Quantitative Risk Indices for Autonomous Vehicle Training Systems

TL;DR

This paper introduces a framework that extends the Responsibility-Sensitive Safety model to quantify collision risk in autonomous vehicle systems, addressing limitations of current safety measurement approaches.

Contribution

It proposes new risk indices based on vehicle dynamics and driver risk aversion, enhancing safety assessment beyond near-miss data.

Findings

Risk indices effectively quantify collision likelihood.

Framework accommodates violations of safe distance assumptions.

Potential for improved safety validation in autonomous vehicle training.

Abstract

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.