# A behavior driven approach for sampling rare event situations for autonomous vehicles

## TL;DR

This paper presents a behavior-driven model for sampling rare traffic events in urban autonomous vehicle environments, enabling better evaluation of AV performance in uncommon but critical scenarios.

## Contribution

It introduces a traffic behavior model based on bounded rationality that estimates rare event probabilities and generates new traffic situations from naturalistic data.

## Key findings

- Model accurately estimates rare event probabilities
- Generates realistic rare traffic scenarios
- Applicable to large naturalistic driving datasets

## Abstract

Performance evaluation of urban autonomous vehicles requires a realistic model of the behavior of other road users in the environment. Learning such models from data involves collecting naturalistic data of real-world human behavior. In many cases, acquisition of this data can be prohibitively expensive or intrusive. Additionally, the available data often contain only typical behaviors and exclude behaviors that are classified as rare events. To evaluate the performance of AV in such situations, we develop a model of traffic behavior based on the theory of bounded rationality. Based on the experiments performed on a large naturalistic driving data, we show that the developed model can be applied to estimate probability of rare events, as well as to generate new traffic situations.