Shared Situational Awareness Using Hybrid Zonotopes with Confidence Metric

TL;DR

This paper introduces a set-based data fusion method using hybrid zonotopes and confidence metrics to improve situational awareness in automated vehicles, especially under measurement inconsistencies.

Contribution

It proposes a novel fusion approach with constrained and hybrid zonotopes that handles sensor noise and false positives for safer vehicle perception.

Findings

Method effectively fuses inconsistent sensor data in simulations.

Real experiments demonstrate improved perception robustness.

Confidence metrics enhance reliability of situational awareness.

Abstract

Situational awareness for connected and automated vehicles describes the ability to perceive and predict the behavior of other road-users in the near surroundings. However, pedestrians can become occluded by vehicles or infrastructure, creating significant safety risks due to limited visibility. Vehicle-to-everything communication enables the sharing of perception data between connected road-users, allowing for a more comprehensive awareness. The main challenge is how to fuse perception data when measurements are inconsistent with the true locations of pedestrians. Inconsistent measurements can occur due to sensor noise, false positives, or unmodeled disturbances. This paper employs set-based estimation with constrained zonotopes to compute a confidence metric for the measurement set from each sensor. Estimated sets and their confidences are then fused using hybrid zonotopes. This method can account for inconsistent measurements, enabling reliable and robust fusion of the sensor data. The effectiveness of the proposed method is demonstrated in both simulation and real experiments.