Perspectives on the system-level design of a safe autonomous driving stack

Abstract

Achieving safe and robust autonomy is the key bottleneck on the path towards broader adoption of autonomous vehicles technology. This motivates going beyond extrinsic metrics such as miles between disengagement, and calls for approaches that embody safety by design. In this paper, we address some aspects of this challenge, with emphasis on issues of motion planning and prediction. We do this through description of novel approaches taken to solving selected sub-problems within an autonomous driving stack, in the process introducing the design philosophy being adopted within Five. This includes safe-by-design planning, interpretable as well as verifiable prediction, and modelling of perception errors to enable effective sim-to-real and real-to-sim transfer within the testing pipeline of a realistic autonomous system.

The Design of Art Design System Based on Image Processing Technology

“…The interpolation operation of directional interpolation takes the method of template interpolation, and the template region size ~ is generally set to a 3×3 or 5×5 region or to a 9×9 template when the image magnification effect is more demanding. It should be noted that the setting of the region size is closely related to the selection of the interpolation method in the subsequent operation [17][18].…”

Multi-agent systems research in the United Kingdom

“…• DeepMind [7] • Five AI [9] • Heriot-Watt University [13] • King's College London [2] • Teesside University [8] • University of Aberdeen [3] • University of Edinburgh [1] • University of Essex [11] • University of Lancaster [4]…”