Enhancing Track Management Systems with Vehicle-To-Vehicle Enabled Sensor Fusion
This addresses reliability issues in autonomous vehicles, particularly in occlusion scenarios, though it appears incremental as it builds on existing sensor fusion methods with V2V integration.
The paper tackles the problem of vehicle perception limitations in connected and automated vehicles by proposing a V2V-enabled track management system that fuses V2V signals with radar and camera detections, demonstrating improved accuracy and robustness in simulated driving scenarios.
In the rapidly advancing landscape of connected and automated vehicles (CAV), the integration of Vehicle-to-Everything (V2X) communication in traditional fusion systems presents a promising avenue for enhancing vehicle perception. Addressing current limitations with vehicle sensing, this paper proposes a novel Vehicle-to-Vehicle (V2V) enabled track management system that leverages the synergy between V2V signals and detections from radar and camera sensors. The core innovation lies in the creation of independent priority track lists, consisting of fused detections validated through V2V communication. This approach enables more flexible and resilient thresholds for track management, particularly in scenarios with numerous occlusions where the tracked objects move outside the field of view of the perception sensors. The proposed system considers the implications of falsification of V2X signals which is combated through an initial vehicle identification process using detection from perception sensors. Presented are the fusion algorithm, simulated environments, and validation mechanisms. Experimental results demonstrate the improved accuracy and robustness of the proposed system in common driving scenarios, highlighting its potential to advance the reliability and efficiency of autonomous vehicles.