Kodiak Robotics, Inc.

Jackson P Rusch

Andreas Wendel

Derek J Phillips

Collin C Otis

This disclosure presents systems and methods for path planning in autonomous vehicles. The method involves a planner receiving perception data from a perception module, which includes tracking or predicted object data about obstacles in the environment of an autonomous vehicle. The tracking or predicted object data are determined based on high recall detection data and high precision detection data.

Systems and methods for path planning of autonomous vehicles

This disclosure provides systems and methods for controlling a vehicle based on a combination of high precision detection and high recall detection. The disclosed systems and methods can efficiently generate trajectories by reducing duplicate detection or duplicate calculation of objects or obstacles of common and known object types and objects or obstacles without class identification.

Systems and methods for controlling a vehicle using high precision and high recall detection

This disclosure provides systems and methods for detecting and tracking objects or obstacles in an environment of an autonomous vehicle. The method may include 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: generating high precision detection data based on the received data; identifying, from the high precision detection data, a set of objects that are classifiable by at least one known classifier; generating high recall detection data based on the received data; identifying from the high recall detection data a set of obstacles; and performing an operation on the high precision detection data of the objects and the high recall detection data of the obstacles, based on a status of the autonomous vehicle or based on one or more characteristics of the objects or the obstacles.

This disclosure provides systems and methods for controlling a vehicle. The method comprises receiving data from a set of sensors, wherein the data represents objects or obstacles in an environment of the autonomous vehicle; identifying objects or obstacles from the received data; 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 an individual sensor; 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.

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Mountain View, CA, United States of America

Jackson P Rusch