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The i2CAT Research Centre and Idneo, a product engineering and complex manufacturing company, have successfully completed a pilot project to develop and validate Collective Perception techniques that combine V2X communication technologies with sensor data fusion, in order to enhance the perception capabilities of on-board vehicle sensors in complex traffic situations (e.g., vulnerable road users hidden by obstacles, adverse weather conditions, etc.).
Modern vehicles are equipped with advanced sensors, such as cameras, LiDAR, and radar, to assist drivers. However, the capabilities of these sensors are limited by their field of view and can be degraded by adverse weather conditions. Obstacles can also obscure vulnerable road users, increasing the risk of accidents.
i2CAT’s core research focused on the development of a solution that uses Collective Perception techniques. This is a method for vehicles and roadside infrastructure to communicate and share information via Vehicle-to-Everything (V2X) communication. This allows a vehicle to extend its situational awareness beyond the range of its own onboard sensors.
i2CAT has been collaborating with Idneo to transfer this research into the mobility industry. As a result of this partnership, a pilot was conducted on the UPC North Campus, successfully validating Late Data Fusion Collective Perception approach. This approach processes sensor data locally at the source (e.g., a vehicle or infrastructure) and then shares detected objects through Collective Perception Messages (CPMs) with the other road users and the roadside infrastructure. The infrastructure fuses vehicle-detected data with its own sensor data to build a comprehensive understanding of the scene. The final set of detected objects is then shared with all surrounding connected vehicles through CPMs, extending their perception capabilities beyond their own sensors and even enabling non-sensor-equipped vehicles to behave more intelligently. This approach minimizes communication load while enhancing the accuracy of individual perception systems.
The project leveraged two key assets developed by i2CAT: MOTCAM (Mobile Object Tracking with CAmeras), a powerful product prototype for analyzing mobility events that provides a complete system for detecting traffic risks and flows via camera analysis; and V2X FlexStack, a flexible, Python-based ETSI-compliant V2X software stack. V2X FlexStack played a crucial role in accelerating the development of a cooperative perception use case in a real urban mobility scenario, facilitating the efficient integration and communication required for collective perception techniques.
The successful pilot demonstrates the real-world potential of Collective Perception technology. The insights gained will be used to enhance Idneo’s Advanced Driver Assistance Systems (ADAS), paving the way for a new generation of vehicles that are smarter, more aware, and ultimately safer for everyone on the road.
This project has been funded by the European Commission within the framework of the European project Digital Innovation Hub of Catalonia (DIH4CAT).
