Cooperative Robotics Reinforced by Collective Perception for Traffic Moderation

2026-05-12

AI Summary

This study addresses the collision risks at non-line-of-sight (NLOS) intersections caused by limited visibility, particularly in low V2X penetration scenarios where non-connected vehicles cannot be effectively managed by conventional systems. To bridge this safety gap, the authors propose deploying humanoid robots endowed with collective perception capabilities as active traffic wardens. By fusing roadside stereo vision and onboard V2X data, the system constructs a real-time traffic scene and employs a novel Zone of Danger (ZoD) dynamic prediction model to proactively identify high-risk merging maneuvers, intervening physically through human-like STOP gestures. This work represents the first integration of embodied collaborative robotics with collective perception for road safety, specifically targeting non-connected vehicles. Real-world evaluations demonstrate that the system can early-detect approaching vehicles, accurately predict potential conflicts, and effectively halt unsafe behaviors, thereby significantly enhancing intersection safety in NLOS conditions.

Abstract

Collisions at non-line-of-sight (NLOS) intersections remain a major safety concern because drivers have limited visibility of approaching traffic. V2X based warnings can reduce these risks, yet many vehicles are not equipped with V2X and drivers may ignore in vehicle alerts. Collective perception (CP) can compensate for low V2X penetration by extending the awareness of connected vehicles, but it cannot influence unconnected vehicles. To fill this gap, our work introduces a complementary concept that adds a cooperative humanoid robot as an active traffic moderator capable of physically stopping a vehicle that attempts to merge into an unseen traffic stream. The system operates on two parallel perception pathways. A dual camera infrastructure unit detects the position, speed and motion of approaching vehicles and transmits this information to the robot as a collective perception message (CPM). The robot also receives cooperative awareness messages (CAM) from connected vehicles through its onboard V2X unit and can act as a relay for decentralized environmental notification messages (DENM) when safety events originate elsewhere along the road. A fusion module combines these streams to maintain a robust real time view of the main road. A Zone of Danger (ZoD) is defined and used to predict whether an approaching vehicle creates a collision risk for a merging road user. When such a risk is detected, the robot issues a human-like STOP gesture and blocks the merging path until the hazard disappears. The full system was deployed at the Future Mobility Park (FMP) in Rotterdam. Experiments show that the combined vision and V2X perception allows the robot to detect approaching vehicles early, predict hazards reliably and prevent unsafe merges in real world NLOS conditions.

Problem

• non-line-of-sight (NLOS)
• traffic safety
• cooperative robotics
• collective perception
• vehicle-to-everything (V2X)

Innovation

• Collective Perception
• Cooperative Robotics
• Non-Line-of-Sight (NLOS)
• V2X
• Traffic Moderation