Autonomy Artificial Intelligence Robotics (AAIR)

Measuring the Performance of Active Safety Algorithms and Systems

by Tony Gioutsos; Jeff Blackburn


In any active safety system, it is desired to measure the “performance”. For the estimation case, generally a cost function like Mean-Square Error is used. For detection cases, the combination of Probability of Detection and Probability of False Alarm is used. Scenarios that would really expose performance measurement involve complex, dangerous and costly driving situations and are hard to recreate while having a low probability of actually being acquired . Using a virtual tool, we can produce the trials necessary to adequately determine the performance of active safety algorithms and systems. In this paper, we will outline the problem of measuring the performance of active safety algorithms or systems. We will then discuss the approach of using complex scenario design and Monte Carlo techniques to determine performance. We then follow with a brief discussion of Prescan and how it can help in this endeavor. Finally, two Monte Carlo type examples for particular active safety algorithms (LDW and AEB) will be presented.