Evaluation of Pedestrian Safety in a High-Fidelity Simulation Environment Framework
This addresses pedestrian safety assessment for autonomous driving systems, representing an incremental improvement by integrating multiple safety factors into existing simulation frameworks.
The paper tackles the problem of pedestrian safety evaluation in autonomous driving by proposing a method that considers collision events, conflict events, and pedestrian characteristics, and demonstrates it through a comparative experiment with single-vehicle and V2I cooperative perception algorithms, showing the framework can evaluate algorithms with detailed quantitative safety indexes.
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.