  The development of Autonomous Vehicles (AV) presents an opportunity to save
and improve lives. However, achieving SAE Level 5 (full) autonomy will require
overcoming many technical challenges. There is a gap in the literature
regarding the measurement of safety for self-driving systems. Measuring safety
and risk is paramount for the generation of useful simulation scenarios for
training and validation of autonomous systems. The limitation of current
approaches is the dependence on near-crash data. Although near-miss data can
substantially increase scarce available accident data, the definition of a
near-miss or near-crash is arbitrary. A promising alternative is the
introduction of the Responsibility-Sensitive Safety (RSS) model by
Shalev-Shwartz et al., which defines safe lateral and longitudinal distances
that can guarantee impossibility of collision under reasonable assumptions for
vehicle dynamics. We present a framework that extends the RSS model for cases
when reasonable assumptions or safe distances are violated. The proposed
framework introduces risk indices that quantify the likelihood of a collision
by using vehicle dynamics and driver's risk aversion. The present study
concludes with proposed experiments for tuning the parameters of the formulated
risk indices.
