Gm Global Technolgoy Operations, Inc.

Gm Cruise Holdings LLC

Gabriel Warshauer-Baker

Kevin Chu

Aman Ashwin Diwakar

Elliot Branson

Benjamin Earle Weinstein-Raun

Siddhartho Bhattacharya

Mark Liu

Nathan Mandi

Haggai Megged Nuchi

Various technologies described herein pertain to generating an occupancy grid movie for utilization in motion planning for the autonomous vehicle. The occupancy grid movie can be generated for a given time and can include time-stepped occupancy grids for future times that are at predefined time intervals from the given time. The time-stepped occupancy grids include cells corresponding to regions in an environment surrounding the autonomous vehicle. Probabilities can be assigned to the cells specifying likelihoods that the regions corresponding to the cells are occupied at the future times. Moreover, cached query objects that respectively specify indices of cells of a grid occupied by a representation of an autonomous vehicle at corresponding orientations are described herein. An occupancy grid for the environment surrounding the autonomous vehicle can be queried to determine whether cells of the occupancy grid are occupied utilizing a cached query object from the cache query objects.

Occupancy grid movie system

A method includes recording a first timestamp of a detection of an event with a first array of an array of light sensors or an array of acoustic sensors. The array of light sensors and the array of acoustic sensors are mounted on a same chassis. The method further includes determining a direction of the event, based on the detection with the first array; recording a second timestamp of a detection of the event by a second array of the array of light sensors or the array of audio sensors; determining a distance of the event from the arrays of light and audio sensors, based on the first timestamp and the second timestamp; and controlling a vehicle, based on the event, the vehicle including the chassis.

Sensors for determining object location

A method includes recording a first timestamp of a detection with a first sensor array of a first physical property of an event; determining a direction of the event, based on the detection with the first sensor array; recording a second timestamp of a detection with a second sensor array of a second physical property of the event; determining a distance of the event from the first and second sensor arrays, based on the first timestamp and the second timestamp; determining the event, based on the first physical property and the second physical property; and taking an action based on the event.

An autonomous vehicle is described herein. The autonomous vehicle comprises sensor systems that generate sensor signals and a computing system. The computing system generates an awareness graph based upon the sensor signals. The awareness graph comprises nodes connected by directed edges. The nodes include a first node assigned to a first maneuver that is to be executed by a vehicle in a vicinity of the autonomous vehicle and a second node assigned to a second maneuver that is to be executed by the autonomous vehicle. The directed edges include a directed edge that extends from the second node to the first node indicating that execution of the second maneuver is dependent upon execution of the first maneuver. The autonomous vehicle determines an ordering of the first maneuver and the second maneuver based on the awareness graph and executes the second maneuver in accordance with the ordering.

Maneuver-based interaction system for an autonomous vehicle

An autonomous vehicle is described herein. The autonomous vehicle includes several different types of sensor systems, such as image, lidar, and radar. The autonomous vehicle additionally includes a computing system that executes several different object classifier modules, wherein the object classifier modules are configured to identify types of objects that are in proximity to the autonomous vehicle based upon outputs of the sensor systems. The computing system additionally executes a Bayesian object classifier system that is configured to receive outputs of the object classifier modules and assign labels to objects captured in sensor signals based upon the outputs of the object classifier modules.

Growing community of inventors

Mountain View, CA, United States of America

Gabriel Warshauer-Baker