Beyond Line of Sight: Hybrid Validation of V2X Collective Perception in Complex Scenarios

2026-07-01

AI Summary

This work addresses the limitations of single-vehicle perception in vehicle-to-everything (V2X) systems, where restricted sensor coverage hinders reliable cooperative awareness. To overcome this challenge, the authors propose a Bayesian fusion–based collaborative perception framework that integrates heterogeneous sensor data from multiple agents to construct interpretable probabilistic occupancy grids. The system’s performance is evaluated through a hybrid validation approach combining CARLA-based virtual simulation with real-vehicle-in-the-loop testing, ensuring reproducible and certifiable assessment. In a roundabout scenario, collaboration among six vehicles expands perceptual coverage by 260% and significantly improves occupancy grid recall from 0.82 (single-vehicle baseline) to 0.94, thereby substantially enhancing environmental awareness in occluded regions beyond line-of-sight.

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.

Problem

• V2X Collective Perception
• Beyond Line of Sight
• Occupancy Grid
• Situational Awareness
• Autonomous Vehicles

Innovation

• Collective Perception
• Bayesian fusion
• probabilistic occupancy grid
• V2X
• hybrid validation