Risk Scenario Generation for Autonomous Driving Systems based on Causal Bayesian Networks

TL;DR

This paper introduces a novel approach using Causal Bayesian Networks to generate risk scenarios for autonomous driving systems, improving safety testing efficiency and effectiveness through data-driven causal modeling and simulation.

Contribution

It presents a new paradigm leveraging Causal Bayesian Networks built from accident data for more realistic risk scenario generation in ADS testing.

Findings

Generated 89 high-risk scenarios from 5 seeds

Outperformed baseline methods in efficiency

Validated with CARLA simulator

Abstract

Advancements in Autonomous Driving Systems (ADS) have brought significant benefits, but also raised concerns regarding their safety. Virtual tests are common practices to ensure the safety of ADS because they are more efficient and safer compared to field operational tests. However, capturing the complex dynamics of real-world driving environments and effectively generating risk scenarios for testing is challenging. In this paper, we propose a novel paradigm shift towards utilizing Causal Bayesian Networks (CBN) for scenario generation in ADS. The CBN is built and validated using Maryland accident data, providing a deeper insight into the myriad factors influencing autonomous driving behaviors. Based on the constructed CBN, we propose an algorithm that significantly enhances the process of risk scenario generation, leading to more effective and safer ADS. An end-to-end testing framework for ADS is established utilizing the CARLA simulator. Through experiments, we successfully generated 89 high-risk scenarios from 5 seed scenarios, outperforming baseline methods in terms of time and iterations required.