Formal Verification of Safety Critical Autonomous Systems via Bayesian Optimization

As control systems become increasingly more complex, there exists a pressing need to find systematic ways of verifying them. To address this concern, there has been significant work in developing test generation schemes for black-box control architectures. These schemes test a black-box control architecture's ability to satisfy its control objectives, when these objectives are expressed as operational specifications through temporal logic formulae. Our work extends these prior, model based results by lower bounding the probability by which the black-box system will satisfy its operational specification, when subject to a pre-specified set of environmental phenomena. We do so by systematically generating tests to minimize a Lipschitz continuous robustness measure for the operational specification. We demonstrate our method with experimental results, wherein we show that our framework can reasonably lower bound the probability of specification satisfaction

Identification of Challenging Highway-Scenarios for the Safety Validation of Automated Vehicles Based on Real Driving Data

For a successful market launch of automated vehicles (AVs), proof of their safety is essential. Due to the open parameter space, an infinite number of traffic situations can occur, which makes the proof of safety an unsolved problem. With the so-called scenario-based approach, all relevant test scenarios must be identified. This paper introduces an approach that finds particularly challenging scenarios from real driving data (\RDDwo) and assesses their difficulty using a novel metric. Starting from the highD data, scenarios are extracted using a hierarchical clustering approach and then assigned to one of nine pre-defined functional scenarios using rule-based classification. The special feature of the subsequent evaluation of the concrete scenarios is that it is independent of the performance of the test vehicle and therefore valid for all AVs. Previous evaluation metrics are often based on the criticality of the scenario, which is, however, dependent on the behavior of the test vehicle and is therefore only conditionally suitable for finding "good" test cases in advance. The results show that with this new approach a reduced number of particularly challenging test scenarios can be derived.Comment: Accepted at 2020 Fifteenth International Conference on Ecological Vehicles and Renewable Energies (EVER

Critical Scenario Identification for Testing of Autonomous Driving Systems

Background: Autonomous systems have received considerable attention from academia and are adopted by various industrial domains, such as automotive, avionics, etc. As many of them are considered safety-critical, testing is indispensable to verify their reliability and safety. However, there is no common standard for testing autonomous systems efficiently and effectively. Thus new approaches for testing such systems must be developed.Aim: The objective of this thesis is two-fold. First, we want to present an overview of software testing of autonomous systems, i.e., relevant concepts, challenges, and techniques available in academic research and industry practice. Second, we aim to establish a new approach for testing autonomous driving systems and demonstrate its effectiveness by using real autonomous driving systems from industry.Research Methodology: We conducted the research in three steps using the design science paradigm. First, we explored the existing literature and industry practices to understand the state of the art for testing of autonomous systems. Second, we focused on a particular sub-domain - autonomous driving - and proposed a systematic approach for critical test scenario identification. Lastly, we validated our approach and employed it for testing real autonomous driving systems by collaborating with Volvo Cars.Results: We present the results as four papers in this thesis. First, we conceptualized a definition of autonomous systems and classified challenges and approaches, techniques, and practices for testing autonomous systems in general. Second, we designed a systematic approach for critical test scenario identification. We employed the approach for testing two real autonomous driving systems from the industry and have effectively identified critical test scenarios. Lastly, we established a model for predicting the distribution of vehicle-pedestrian interactions for realistic test scenario generation for autonomous driving systems. Conclusion: Critical scenario identification is a favorable approach to generate test scenarios and facilitate the testing of autonomous driving systems in an efficient way. Future improvement of the approach includes (1) evaluating the effectiveness of the generated critical scenarios for testing; (2) extending the sub-components in this approach; (3) combining different testing approaches, and (4) exploring the application of the approach to test different autonomous systems

Trajectory planning based on adaptive model predictive control: Study of the performance of an autonomous vehicle in critical highway scenarios

Increasing automation in automotive industry is an important contribution to overcome many of the major societal challenges. However, testing and validating a highly autonomous vehicle is one of the biggest obstacles to the deployment of such vehicles, since they rely on data-driven and real-time sensors, actuators, complex algorithms, machine learning systems, and powerful processors to execute software, and they must be proven to be reliable and safe. For this reason, the verification, validation and testing (VVT) of autonomous vehicles is gaining interest and attention among the scientific community and there has been a number of significant efforts in this field. VVT helps developers and testers to determine any hidden faults, increasing systems confidence in safety, security, functional analysis, and in the ability to integrate autonomous prototypes into existing road networks. Other stakeholders like higher-management, public authorities and the public are also crucial to complete the VTT process. As autonomous vehicles require hundreds of millions of kilometers of testing driven on public roads before vehicle certification, simulations are playing a key role as they allow the simulation tools to virtually test millions of real-life scenarios, increasing safety and reducing costs, time and the need for physical road tests. In this study, a literature review is conducted to classify approaches for the VVT and an existing simulation tool is used to implement an autonomous driving system. The system will be characterized from the point of view of its performance in some critical highway scenarios.O aumento da automação na indústria automotiva é uma importante contribuição para superar muitos dos principais desafios da sociedade. No entanto, testar e validar um veículo altamente autónomo é um dos maiores obstáculos para a implantação de tais veículos, uma vez que eles contam com sensores, atuadores, algoritmos complexos, sistemas de aprendizagem de máquina e processadores potentes para executar softwares em tempo real, e devem ser comprovadamente confiáveis e seguros. Por esta razão, a verificação, validação e teste (VVT) de veículos autónomos está a ganhar interesse e atenção entre a comunidade científica e tem havido uma série de esforços significativos neste campo. A VVT ajuda os desenvolvedores e testadores a determinar quaisquer falhas ocultas, aumentando a confiança dos sistemas na segurança, proteção, análise funcional e na capacidade de integrar protótipos autónomos em redes rodoviárias existentes. Outras partes interessadas, como a alta administração, autoridades públicas e o público também são cruciais para concluir o processo de VTT. Como os veículos autónomos exigem centenas de milhões de quilómetros de testes conduzidos em vias públicas antes da certificação do veículo, as simulações estão a desempenhar cada vez mais um papel fundamental, pois permitem que as ferramentas de simulação testem virtualmente milhões de cenários da vida real, aumentando a segurança e reduzindo custos, tempo e necessidade de testes físicos em estrada. Neste estudo, é realizada uma revisão da literatura para classificar abordagens para a VVT e uma ferramenta de simulação existente é usada para implementar um sistema de direção autónoma. O sistema é caracterizado do ponto de vista do seu desempenho em alguns cenários críticos de autoestrad