The funding is being provided by Germany’s Federal Ministry for Economic Affairs and Climate Action in response to its call for funding applications in the area of “Artificial intelligence as a key technology for the vehicle of the future”.
Urban traffic has limited space, varying flows and different road users. Despite increasing automation and traffic networking, the fact that traffic areas are used by multiple parties is and will continue to be a major risk for vulnerable road users (“VRUs”). VRUs are road users whose risk is higher because they are not inside a protective enclosure like the body of a car. They include pedestrians and cyclists. To maximize safety in automated traffic, the severity of the injuries sustained by vulnerable parties in unavoidable collisions needs to be minimized. ATTENTION’s aim is to develop a method that uses learning algorithms to predict VRU injury in real time.
Data-driven artificial intelligence (AI) methods are being used to determine the risk of injury for a specific situation based on video data from the automated vehicle, virtual tests and digital human models. This will help make traffic safer and more efficient thanks to risk minimization strategies for the automated vehicle. Prof. Dr. Syn Schmitt and his team at Stuttgart University’s Institute for Modelling and Simulation of Biomechanical Systems are concentrating specifically on this aspect. The research on a digital human model also reflects the vision of the SimTech Cluster of Excellence as well as creating a connection between the field of simulation science and the focus of Cyber Valley research. The work uses the whole-body and body-part human models developed by Schmitt’s team and combines them with AI methods to generate the models’ movements and thus synthetic training data. The latter will then be used for an injury prediction model that can be evaluated quickly and covers a variety of collisions. An important aspect of this modeling is the need to consider who the parties to the collision are. “The project enables us to start testing our models and computation methods under real conditions in an applied research environment, the aim being for our industry partners to drive the transfer to real-world application. I’m looking forward to it and can’t wait to see the results,” said Syn Schmitt.