Title: Road to safe autonomy with data and formal reasoning,
Abstract: We present an overview of recently developed data-driven tools for safety
analysis of autonomous vehicles and advanced driver assist systems. The core
algorithms combine model-based, hybrid system reachability analysis with
sensitivity analysis of components with unknown or inaccessible models. We
illustrate the applicability of this approach with a new case study of
emergency braking systems in scenarios with two or three vehicles. This problem
is representative of the most common type of rear-end crashes, which is
relevant for safety analysis of automatic emergency braking (AEB) and forward
collision avoidance systems. We show that our verification tool can effectively
prove the safety of certain scenarios (specified by several parameters like
braking profiles, initial velocities, uncertainties in position and reaction
times), and also compute the severity of accidents for unsafe scenarios.
Through hundreds of verification experiments, we quantified the safety envelope
of the system across relevant parameters. These results show that the approach
is promising for design, debugging and certification. We also show how the
reachability analysis can be combined with statistical information about the
parameters, to assess the risk level of the control system, which in turn is
essential, for example, for determining Automotive Safety Integrity Levels
(ASIL) for the ISO26262 standard.