Shared Situational Awareness Using Hybrid Zonotopes with Confidence Metric
For connected and automated vehicles, this work provides a robust fusion method to handle inconsistent measurements, improving safety in occluded pedestrian scenarios.
The paper addresses the challenge of fusing inconsistent perception data for shared situational awareness in connected vehicles, using set-based estimation with hybrid zonotopes and a confidence metric. The method is validated in simulation and real experiments, showing reliable fusion despite occlusions and sensor noise.
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.