Robust Automated Driving in Extreme Weather

Complex environment and traffic conditions have major impact on the safety and operations of Connected and Automated Vehicles (CAVs).

Weather affects not only the vehicle performance but also the roadway infrastructure, thereby increases the risk of collision and traffic scenarios variations.

So far, most automated vehicles have been primarily trained and tested under optimal weather and road conditions with clear visibility.

However, the systems will have to prove that they are equally reliable and accurate under any weather and road condition before they can see widespread acceptance and adoption.

ROADVIEW integrates a complex in-vehicle system-of-systems able to perform advanced environment and traffic recognition and prediction and determine the appropriate course of action of a CAV in a real-world environment, including harsh weather conditions.

ROADVIEW develops an embedded in-vehicle perception and decision-making system based on enhanced sensing, localisation, and improved object/person classification (including vulnerable road users).

ROADVIEW ground-breaking innovations are grounded on a cost-effective multisensory setup, sensor noise modelling and filtering, collaborative perception, testing by simulation-assisted methods and integration and demonstration under different scenarios and weather conditions, reaching TRL 7 by the end of the project.

ROADVIEW implements the co-programmed European Partnership “Connected, Cooperative and Automated Mobility” (CCAM) partnership by contributing to the development of a more powerful, fail-safe, resilient and weather-aware technologies.

The consortium is a perfect combination of leading universities in the field and research institutes, high-tech SMEs, and strong industry leaders.

Beyond their research excellence, the consortium members bring a unique portfolio of testing sites and testing infrastructure, ranging from hardware-testing facilities and rain and wind tunnels to test tracks north of the Arctic Circle.