Identification of test cases for Automated Driving Systems using Bayesian optimization

With advancements in technology, the automotive industry is experiencing a paradigm shift from assisted driving to highly automated driving. However, autonomous driving systems are highly safety critical in nature and need to be thoroughly tested for a diverse set of conditions before being commercially deployed. Due to the huge complexities involved with Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS), traditional software testing methods have well-known limitations. They also fail to cover the infinite number of adverse conditions that can occur due to a slight change in the interactions between the environment and the system. Hence, it is important to identify test conditions that push the vehicle under test to breach its safe boundaries. Hazard Based Testing (HBT) methods, inspired by Systems-Theoretic Process Analysis (STPA), identify such parameterized test conditions that can lead to system failure. However, these techniques fall short of discovering the exact parameter values that lead to the failure condition. The presented paper proposes a test case identification technique using Bayesian Optimization. For a given test scenario, the proposed method learns parameter values by observing the system's output. The identified values create test cases that drive the system to violate its safe boundaries. STPA inspired outputs (parameters and pass/fail criteria) are used as inputs to the Bayesian Optimization model. The proposed method was applied to an SAE Level-4 Low Speed Automated Driving (LSAD) system which was modelled in a driving simulator

Comprehensive concept-phase system safety analysis for hybrid-electric vehicles utilizing automated driving functions

2019 Summer.Includes bibliographical references.Automotive system safety (SS) analysis involving automated driving functions (ADFs) and advanced driver assistance systems (ADAS) is an active subject of research but highly proprietary. A comprehensive SS analysis and a risk informed safety case (RISC) is required for all complex hybrid-vehicle builds especially when utilizing ADFs and ADAS. Industry standard SS procedures have been developed and are accessible but contain few detailed instructions or references for the process of completing a thorough automotive SS analysis. In this work, a comprehensive SS analysis is performed on an SAE-Level 2 autonomous hybrid-vehicle architecture in the concept phase which utilizes lateral and longitudinal automated corrective control actions. This paper first outlines a proposed SS process including a cross-functional SS working group procedure, followed by the development of an item definition inclusive of the ADFs and ADAS and an examination of 5 hazard analysis and risk assessment (HARA) techniques common to the automotive industry that were applied to 11 vehicle systems, and finally elicits the safety goals and functional requirements necessary for safe vehicle operation. The results detail functional failures, causes, effects, prevention, and mitigation methods as well as the utility of, and instruction for completing the various HARA techniques. The conclusion shows the resulting critical safety concerns for an SAE Level-2 autonomous system can be reduced through the use of the developed list of 116 safety goals and 950 functional safety requirements

Have We Ever Encountered This Before? Retrieving Out-of-Distribution Road Obstacles from Driving Scenes

In the life cycle of highly automated systems operating in an open and dynamic environment, the ability to adjust to emerging challenges is crucial. For systems integrating data-driven AI-based components, rapid responses to deployment issues require fast access to related data for testing and reconfiguration. In the context of automated driving, this especially applies to road obstacles that were not included in the training data, commonly referred to as out-of-distribution (OoD) road obstacles. Given the availability of large uncurated recordings of driving scenes, a pragmatic approach is to query a database to retrieve similar scenarios featuring the same safety concerns due to OoD road obstacles. In this work, we extend beyond identifying OoD road obstacles in video streams and offer a comprehensive approach to extract sequences of OoD road obstacles using text queries, thereby proposing a way of curating a collection of OoD data for subsequent analysis. Our proposed method leverages the recent advances in OoD segmentation and multi-modal foundation models to identify and efficiently extract safety-relevant scenes from unlabeled videos. We present a first approach for the novel task of text-based OoD object retrieval, which addresses the question ''Have we ever encountered this before?''.Comment: 11 pages, 7 figures, and 3 table

Проблемные вопросы правового регулирования использования автомобилей с автоматизированной системой вождения

In the context of a fundamental change in the fundamental approaches to building a traffic management system, traditionally based on establishing the driver’s duty to ensure constant control over the traffic situation and, accordingly, presuming his responsibility for harm caused by a source of increased danger, the problem of legal regulation of the use of highly automated vehicles equipped with an automated driving system that does not provide for participation of the driver in the dynamic control of the car becomes not only relevant in theoretical, but also especially significant from practical aspects.The objective of the comprehensive study being conducted by the authors was to identify and visualise key groups of problems of legal regulation of the operation of cars with an automated driving system, to formulate proposals for their solution as part of a subsequent systemic legal study. This article is devoted to the results of consideration of the first block of the identified issues.Using the methods of the system-legal approach, formal-logical and formal-dogmatic analysis, the authors have identified the most problematic issues of legalising the terminology used in positive law and scientific sources. In particular, options for identifying the essential features of highly automated cars are proposed with the purpose to further legislatively determine the cars that should be classified as highly automated, to reveal which software and hardware complex can be considered an automated driving system, etc.Based on the results of solving the scientific problem, which consists in determining the directions for adapting the legislation governing the requirements for safety of vehicles and the procedure for their admission to operation for the needs of the widespread introduction of highly automated vehicles, the research can be carried out in two directions at the same time: to develop upper-level, essential requirements to safety and to develop specific, purely technical requirements for automated driving systems, as well as to develop a methodology for testing them.As a part of the taxonomic analysis carried out by the authors to determine, on a fundamentally new basis, the range of rights and obligations of the participants in the relations under the study, it is proposed to highlight the problem of distinguishing between situations in which the driver needs to take an active part in driving a car from situations in which he is not required to be actively involved. into this process.When considering issues of liability for harm caused by a car with an automated driving system, the article focuses on the need to solve the problem of balancing the responsibility of the car owner and the manufacturer, which can be facilitated by the application of methods of comparative legal analysis.В условиях коренного изменения принципиальных подходов к построению системы организации дорожного движения, традиционно основанной на установлении обязанности водителя обеспечивать постоянный контроль за дорожной обстановкой и, соответственно, презюмировании его ответственности за вред, причинённый источником повышенной опасности, проблема правового регулирования применения высокоавтоматизированных транспортных средств, оснащённых автоматизированной системой вождения, не предусматривающей участия водителя в динамическом управлении автомобилем, становится не только актуальной в теоретическом, но и особо значимой в практическом аспектах.Целями ведущегося авторами комплексного исследования стали выявление и визуализация ключевых групп проблем правового обеспечения использования автомобилей с автоматизированной системой вождения, формулирование предложений по их решению в рамках последующего системно-правового исследования. В данной публикации приведены результаты рассмотрения первого блока обозначенных вопросов.С использованием методик системно-правового подхода, формальнологического и формально-догматического анализа авторами определены наиболее проблемные вопросы легализации терминологии, используемой в позитивном праве и научных источниках. В частности, предложены варианты выявления сущностных признаков высокоавтоматизированных автомобилей в интересах последующего законодательного определения того, какие автомобили должны быть отнесены к высокоавтоматизированным, какой программно-аппаратный комплекс может считаться автоматизированной системой вождения и т.п.По результатам решения научной задачи, состоящей в определении направлений адаптации законодательства, регулирующего требования к безопасности транспортных средств и процедуру их допуска к эксплуатации к потребностям широкого внедрения высокоавтоматизированных автомобилей, авторами сделан вывод, что данная работа может вестись в двух направлениях одновременно: по разработке верхнеуровневых, сущностных требований к безопасности и по выработке точечных, сугубо технических требований к автоматизированным системам вождения, а также по разработке методологии их испытаний.В рамках осуществления таксономического анализа, осуществлённого авторами в целях определения круга прав и обязанностей участников исследуемых отношений предлагается особо выделить проблему разграничения ситуаций, в которых водителю необходимо принимать активное участие в управлении автомобилем, от ситуаций, в которых от него не требуется активного вовлечения в этот процесс.При рассмотрении вопросов ответственности за вред, причинённый автомобилем с автоматизированной системой вождения, делается акцент на необходимость решения проблемы баланса ответственности владельца автомобиля и изготовителя, чему может способствовать применение методов компаративно-правового анализа

The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems

Scenario-based testing for the safety validation of highly automated vehicles is a promising approach that is being examined in research and industry. This approach heavily relies on data from real-world scenarios to derive the necessary scenario information for testing. Measurement data should be collected at a reasonable effort, contain naturalistic behavior of road users and include all data relevant for a description of the identified scenarios in sufficient quality. However, the current measurement methods fail to meet at least one of the requirements. Thus, we propose a novel method to measure data from an aerial perspective for scenario-based validation fulfilling the mentioned requirements. Furthermore, we provide a large-scale naturalistic vehicle trajectory dataset from German highways called highD. We evaluate the data in terms of quantity, variety and contained scenarios. Our dataset consists of 16.5 hours of measurements from six locations with 110 000 vehicles, a total driven distance of 45 000 km and 5600 recorded complete lane changes. The highD dataset is available online at: http://www.highD-dataset.comComment: IEEE International Conference on Intelligent Transportation Systems (ITSC) 201

Paving the Roadway for Safety of Automated Vehicles: An Empirical Study on Testing Challenges

The technology in the area of automated vehicles is gaining speed and promises many advantages. However, with the recent introduction of conditionally automated driving, we have also seen accidents. Test protocols for both, conditionally automated (e.g., on highways) and automated vehicles do not exist yet and leave researchers and practitioners with different challenges. For instance, current test procedures do not suffice for fully automated vehicles, which are supposed to be completely in charge for the driving task and have no driver as a back up. This paper presents current challenges of testing the functionality and safety of automated vehicles derived from conducting focus groups and interviews with 26 participants from five countries having a background related to testing automotive safety-related topics.We provide an overview of the state-of-practice of testing active safety features as well as challenges that needs to be addressed in the future to ensure safety for automated vehicles. The major challenges identified through the interviews and focus groups, enriched by literature on this topic are related to 1) virtual testing and simulation, 2) safety, reliability, and quality, 3) sensors and sensor models, 4) required scenario complexity and amount of test cases, and 5) handover of responsibility between the driver and the vehicle.Comment: 8 page