Deriving System Parameters of a 360° Low Speed Autonomous Emergency Braking Driver Assistance System for Parking and Maneuvering Based on Naturalistic Driving Studies

Abstract

Parking and maneuvering accidents are responsible for a significant amount of real-world – especially property damage – accidents. Insurance research estimates up to 40 % of all claims and up to 30 % of all claim-associated costs around the world are caused by these type of accidents. Therefore, a 360° low speed autonomous emergency braking system could have a high monetary effectiveness. To design such a system, parameters like initial accident velocity and environmental properties (like lighting and surface conditions) for an effective actuation strategy have to be derived. Firstly, this paper uses a state-of-the-art approach: an analysis of an accident database of reconstructed crashes. Due to the low velocity at parking and maneuvering, the tolerance caused within the accident reconstruction and the usability for further effective assessment were discussed. Secondly, a naturalistic big data analysis and a real-world accidents approach were conducted. Naturalistic driving studies enable a more precise evaluation of parking and maneuvering behavior. Finally, results were discussed and advices for effective system parameters were given.