Aptiv Technologies Ag

Aptiv Technologies Limited

Syed Asif Imran

Jan Konried Schiffmann

Nianxia Cao

Zixin Liu

Ganesh Sevagamoorthy

Perception systems and methods include use of occlusion constraints for resolving tracks from multiple types of sensors. An occlusion constraint is applied to an association between a radar track and vision track to indicate a probability of occlusion. The perception systems and methods refrain from evaluating occluded and collected radar tracks and vision tracks. The probability of occlusion is utilized for deemphasizing pairs of radar tracks and vision tracks with a high likelihood of occlusion and therefore, not useful for tracking. Improved perception data is provided and more closely represents multiple complex data sets for a vehicle to prevent a collision with an occluded object as the vehicle operates in an environment.

Occlusion constraints for resolving tracks from multiple types of sensors

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

This document describes systems and techniques for clustering track pairs for multi-sensor track association. Many track-association algorithms use pattern-matching processes that can be computationally complex. Clustering tracks derived from different sensors present on a vehicle may reduce the computational complexity by reducing the pattern-matching problem into groups of subproblems. The weakest connection between two sets of tracks is identified based on both the perspective from each track derived from a first sensor and the perspective of each track derived from a second sensor. By identifying and pruning the weakest connection between two sets of tracks, a large cluster of tracks may be split into smaller clusters. The smaller clusters may require fewer computations by limiting the quantity of candidate track pairs to be evaluated. Fewer computations result in processing the sensor information more efficiently that, in turn, may increase the safety and reliability of an automobile.

Clustering track pairs for multi-sensor track association

This document describes methods and systems directed at history-based identification of incompatible tracks. The historical trajectory of tracks can be advantageous to accurately determine whether tracks originating from different sensors identify the same object or different objects. However, recording historical data of several tracks may consume vast amounts of memory or computing resources, and related computations may become complex. The methods and systems described herein enable a sensor fusion system of an automobile or other vehicle to consider historical data when associating and pairing tracks, without requiring large amounts of memory and without tying up other computing resources.

History-based identification of incompatible tracks

Kurtosis based pruning for a sensor-fusion system includes aligning candidate radar tracks with one of multiple candidate visual tracks. For each candidate vision track, a weight or other evidence of matching is assigned to each candidate radar track. An inverse of matching errors between each candidate vision track and each candidate radar track contributes to the evidence, which is normalized to produce, for each candidate vision track, a distribution associated with all candidate radar tracks. A Kurtosis or a shape of this distribution is calculated. Based on Kurtosis values, some candidate radar tracks are selected for matching and other remaining candidate radar tracks are pruned. The Kurtosis is used to determine how many candidates to retain and how many to prune.

Kurtosis based pruning for sensor-fusion systems

This document describes Kurtosis based pruning for sensor-fusion systems. Kurtosis based pruning minimizes a total quantity of comparisons performed when fusing together large sets of data. Multiple candidate radar tracks may possibly align with one of multiple candidate visual tracks. For each candidate vision track, a weight or other evidence of matching is assigned to each candidate radar track. An inverse of matching errors between each candidate vision and each candidate radar track contributes to this evidence, which may be normalized to produce, for each candidate vision track, a distribution associated with all candidate radar tracks. A Kurtosis or shape of this distribution is calculated. Based on the Kurtosis values, some candidate radar tracks are selected for matching and other remaining candidate radar tracks are pruned. The Kurtosis aids in determining how many candidates to retain and how many to prune. In this way, Kurtosis based pruning can prevent combinatorial explosions due to large-scale matching.

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Moorpark, CA, United States of America

Syed Asif Imran