Stable radar track velocity initialization using multiple hypotheses

Abstract

This document describes an object tracker that performs stable velocity initialization for radar tracks, using multiple hypotheses, including when only sparse radar point clouds are available. With just a single point per scan, the tracker creates multiple hypotheses for the direction and speed of an object. A least square function can be applied to each hypothesis to derive each respective initial velocity, which are tracked using a Kalman Filter during a hypotheses tracking period. When hypotheses are initialized and tracked on each hypothesis tracking period, their track error scores are computed. Based on their track error scores, the hypotheses that have low evidence are discarded during the hypotheses tracking period. When the hypotheses tracking period ends, a hypothesis with high evidence initializes the track's velocity. Parallel hypothesis evaluation enables the tracker to initialize a velocity quickly and accurately by merely selecting the best hypothesis, which may enable safer driving.