Study on the Impacts of Hazardous Behaviors on Autonomous Vehicle Collision Rates Based on Humanoid Scenario Generation in CARLA

TL;DR

This paper introduces a Humanoid Scenario Generation scheme for virtual testing of autonomous vehicles in CARLA, improving realism and diversity in hazard scenarios to better assess collision risks.

Contribution

It presents a novel HSG method that captures human driver behaviors, enhancing simulation realism and enabling safer, more effective AV hazard testing.

Findings

HSG improves simulation realism and diversity.

Hazardous behaviors significantly impact collision rates.

Validation shows behaviors follow predefined speed distributions.

Abstract

Testing of function safety and Safety Of The Intended Functionality (SOTIF) is important for autonomous vehicles (AVs). It is hard to test the AV's hazard response in the real world because it would involve hazards to passengers and other road users. This paper studied on virtual testing of AV on the CARLA platform and proposed a Humanoid Scenario Generation (HSG) scheme to investigate the impacts of hazardous behaviors on AV collision rates. The HSG scheme breakthrough the current limitation on the rarity and reproducibility of real scenes. By accurately capturing five prominent human driver behaviors that directly contribute to vehicle collisions in the real world, the methodology significantly enhances the realism and diversity of the simulation, as evidenced by collision rate statistics across various traffic scenarios. Thus, the modular framework allows for customization, and its seamless integration within the CARLA platform ensures compatibility with existing tools. Ultimately, the comparison results demonstrate that all vehicles that exhibited hazardous behaviors followed the predefined random speed distribution and the effectiveness of the HSG was validated by the distinct characteristics displayed by these behaviors.