Shared Situational Awareness Using Hybrid Zonotopes with Confidence Metric

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 communication issues. This paper employs set-based estimation with constrained zonotopes to compute a confidence metric for the measurement set from each sensor. These 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.

💡 Research Summary

The advancement of Connected and Automated Vehicles (CAVs) relies heavily on the ability to maintain high-level situational awareness. A critical challenge in this domain is the “occlusion” problem, where pedestrians or other road users are hidden from a vehicle’s direct line of sight by obstacles like other cars or infrastructure. While Vehicle-to-Everything (V2X) communication offers a solution by allowing vehicles to share perception data, it introduces a new layer of complexity: the problem of data inconsistency. Sensor noise, false positive detections, and communication-related errors (such as latency or packet loss) can lead to conflicting information between different road users.

This paper proposes a robust framework for Shared Situational Awareness (SSA) using a novel approach based on hybrid zonotopes and confidence metrics. The core innovation lies in moving away from traditional point-based estimation toward a set-based estimation approach. The authors utilize “constrained zonotopes” to represent the measurement sets from various sensors. Unlike traditional probabilistic methods (like the Kalman Filter) that may struggle with outliers and false positives, this method allows for the calculation of a specific “confidence metric” for each sensor’s measurement set. This metric quantifies the reliability of the data, accounting for potential errors and providing a mathematical way to bound the uncertainty.

The integration of these disparate data sources is then achieved through “hybrid zonotopes.” This fusion mechanism is designed to intelligently combine the geometric sets and their respective confidence levels. By incorporating the confidence metric directly into the fusion process, the system can effectively mitigate the impact of inconsistent or unreliable measurements, ensuring that the final shared perception remains accurate and stable even in the presence of erroneous data.

The effectiveness of this proposed method was rigorously evaluated through both computer simulations and real-world experiments. The results demonstrate that the hybrid zonotope-based approach provides superior robustness and reliability compared to conventional fusion techniques, particularly in scenarios involving high levels of sensor uncertainty and communication issues. This research provides a significant step forward in developing the safety-critical perception systems required for the future of fully autonomous driving ecosystems, where reliable data fusion is paramount for navigating complex urban environments.