In traffic accidents cyclists are always counted as a vulnerable group suffering from heavy injuries and fatalities. A particularly dangerous type of collision involves trucks that turn to the right without recognizing cyclists driving on their lane in a blind spot. Even though it is not the most frequent type of accident the chance of survival for a cyclist involved is low.
One of the main causes for the collision is that the truck drivers only have a limited field of vision. The cyclists in the surroundings are hard to perceive due to their smaller size. Besides, it is difficult to predict their behavior. The velocity of a cyclist is usually comparable to a slowly running car and they must share the same road with other traffic participants, which makes them easy to be occluded by other vehicles. Hence, this reduces the truck driver's reaction time once they are noticed. This also explains that the heaviest accidents involving trucks and cyclists often happen when a truck turns right at an intersection.
In order to solve this problem on an intelligent level we are aiming at developing a driving assistance system for trucks to avoid possible accidents with cyclists. The main task is to detect the cyclists with the help of a state-of-the-art hardware setup consisting of a miniature-3D-LIDAR-Sensor in combination with a CMOS-camera. Based on the detection, the movement of the cyclist will be estimated and its behavior will be predicted so that the risk of accidents can be assessed. An intelligent warning strategy will warn the truck driver in dangerous situations to avoid accidents.
In this project we are focusing on developing algorithms for robust detection and motion estimation for cyclists. To achieve this goal modern techniques in computer vision as well as new ideas and effective implementations will be applied. The project is a cooperation with HFC Human-Factors-Consult (Berlin) and Ingenieurbüro Spies (Hohenwart) funded by the German Federal Ministry of Education and research (BMBF).