Thanks to the evolution of robotics, communication infrastructures, and sensor technologies, the Cooperative, Connected, and Automated Mobility (CCAM) industry has grown exponentially in the past decade. Automated Vehicles (AVs), often referred to as self-driving vehicles, have the potential to revolutionise the mobility industry, making it safer, cleaner, more efficient, and user-friendly. However, although giant steps have been made in this regard, most of AVs have been trained and tested under optimal weather conditions and with clear visibility. Complex environment and traffic conditions have a major impact on the safety and operations of AVs. Weather affects not only the vehicle performance but also the roadway infrastructure, thereby increasing the risk of collision and traffic scenario variations. Thus, to ensure wide acceptance, and exploit the all the benefits of CCAM, a technology capable of performing equally well under any weather conditions must be developed.
In response to this challenge, funded through the Horizon Europe programme of the European Union and bringing together partners from academia and industry, the ROADVIEW project aims to develop a complex in-vehicle system able to perform advanced traffic recognition and prediction under severe weather conditions, such as snow, fog, and rain. Based on a cost-efficient multisensory setup, the revolutionary ROADVIEW systems will independently perceive the environment conditions and make decisions based on its enhanced sensing, localisation, and improved object and person classification.