Evaluation of Pedestrian Safety in a High-Fidelity Simulation Environment Framework

TL;DR

This paper introduces a high-fidelity simulation framework for evaluating pedestrian safety in autonomous driving, considering collision and conflict events, and demonstrates its effectiveness through comparative experiments.

Contribution

It presents a novel pedestrian safety evaluation method and a high-fidelity simulation framework that incorporates pedestrian safety-critical characteristics.

Findings

The framework can evaluate different autonomous driving perception algorithms.

It provides detailed quantitative pedestrian safety indexes.

The simulation framework outperforms existing methods in safety assessment.

Abstract

Pedestrians' safety is a crucial factor in assessing autonomous driving scenarios. However, pedestrian safety evaluation is rarely considered by existing autonomous driving simulation platforms. This paper proposes a pedestrian safety evaluation method for autonomous driving, in which not only the collision events but also the conflict events together with the characteristics of pedestrians are fully considered. Moreover, to apply the pedestrian safety evaluation system, we construct a high-fidelity simulation framework embedded with pedestrian safety-critical characteristics. We demonstrate our simulation framework and pedestrian safety evaluation with a comparative experiment with two kinds of autonomous driving perception algorithms -- single-vehicle perception and vehicle-to-infrastructure (V2I) cooperative perception. The results show that our framework can evaluate different autonomous driving algorithms with detailed and quantitative pedestrian safety indexes. To this end, the proposed simulation method and framework can be used to access different autonomous driving algorithms and evaluate pedestrians' safety performance in future autonomous driving simulations, which can inspire more pedestrian-friendly autonomous driving algorithms.