Safety-critical scenarios and virtual testing procedures for automated cars at road intersections

This thesis addresses the problem of road intersection safety with regard to a mixed population of automated vehicles and non-automated road users. The work derives and evaluates safety-critical scenarios at road junctions, which can pose a particular safety problem involving automated cars. A simulation and evaluation framework for car-to-car accidents is presented and demonstrated, which allows examining the safety performance of automated driving systems within those scenarios. Given the recent advancements in automated driving functions, one of the main challenges is safe and efficient operation in complex traffic situations such as road junctions. There is a need for comprehensive testing, either in virtual testing environments or on real-world test tracks. Since it is unrealistic to cover all possible combinations of traffic situations and environment conditions, the challenge is to find the key driving situations to be evaluated at junctions. Against this background, a novel method to derive critical pre-crash scenarios from historical car accident data is presented. It employs k-medoids to cluster historical junction crash data into distinct partitions and then applies the association rules algorithm to each cluster to specify the driving scenarios in more detail. The dataset used consists of 1,056 junction crashes in the UK, which were exported from the in-depth On-the-Spot database. The study resulted in thirteen crash clusters for T-junctions, and six crash clusters for crossroads. Association rules revealed common crash characteristics, which were the basis for the scenario descriptions. As a follow-up to the scenario generation, the thesis further presents a novel, modular framework to transfer the derived collision scenarios to a sub-microscopic traffic simulation environment. The software CarMaker is used with MATLAB/Simulink to simulate realistic models of vehicles, sensors and road environments and is combined with an advanced Monte Carlo method to obtain a representative set of parameter combinations. The analysis of different safety performance indicators computed from the simulation outputs reveals collision and near-miss probabilities for selected scenarios. The usefulness and applicability of the simulation and evaluation framework is demonstrated for a selected junction scenario, where the safety performance of different in-vehicle collision avoidance systems is studied. The results show that the number of collisions and conflicts were reduced to a tenth when adding a crossing and turning assistant to a basic forward collision avoidance system. Due to its modular architecture, the presented framework can be adapted to the individual needs of future users and may be enhanced with customised simulation models. Ultimately, the thesis leads to more efficient workflows when virtually testing automated driving at intersections, as a complement to field operational tests on public roads

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

12th EASN International Conference on "Innovation in Aviation & Space for opening New Horizons"

Epoxy resins show a combination of thermal stability, good mechanical performance, and durability, which make these materials suitable for many applications in the Aerospace industry. Different types of curing agents can be utilized for curing epoxy systems. The use of aliphatic amines as curing agent is preferable over the toxic aromatic ones, though their incorporation increases the flammability of the resin. Recently, we have developed different hybrid strategies, where the sol-gel technique has been exploited in combination with two DOPO-based flame retardants and other synergists or the use of humic acid and ammonium polyphosphate to achieve non-dripping V-0 classification in UL 94 vertical flame spread tests, with low phosphorous loadings (e.g., 1-2 wt%). These strategies improved the flame retardancy of the epoxy matrix, without any detrimental impact on the mechanical and thermal properties of the composites. Finally, the formation of a hybrid silica-epoxy network accounted for the establishment of tailored interphases, due to a better dispersion of more polar additives in the hydrophobic resin

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

Proceedings of the 4th International Conference on Transport, Atmosphere and Climate

The "4th International Conference on Transport, Atmosphere and Climate (TAC-4)" held in Bad Kohlgrub (Germany), 2015, was organised with the objective of updating our knowledge on the impacts of transport on the composition of the atmosphere and on climate, three years after the TAC-3 conference in Prien am Chiemsee (Germany). The TAC-4 conference covered all aspects of the impact of the different modes of transport (aviation, road transport, shipping etc.) on atmospheric chemistry, microphysics, radiation and climate, in particular

Safety Evaluation Using Counterfactual Simulations: The use of computational driver behavior models in crash avoidance systems and virtual simulations with optimal subsampling

Traffic safety is a problem worldwide. In-vehicle conflict and crash avoidance systems have been under development and assessment for some time, as integral parts of Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS). Among the methods used to assess conflict and crash avoidance systems developed by the automotive industry, virtual safety assessment methods have been shown to have great potential and efficiency. In fact, scenario generation-based virtual safety assessments play—and are likely to continue to play—a very important role in the assessments of vehicles of all levels of automation. The ultimate aim of this thesis is to improve the safety performance of conflict and crash avoidance systems. This aim is addressed through the use of computational driver models in two different ways. First, by using comfort-zone boundaries in system design, and second, by using a behavior-based crash-causation model together with a novel optimized scenario generation method for virtual safety assessment.The first objective of this thesis is to investigate how a driver model which includes road users’ comfortable behaviors in crash avoidance algorithms impacts the systems’ safety performance and the residual crash characteristics. Chinese car-to-two-wheeler crashes were targeted; Automated Emergency Braking (AEB) algorithms, which comprised the proposed crash avoidance systems, were compared to a traditional AEB algorithm. The proposed algorithms showed larger safety performance benefits. In addition, the similarities in residual crash characteristics regarding impact speed and location after different AEB implementations can potentially simplify the designs of in-crash protection system in future.The second objective is to develop and apply a method for efficient subsampling in crash-causation-model-based scenario generation for virtual safety assessment. The method, which is machine-learning-assisted, actively and iteratively updates the sampling probability based on new simulation results. The crash-causation model is based on off-road glances and a distribution of driver maximum decelerations in critical situations. A simple time-to-collision-based AEB algorithm was used to demonstrate the assessment process as well as the benefits of combining crash-causation-model-based scenario generation and optimal subsampling. The sampling methods are designed to target specific safety benefit indicators, such as impact speed reduction and crash avoidance rate. The results of the study show that the proposed sampling method requires almost 50% fewer simulations than traditional importance sampling.Future work aims to focus on applying the active sampling method to driver-model-based car-to-vulnerable road user (VRU) scenario generation. In addition to assessing conflict and crash avoidance system performance, a novel stopping criterion based on Bayesian future prediction will be further developed and demonstrated for use in experiments (e.g., as part of developing driver models) and virtual simulations (e.g., using driver-behavior-based crash-causation models). This criterion will be able to indicate when studies are unlikely to yield actionable results within the budget available, facilitating the decision to discontinue them while they are being run

The Integration of Machine Learning into Automated Test Generation: A Systematic Mapping Study

Context: Machine learning (ML) may enable effective automated test generation. Objective: We characterize emerging research, examining testing practices, researcher goals, ML techniques applied, evaluation, and challenges. Methods: We perform a systematic mapping on a sample of 102 publications. Results: ML generates input for system, GUI, unit, performance, and combinatorial testing or improves the performance of existing generation methods. ML is also used to generate test verdicts, property-based, and expected output oracles. Supervised learning - often based on neural networks - and reinforcement learning - often based on Q-learning - are common, and some publications also employ unsupervised or semi-supervised learning. (Semi-/Un-)Supervised approaches are evaluated using both traditional testing metrics and ML-related metrics (e.g., accuracy), while reinforcement learning is often evaluated using testing metrics tied to the reward function. Conclusion: Work-to-date shows great promise, but there are open challenges regarding training data, retraining, scalability, evaluation complexity, ML algorithms employed - and how they are applied - benchmarks, and replicability. Our findings can serve as a roadmap and inspiration for researchers in this field.Comment: Under submission to Software Testing, Verification, and Reliability journal. (arXiv admin note: text overlap with arXiv:2107.00906 - This is an earlier study that this study extends

Precise Point Positioning Augmentation for Various Grades of Global Navigation Satellite System Hardware

The next generation of low-cost, dual-frequency, multi-constellation GNSS receivers, boards, chips and antennas are now quickly entering the market, offering to disrupt portions of the precise GNSS positioning industry with much lower cost hardware and promising to provide precise positioning to a wide range of consumers. The presented work provides a timely, novel and thorough investigation into the positioning performance promise. A systematic and rigorous set of experiments has been carried-out, collecting measurements from a wide array of low-cost, dual-frequency, multi-constellation GNSS boards, chips and antennas introduced in late 2018 and early 2019. These sensors range from dual-frequency, multi-constellation chips in smartphones to stand-alone chips and boards. In order to be comprehensive and realistic, these experiments were conducted in a number of static and kinematic benign, typical, suburban and urban environments. In terms of processing raw measurements from these sensors, the Precise Point Positioning (PPP) GNSS measurement processing mode was used. PPP has become the defacto GNSS positioning and navigation technique for scientific and engineering applications that require dm- to cm-level positioning in remote areas with few obstructions and provides for very efficient worldwide, wide-array augmentation corrections. To enhance solution accuracy, novel contributions were made through atmospheric constraints and the use of dual- and triple-frequency measurements to significantly reduce PPP convergence period. Applying PPP correction augmentations to smartphones and recently released low-cost equipment, novel analyses were made with significantly improved solution accuracy. Significant customization to the York-PPP GNSS measurement processing engine was necessary, especially in the quality control and residual analysis functions, in order to successfully process these datasets. Results for new smartphone sensors show positioning performance is typically at the few dm-level with a convergence period of approximately 40 minutes, which is 1 to 2 orders of magnitude better than standard point positioning. The GNSS chips and boards combined with higher-quality antennas produce positioning performance approaching geodetic quality. Under ideal conditions, carrier-phase ambiguities are resolvable. The results presented show a novel perspective and are very promising for the use of PPP (as well as RTK) in next-generation GNSS sensors for various application in smartphones, autonomous vehicles, Internet of things (IoT), etc

Doctor of Philosophy

dissertationIn computer science, functional software testing is a method of ensuring that software gives expected output on specific inputs. Software testing is conducted to ensure desired levels of quality in light of uncertainty resulting from the complexity of software. Most of today's software is written by people and software development is a creative activity. However, due to the complexity of computer systems and software development processes, this activity leads to a mismatch between the expected software functionality and the implemented one. If not addressed in a timely and proper manner, this mismatch can cause serious consequences to users of the software, such as security and privacy breaches, financial loss, and adversarial human health issues. Because of manual effort, software testing is costly. Software testing that is performed without human intervention is automatic software testing and it is one way of addressing the issue. In this work, we build upon and extend several techniques for automatic software testing. The techniques do not require any guidance from the user. Goals that are achieved with the techniques are checking for yet unknown errors, automatically testing object-oriented software, and detecting malicious software. To meet these goals, we explored several techniques and related challenges: automatic test case generation, runtime verification, dynamic symbolic execution, and the type and size of test inputs for efficient detection of malicious software via machine learning. Our work targets software written in the Java programming language, though the techniques are general and applicable to other languages. We performed an extensive evaluation on freely available Java software projects, a flight collision avoidance system, and thousands of applications for the Android operating system. Evaluation results show to what extent dynamic symbolic execution is applicable in testing object-oriented software, they show correctness of the flight system on millions of automatically customized and generated test cases, and they show that simple and relatively small inputs in random testing can lead to effective malicious software detection