The IEEE P2020 Automotive Image Quality working group is proposing new metrics and test protocols to measure image flicker. A comprehensive validation activity is therefore required. Light source flicker (often LED flicker), as captured in a camera output, is a product of camera exposure time, sensitivity, full well capacity, readout timing, HDR scheme, and the light source frequency, duty cycle, intensity, waveform and spectrum. The proposed LED flicker metrics have to be tested and validated for a sufficient number of combinations of these camera and lighting configurations. The test space of the combinations of camera and lighting parameters is unfeasibly large to test with physical cameras and lighting setups. A numerical simulation study to validate the proposed metrics has therefore been performed. To model flicker, a representative pixel model has been implemented in code. The pixel model incorporates exposure time, sensitivity, full well capacity, and representative readout timings. The implemented light source model comprises an hybrid analyticnumerical approach that allows for efficient generation of complex temporal lighting profiles. It simulates full and half wave rectified sinusoidal waveforms, representative of AC lighting, as well as pulse width modulated lighting with variable frequency, duty cycle, intensity, and complex edge rise/fall time behaviour. In this article, both initial results from the flicker simulation model, and evaluation of proposed IEEE metrics, are presented.
The recent established goal of autonomous driving cars, motivates the discussion about safety relevant performance parameters in the automotive industry. The majority of currently accepted key performance indicators (KPIs) do not allow a good prediction over the system performance along a safety relevant critical effect chain. A breakdown of the functional system down to component and sensor levels makes this KPI problem evident. We will present a methodology for sensor performance prediction by a probabilistic approach, on the basis of significant critical use cases. As a result the requirement engineering along the effect chain especially for safety relevant processes appears transparent and understandable. Specific examples from the field of image quality will concentrate on the proposal of a new KPI, the contrast detection probability (CDP). This proposal is currently under discussion within the P2020 work group on automotive image quality and challenges known KPIs such as SNR, especially with respect their effects on automotive use cases.