenarios based on a relevance indicator, i.e., the risk level being the multiplication of the occurrence frequency and IR for a specific scenario. For each relevant scenario, a risk level-based strategy to identify one significant closing speed between vehicles for the tests is defined; the test collision speed for the two vehicles is determined analogously, and the risk level for each combination of speeds in a scenario represents the maximum achievable score by the ADAS if the collision is averted. Considering the well-established Euro NCAP framework as a relevant starting point for the definition of test protocols, two examples are highlighted regarding the proposal of a new test protocol and an update of an already existing one. Finally, a method is proposed for ADAS performance assessment if the impact is not avoided, scaling the maximum achievable score based on the IR reduction consequent to the ADAS intervention.

Peculiarities of safety assessment and quality assurance for automotive vehicles produced in Russia under present-day conditions

The paper is dedicated to systematization and description of technical regulation measures taken by the Government of the Russian Federation in the field of automotive vehicle safety assessment to ensure stable production under the sanctions imposed by unfriendly countries, interruption of logistic chains in vehicle component supplies and deficiency of such components. The paper analyzes differences in technical requirements for new-type vehicles released for circulation in the territory of Russia, and procedures to assess compliance with those requirements stated by the Eurasian Economic Union Technical Regulations “On Wheeled Vehicles Safety” and temporary procedure for application of obligatory requirements for wheeled vehicles and their compliance assessment introduced by the decree of the Russian Government, as well as dynamic of changes in such differences. There is also a description given for the quality management systems voluntary certification system developed within the Association of Russian Automakers (OAR), and proposals drawn up to supplement Decree of the Government of the Russian Federation No. 719 dated July 17, 2015, On Confirmation of Industrial Manufacturing in the Russian Federation with conditions for additional scoring in case vehicle assembly plants have a certain number of Tier I suppliers that have certified their quality system in terms of compliance with the OAR voluntary certification system

Dynamic Identification Method for Potential Threat Vehicles beyond Line of Sight in Expressway Scenarios

Due to the challenge of limited line of sight in the perception system of intelligent driving vehicles (cameras, radar, body sensors, etc.), which can only perceive threats within a limited range, potential threats outside the line of sight cannot be fed back to the driver. Therefore, this article proposes a safety perception detection method for beyond the line of sight for intelligent driving. This method can improve driving safety, enabling drivers to perceive potential threats to vehicles in the rear areas beyond the line of sight earlier and make decisions in advance. Firstly, the electronic toll collection (ETC) transaction data are preprocessed to construct the vehicle trajectory speed dataset; then, wavelet transform (WT) is used to decompose and reconstruct the speed dataset, and lightweight gradient noosting machine learning (LightGBM) is adopted to train and learn the features of the vehicle section speed. On this basis, we also consider the features of vehicle type, traffic flow, and other characteristics, and construct a quantitative method to identify potential threat vehicles (PTVs) based on a fuzzy set to realize the dynamic safety assessment of vehicles, so as to effectively detect PTVs within the over-the-horizon range behind the driver. We simulated an expressway scenario using an ETC simulation platform to evaluate the detection of over-the-horizon PTVs. The simulation results indicate that the method can accurately detect PTVs of different types and under different road scenarios with an identification accuracy of 97.66%, which verifies the effectiveness of the method in this study. This result provides important theoretical and practical support for intelligent driving safety assistance in vehicle–road collaboration scenarios.

Research on the Prediction of Tire Radial Load Based on 1D CNN and BiGRU

As an important indicator of vehicle systems, tire load is a key factor in the structural design and safety assessment of vehicles. Direct measurement methods for tire loads are expensive and complicated, while conventional load identification methods are limited by low accuracy and poor robustness. This study aimed to propose a radial load identification method for rubber-tired vehicles based on a one-dimensional convolutional neural network (1D CNN) and bidirectional gated recurrent unit (BiGRU). Considering a priori information of the radial load data of tires and based on the observability of the vehicle vibration system, the proposed method selected feature sets and then retained the effective feature subsets through feature selection to construct samples with multiple time steps as input and with a single time step as output for network training. In doing so, the load prediction results were obtained, and the theoretical model was modified by integrating prediction accuracy, generalization performance, and robustness. Compared with traditional algorithms, the proposed method could effectively reduce the error of load identification, improve adaptability under different operating conditions, and handle the measurement error of different noise levels, which are of practical application value in the engineering field.

Formal Certification Methods for Automated Vehicle Safety Assessment

Challenges related to automated driving are no longer focused on just the construction of such automated vehicles (AVs), but in assuring the safety of their operation. Recent advances in Level 3 and Level 4 autonomous driving have motivated more extensive study in safety guarantees of complicated AV maneuvers, which aligns with the goal of ISO 21448 (Safety of the Intended Functions, or SOTIF), i.e. minimizing unsafe scenarios both known and unknown, as well as Vision Zero -- eliminating highway fatalities by 2050. A majority of approaches used in providing safety guarantees for AV motion control originate from formal methods, especially reachability analysis (RA), which relies on mathematical models for the dynamic evolution of the system to provide guarantees. However, to the best of the authors' knowledge, there have been no review papers dedicated to describing and interpreting state-of-the-art of formal methods in the context of AVs. In this work, we provide both an overview of the safety verification, validation and certification process, as well as review formal safety techniques that are best suited to AV applications. We also propose a unified scenario coverage framework that can provide either a formal or sample-based estimate of safety verification for full AVs. Finally, remaining challenges and future opportunities beyond the scope of current published research for assured AV safety are presented.

Injury Criteria for Vehicle Safety Assessment: A Review with a Focus Using Human Body Models

This paper aims at providing an overview of the most used injury criteria (IC) and injury metrics for the study of the passive safety of vehicles. In particular, the work is focused on the injury criteria that can be adopted when finite element simulations and Human Body Models (HBMs) are used. The HBMs will result in a fundamental instrument for studying the occupant’s safety in Autonomous Vehicles (AVs) since they allow the analysis of a larger variety of configurations compared to the limitations related to the traditional experimental dummies. In this work, the most relevant IC are reported and classified based on the body segments. In particular, the head, the torso, the spine, the internal organs, and the lower limbs are here considered. The applicability of the injury metrics to the analyses carried out with the HBMs is also discussed. The paper offers a global overview of the injury assessment useful to choose the injury criteria for the study of vehicle passive safety. To this aim, tables of the presented criteria are also reported to provide the available metrics for the considered body damage.

Vision Transformer for Detecting Critical Situations and Extracting Functional Scenario for Automated Vehicle Safety Assessment

Automated Vehicles (AVs) are attracting attention as a safer mobility option thanks to the recent advancement of various sensing technologies that realize a much quicker Perception–Reaction Time than Human-Driven Vehicles (HVs). However, AVs are not entirely free from the risk of accidents, and we currently lack a systematic and reliable method to improve AV safety functions. The manual composition of accident scenarios does not scale. Simulation-based methods do not fully cover the peculiar AV accident patterns that can occur in the real world. Artificial Intelligence (AI) techniques are employed to identify the moments of accidents from ego-vehicle videos. However, most AI-based approaches fall short in accounting for the probable causes of the accidents. Neither of these AI-driven methods offer details for authoring accident scenarios used for AV safety testing. In this paper, we present a customized Vision Transformer (named ViT-TA) that accurately classifies the critical situations around traffic accidents and automatically points out the objects as probable causes based on an Attention map. Using 24,740 frames from Dashcam Accident Dataset (DAD) as training data, ViT-TA detected critical moments at Time-To-Collision (TTC) ≤ 1 s with 34.92 higher accuracy than the state-of-the-art approach. ViT-TA’s Attention map highlighting the critical objects helped us understand how the situations unfold to put the hypothetical ego vehicles with AV functions at risk. Based on the ViT-TA-assisted interpretation, we systematized the composition of Functional scenarios conceptualized by the PEGASUS project for describing a high-level plan to improve AVs’ capability of evading critical situations. We propose a novel framework for automatically deriving Logical and Concrete scenarios specified with 6-Layer situational variables defined by the PEGASUS project. We believe our work is vital towards systematically generating highly reliable and trustworthy safety improvement plans for AVs in a scalable manner.

Simulations on Reliability-based Safety Assessment of Vehicles Crossing Highway Interchanges during Earthquakes

ABSTRACT Safety assessment of vehicles crossing highway interchanges during earthquakes by reliability-based numerical simulations is presented in this paper. The assessment was performed for two limit states: vehicle rollover and sideslipping accidents. Simulations were conducted using three-dimensional finite element models of two existing highway interchanges consisting of straight and curved bridges under site-specific earthquakes. Effects of curved bridges radii, levels of earthquake, vehicle type, driving velocity, and road surface conditions on the safety limit states of five types of vehicles, namely, sedan-type, SUV, small-box truck, bus, and large truck, were investigated and clarified in the Monte Carlo simulation.

Vision Transformer for Detecting Critical Situations And Extracting Functional Scenario for Automated Vehicle Safety Assessment

Vision Transformer for Detecting Critical Situations And Extracting Functional Scenario for Automated Vehicle Safety Assessment

Logical Scenarios Parameterization for Automated Vehicle Safety Assessment: Comparison of Deceleration and Cut-In Scenarios From Japanese and German Highways

This study compares real-traffic deceleration and cut-in scenarios, which were established as critical to automated vehicles (AVs) safety, between Japanese and German highway trajectory datasets. Both scenarios were extracted from two different traffic data previously collected in Japan with both instrumented vehicles and fixed cameras over highways (SAKURA dataset) and in Germany with drones (highD dataset). Five vehicle kinematic variables (lateral and longitudinal distances, velocities, and accelerations) were used to parameterize both scenarios and compared them between datasets using correlation and intersection objective measures and safety metrics: Time-to-Collision and Time Headway. Despite the differences in the rule of the road (e.g. speed limits left- and right-hand traffic), road design, and data sources between the two countries, data comparison results revealed significant correlations and intersections of parameters distribution for both scenarios. The Time-to-Collision significantly overlapped between countries for both scenarios. However, differences in the Time Headway indicate that the safety distance varied across both countries, suggesting that safety assessment methodologies need to be tailored to different environments and regions to ensure safety. These results highlight the potential to develop safety indicators applicable at the international level and warrant further data collection and comparative studies that support the development of harmonized, widely applicable, and region-neutral AVs safety assessment methodologies.

Scenario-Mining for Level 4 Automated Vehicle Safety Assessment from Real Accident Situations in Urban Areas Using a Natural Language Process.

As the research and development activities of automated vehicles have been active in recent years, developing test scenarios and methods has become necessary to evaluate and ensure their safety. Based on the current context, this study developed an automated vehicle test scenario derivation methodology using traffic accident data and a natural language processing technique. The natural language processing technique-based test scenario mining methodology generated 16 functional test scenarios for urban arterials and 38 scenarios for intersections in urban areas. The proposed methodology was validated by determining the number of traffic accident records that can be explained by the resulting test scenarios. That is, the resulting test scenarios are valid and represent a matching rate between the test scenarios and the increased number of traffic accident records. The resulting functional scenarios generated by the proposed methodology account for 43.69% and 27.63% of the actual traffic accidents for urban arterial and intersection scenarios, respectively.

Establishment of 3D-printed Child Manikins Using Anthropometric Data of 6- and 10-year-old ASEAN Children


 
 
 The New Car Assessment Programme for Southeast Asian Countries (ASEAN NCAP) has been working hard to increase vehicle safety levels in the ASEAN region. To ensure a significant impact on the ASEAN community, few enhancements need to be made to their test methodology and facilities. As an example, ASEAN NCAP is using child manikins with generic build in their vehicle safety assessment. Meanwhile, vehicle manufacturers and product developers are also using these generic references in their product designs such as the seat restraint system, child seat design, etc. For the improvement of vehicle safety especially for the ASEAN region, dedicated manikins with ASEAN anthropometric measurements are necessary. This paper aims to describe the development of 3D-printed manikins based on anthropometric data of ASEAN children aged 6 and 10 years old. The completed prototypes will be tested using a selected vehicle safety assessment (i.e., Child Restraint System installation) and intended to be applied for design refinement of vehicle safety systems.
 
 

A Comparative Study Between Different NCAPs and The Future of BNVSAP

India is the third largest road network which is used by one percent of the world’s total vehicle population. On this road network six percent of the total of global road traffic accidents occur. India is the sixth-largest car market in the world but is still the only country amongst the global top 10 car markets to not have a testing program. To reduce the number of road traffic accidents, many countries have come up with safety programmes such as Euro NCAP (New Car Assessment Program), US NCAP, Latin NCAP, etc. In 2014, Indian government proposed a similar safety assessment program called as Bharat New Vehicle Safety Assessment Program (BNVSAP) but it is since delayed in its implementation due to a number of reasons. The aim of this study is to compare existing NCAPS and the new BNVSAP and propose some improvements in it by studying the experiences gained by the older NCAPS and observing the difference in the situation of road traffic in India and in other countries by analysing real world accident data.

Autonomous Vehicle Safety Assessment with Fully Quantified ODDs

<div class="section abstract"><div class="htmlview paragraph">We are in the midst of a vehicle safety revolution. Current vehicle safety standards, best practices, and approaches are not adequate to ensure the safety of an Automated Vehicle (AV), the motoring public, and vulnerable road users. Continued application of these nascent technologies prompts the question: How safe is safe enough and how do we know that these systems can handle inherent risks of a given deployment area? Current practices are very focused on vehicle safety elements. In fact, there is currently only one published safety standard, specifically for AVs [<span class="xref">1</span>], though there are instances where some vehicle system safety standards are being adapted for AV application and some safety standards from other industries (e.g. aerospace and nuclear) are being considered. Specific guidelines for AV safety metrics and AV safety performance are currently in the development stages and once published will require time to be fully understood, thresholds defined, and data collected and accepted by the AV community. A holistic approach to safety that considers all aspects of a safe AV deployment beyond increasing levels of vehicle technology is crucial. Included in this holistic approach, as a foundational element, is a fundamentally new way of assessing and quantifying risks within the Operational Design Domain (ODD). This is produced by breaking down the risk of a given ODD as a sum over the risk of component scenarios which make up the ODD. This means ODDs are fully specified and not defined by subjective assumptions. With no single standard, best practice, or guideline that covers AV safety in a holistic manner, an assessment process including a fully quantified ODD is seen as the most effective way to cover all aspects of safety, including the environment, management practices, and the vehicles.</div></div>

Evaluation of brain injury criteria based on reliability analysis

Among the proposed brain injury metrics, Brain Injury Criteria (BrIC) is a promising tool for performing safety assessment of vehicles in the future. In this paper, the available risk curves of BrIC were re-evaluated with the use of reliability analysis and new risk curves were constructed for different injury types based on literature data of tissue-level tolerances. Moreover, the comparison of different injury metrics and their corresponding risk curves were performed. Tissue-level uncertainties of the effect and resistance were considered by random variables. The variability of the tissue-level predictors was quantified by the finite element reconstruction of 100 frontal crash tests which were performed in Simulated Injury Monitor environment. The applied tests were scaled to given BrIC magnitudes and the injury probabilities were calculated by Monte Carlo simulations. New risk curves were fitted to the observed results using Weibull and Lognormal distribution functions. The available risk curves of diffuse axonal injury (DAI) could be slightly improved, and combined AIS 4+ risk curves were obtained by considering subdural hematoma and contusion as well. The performance of several injury metrics and their risk curves were evaluated based on the observed correlations with the tissue-level predictors. The cumulative strain damage measure and the BrIC provide the highest correlation (R² = 0.61) and the most reliable risk curve for the evaluation of DAI. Although the observed correlation is smaller for other injury types, the BrIC and the associated reliability analysis-based risk curves seem to provide the best available method for estimating the brain injury risk for frontal crash tests.

Malaysian Consumer Awareness and Perspectives towards ASEAN NCAP


 
 
 ASEAN NCAP has been in existence for the last eight years. Over these years, multiple initiatives had been undertaken to stress the importance and relevance of vehicle safety to the consumers. This study is undertaken to study and gauge the levels of understanding with regards to the vehicle safety assessment through ASEAN NCAP, amongst the consumer in Malaysia. A total of 428 respondents was involved in this study and the survey had been conducted throughout the major cities in Malaysia. The analysis shows that three main factors contributes to the buying decision of vehicles in Malaysia, they are fuel economy, comfort technology and safety assist technology. Air conditioning and the navigation system were the most important technology that is preferred by the consumers in their selection of vehicles to purchase. The study also reveals that an emphasis is placed on vehicle safety, as the Anti-Lock Braking System (ABS), the Anti-Theft Devices/Alarms and the Emergency Braking Assist (EBA), as being the required priority when choosing a vehicle to be purchased. In comparison with a previous study done, it further shows that the awareness and the level of understanding of ASEAN NCAP had increased to 36 %. This value indicates that the effectiveness of these numerous promotional activities conjured by various stakeholders has had an impact. Nevertheless, continuous effort needs to be effected to ensure the ASEAN NCAP becomes a sensible benchmark for potential buyers in Malaysia in making purchase decisions.
 
 

Young drivers and their cars: Safe and sound or the perfect storm?

Consistent with the experiences in high-income countries, young drivers remain overrepresented in road trauma statistics in low- and middle-income countries. This article pursues the emerging interest of approaching the young driver problem from a systems thinking perspective in order to design and deliver robust countermeasures. Specifically, the focus of this paper is the cars driven by young drivers. The study of vehicles’ characteristics and their interaction with driving behaviour is, more often than not, considered a minor concern when developing countermeasures in young drivers’ safety not only in developed nations, but especially in developing nations. Participants completed an online survey containing the 44-item Behaviour of Young Novice Drivers Scale Spanish version (BYNDS-Sp), in addition to providing information regarding their vehicle, any crash involvement, and driving offences. Based on the vehicle model information, the assessment of vehicle safety was conducted for three safety programs (ANCAP, Latin NCAP, U.S. NCAP). Young drivers in Colombia reported a breadth of risky driving behaviours worth targeting in broader interventions. For example, interventions can target speeding, particularly as three quarters of the participants drove small-medium cars associated with poorer road safety outcomes. Moreover, risky driving exposure was highly prevalent amongst the young driver participants, demonstrating the need for them to be driving the safest vehicles possible. It is noteworthy that few cars were able to be assessed by the Latin NCAP (with half of the cars rated having only 0–2 star ratings), and that there was considerable discrepancy between ANCAP, U.S. NCAP, and Latin NCAP ratings. The need for system-wide strategies to increase young driver road safety—such as improved vehicle safety—is vital to improve road safety outcomes in jurisdictions such as Colombia. Such improvements may also require systemic changes such as enhanced vehicle safety rating scales and investigation of the nature of vehicles sold in developing nations, particularly as these vehicles typically contain fewer safety features than their counterparts sold in developed nations.