Aptiv Technologies Limited

Delphi Technologies, Inc.

Aptiv Technologies Ag

Susan Chen

Jan Konried Schiffmann

Kumar Vishwajeet

Wenbing Dang

David Aaron Schwartz

Izzat H Izzat

Jan Siegemund

Anuradha Mangalgiri

Yu Liu

An illustrative example method of tracking an object includes detecting one or more points on the object over time to obtain a plurality of detections, determining a position of each of the detections, determining a relationship between the determined positions, and determining an estimated heading angle of the object based on the relationship.

Heading angle estimation for object tracking

An object detection system includes a lidar-unit and a controller. The controller defines an occupancy-grid that segregates the field-of-view into columns, determine a first-occupancy-status of a column based on first-cloud-points detected by the lidar-unit in the column by a first-scan, determine a second-occupancy-status of the column based second-cloud-points detected in the column by a second-scan, determine a first-number of the first-cloud-points and a second-number of the second-cloud-points, and determine a dynamic-status of the column only if the column is classified as occupied by either the first-occupancy-status or the second-occupancy-status. The dynamic-status of the column is determined to be moving when a count-difference between the first-number and the second-number is greater than a difference-threshold, and the dynamic-status of the column is determined to be static when the count-difference is not greater than the difference-threshold and a registration-factor that aligns the first-cloud-points to the second-cloud-points is less than a registration-threshold.

Object detection system

An object detection system for an automated vehicle includes a radar, a camera, and a controller. The radar detects a cluster of targets characterized by a radar-distance (x) from the host-vehicle. The camera renders an image of an object and a lane-marking present in the area. The controller is configured to determine an equation that is a function of a longitudinal-distance from the host-vehicle and corresponds to the lane-marking, determine a first-optical-angle (&#x3b8;) based on a first-image-distance between a reference-point on the object and a spot on the image indicated by the equation where the longitudinal-distance is equal to the radar-distance, determine a lateral-distance (Dist_LM) between the object and the lane-marking based on the first-optical-angle and the radar-distance, and determine a first-lateral-offset (y) between the object and a longitudinal-axis of the host-vehicle based on the lateral-distance and a first-value of the equation where the longitudinal-distance is equal to the radar-distance.

Automated vehicle object detection system with camera image and radar data fusion

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Los Angeles, CA, United States of America

Susan Chen