Probabilistic Collision Risk Estimation for Pedestrian Navigation

TL;DR

This paper adapts a probabilistic collision risk model from autonomous driving to assist visually impaired individuals, demonstrating improved warning accuracy over traditional measures in real-world scenarios.

Contribution

It introduces the application of a collision risk model to pedestrian assistance devices, enhancing warning accuracy for visually impaired users.

Findings

Risk model achieves 67% warning accuracy.

Distance and time-to-contact measures reach only 51%.

Risk model outperforms traditional measures in real-world tests.

Abstract

Intelligent devices for supporting persons with vision impairment are becoming more widespread, but they are lacking behind the advancements in intelligent driver assistant system. To make a first step forward, this work discusses the integration of the risk model technology, previously used in autonomous driving and advanced driver assistance systems, into an assistance device for persons with vision impairment. The risk model computes a probabilistic collision risk given object trajectories which has previously been shown to give better indications of an object's collision potential compared to distance or time-to-contact measures in vehicle scenarios. In this work, we show that the risk model is also superior in warning persons with vision impairment about dangerous objects. Our experiments demonstrate that the warning accuracy of the risk model is 67% while both distance and time-to-contact measures reach only 51% accuracy for real-world data.