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Increasing automation in the automotive systems has re-focused the industry’s attention on verification and validation methods and especially on the development of test scenarios. The complex nature of Advanced Driver Assistance Systems (ADASs) and Automated Driving (AD) systems warrant the adoption of new and innovative means of evaluating and establishing the safety of such systems. In this paper, the authors discuss the results from a semi-structured interview study, which involved interviewing ADAS and AD experts across the industry supply chain. Eighteen experts (each with over 10 years’ of experience in testing and development of automotive systems) from different countries were interviewed on two themes: test methods and test scenarios. Each of the themes had three guiding questions which had some follow-up questions. The interviews were transcribed and a thematic analysis via coding was conducted on the transcripts.

Automotive electronic control systems are expected to respond to input demands in real-time (circa: milliseconds) to ensure occupant and road user safety and comfort. System complexity and real-time computing requirements create significant challenges in proving the robustness of control systems; here robustness is the degree to which a system can function correctly in the presence of unexpected inputs. Evidence shows that faults still escape to customers incurring large warranty costs. Existing test methods can be ineffective in testing robustness with the primary focus being on requirements validation. Evidence from other industries such as IT and medical suggests faults that are difficult to find, manifest due to complex interactions and sequences of events. Research in model based software design, test optimization and formal methods - mathematical based approaches to prove robustness, is abundant in literature.

PICASSOS was a UK government funded programme to improve the ability of automotive supply chains to develop complex software-intensive systems with high safety assurance and at an acceptable cost. This was executed by a consortium of three universities and five companies including an automotive OEM and suppliers. Three major elements of the PICASSOS project were: use of automated model based verification technology utilising formal methods; application of this technology in the context of ISO 26262; and evaluation to measure the impact of this approach to inform key management decisions on the costs, benefits and risks of applying this technology on live projects. The project spanned system level design and software development. This was achieved by using a unified model based process incorporating SysML at the system level and using Simulink and Stateflow auto-coded into C at the software level.

The advent of Advanced Driver Assistance Systems (ADAS) and automated driving has offered a new challenge for functional verification and validation. The explosion of the test sample space for possible combinations of inputs needs to be handled in an intelligent manner to meet cost and time targets for the development of such systems. This paper addresses this research gap by using constrained randomization techniques for the creation of the required test scenarios and test cases. Furthermore, this paper proposes an automated constrained randomized test scenario generation framework for testing of ADAS and automated systems in a driving simulator setup. The constrained randomization approach is deployed at two levels: 1) test scenario randomization 2) test case randomization. The novelty of the proposed approach is in applying the constrained randomization method to generate test scenarios and test cases for automotive system and system of systems in a driving simulator environment.

The introduction of ISO 26262 concepts has brought important changes in the software development process for automotive software. While making the process more robust by introducing various additional methods of verification and validation, there has been a substantial increase in the development time. Thus, test automation and front loading approaches have become important to meet product timelines and quality. This paper proposes automated testing methods using formal analysis tools like Simulink Design Verifier™ (SLDV) for boundary value testing and interface testing to address the demands of ISO 26262 concepts at unit and component level. In addition, the method of automated boundary value testing proposed differs from the traditional methods and the authors offer an argument as to why the traditional boundary value testing is not required at unit (function) level. There are two aspects of the proposed method: automated test case generation and automated test case execution.

Complexity of electronics and embedded software systems in automobiles has been increasing over the years. This necessitates the need for an effective and exhaustive development and validation process in order to deliver fault free vehicles at reduced time to market. Model-based Product Engineering (MBPE) is a new process for development and validation of embedded control software. The process is generic and defines the engineering activities to plan and assess the progress and quality of the software developed for automotive applications. The MBPE process is comprised of six levels (one design level and five verification and validation levels) ranging from the vehicle requirements phase to the start of production. The process describes the work products to be delivered during the course of product development and also aligns the delivery plan to overall vehicle development milestones.

Testing real-time vehicular systems challenges the tester to design test cases for concurrent and sequential input events, emulating unexpected user and usage profiles. The vehicle response should be robust to unexpected user actions. Sequence Covering Arrays (SCA) offer an approach which can emulate such unexpected user actions by generating an optimized set of test vectors which cover all possible t-way sequences of events. The objective of this research was to find an efficient nonfunctional sequence testing (NFST) strategy for testing the robustness of real-time automotive embedded systems measured by their ability to recover (prove-out test) after applying sequences of user and usage patterns generated by combinatorial test algorithms, considered as “noisy” inputs. The method was validated with a case study of an automotive embedded system tested at Hardware-In-the-Loop (HIL) level. The random sequences were able to alter the system functionality observed at the prove-out test.

The increasing use of distributed functions in vehicles can introduce unexpected and undesirable emergent behavior. This can be as a result of transient events such as sudden drops in the supply voltage. In this situation system behavior is often not adequately specified or controlled. This paper presents a novel approach to automotive electronic systems testing addressing robustness against low voltage transient conditions. The paper will discuss the technical output as well as performance in real-world test usage. The proposed approach uses a combination of pseudo-random number generator algorithms to generate parameterized supply voltage waveforms simulating low voltage transient conditions, used to drive the system-under-test (SUT).

In a current premium vehicle the infotainment system is typically implemented as a distributed system consisting of a number of modules communicating via a Media Oriented Systems Transport (MOST) network. Typical issues with such systems of systems (SoS) are emergent behaviour as systems interact in an unanticipated manner particularly during some initialisation conditions where it may be possible to get delays and failures in individual systems. Testing of infotainment systems at an overall level is conventionally carried out manually by an expert who can observe at a customer level but this has limitations affecting test coverage and effectiveness. Hence there is a requirement for an automated infotainment testing system which replicates a human expert encompassing relevant sensory modalities relating to control (i.e. touch) and observation (i.e. sight and sound) of the system under test.