Hybrid AI towards Large Scale Data and Behavior Modeling for Automated Driving – HAIBrid

We propose to improve behavior prediction for autonomous driving by extracting trajectories and learning their interactions unsupervised from large scale data. We will leverage the experience and infrastructure of the consortium to collect a large body of traffic data from traffic cameras, drones and on-board vehicle sensors. Labeling vehicle trajectories and classifying interactions is hard due to their high variance in time and progression. Meanwhile, unsupervised methods push performance on image classification, segmentation and tracking, slowly approaching their supervised counterparts. Therefore, we will develop efficient methods for trajectory extraction and interaction clustering to take advantage of this recent progress but also generate 3D maps automatically. The static environment and the traffic rules, commonly encoded in semantic maps, are crucial not only to motion planning for automated vehicles but also to understand the behavior of other traffic participants. We will contribute collected data, developed methods and extracted knowledge about traffic participant trajectories and interactions to support the autonomous driving community and advance Franco-German AI competencies. Thereby the project will not only contribute novel multi-sensor recordings of scenes but methods and tools to support data extraction and labeling for future data recordings.