  This paper proposes a mechanism to assess the safety of autonomous cars. It
assesses the car's safety in scenarios where the car must avoid collision with
an adversary. Core to this mechanism is a safety measure, called Safe-Kamikaze
Distance (SKD), which computes the average similarity between sets of safe
adversary's trajectories and kamikaze trajectories close to the safe
trajectories. The kamikaze trajectories are generated based on planning under
uncertainty techniques, namely the Partially Observable Markov Decision
Processes, to account for the partially observed car policy from the point of
view of the adversary. We found that SKD is inversely proportional to the upper
bound on the probability that a small deformation changes a collision-free
trajectory of the adversary into a colliding one. We perform systematic tests
on a scenario where the adversary is a pedestrian crossing a single-lane road
in front of the car being assessed --which is, one of the scenarios in the
Euro-NCAP's Vulnerable Road User (VRU) tests on Autonomous Emergency Braking.
Simulation results on assessing cars with basic controllers and a test on a
Machine-Learning controller using a high-fidelity simulator indicates promising
results for SKD to measure the safety of autonomous cars. Moreover, the time
taken for each simulation test is under 11 seconds, enabling a sufficient
statistics to compute SKD from simulation to be generated on a quad-core
desktop in less than 25 minutes.
