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Learning from drone observations on highways: characteristics of vehicular traffic at an on-ramp

This paper describes naturalistic driving on German highways with the help of highway observations recorded by a drone and the subsequent processing of the footage. The on-ramp merging trajectories of real traffic are analysed to collect characteristics about the behavior of manually driven vehicles at on-ramps. Therefore, a statistical analysis of the location of lane changes within the merging section of the highway is presented. Typically, we can identify at least one specific location on a merging lane at which vehicles drive onto the highway lane with the highest probability. Additionally, the natural trajectories are replayed offline with a share of vehicle-to-vehicle (V2V) capable vehicles to simulate networks in real traffic scenarios. We show that the distribution of lane-changing locations is multimodal along the merging ramp and depends on the existing gaps in the rightmost lane of the highway. In addition, different measurements of multiple shares of V2V capable vehicles in a simulated environment of the real recordings are provided and discussed. With a penetration rate of 10%, a communication uptime of over 80% is achieved for this section.