Abstract: A system uses a machine learning based model to determine attributes describing states of mind and behavior of traffic entities in video frames captured by an autonomous vehicle. The system classifies video frames according to traffic scenarios depicted, where each scenario is associated with a filter based on vehicle attributes, traffic attributes, and road attributes. The system identifies a set of video frames associated with ground truth scenarios for validating the accuracy of the machine learning based model and predicts attributes of traffic entities in the video frames. The system analyzes video frames captured after the set of video frames to determine actual attributes of the traffic entities. Based on a comparison of the predicted attributes and actual attributes, the system determines a likelihood of the machine learning based model making accurate predictions and uses the likelihood to generate a navigation action table for controlling the autonomous vehicle.

Abstract: A broadband doppler simulator is described in which signals from a plurality of hydrophones are simulated by selecting storage addresses of a random access memory whose successive addresses contain samples of an input signal. The range of each hydrophone from the target whose signal it is receiving is determined by the address assigned to the hydrophone relative to the address of the input signal in the random access memory. In order to produce a doppler shift (time compression/expansion), the address of the simulated hydrophone signal is changed with respect to the signal which is being stored in the memory. The rate at which the address of the simulated hydrophone signal is being changed relative to the input signal determines the doppler shift.