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Radar sensors have been used for active safety in cars for many years. An ongoing research project explores how radar sensors and technology common in automotive vehicles can be transferred for use on bicycles. Workshops have been used to generate ideas. A bicycle simulator is planned to be used for test and evaluation. Tests on a test track has been used to simulate high-risk scenarios. This paper describes the design process of this project, with focus on the user interface. High-risk scenarios and requirements are identified, followed by identified design challenges and design activities, including evaluation. Ideas for a dual HMI approach, directed towards the bicyclist and towards surrounding traffic are presented.

In applications of principal component analysis (PCA) it has often been observed that the eigenvector with the largest eigenvalue has only nonnegative entries when the vectors of the underlying stochastic process have only nonnegative values. This has been used to show that the coordinate vectors in PCA are all located in a cone. We prove that the nonnegativity of the first eigenvector follows from the Perron-Frobenius (and Krein-Rutman theory). Experiments show also that for stochastic processes with nonnegative signals the mean vector is often very similar to the first eigenvector. This is not true in general, but we first give a heuristical explanation why we can expect such a similarity. We then derive a connection between the dominance of the first eigenvalue and the similarity between the mean and the first eigenvector and show how to check the relative size of the first eigenvalue without actually computing it. In the last part of the paper we discuss the implication of theoretical results for multispectral color processing.