Beyond Line of Sight: Hybrid Validation of V2X Collective Perception in Complex Scenarios
2026-07-01 • Robotics
Robotics
AI summaryⓘ
The authors created a new method that helps self-driving cars share and combine what they sense around them to get a better understanding of their surroundings, especially in tricky traffic situations like roundabouts. They used a smart math approach called Bayesian fusion to merge information from different cars into a shared map that shows both how likely something is there and how uncertain that is. To test this, they used a mix of computer simulations and real car experiments. Their method made the cars see a lot more of the area around them and detect obstacles more accurately than just relying on one car's sensors.
V2X (Vehicle-to-Everything)Collective PerceptionBayesian FusionOccupancy GridAutonomous VehiclesSensor FusionCARLA SimulatorVehicle-in-the-loop TestingField-of-ViewLocalization
Authors
Markos Antonopoulos, Anastasia Bolovinou, Bill Roungas, Elena Daskalaki, Angelos Amditis
Abstract
This paper introduces a probabilistic framework and hybrid validation methodology for V2X-enabled Collective Perception (CP) in complex traffic scenarios. The proposed Bayesian fusion algorithm extends the perceptual horizon of connected and autonomous vehicles by integrating heterogeneous sensor observations from multiple agents into a shared probabilistic occupancy grid. Each cell of this grid encapsulates both occupancy likelihood and uncertainty, enabling explainable and trustworthy situational awareness beyond the ego vehicle's field of view. To bridge the gap between simulation and real-world evaluation, a hybrid testing framework is developed, combining CARLA-based virtual environments with vehicle-in-the-loop experimentation. Experimental results in a roundabout scenario demonstrate a 260 percent increase in field-of-view coverage and a rise in occupied-cell recall from 0.82 (ego-only) to 0.94 (six-agent CP) under nominal localization conditions. Overall, the proposed approach provides a reproducible and interpretable foundation for validating CP systems, supporting the safe and certifiable deployment of cooperative autonomous vehicles.