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We describe INTO-CPS, a project that aims to realise the goal of integrated tool chains for the collaborative and multidisciplinary engineering of dependable Cyber-Physical Systems (CPSs). Challenges facing model-based CPS engineering are described, focussing on the semantic diversity of models, management of the large space of models and artefacts produced in CPS engineering, and the need to evaluate effectiveness in industrial settings. We outline the approach taken to each of these issues, particularly on the use of semantically integrated multi-models, links to architectural modelling, code generation and testing, and evaluation via industry-led studies. We describe progress on the development of a prototype tool chain from baseline tools, and discuss ongoing challenges and open research questions in this area.

In this paper, we present a multi-modal dataset for obstacle detection in agriculture. The dataset comprises approximately 2 h of raw sensor data from a tractor-mounted sensor system in a grass mowing scenario in Denmark, October 2016. Sensing modalities include stereo camera, thermal camera, web camera, 360∘ camera, LiDAR and radar, while precise localization is available from fused IMU and GNSS. Both static and moving obstacles are present, including humans, mannequin dolls, rocks, barrels, buildings, vehicles and vegetation. All obstacles have ground truth object labels and geographic coordinates.

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