Aptiv Technologies Ag

Zixin Liu

Yu Wang

Syed Asif Imran

Yang Wang

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.

Stable radar track velocity initialization using multiple hypotheses

The techniques and systems herein enable track association based on azimuth extension and compactness errors. Specifically, first and second tracks comprising respective locations and footprints of respective objects are received. An azimuth distance is determined based on an azimuth extension error that corresponds to azimuth spread between the first and second tracks with respect to a host vehicle. A position distance is also determined based on a compactness error that corresponds to footprint difference between the first and second tracks. Based on the azimuth and position distances, it is established whether the first object and the second object are a common object. By doing so, the system can better determine if the tracks are of the common object when the tracks are extended (e.g., not point targets) and/or partially observed (e.g., the track is not of an entire object).

Track association based on azimuth extension and compactness errors

The techniques and systems herein enable ghost object detection. Specifically, a reflection line indicative of a potential reflection surface between first and second moving objects is determined. If enough stationary objects are within an area of the reflection line, it is determined whether one or more of the stationary objects within the area are within a distance of a reflection point. An expected velocity of the second object is then determined and checked against a velocity of the second object. If the expected velocity is near the velocity, it is determined that the second object is a ghost object. By doing so, the system can effectively identify ghost objects in a wide variety of environments, thereby allowing for downstream operations to function as designed.

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Auburn Hills, MI, United States of America

Zixin Liu