Data collection is crucial in modern automotive engineering. Yet, the focus on technological development often overlooks legal, ethical, and privacy aspects. This feasibility study aims to bridge this knowledge gap for SMEs and research entities by examining the technical, legal, and ethical aspects impacting vehicle data collection. It provides a guide for organisations within the European context with a global perspective. The crucial importance of each aspect is highlighted within the modern context of connected vehicles, increasing cyber-attacks and the legal demands of handling and processing data. This report provides guidelines for hardware selection, software development, data handling and storage, cybersecurity, and privacy and legal compliance. It discusses the considerations for choosing data collection hardware, outlines the software methodologies for data acquisition and cloud synchronization, and provides an overview of cybersecurity in the context of automotive applications. The report also covers the handling and storage of both low-throughput and high-throughput data, with a focus on data types, retrieval, and storage options. It concludes with a discussion on legal aspects, particularly data ownership, protection under GDPR, and liability implications. The importance of these topics in the modern context of IoT connectivity, edge computing and the application of various AI technologies cannot be understated. The broad applications of such technologies encourages the use of data standards and interoperability for modern connected and autonomous vehicles. This document serves as a guide for SMEs and research entities involved in automotive data collection, providing information so they may better navigate and understand the complexities of modern automotive data collection, ensuring they consider all aspects. It highlights the importance of interdisciplinary expertise for modern automotive data collection and bridges the gaps in knowledge that organisations may have within a number of important topics for data collection. By bridging knowledge gaps, the report empowers organisations to make informed decisions about automotive data collection, ensuring accuracy, efficiency, and legal compliance.
Data, the raw material from which information is derived, is stored, copied, moved and modified more easily than ever. This quantum leap reaches levels outside our imagination. Surrounded by sensors, recommendation systems, invisible algorithms, spreadsheets and blockchains, the ‘difference that makes a difference’ can no longer be identified. Big Data is a More Data ideology, driven by old school hypergrowth premisses. As Nathan Jurgenson once observed: “Big Data always stands in the shadow of the bigger data to come. The assumption is that there is more data today and there will necessarily be even more tomorrow, an expansion that will bring us ever closer to the inevitable pure ‘data totality.” (2) Nothing symbolizes the current hypergrowth obsession better than Big Data. Let’s investigate what happens when we apply degrowth to data and reserve datafication–as a decolonial project, a collective act of refusal, an ultimate sign of boredom. We’re done with you, data system, stand out of my light.
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Many studies have shown that experts possess better perceptual-cognitive skills than novices (e.g., in anticipation, decision making, pattern recall), but it remains unclear whether a relationship exists between performance on those tests of perceptual-cognitive skill and actual on-field performance. In this study, we assessed the in situ performance of skilled soccer players and related the outcomes to measures of anticipation, decision making, and pattern recall. In addition, we examined gaze behaviour when performing the perceptual-cognitive tests to better understand whether the underlying processes were related when those perceptual-cognitive tasks were performed. The results revealed that on-field performance could not be predicted on the basis of performance on the perceptual-cognitive tests. Moreover, there were no strong correlations between the level of performance on the different tests. The analysis of gaze behaviour revealed differences in search rate, fixation duration, fixation order, gaze entropy, and percentage viewing time when performing the test of pattern recall, suggesting that it is driven by different processes to those used for anticipation and decision making. Altogether, the results suggest that the perceptual-cognitive tests may not be as strong determinants of actual performance as may have previously been assumed.
