Polar Occupancy Map - A Compact Traffic Representation for Deep Learning Scenario Classification

In order to accelerate development, testing and verification of automated vehicles, it is crucial to classify a wide range of driving scenarios. Scenario classification is usually done by rule-based algorithms or even manual video or signal inspection. A promising alternative is to use machine learning and let neural networks extract the relevant classification features. Since inputs to neural networks need to have a fixed size, an abstract representation of the driving scenario is necessary. In this paper, a scenario representation that captures the dynamic traffic behavior, without loss of relevant information, is presented.