Abstract
Abstract. The environment of the vehicle can significantly influence the driving situation. Which conditions lead to unsafe driving behaviour is not always clear, also not to a human driver, as the causes might be unconscious, and thus cannot be revealed by expert interviews. Therefore, it is important to investigate how such situations can be reliably detected, and then search for their triggers. It is conceivable that such insecure situations (e.g. near-accidents, U-turns, avoiding obstacles) are reflected, for example, as anomalies in the movement trajectories of road users.Collecting real world traffic data in driving studies is very time consuming and expensive. However, a lot of roads or public areas are already monitored with video cameras. In addition, nowadays more and more of such video data is made publicly available over the internet so that the amount of free video data is increasing. This research will exploit the use of such kind of opportunistic VGI. In the paper the first step of an automatic analysis are presented, namely: to introduce a real time processing pipeline to extract road user trajectories from surveillance video data.
Highlights
Getting insight into critical driving maneuvers is an important prerequisite for improving the safety in traffic
Figure 7) it can be seen that the sidewalks and zebra crossing
Rules and illegally cross the road. This is the reason why some some individual trajectories of vehicles and pedestrians are shown. trajectories of pedestrians are on places of the road where they
Summary
Getting insight into critical driving maneuvers is an important prerequisite for improving the safety in traffic. As a matter of fact, the environment of a driver has a big influence on the driving behaviour Such information ranges from the static environment in terms of lanes, the infrastructure, and the dynamic environment in terms of other traffic participants, changing conditions (such as weather). The idea of so-called Naturalistic Driving Studies (NDS) is to capture normal traffic situations with a set of different sensors, e.g. cameras inside and outside the car (Campbell, 2012). Such sensor data have the potential to detect critical situation - and once they are detected, it is possible to infer their triggers in the data as well. The idea in this paper is to observe the behaviour of traffic participants in terms of movement trajectories and analyze it
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