US 12,258,041 B2
Systems and methods for controlling a vehicle using high precision and high recall detection
Derek J. Phillips, Mountain View, CA (US); Collin C. Otis, Driggs, ID (US); Andreas Wendel, Mountain View, CA (US); and Jackson P. Rusch, Mountain View, CA (US)
Assigned to Kodiak Robotics, Inc., Mountain View, CA (US)
Filed by Kodiak Robotics, Inc., Mountain View, CA (US)
Filed on Dec. 13, 2022, as Appl. No. 18/065,419.
Prior Publication US 2024/0190466 A1, Jun. 13, 2024
Int. Cl. B60W 60/00 (2020.01); G01S 13/86 (2006.01)
CPC B60W 60/0011 (2020.02) [G01S 13/862 (2013.01); G01S 13/865 (2013.01); B60W 2420/403 (2013.01); B60W 2420/408 (2024.01); B60W 2420/54 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method of controlling an autonomous vehicle, comprising:
receiving data from a set of sensors, wherein the data represents objects or obstacles in an environment of the autonomous vehicle; and
using a processor:
identifying objects or obstacles from the received data from the set of sensors;
determining multiple sets of attributes of the objects or obstacles, wherein each set of attributes of the objects or obstacles are determined based on data received by a sensor of the set of sensors;
determining a candidate trajectory for the autonomous vehicle based on the multiple sets of attributes of the objects or obstacles; and
controlling the autonomous vehicle according to the candidate trajectory,
wherein the method further comprises:
generating high precision detection data based on the received data from the set of sensors;
identifying from the high precision detection data a first set of objects or obstacles that are classifiable by at least one known classifier;
tracking movement of one or more objects in the first set of objects or obstacles over time and maintaining identity of the tracked one or more objects in the first set of objects or obstacles;
generating high recall detection data based on the received data from the set of sensors;
identifying from the high recall detection data a second set of objects or obstacles without using any classifier;
filtering out objects, from the second set of objects or obstacles, that correspond to the tracked one or more objects in the first set of objects or obstacles to obtain a filtered set of objects or obstacles; and
determining a candidate trajectory for the autonomous vehicle to avoid at least the tracked one or more objects in the first set of objects or obstacles and the filtered set of objects or obstacles.