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Beschreibung:
In this dissertation, we introduce a digital forensic approach to investigate hit-and-run accidents. The result of this investigation is a priority ranking of suspects that can be used to optimize subsequent investigation steps. In the proposed approach, we first investigate which of the suspects could have taken a route that leads through the accident location. For this, we introduce an algorithm that reconstructs the likely traveled routes of a driver by mapping the distances and turns caused by traveling the actual route onto the street network of the area in which the trip took place. The distances and turns are calculated from the wheel speeds of the suspect's vehicle recorded by a forensic data logger while driving. The presence of the accident location on any of the reconstructed routes is an indication of a possible involvement of the suspect in the accident. We show that the algorithm is suitable for the reconstruction of likely traveled routes in urban areas by means of a simulation based on real driving data. Furthermore, we demonstrate the applicability of the algorithm in the investigation of hit-and-run accidents with real driving experiments. Next, we investigate the aggressiveness of the suspects' driving behavior, since aggressive driving behavior increases the risk of accidents. In addition, we analyze the driving behavior of the suspects near the accident location to determine which of the suspects performed risky driving maneuvers there. To this end, we introduce an algorithm for assessing driving behavior using wheel speeds. Based on this algorithm, we categorize the driving maneuvers performed by a suspect while driving according to their severity. This enables to discover risky driving maneuvers near the accident location. By means of real driving experiments, we show that the algorithm can identify aggressive driving behavior and can be used to discover risky driving maneuvers at any point of a trip. These driving experiments also include common accident maneuvers, which shows the ...