Evaluation of Histogram of Oriented Gradients Soft Errors Criticality for Automotive Applications

Pedestrian detection reliability is a key problem for autonomous or aided driving, and methods that use Histogram of Oriented Gradients (HOG) are very popular. Embedded Graphics Processing Units (GPUs) are exploited to run HOG in a very efficient manner. Unfortunately, GPUs architecture has been shown to be particularly vulnerable to radiation-induced failures. This article presents an experimental evaluation and analytical study of HOG reliability. We aim at quantifying and qualifying the radiation-induced errors on pedestrian detection applications executed in embedded GPUs. We analyze experimental results obtained executing HOG on embedded GPUs from two different vendors, exposed for about 100 hours to a controlled neutron beam at Los Alamos National Laboratory. We consider the number and position of detected objects as well as precision and recall to discriminate critical erroneous computations. The reported analysis shows that, while being intrinsically resilient (65% to 85% of output errors only slightly impact detection), HOG experienced some particularly critical errors that could result in undetected pedestrians or unnecessary vehicle stops. Additionally, we perform a fault-injection campaign to identify HOG critical procedures. We observe that Resize and Normalize are the most sensitive and critical phases, as about 20% of injections generate an output error that significantly impacts HOG detection. With our insights, we are able to find those limited portions of HOG that, if hardened, are more likely to increase reliability without introducing unnecessary overhead.