Automatic Interaction Detection Between Vehicles and Vulnerable Road Users During Turning at an Intersection

Interaction detection between vehicles and vulnerable road users (e.g. pedestrians and cyclists) is important for e.g. safety control and autonomous driving. However, there are many challenges for automatically detecting interactions, such as the ambiguity of defining when interaction is required in dynamic traffic activities among different road users and the lack of labeled data for training a machine learning detector. To overcome the challenges, we introduce a way to define whether or not interaction is required in various traffic scenes and create a large real-world dataset from a very challenging intersection. A sequence-to-sequence method that uses the object information and motion information of the traffic scenes extracted by a state-of-the-art object detector and from optical flow, respectively, is proposed for automatic interaction detection. The proposed method generates a probability of interaction at each short interval (< 0.1 s) that represents the changing of interaction along a sequence. We obtain a baseline model that differentiates no interaction from interaction on the basis of the location and road user type from the detected object information. Compared with the baseline model, the empirical results of the proposed method demonstrate very accurate predictions for vehicle turning sequences with varying length.