In the automotive domain, as an example of cyber-physical systems, continuous software deployment is actively explored to deliver increasingly capable features to existing fleets of vehicles. The distributed nature of software coupled with tight hardware integration and potentially tremendous variability between vehicles make ensuring compatibility of updated software a significant challenge – both technically and managerially. While the automotive industry commonly forms larger multi-brand organisations to utilise economies of scale, processes for continuous deployment contradictory assumes a single organisation with full control. This paper sets out to shed light on challenges of adopting continuous deployment in the context of such a multi-brand cyber-physical systems organisation. Following a case study, the paper describes a tension between the managerial perspective concerned with platform strategies, and the engineering perspective responsible for developing products from those platforms. The paper highlights software dependencies as a barrier to continuous innovation of cyber-physical systems in multi-brand organisations.

Continuous integration promises earlier defect detection, quality improvements, and more customer value delivered faster. In this case study, we examined development of software for the advanced safety and driver support component of a Swedish vehicle manufacturer in two consecutive projects.

Software is an enabler for continuously delivering product innovations to customers. In the automotive domain, continuous software deployment is actively investigated to deliver increasingly capable features to existing fleets of vehicles. The distributed nature of vehicle software coupled with tight hardware integration and a potentially tremendous variability between vehicle individuals makes ensuring compatibility of updated software a significant challenge—both technically and managerially. Agile development processes address such challenges with short development cycles, informal inter-team communication, and tool-support for continuous integration and automated testing. This, however, normally assumes a single organisation with full control of their processes, while the automotive industry commonly forms larger multi-brand organisations to utilise economy of scale—a case not sufficiently investigated by current literature. In this paper we identify and describe organisational challenges with managing software dependencies in the context of a multi-brand automotive organisation. Through an exploratory, qualitative case[1]study we characterise software dependencies to link their technical nature with managerial challenges facing the organisations, which can support an organisation to formulate efficient management strategies.

Continuous integration promises earlier defect detection, quality improvements and more customer value delivered faster. But what evidence is there? In this longitudinal case study we examined the development of software for the advanced safety and driver support component of a Swedish vehicle manufacturer in two consecutive projects, where the first was developed in a big bang fashion, typical to a traditional waterfall process, while the second project utilized continuous integration. By contrasting the two projects, we evaluated the introduction of continuous integration and supplement earlier claims based on interview studies with a quantitative analysis of defect reports.

This article reports article reports on a mapping study to investigate the C-ROADS Day 1 C-ITS application Green Light Optimal Speed Advisory (GLOSA). In the study, 64 publications between 2006 and 2019 where reviewed and classified according to a schema developed using thematic analysis of the selected publications. Among the findings were that the typical publication evaluates through simulation benefits for the equipped vehicle, leaving considerable gaps in investigations of societal effects. Additionally, there is a lack of investigation on driver behaviour, both for the equipped vehicle and for fellow road users—the ability to accurately model such behaviour is necessary for reliable simulation results.

Maritime transports are to be regarded as a self-organized ecosystem (Kay et al., 1999) characterized by sub-optimization where historically each actor to has optimized its own operations, often giving rise to inefficiencies as a whole. In recent years however, digital transformation has challenged this by providing means for enhanced transparency in data sharing and situational awareness, enabling better coordination and improved efficiency on the whole (Lind et al. 2018a). Digital transformation drives the possibilities of creating new value by enabling higher degrees of connectivity between actors, digitally twin physical objects, drawing patterns of behaviour based on extensive sets of historical data, as well as harmonizing data sharing through standardized interfaces and communication protocols (e.g. Almirall and Casadesus-Masanell 2010; Gassman et al. 2010; Lakhani et al. 2006). To break existing patterns of behaviour and to avoid the creation of proprietary solutions that feed sub-optimization, there is a need for new inspiration and perspectives that capitalize on the opportunities that digital transformation provides. From an open innovation point of view, this means that innovators both having experience from the sector as well as from other sectors would come together, come up with, and provide new applications not previously possible or never thought about before. A core capability that the ecosystem needs to develop and ensure is data streams made accessible for those that can provide new applications aimed for the single actor and/or clusters of actors, within or outside the maritime sector (Lind et al. 2018).This has also been one of the objectives for Port Collaborative Decision Making (PortCDM), which is a concept that provides guidelines and standards for the data exchange within and between ports, between ships and ports, and between ports and hinterland operators (Lind et al 2018). Such data exchange is necessary if enhanced efficiency during port call operations is to be achieved but also facilitates open 

innovation within the maritime sector. In order to realise that potential, a purposive transfer of knowledge between the established actors and potential new service providers has to be established (Chesbrough 2006). We therefore set out to explore How can open innovation intermediaries accelerate acquisition in an ecosystem through the management and throughput of knowledge transfer?We address the question through a longitudinal study by applying an action research approach involving actors from the local port and students from three bachelor programs. Before we describe the specifics of the research methodology, we outline our theoretical framework in terms of how knowledge transfer can be framed within an open innovation ecosystem. After the research methodology we detail the five iterations and then discuss the effect on knowledge transfer within the ecosystem. Finally, we conclude and give directions on future research.

Simulation is often used as a technique to test and evaluate systems, as it provides a cost-efficient and safe alternative for testing and evaluation. A combination of simulators can be used to create high-fidelity and realistic test scenarios, especially when the systems-under-test are complex. An example of such complex systems is Cooperative Intelligent Transport Systems (C-ITS), which include many actors that are connected to each other via wireless communication in order to interact and cooperate. The majority of the actors in the systems are vehicles equipped with wireless communication modules, which can range from fully autonomous vehicles to manually driven vehicles. In order to test and evaluate C-ITS, this paper presents a distributed simulation framework that consists of (a) a moving base driving simulator; (b) a real-time vehicle simulator; and (c) network and traffic simulators. We present our approach for connecting and co-simulating the simulators. We report on limitation and performance that this simulation framework can achieve. Lastly, we discuss potential benefits and feasibility of using the simulation framework for testing of C-ITS. 

This paper presents a systematic mapping study of the research on crowdsourced security vulnerability discovery. The aim is to identify aspects of bug bounty program (BBP) research that relate to product owners, the bug-hunting crowd or vulnerability markets. Based on 72 examined papers, we conclude that research has mainly been focused on the organisation of BBPs from the product owner perspective, but that aspects such as mechanisms of the white vulnerability market and incentives for bug hunting have also been addressed. With the increasing importance of cyber security, BBPs need more attention in order to be understood better. In particular, datasets from more diverse types of companies (e.g. safety-critical systems) should be added, as empirical studies are generally based on convenience sampled public data sets. Also, there is a need for more in-depth, qualitative studies in order to understand what drives bug hunters and product owners towards finding constructive ways of working together. 

Decodinglarge AIS encoded data sets into clear text is time consuming. This paperdetails an approach on how to structure the innermost part of AIS decoding toincrease performance. The method is compared to existing Open Sourceimplementations, as well as to a straight forward example. The proposedapproach can increase decoding performance 20-30 times compared to these.

Cooperative IntelligentTransport Systems include many actors in the transport system that are con-nected to each other via wireless communication in order to interact andcooperate. Majority of the actors in thesystems are vehicles, which can range from fully autonomous vehicles tomanually driven vehicles, equipped withwireless communication modules. Creating realistic scenarios for testing suchcomplex systems often need a com-bination of simulators. This paper presents a distributed simulation frameworkthat consists of a) a moving basedriving simulator; b) a real-time hardware-in-the-loop simulator; and c) anetwork simulator and traffic simulator. Wepresent our approach for connecting and co-simulating the mentioned simulators.As a first test of our simulationframework, a crossing scenario is simulated. Lastly, we discuss potentialbenefits and future work.