Khatun, Marzana
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Institute
Safe Scenario Boundaries Determination by Parameter Variation for an Automated Driving System
(2023)
An expanding area of research interest is the scenario-based testing and development of Automated Driving Systems (ADS). In scenario-based testing, a system is examined in a set of pre-defined scenarios to inspect its behavior. Scenarios are described by a set of parameters, such as velocities and distances. For a safety-related system, identifying the parameter limits for safe operation is essential to reduce harm. Hence, there is a need to determine safe boundaries considering the assumed parameter set in a scenario to support the Verification and Validation (V&V) of an ADS. This paper presents a systematic approach to determining safe boundaries of parameters by scenario-based testing. The contributions of this work are: (i) performing scenariobased parameter variation to detect collisions, (ii) identifying safe boundaries of each single parameter from a specific Operation Design Domain (ODD) and (iii) providing safety-related evidence to identify safe boundaries from defined ODD. The results of this work can assist scenario reduction techniques to derive nothazardous scenarios and hazardous scenarios and support the V&V Processes of ADS.
Scenario-based testing is essential for Highly Automated Driving (HAD) vehicles to determine the safety-related input parameters and their boundaries. The increasing complexity, vehicle functions, and operational design pose new challenges for scenario-based testing, as the number of scenarios is enormous. Therefore, an efficient and systematic process is required in the various stages of scenario-based testing. The contribution of this study is to provide sensitivity information of safety related parameters and support logical scenario reduction. This paper presents an approach that supports to optimize the safety-related parameters boundary towards logical scenario reduction. Additionally, sensitivity analysis is applied by computing Variance- Based Sensitivity Analysis (VBSA) indices and prioritize the input parameters. Two datasets are investigated by VBSA based on the input parameters. One dataset is based on the samples from realworld scenarios and other dataset is derived from the samples considering statistic distributions with a specific parameter range. Moreover, the proposed approach is applied to an exemplary use case and the outcomes are demonstrated.
To ensure the safety and security of Automated Vehicles (Avs), the interaction between the Functional Safety (FuSa) and the Cybersecurity (CS) domains needs to be managed systematically. There is a demand to develop effective and structured management systems to support the homologation process. From this motivation, identifying the interaction between the Safety Management System (SMS) and the Cybersecurity Management System (CSMS) is a fundamental aspect and needs to be improved for HAD systems. Hence, the classical Decision Making Trial and Evaluation Laboratory (DEMATEL) method and fuzzy DEMATEL are applied to evaluate the influential factors that can impact the safety and security of the HAD systems. This paper proposes a list of influencing factors focusing on the interaction between SMS and CSMS for HAD systems. Additionally, the results of an anonymously conducted survey among experts from industry and research are presented and used as inputs for the methods. This work helps to understand the relationship between influencing factors and provides a simplified, easy-to-visualized, and valuable guide for developing HAD systems. The result of this study shows that the most important influential factor is F13. Moreover, the cause and effect of the factors are illustrated numerically and graphically. The influential factors F1 to F7 are identified as the cause and F8 to F13 are reasoned to effect. Finally, a circular representation of the influential factors and their interaction is presented in this paper.
Functional safety and cybersecurity are essential parts of the development of automated vehicles to ensure vehicle safety. Highly automated driving (HAD) vehicles require safe and secure development and communication processes that have to be monitored, maintained and improved through management processes. Hence, interface management systems are required to confirm HAD vehicle safety. The acceptance level of the interface between functional safety and cybersecurity in management systems is crucial for the development of Highly Automated Driving (HAD) vehicles. The Safety Management System (SMS) needs to consider the aspect of cybersecurity to ensure the overall safety of the vehicles or vice-versa. However, the interface methods of SMS and Cybersecurity Management System (CSMS) is challenging given the complexity of the system development and constraints from the company culture. The objective of this study is to present an interface approach in between management systems with a set of interface specifications including communication adaption processes. The main contributions of the paper are, (i) Illustrating the interface areas of the SMS and CSMS by identifying the management factors, (ii) Presenting the degree of influence of the management factors based on the survey results, and (iii) Providing a support to deal with SMS and CSMS interface for HAD vehicle development. A list of interface-related management factors is presented in this paper based on the literature study and findings from other disciplines. Additionally, the degree of influence of the management factors is presented as a result of this research based on the survey results from functional safety and cybersecurity experts.
The development of Highly Automated Driving (HAD) systems is necessary for automated vehicles in termsof various complex functionalities. HAD systems consist of complex structures containing different types ofsensors. The functionality of HAD systems needs be tested to ensure the overall safety of automated vehicles.Methods such as real-world testing require a large number of driving miles and are enormously expensive andtime-consuming. Therefore, simulation-based testing is widely accepted and applicable in the development ofHAD systems, including sensor performance improvement. In order to identify the functional insufficiencyof such sensors that affect the safety of HAD systems, it is critical to test these sensors extensively under avariety of conditions such as, road types, environment and traffic situations. Based on this motivation, the maincontributions of this paper are as follows: First, a simulation-based test concept of radar sensors with methodsfor the Safety Of the Intended Functionality (SOTIF) use case is presented. Second, a specific radar effect isevaluated through simulation-based testing of two different radar models to support and realize the sensor’sfunctional insufficiency. Finally, the development of a filter is proposed to improve the sensor performanceconsidering the radar specific multipath propagation effects.
Scenario-based collision detection using machine learning for highly automated driving systems
(2023)
Highly Automated Driving (HAD) systems implement new features to improve the performance, safety and comfort of partially or fully automated vehicles. The identification of safety parameters by means of complex systems and the driving environment is a fundamental aspect that require great attention. Therefore, much research has been conducted in the field of collision detection in the development of automated vehicles. However, the development of HAD systems faces the challenge of ensuring zero accidents. For this reason, collision detection in the safety-related concept phase as hazard identification is one of the key research points in HAD system. In this paper, a systematic approach to detect potential collisions for scenario-based hazard analysis of HAD systems is presented by using Multilayer Perceptron (MLP) as a Machine Learning (ML) technique. Moreover, the proposed approach assists in reducing the number of observed scenarios for hazard analysis and risk assessment. Additionally, two simulation-based scenario datasets are examined in the ML model to identify potential hazard scenarios. The results of this study show that MLP can support to detect the collision at safety-related concept phase. Furthermore, this paper contributes to providing arguments and evidence for ML techniques in HAD systems safety by selecting relevant use cases.
To ensure safety and security of highly automated driving systems one shall make sure all risks are reduced to a reasonable level and an all potential cyberattacks are addressed with necessary protection. Because of the complexity of such vehicle systems, systematic and structured management approaches are vital to maintaining safety via cybersecurity (CS). The interface of Safety Management System (SMS) with Cybersecurity Management System (CSMS) is one of the key aspects to ensuring that potential safety issues are addressed. Both management systems include planning, concepts, and process development, with significant areas of overlapping management systems is required. Regarding the management systems interface and distribution, it is still a challenge that Highly Automated Driving (HAD) vehicles needs to overcome by means of effective implementation and strategies with continuous improvement and a reduction of miscommunication. From that motivation, a set of engineering risk management framework are proposed in this paper. Subsequently, introducing the interface areas between the safety and the cybersecurity domain is one of the focus areas of this paper, together with the representation of the interface management activities with exemplary interaction template. Additionally, mapping in between safety and cybersecurity related standards in terms of evidence and management systems is represented partially to support both safety case and security assurance.
Scenario analysis is essential for the validation of highly automated driving (HAD) systems. The complexity of overall system safety is increasing in terms of Functional Safety (FuSa) and Safety of Intended Functionality (SOTIF). However, field testing of all possible safety-critical scenarios is hardly possible for automated vehicles. Therefore, scenario simulation is necessary for HAD to support the validation process and has gained acceptance in recent years. However, scenariobased analysis leads to an explosion of scenarios, so a scenario database is required at the beginning of the development phase. Hence, scenario reduction approaches need to be integrated in the conceptual phase to reduce the scenario modeling and testing effort. The contribution of this paper is to present simulation-based testing approaches for determining a reduced set of collision scenarios, taking into account the sensitivity of safety-critical parameters. Furthermore, a set of application scenarios is simulated to demonstrate the scenario reduction approaches by considering the detected collisions under a specific or restricted Operational Design Domain (ODD). In addition, this study supports the provision of a safety argument with evidence by introducing a variance-based sensitivity analysis and a scenario database that can be used as an input data set for an artificial intelligence system.
Modeling and simulation techniques are a necessity to solve the problems and aid the automated driving verification and validation process. The scenario-based analysis like hazard analysis and risk assessment is counting as an essential method not only to understand the system behavior in the field of an automated vehicle but also to reduce the development and communication gaps. In terms of functional safety and safety of the intended functionality the number of hazardous scenarios increases that need to be reduced. Scenario reduction is a challenge that yet needs to be solved. Therefore, this paper proposes a probability approach like the Monte Carlo method at the logical scenario level. Additionally, the safety-critical vehicle parameter range has been optimized based on collision detection. Furthermore, the result realized by the Monte Carlo experiment has been used to model the concrete scenarios in CarMaker in a time-efficient manner. The approach of modeling for a specific function like transverse guidance can be utilized to build a full scenario database for the highly automated driving vehicle.
To allow a vehicle with highly automated driving functions to operate on the road the overall safety (safe functionalities, functional insufficiencies including cybersecurity) of the driving system must be guaranteed. Therefore, a generic Safety Management System (SMS), that includes all useful and necessary regulations should be applied. The given specifications regarding the safety of driving systems shall be understood and considered in order to define an acceptable SMS for the Highly Automated Driving Function (HADF). Derived from the generic SMS a specific management system has to be developed to guide the development and deployment of the HADF. The research presented in this paper investigates the currently available SMS in different sectors like aviation, marine, and railway to propose a new set of components and elements that are useful and modified for a HADF’s SMS. Moreover, the paper provides a systematic approach to how the new set of components and elements can be applied for a HADF SMS. Additionally, well established hazard identification methods (scenario-based HARA and STPA) are compared and integrated into the safety concept phase. The complexity of scenarios is unique for a HADF. Since the SMS is going to be complicated for automotive HADFs and a constantly developing process, methods for evaluation and continuous improvement are needed. This paper gives insight into the structure of the SMS and to guarantee its applicability the use of a helpful software tool is considered. Furthermore, the proposed SMS approach can be adjusted as a groundwork for research concerns like validation for homologation and assess the safety of a HADF.