Combined automotive safety and security pattern engineering approach

Automotive systems will exhibit increased levels of automation as well as ever tighter integration with other vehicles, traffic infrastructure, and cloud services. From safety perspective, this can be perceived as boon or bane - it greatly increases complexity and uncertainty, but at the same time opens up new opportunities for realizing innovative safety functions. Moreover, cybersecurity becomes important as additional concern because attacks are now much more likely and severe. However, there is a lack of experience with security concerns in context of safety engineering in general and in automotive safety departments in particular. To address this problem, we propose a systematic pattern-based approach that interlinks safety and security patterns and provides guidance with respect to selection and combination of both types of patterns in context of system engineering. A combined safety and security pattern engineering workflow is proposed to provide systematic guidance to support non-expert engineers based on best practices. The application of the approach is shown and demonstrated by an automotive case study and different use case scenarios.EC/H2020/692474/EU/Architecture-driven, Multi-concern and Seamless Assurance and Certification of Cyber-Physical Systems/AMASSEC/H2020/737422/EU/Secure COnnected Trustable Things/SCOTTEC/H2020/732242/EU/Dependability Engineering Innovation for CPS - DEIS/DEISBMBF, 01IS16043, Collaborative Embedded Systems (CrESt

Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS - a collection of Technical Notes Part 1

This report provides an introduction and overview of the Technical Topic Notes (TTNs) produced in the Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS (Tigars) project. These notes aim to support the development and evaluation of autonomous vehicles. Part 1 addresses: Assurance-overview and issues, Resilience and Safety Requirements, Open Systems Perspective and Formal Verification and Static Analysis of ML Systems. Part 2: Simulation and Dynamic Testing, Defence in Depth and Diversity, Security-Informed Safety Analysis, Standards and Guidelines

The Evaluation of Route Guidance Systems

BACKGROUND We were commissioned by the Transport and Road Research Laboratory to: "collaborate with the German government and their representatives who are responsible for conducting the LISB trial in Berlin in order to produce an agreed methodology, which is acceptable in both Germany and the UK, for assessing the automatic route guidance systems which will be provided in Berlin and London." The brief suggested a number of aspects to be included, and required detailed proposals, timescales and costs for implementation in London. 1.1.2 The background to the brief lies in decisions to introduce pilot automatic route guidance systems in the two cities. The principles of the systems are similar, and have been described in detail elsewhere (Jeffery, 1987). In brief, they involve : (i) a central computer which retains information on a specified road network, which is updated using real time information from the equipment users; (ii) infra red beacons at selected junctions which transmit information to equipped vehicles and receive information from those vehicles; (iii) in-vehicle equipment which includes a dead-reckoning system for position finding, a device for requesting guidance and specifying the destination, a micro-computer which selects the optimal route, and a display which indicates when a turn is required on the main network, and the compass direction and distance to the final destination; iv) transmission from the equipped vehicles of origin, requested destination, links used since passing the last beacon and, for each link, the time of entry and departure and time spent delayed. It is this travel time information which is used to update the central computer's knowledge of the best routes. (Continues..

Disruptive Innovations and Disruptive Assurance: Assuring Machine Learning and Autonomy

Autonomous and machine learning-based systems are disruptive innovations and thus require a corresponding disruptive assurance strategy. We offer an overview of a framework based on claims, arguments, and evidence aimed at addressing these systems and use it to identify specific gaps, challenges, and potential solutions