A systematic literature review on the relationship between autonomous vehicle technology and traffic-related mortality.

학위논문(석사) -- 서울대학교대학원 : 행정대학원 글로벌행정전공, 2023. 2. 최태현.The society is anticipated to gain a lot from Autonomous Vehicles (AV), such as improved traffic flow and a decrease in accidents. They heavily rely on improvements in various Artificial Intelligence (AI) processes and strategies. Though some researchers in this field believe AV is the key to enhancing safety, others believe AV creates new challenges when it comes to ensuring the security of these new technology/systems and applications. The article conducts a systematic literature review on the relationship between autonomous vehicle technology and traffic-related mortality. According to inclusion and exclusion criteria, articles from EBSCO, ProQuest, IEEE Explorer, Web of Science were chosen, and they were then sorted. The findings reveal that the most of these publications have been published in advanced transport-related journals. Future improvements in the automobile industry and the development of intelligent transportation systems could help reduce the number of fatal traffic accidents. Technologies for autonomous cars provide effective ways to enhance the driving experience and reduce the number of traffic accidents. A multitude of driving-related problems, such as crashes, traffic, energy usage, and environmental pollution, will be helped by autonomous driving technology. More research is needed for the significant majority of the studies that were assessed. They need to be expanded so that they can be tested in real-world or computer-simulated scenarios, in better and more realistic scenarios, with better and more data, and in experimental designs where the results of the proposed strategy are compared to those of industry standards and competing strategies. Therefore, additional study with improved methods is needed. Another major area that requires additional research is the moral and ethical choices made by AVs. Government, policy makers, manufacturers, and designers all need to do many actions in order to deploy autonomous vehicles on the road effectively. The government should develop laws, rules, and an action plan in particular. It is important to create more effective programs that might encourage the adoption of emerging technology in transportation systems, such as driverless vehicles. In this regard, user perception becomes essential since it may inform designers about current issues and observations made by people. The perceptions of autonomous car users in developing countries like Azerbaijan haven't been thoroughly studied up to this point. The manufacturer has to fix the system flaw and needs a good data set for efficient operation. In the not-too-distant future, the widespread use of highly automated vehicles (AVs) may open up intriguing new possibilities for resolving persistent issues in current safety-related research. Further research is required to better understand and quantify the significant policy implications of Avs, taking into consideration factors like penetration rate, public adoption, technological advancements, traffic patterns, and business models. It only needs to take into account peer-reviewed, full-text journal papers for the investigation, but it's clear that a larger database and more documents would provide more results and a more thorough analysis.자율주행차(AV)를 통해 교통 흐름이 개선되고 사고가 줄어드는 등 사회가 얻는 것이 많을 것으로 예상된다. 그들은 다양한 인공지능(AI) 프로세스와 전략의 개선에 크게 의존한다. 이 분야의 일부 연구자들은 AV가 안전성을 향상시키는 열쇠라고 믿지만, 다른 연구자들은 AV가 이러한 새로운 기술/시스템 및 애플리케이션의 보안을 보장하는 것과 관련하여 새로운 문제를 야기한다고 믿는다. 이 논문은 자율주행차 기술과 교통 관련 사망률 사이의 관계에 대한 체계적인 문헌 검토를 수행한다. 포함 및 제외 기준에 따라 EBSCO, ProQuest, IEEE Explorer 및 Web of Science의 기사를 선택하고 분류했다.연구 결과는 이러한 출판물의 대부분이 고급 운송 관련 저널에 게재되었음을 보여준다. 미래의 자동차 산업의 개선과 지능형 교통 시스템의 개발은 치명적인 교통 사고의 수를 줄이는 데 도움이 될 수 있다. 자율주행 자동차 기술은 운전 경험을 향상시키고 교통 사고의 수를 줄일 수 있는 효과적인 방법을 제공한다. 충돌, 교통, 에너지 사용, 환경 오염과 같은 수많은 운전 관련 문제들은 자율 주행 기술에 의해 도움을 받을 것이다. 평가된 대부분의 연구에 대해 더 많은 연구가 필요하다. 실제 또는 컴퓨터 시뮬레이션 시나리오, 더 좋고 현실적인 시나리오, 더 좋고 더 많은 데이터, 그리고 제안된 전략 결과가 산업 표준 및 경쟁 전략의 결과와 비교되는 실험 설계에서 테스트될 수 있도록 확장되어야 한다. 따라서 개선된 방법에 대한 추가 연구가 필요하다. 추가 연구가 필요한 또 다른 주요 분야는 AV의 도덕적, 윤리적 선택이다. 정부, 정책 입안자, 제조업체 및 설계자는 모두 자율 주행 차량을 효과적으로 도로에 배치하기 위해 많은 조치를 취해야 한다. 정부는 특히 법, 규칙, 실행 계획을 개발해야 한다. 운전자 없는 차량과 같은 운송 시스템에서 새로운 기술의 채택을 장려할 수 있는 보다 효과적인 프로그램을 만드는 것이 중요하다. 이와 관련하여, 설계자에게 현재 이슈와 사람에 의한 관찰을 알려줄 수 있기 때문에 사용자 인식이 필수적이 된다.제조업체는 시스템 결함을 수정해야 하며 효율적인 작동을 위해 좋은 데이터 세트가 필요하다. 멀지 않은 미래에, 고도로 자동화된 차량(AV)의 광범위한 사용은 현재의 안전 관련 연구에서 지속적인 문제를 해결하기 위한 흥미로운 새로운 가능성을 열어줄 수 있다. 보급률, 공공 채택, 기술 발전, 교통 패턴 및 비즈니스 모델과 같은 요소를 고려하여 Avs의 중요한 정책 영향을 더 잘 이해하고 정량화하기 위한 추가 연구가 필요하다. 조사를 위해 동료 검토를 거친 전문 저널 논문만 고려하면 되지만, 데이터베이스가 커지고 문서가 많아지면 더 많은 결과와 더 철저한 분석이 제공될 것이 분명하다.Abstract 3 Table of Contents 6 List of Tables 7 List of Figures 7 List of Appendix 7 CHAPTER 1: INTRODUCTION 8 1.1. Background 8 1.2. Purpose of Research 13 CHAPTER 2: AUTONOMOUS VEHICLES 21 2.1. Intelligent Traffic Systems 21 2.2. System Architecture for Autonomous Vehicles 22 2.3. Key components in AV classification 27 CHAPTER 3: METHODOLOGY AND DATA COLLECTION PROCEDURE 35 CHAPTER 4: FINDINGS AND DISCUSSION 39 4.1. RQ1: Do autonomous vehicles reduce traffic-related deaths 40 4.2. RQ2: Are there any challenges to using autonomous vehicles 63 4.3. RQ3: As a developing country, how effective is the use of autonomous vehicles for reducing traffic mortality 72 CHAPTER 5: CONCLUSION 76 5.1. Summary 76 5.2. Implications and Recommendations 80 5.3. Limitation of the study 91 Bibliography 93 List of Tables Table 1: The 6 Levels of Autonomous Vehicles Table 2: Search strings Table 3: Inclusion and exclusion criteria List of Figures Figure 1: Traffic Death Comparison with Europe Figure 2: Research strategy and study selection process List of Appendix Appendix 1: List of selected articles석

Automatic Dense 3D Scene Mapping from Non-overlapping Passive Visual Sensors for Future Autonomous Systems

The ever increasing demand for higher levels of autonomy for robots and vehicles means there is an ever greater need for such systems to be aware of their surroundings. Whilst solutions already exist for creating 3D scene maps, many are based on active scanning devices such as laser scanners and depth cameras that are either expensive, unwieldy, or do not function well under certain environmental conditions. As a result passive cameras are a favoured sensor due their low cost, small size, and ability to work in a range of lighting conditions. In this work we address some of the remaining research challenges within the problem of 3D mapping around a moving platform. We utilise prior work in dense stereo imaging, Stereo Visual Odometry (SVO) and extend Structure from Motion (SfM) to create a pipeline optimised for on vehicle sensing. Using forward facing stereo cameras, we use state of the art SVO and dense stereo techniques to map the scene in front of the vehicle. With significant amounts of prior research in dense stereo, we addressed the issue of selecting an appropriate method by creating a novel evaluation technique. Visual 3D mapping of dynamic scenes from a moving platform result in duplicated scene objects. We extend the prior work on mapping by introducing a generalized dynamic object removal process. Unlike other approaches that rely on computationally expensive segmentation or detection, our method utilises existing data from the mapping stage and the findings from our dense stereo evaluation. We introduce a new SfM approach that exploits our platform motion to create a novel dense mapping process that exceeds the 3D data generation rate of state of the art alternatives. Finally, we combine dense stereo, SVO, and our SfM approach to automatically align point clouds from non-overlapping views to create a rotational and scale consistent global 3D model

Vehicular Instrumentation and Data Processing for the Study of Driver Intent

The primary goal of this thesis is to provide processed experimental data needed to determine whether driver intentionality and driving-related actions can be predicted from quantitative and qualitative analysis of driver behaviour. Towards this end, an instrumented experimental vehicle capable of recording several synchronized streams of data from the surroundings of the vehicle, the driver gaze with head pose and the vehicle state in a naturalistic driving environment was designed and developed. Several driving data sequences in both urban and rural environments were recorded with the instrumented vehicle. These sequences were automatically annotated for relevant artifacts such as lanes, vehicles and safely driveable areas within road lanes. A framework and associated algorithms required for cross-calibrating the gaze tracking system with the world coordinate system mounted on the outdoor stereo system was also designed and implemented, allowing the mapping of the driver gaze with the surrounding environment. This instrumentation is currently being used for the study of driver intent, geared towards the development of driver maneuver prediction models

자율 주행 시스템의 차량 안전을 위한 적응형 관심 영역 기반 효율적 환경 인지

학위논문(박사)--서울대학교 대학원 :공과대학 기계항공공학부,2020. 2. 이경수.전 세계적으로 자동차 사고로 120 만 명이 사망하기 때문에 교통 사고에 대한 기본적인 예방 조치에 대한 논의가 진행되고 있다. 통계 자료에 따르면 교통 사고의 94 %가 인적 오류에 기인한다. 도로 안전 확보의 관점에서 자율 주행 기술은 이러한 심각한 문제를 해결하는 방법으로써 관심이 높아졌으며, 연구 개발을 통해 단계적 상용화가 이루어지고 있다. 주요 자동차 제조업체는 이미 차선 유지 보조장치 (LKAS: Lane Keeping Assistant System), 적응형 순항 제어 시스템(ACC: Adaptive Cruise Control), 주차 보조 시스템 (PAS: Parking Assistance System), 자동 긴급 제동장치 (AEB: Automated Emergency Braking) 등의 첨단 운전자 보조 시스템 (ADAS)을 개발하고 상용화하였다. 또한 Audi의 Audi AI Traffic Jam Pilot, Tesla의 Autopilot, Mercedes-Benz의 Distronic Plus, 현대자동차의 Highway Driving Assist 및 BMW의 Driving Assistant Plus 와 같은 부분 자율 주행 시스템이 출시되고 있다. 이러한 부분 자율 주행 시스템은 여전히 운전자의 주의가 수반되어야 함에도 불구하고, 안전성을 크게 향상시키는 데 효과적이기 때문에 지속적으로 그 수요가 증가하고 있다. 최근 몇 년간 많은 수의 자율주행 사고가 발생하였으며, 그 빈도수가 빠르게 증가하여 사회적으로 주목받고 있다. 차량 사고는 인명 사고와 직접적으로 연관되기 때문에 자율 주행 차량의 사고들은 자율 주행 기술 신뢰성의 저하를 야기하여 사회적인 불안감을 키운다. 최근 자율 주행 관련 사고들로 인해, 자율주행 차량의 안전성의 보장이 더욱 강조되고 있다. 따라서 본 연구에서는 자율 주행 차량의 거동 제어를 고려하여 자율 주행 시스템 관점에서 차량의 안전성을 우선적으로 확보하는 접근 방식을 제안한다. 또한 자율주행 기술 개발은 단순하게 운전을 대체하는 기술이 아니라, 첨단기술의 집약 체로써 산업적으로 매우 큰 파급력을 가진다고 전망된다. 현재 자율주행 시스템은 기존 자동차 산업의 고전적인 틀에서 확장되어, 다양한 분야의 관점에서 주도적으로 개발이 진행되고 있다. 자율 주행은 다양한 기술의 복합적인 결합으로 구성되기 때문에, 현재 각기 다른 다양한 환경에서 개발이 진행 중이며, 아직 표준화되어 있지 않은 실정이다. 대부분 각 모듈 단위의 지엽적인 성능향상을 추구하는 경향이 있으며, 구성 모듈 간 관계가 고려된 전체 시스템 단위의 접근방식은 미흡한 실정이다. 세부 모듈 단위의 지엽적인 연구 개발은 통합 시, 모듈 간 상호작용으로 인한 영향으로 시스템 관점에서 적절한 성능을 확보하기 어려울 수 있다. 각 모듈의 성능만을 고려한 일방적인 방향의 연구는 한계가 명확하며, 연관된 모듈들의 특성을 고려하여 반영할 필요가 있다. 따라서 자율주행 전체 시스템의 관점에서, 차량 안전을 우선적으로 확보하고 전체 성능을 극대화하는 효과적인 접근 방식을 본 연구에서 제안하고자 한다. 본 연구는 자율 주행 시스템의 안정적이고 높은 성능을 확보하기 위해 전체 시스템 작동 측면에서 구성된 모듈 간의 상호 작용을 고려하여 효율적인 환경 인식 알고리즘을 개발하는데 중점을 둔다. 실질적인 관점에서 효과적인 정보 처리를 수행하고 차량 안전을 확보하기 위해 적응형 관심 영역 (ROI) 기반 계산 부하 관리 전략을 제안한다. 차량의 거동 특성, 도로 설계 표준, 추월 및 차선 변경과 같은 주변 차량의 주행 특성이 적응형 ROI 설계 및 주행 상황에 따른 영역 확장에 반영된다. 또한, 자율 주행 차량의 실질적인 안전을 보장하기 위해 ROI 설계에서 자율 주행 제어를 위한 거동 계획 결과가 고려된다. 보다 넓은 주변 영역에 대한 환경 정보를 확보하기 위해 라이다 데이터는 설계된 ROI별로 분류되며, 영역별 중요도에 따라 연산 과정이 분리되어 수행된다. 목표 시스템을 구성하는 모듈 별 연산 시간이 측정된 데이터 기반으로 통계적으로 분석된다. 운전자의 반응 시간, 산업 표준, 대상 하드웨어 사양 및 센서 성능을 기반으로 결정된 시스템 성능 조건을 고려하여, 안전성을 확보하기 위한 자율 주행 시스템의 적절한 샘플링 주기가 정의된다. 데이터 기반 다중 선형 회귀 분석은 인식 모듈을 구성하는 함수 별 실행 시간을 예측하기 위해 적용되며, 안정적인 실시간 성능을 보장하기 위해 적응형 ROI를 기반으로 자율 주행 안전에 필요한 데이터를 선택적으로 분류하여 연산 부하가 감축된다. 연산 부하 평가 관리에서 환경 인지 모듈과 전체 시스템의 연산 부하가 대상 환경에서의 적절성을 평가하고, 연산 부하 관리에 문제가 있을 때 자율 주행 차량의 거동을 제한하여 시스템 안정성을 유지함으로써 차량 안전성을 확보한다. 제안된 자율주행 인지 전략 및 알고리즘의 성능은 데이터 기반 시뮬레이션 및 실차 실험을 통해 검증되었다. 실험 결과를 통해 제안된 환경 인식 알고리즘은 자율 주행 시스템을 구성하는 모듈 간의 상호 작용을 고려하여 도심 도로 환경에서 자율 주행 차량의 안전성과 시스템의 안정적인 성능을 보장할 수 있음을 확인하였다.Since annually 1.2 million people die from car crashes worldwide, discussions about fundamental preventive measures for traffic accidents are taking place. According to the statistical survey, 94 percent of all traffic accidents are caused by human error. From the perspective of securing road safety, automated driving technology became interesting as a way to solve this serious problem, and its commercialization was considered through a step-by-step application through research and development. Major carmakers already have developed and commercialized advanced driver assistance systems (ADAS), such as lane keeping assistance system (LKAS), adaptive cruise control (ACC), parking assistance system (PAS), automated emergency braking (AEB), and so on. Furthermore, partially automated driving systems are being installed in vehicles and released by carmakers. Audi AI Traffic Jam Pilot (Audi), Autopilot (Tesla), Distronic Plus (Mercedes-Benz), Highway Driving Assist (Hyundai Motor Company), and Driving Assistant Plus (BMW) are typical released examples of the partially automated driving system. These released partially automated driving systems are still must be accompanied by driver attention. Nevertheless, it is proving to be effective in significantly improving safety. In recent years, several automated driving accidents have occurred, and the frequency is rapidly increasing and attracting social attention. Since vehicle accidents are directly related to human casualty, accidents of automated vehicles cause social insecurity by causing a decrease in the reliability of automated driving technology. Due to recent automated driving-related accidents, the safety of the automated vehicle has been emphasized more. Therefore, in this study, we propose an approach to secure vehicle safety in terms of the entire system in consideration of the behavior control of the automated driving vehicle. In addition, the development of automated driving is not merely a replacement technology for driving, but it is expected to have an industrial assembly as integration of high technology. Currently, automated driving systems have been extended from the conventional framework of the existing automotive industry, and are being developed in various fields. Since automated driving is composed of a complex combination of various technologies, development is currently underway in various conditions and has not been standardized yet. Most developments tend to pursue local performance improvement in each module unit, and the overall system unit approaches considering the relationship between component modules is insufficient. Local research and development at the submodule level can be challenging to achieve adequate performance from a system-level due to the effects of module interaction in terms of system integration perspective. The one-way approach that considers only the performance of each module has its limitations. To overcome this problem, it is necessary to consider the characteristics of the modules involved. This dissertation focuses on developing an efficient environment perception algorithm by considering the interaction between configured modules in terms of entire system operation to secure the stable and high performance of an automated driving system. In order to perform effective information processing and secure vehicle safety from a practical perspective, we propose an adaptive ROI based computational load management strategy. The motion characteristics of the subject vehicle, road design standards, and driving tasks of the surrounding vehicles, such as overtaking, and lane change, are reflected in the design of adaptive ROI, and the expansion of the area according to the driving task is considered. Additionally, motion planning results for automated driving are considered in the ROI design in order to guarantee the practical safety of the automated vehicle. In order to secure reasonable and appropriate environment information for the wider areas, lidar sensor data is classified by the designed ROI, and separated processing is conducted according to area importance. Based on the driving data, the calculation time of each module constituting the target system is statistically analyzed. In consideration of the system performance constraint determined by using human reaction time and industry standards, target hardware specification and the performance of sensor, the appropriate sampling time for automated driving system is defined to enhance safety. The data-based multiple linear regression is applied to predict the computation time by each function constituting perception module, and the computational load reduction is applied sequentially by selecting the data essential for automated driving safety based on adaptive ROI to secure the stable real-time execution performance of the system. In computational load assessment, it evaluates whether the computational load of the environmental perception module and entire system are appropriate and restricts the vehicle behavior when there is a problem in the computational load management to ensure vehicle safety by maintaining system stability. The performance of the proposed strategy and algorithms is evaluated through driving data-based simulation and actual vehicle tests. Test results show that the proposed environment recognition algorithm, which considers the interactions between the modules that make up the automated driving system, guarantees the safety of automated vehicle and reliable performance of system in an urban environment scenario.Chapter 1 Introduction 1 1.1. Background and Motivation 1 1.2. Previous Researches 6 1.3. Thesis Objectives 11 1.4. Thesis Outline 13 Chapter 2 Overall Architecture 14 2.1. Automated Driving Architecture 14 2.2. Test Vehicle Configuration 19 Chapter 3 Design of Adaptive ROI and Processing 21 3.1. ROI Definition 25 3.1.1. ROI Design for Normal Driving Condition 30 3.1.2. ROI Design for Lane Change 50 3.1.3. ROI Design for Intersection 56 3.2. Data Processing based on Adaptive ROI 62 3.2.1. Point Cloud Categorization by Adaptive ROI 63 3.2.2. Separated Voxelization 66 3.2.3. Separated Clustering 70 Chapter 4 Environment Perception Algorithm for Automated Driving 75 4.1. Time Delay Compensation of Environment Sensor 77 4.1.1. Algorithm Structure of Time Delay Estimation and Compensation 78 4.1.2. Time Delay Compensation Algorithm 79 4.1.3. Analysis of Processing Delay 84 4.1.4. Test Data based Open-loop Simulation 91 4.2. Environment Representation 96 4.2.1. Static Obstacle Map Construction 98 4.2.2. Lane and Road Boundary Detection 100 4.3. Multiple Object State Estimation and Tracking based on Geometric Model-Free Approach 107 4.3.1. Prediction of Geometric Model-Free Approach 109 4.3.2. Track Management 111 4.3.3. Measurement Update 112 4.3.4. Performance Evaluation via vehicle test 114 Chapter 5 Computational Load Management 117 5.1. Processing Time Analysis of Driving Data 121 5.2. Processing Time Estimation based on Multiple Linear Regression 128 5.2.1. Clustering Processing Time Estimation 129 5.2.2. Multi Object Tracking (MOT) Processing Time Estimation 138 5.2.3. Validation through Data-based Simulation 146 5.3. Computational Load Management 149 5.3.1. Sequential Processing to Computation Load Reduction 151 5.3.2. Restriction of Driving Control 154 5.3.3. Validation through Data-based Simulation 159 Chapter 6 Vehicle Tests based Performance Evaluation 163 6.1. Test-data based Simulation 164 6.2. Vehicle Tests: Urban Automated Driving 171 6.2.1. Test Configuration 171 6.2.2. Motion Planning and Vehicle Control 172 6.2.3. Vehicle Tests Results 174 Chapter 7 Conclusions and Future Works 184 Bibliography 188 Abstract in Korean 200Docto

Development of a light-based driver assistance system

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Machine learning and blockchain technologies for cybersecurity in connected vehicles

Future connected and autonomous vehicles (CAVs) must be secured againstcyberattacks for their everyday functions on the road so that safety of passengersand vehicles can be ensured. This article presents a holistic review of cybersecurityattacks on sensors and threats regardingmulti-modal sensor fusion. A compre-hensive review of cyberattacks on intra-vehicle and inter-vehicle communicationsis presented afterward. Besides the analysis of conventional cybersecurity threatsand countermeasures for CAV systems,a detailed review of modern machinelearning, federated learning, and blockchain approach is also conducted to safe-guard CAVs. Machine learning and data mining-aided intrusion detection systemsand other countermeasures dealing with these challenges are elaborated at theend of the related section. In the last section, research challenges and future direc-tions are identified

On Semantic Segmentation and Path Planning for Autonomous Vehicles within Off-Road Environments

There are many challenges involved in creating a fully autonomous vehicle capable of safely navigating through off-road environments. In this work we focus on two of the most prominent such challenges, namely scene understanding and path planning. Scene understanding is a challenging computer vision task with recent advances in convolutional neural networks (CNN) achieving results that notably surpass prior traditional feature driven approaches. Here, we build on recent work in urban road-scene understanding, training a state of the art CNN architecture towards the task of classifying off-road scenes. We analyse the effects of transfer learning and training data set size on CNN performance, evaluating multiple configurations of the network at multiple points during the training cycle, investigating in depth how the training process is affected. We compare this CNN to a more traditional feature-driven approach with Support Vector Machine (SVM) classifier and demonstrate state-of-the-art results in this particularly challenging problem of off-road scene understanding. We then expand on this with the addition of multi-channel RGBD data, which we encode in multiple configurations for CNN input. We evaluate each of these configuration over our own off-road RGBD data set and compare performance to that of the network model trained using RGB data. Next, we investigate end-to-end navigation, whereby a machine learning algorithm optimises to predict the vehicle control inputs of a human driver. After evaluating such a technique in an off-road environment and identifying several limitations, we propose a new approach in which a CNN learns to predict vehicle path visually, combining a novel approach to automatic training data creation with state of the art CNN architecture to map a predicted route directly onto image pixels. We then evaluate this approach using our off-road data set, and demonstrate effectiveness surpassing existing end-to-end methods

Measurable Safety of Automated Driving Functions in Commercial Motor Vehicles - Technological and Methodical Approaches

Fahrerassistenzsysteme sowie automatisiertes Fahren leisten einen wesentlichen Beitrag zur Verbesserung der Verkehrssicherheit von Kraftfahrzeugen, insbesondere von Nutzfahrzeugen. Mit der Weiterentwicklung des automatisierten Fahrens steigt hierbei die funktionale Leistungsfähigkeit, woraus Anforderungen an neue, gesamtheitliche Erprobungskonzepte entstehen. Um die Absicherung höherer Stufen von automatisierten Fahrfunktionen zu garantieren, sind neuartige Verifikations- und Validierungsmethoden erforderlich. Ziel dieser Arbeit ist es, durch die Aggregation von Testergebnissen aus wissensbasierten und datengetriebenen Testplattformen den Übergang von einer quantitativen Kilometerzahl zu einer qualitativen Testabdeckung zu ermöglichen. Die adaptive Testabdeckung zielt somit auf einen Kompromiss zwischen Effizienz- und Effektivitätskriterien für die Absicherung von automatisierten Fahrfunktionen in der Produktentstehung von Nutzfahrzeugen ab. Diese Arbeit umfasst die Konzeption und Implementierung eines modularen Frameworks zur kundenorientierten Absicherung automatisierter Fahrfunktionen mit vertretbarem Aufwand. Ausgehend vom Konfliktmanagement für die Anforderungen der Teststrategie werden hochautomatisierte Testansätze entwickelt. Dementsprechend wird jeder Testansatz mit seinen jeweiligen Testzielen integriert, um die Basis eines kontextgesteuerten Testkonzepts zu realisieren. Die wesentlichen Beiträge dieser Arbeit befassen sich mit vier Schwerpunkten: * Zunächst wird ein Co-Simulationsansatz präsentiert, mit dem sich die Sensoreingänge in einem Hardware-in-the-Loop-Prüfstand mithilfe synthetischer Fahrszenarien simulieren und/ oder stimulieren lassen. Der vorgestellte Aufbau bietet einen phänomenologischen Modellierungsansatz, um einen Kompromiss zwischen der Modellgranularität und dem Rechenaufwand der Echtzeitsimulation zu erreichen. Diese Methode wird für eine modulare Integration von Simulationskomponenten, wie Verkehrssimulation und Fahrdynamik, verwendet, um relevante Phänomene in kritischen Fahrszenarien zu modellieren. * Danach wird ein Messtechnik- und Datenanalysekonzept für die weltweite Absicherung von automatisierten Fahrfunktionen vorgestellt, welches eine Skalierbarkeit zur Aufzeichnung von Fahrzeugsensor- und/ oder Umfeldsensordaten von spezifischen Fahrereignissen einerseits und permanenten Daten zur statistischen Absicherung und Softwareentwicklung andererseits erlaubt. Messdaten aus länderspezifischen Feldversuchen werden aufgezeichnet und zentral in einer Cloud-Datenbank gespeichert. * Anschließend wird ein ontologiebasierter Ansatz zur Integration einer komplementären Wissensquelle aus Feldbeobachtungen in ein Wissensmanagementsystem beschrieben. Die Gruppierung von Aufzeichnungen wird mittels einer ereignisbasierten Zeitreihenanalyse mit hierarchischer Clusterbildung und normalisierter Kreuzkorrelation realisiert. Aus dem extrahierten Cluster und seinem Parameterraum lassen sich die Eintrittswahrscheinlichkeit jedes logischen Szenarios und die Wahrscheinlichkeitsverteilungen der zugehörigen Parameter ableiten. Durch die Korrelationsanalyse von synthetischen und naturalistischen Fahrszenarien wird die anforderungsbasierte Testabdeckung adaptiv und systematisch durch ausführbare Szenario-Spezifikationen erweitert. * Schließlich wird eine prospektive Risikobewertung als invertiertes Konfidenzniveau der messbaren Sicherheit mithilfe von Sensitivitäts- und Zuverlässigkeitsanalysen durchgeführt. Der Versagensbereich kann im Parameterraum identifiziert werden, um die Versagenswahrscheinlichkeit für jedes extrahierte logische Szenario durch verschiedene Stichprobenverfahren, wie beispielsweise die Monte-Carlo-Simulation und Adaptive-Importance-Sampling, vorherzusagen. Dabei führt die geschätzte Wahrscheinlichkeit einer Sicherheitsverletzung für jedes gruppierte logische Szenario zu einer messbaren Sicherheitsvorhersage. Das vorgestellte Framework erlaubt es, die Lücke zwischen wissensbasierten und datengetriebenen Testplattformen zu schließen, um die Wissensbasis für die Abdeckung der Operational Design Domains konsequent zu erweitern. Zusammenfassend zeigen die Ergebnisse den Nutzen und die Herausforderungen des entwickelten Frameworks für messbare Sicherheit durch ein Vertrauensmaß der Risikobewertung. Dies ermöglicht eine kosteneffiziente Erweiterung der Validität der Testdomäne im gesamten Softwareentwicklungsprozess, um die erforderlichen Testabbruchkriterien zu erreichen

Measurable Safety of Automated Driving Functions in Commercial Motor Vehicles

With the further development of automated driving, the functional performance increases resulting in the need for new and comprehensive testing concepts. This doctoral work aims to enable the transition from quantitative mileage to qualitative test coverage by aggregating the results of both knowledge-based and data-driven test platforms. The validity of the test domain can be extended cost-effectively throughout the software development process to achieve meaningful test termination criteria