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With the introduction of vehicular digitization and automation, there has been significant growth in the number of Electronic Control Units (ECUs) inside vehicles, followed by the broader use of Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADSs). The growth of the number of ECUs has also significantly increased the number of user interfaces. To conduct safe driving, in addition to those related to the real-time control of the vehicle, a driver also needs to be able to digest information effectively and efficiently from various ECUs via the Human-Machine Interface (HMI). To evaluate the safety of ADS, including its interactions with system users, some work has suggested that they will need to be driven for over 11 billion miles. However, the number of test miles driven is not a meaningful metric for judging safety. Instead, the types of scenarios encountered by the driver-vehicle interactions during testing are critically important.

Due to the increasing complexities, the safety assurances for Automated Driving Systems (ADSs) and Advanced Driver Assistance Systems (ADASs) pose challenges. Recent development within the industry and academia suggests a scenario-based approach underpinned by the system’s Operational Design Domain (ODD) for its safety assurance. In such framework, the ODD defines the safe operating boundary, whereas the scenarios set out individual test conditions. To assess the behavior of the system, a critical element for road safety is the ability to respect the rules of the road. This paper joins together ODDs, scenarios, and rules of the road to form a scalable ODD-based safety assurance framework. The backbone of the framework contains a coherent and common taxonomy to describe the ODDs and behavior library, the scenario tagging structure from the ASAM OpenLABEL standard has been used in the example use case.

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.

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.

Plug-in hybrid electric vehicles with its low local emissions are very promising alternative to conventional vehicles. Similar to conventional hybrid vehicles, plug-in hybrid electric vehicles use multiple energy sources for propulsive power. The power split between these energy sources must be managed carefully to achieve reduction in fuel consumption and carbon dioxide emissions. The decision to use either or both of the power sources is made by the vehicle supervisory controller. Depending on how the supervisory controller uses the off-board electricity source, supervisory control strategies can significantly impact the component size, cost and overall vehicle energy efficiency. This paper reports the impact of supervisory control strategies on a full parallel plug-in hybrid electric vehicle's emission performance over two drive cycles.

To improve the effectiveness and efficiency of decision making during vehicle development programmes, new interactive NVH simulation tools have been introduced. For their optimum use during the target setting of NVH attributes new methodologies need to be established. A valuable step in achieving this is to further understand opinion forming processes during on-road appraisals. To achieve this however new approaches to capture the events of an appraisal are needed. This paper reports on the first steps taken towards developing these new approaches and demonstrates how attitudes towards vehicle sound quality and driver behavior can vary depending on the personality traits of the assessor and their role within the OEM. This insight will provide a benchmark to compare how expert assessors and customers make decisions.

A full vehicle NVH Simulator has been developed to provide a realistic interactive in-car environment where a subject can experience multi-modal stimuli ( accurately reproduced sound and vibration as well as visual ) whilst either driving or being driven. This paper describes its use in learning about the strategies subjects employ during sound evaluations, and how this information can help optimise decision making during product development. It is possible to understand how subjects assess the sound of vehicles, both in the way that they drive the vehicle and importantly which elements of the sound character have greatest influence on their evaluation of the vehicle. It is also possible to compare the strategies employed by NVH engineers, company decision-makers and non-experts such as customers.

Many different control strategies have been developed to optimize a hybrid electric (HE) power train. Recently these have included the use of adaptive approaches. Such adaptive strategies attempt to alter the behavior of the HE power train based on the journey being undertaken. Unlike the journeys undertaken in personal vehicles, passenger buses often traverse the same unchanged route several times a day. If such a journey could be suitably modeled the model may then form part of an optimized control strategy. This paper describes the collection and analysis of a large number of bus journeys over a period of 10 months. Journey data was collected from a real route using a GPS receiver in conjunction with a handheld computer running bespoke logging software. A method has been developed to reduce each journey into a simple statistical representation based on power/energy use.

The objective of the Project is to develop a low cost lead-acid battery solution for hybrid electric vehicles based on a novel, individual, spirally wound valve-regulated lead-acid 2V cell optimized for this application. This cell will be used as a building block for the development of a complete battery pack that is managed at the cell level. Following bench testing, this battery pack will be thoroughly evaluated by substituting it for the Nickel/Metal Hydride pack in a Honda Insight A paper given at the Future Car Congress in June 2002 covered the first half of the 3-year project and described work carried out in the following areas: Development of cell and battery testing facilities. The design and development of the prototype double-ended cell. The development of the battery pack specification and pack design. The development of the battery management system. It also gave details of the test results obtained on the demonstration vehicle with its original NiMH battery.

Sound quality targets for new vehicles are currently specified by jury evaluation techniques based upon listening studies in a sound laboratory. However, jury testing is costly, time consuming and at present there are no methods to include customer expectations or brand requirements. This paper describes a neural computing approach that is being developed to generate knowledge and tools to enable objective measures of a product's sound to be converted into a prediction of the subjective impression of potential customers without carrying out the traditional jury evaluation tests.

All new European vehicles face strict drive-by noise regulations. It would help vehicle designers if they could predict drive-by noise given parameters available early in the design process. The large amount of data from previous tests suggests a new approach, using neural networks. This paper introduces neural networks and describes how to apply them to the prediction problem. The selection of suitable inputs and amount of data required is discussed. The problem can be simplified by first predicting vehicle performance. Interim results for a vehicle performance neural network are presented. Further work towards a drive-by noise neural network is proposed.