Statistical accident analysis supporting the control of autonomous vehicles

Autonomous vehicles offer the potential to drastically decrease the number and severity of road accidents. Most accidents occur due to human inattention or wrong decisions, whose factors can be eliminated by autonomous vehicles. However, not all accidents are avoidable through automation. Complying with the law is not always enough, there can be environmental problems (bad weather, road surface, etc.) causing accidents, and other actors (human drivers, pedestrians) making mistakes. These are unexpected situations, and the real-time sensors of vehicles are currently limited in their ability to predict them (a slippery road surface for example) in time, and deliver a programed response to a dangerous situation. This paper presents a method based on the analysis of historical accident records, to find danger zones of public road networks. A further statistical approach is used to find the significant risk factors of these zones, which data can be built into the controlling algorithms of autonomous vehicles, to prepare for these situations and avoid, or at least decrease the seriousness, of the potential incidents. It is concluded that the proposed method can find the black spots of a given road section and give assumptions about the main local risk factors.