Gm Cruise Holdings LLC

Gm Global Technolgoy Operations, Inc.

Kyle Vogt

Stephanie Olivia Engle

Michael Frank Schmidt

Kevin Chu

Eric Lujan

Ian Rust

Chase Brian Kaufman

Samuel Harrison Glidden

Drew Gross

Sohrab Haghighat

Gautier Minster

Solomon Bier

Oliver Cameron

Lutfollah Maleki

Juan Fasola

Stuart Glaser

Divya Thakur

Elliot Branson

Adam Mandel-Senft

Mohamed Mostafa Elshenawy

Sandeep Gangundi

Daniel Kan

Yung-Chang Ko

Abdelrahman Elogeel

Matthew Graney

Cornelis Jacob Lekkerkerker

Rita Ciarvino

Lukas Holoubek

Robert Tweedy

Avinash Nair

Thomas Busser

Benjamin Dunkin

Systems and methods for providing indications to identify a selected autonomous vehicle, thereby distinguishing the selected autonomous vehicle from other autonomous vehicles in a fleet. In particular, autonomous vehicles that are not paired with a specific nearby user may employ various exterior indicators to dissuade the user from entering. Additionally, autonomous vehicles that are not paired with the specific nearby user may provide guidance toward the user's autonomous vehicle.

Autonomous vehicle identification

Described herein is a system and method for detecting malodor within a cabin of an autonomous vehicle, wherein initiation of a remediation system and/or dispatch of the autonomous vehicle to a service hub for mitigating the malodor is based upon sensor signals that monitor the cabin of the autonomous vehicle. The remediation system can include devices such as an HVAC system or a window, which are operated to reduce the concentration of airborne molecules in the cabin of the autonomous vehicle when the concentration of airborne molecules exceeds an air quality threshold. A dispatch protocol may be initiated to dispatch the autonomous vehicle to a service hub when the malodor is associated with certain predefined incidents or when remediation fails to reduce the concentration of airborne molecules below the air quality threshold.

System and method for malodor detection and remediation

The subject disclosure relates to techniques for optimizing autonomous vehicle activities at a fleet level. A process of the disclosed technology can include measuring a first set of cost metrics for a first plurality of autonomous vehicles (AVs) associated with a first vehicle routing version, wherein the first set of cost metrics comprises energy consumption values for two or more of the first plurality of AVs, measuring a second set of cost metrics for a second plurality of AVs associated with a second vehicle routing version, wherein the second set of cost metrics comprises energy consumption values for two or more of the second plurality of AVs, and comparing the first set of cost metrics with the second set of cost metrics to determine routing updates configured to reduce fleetwide energy consumption.

Fleet-level AV simulation system and method

An autonomous vehicle (AV) implements a notification system that provides notifications to user devices in nearby vehicles to alert drivers of the nearby vehicles of the AV's planned behavior. The notifications are delivered wirelessly and output by the user device to the drivers. The planned behaviors of the AV may not be conveyed by existing mechanisms, such as turning signals or hazard lights. The AV or the receiving user device may determine when an AV's planned behavior may impact a particular driver, and provide relevant notifications to the driver.

Notifications from an autonomous vehicle to a driver

Various technologies described herein pertain to controlling an autonomous vehicle to provide indicators to distinguish the autonomous vehicle from other autonomous vehicles in a fleet. The autonomous vehicle includes a vehicle propulsion system, a braking system, a notification system, and a computing system. The notification system outputs an indicator that is perceivable external to the autonomous vehicle. The computing system receives data specifying an identity of a passenger to be picked up by the autonomous vehicle. Moreover, the computing system controls at least one of the vehicle propulsion system or the braking system to stop the autonomous vehicle for passenger pickup. Further, the computing system controls the notification system to output the indicator; a characteristic of the indicator outputted by the notification system is controlled based on the identity of the passenger to be picked up and whether the autonomous vehicle is stopped for passenger pickup.

The subject disclosure relates to ways to identify self-hit data collected by autonomous vehicle (AV) sensors. In some aspects, a method of the disclosed technology includes steps for generating a geometric model of an autonomous vehicle (AV), wherein the geometric model specifies physical boundaries of the AV in three-dimensional (3D) space, collecting sensor data for an environment around the AV, and identifying one or more data points, from among the collected sensor data, that correspond with a surface of the AV. Systems and machine-readable media are also provided.

Eliminating sensor self-hit data

Various technologies described herein pertain to context aware real-time power adjusting for steerable lidar. A lidar system can include a laser source (e.g., FMCW) configured to emit an optical signal. The lidar system can further include a scanner configured to direct the optical signal emitted by the laser source from the lidar system into an environment. The optical signal can be directed over a field of view in the environment during time periods of frames. The lidar system can further include a controller configured to modulate a power of the optical signal emitted by the laser source between the frames and/or within one or more of the frames. The controller can modulate the power of the optical signal emitted by the laser source based on a position of the lidar system in the environment and a direction in which the optical signal is to be transmitted into the environment.

Context aware real-time power adjustment for steerable lidar

The subject disclosure relates to ways to determine the mass of a vehicle, such as an autonomous vehicle (AV), based on the vehicle pose. In some aspects, a method of the disclosed technology includes steps for measuring a first pose of a vehicle using one or more environmental sensors, measuring a second pose of the vehicle using the one or more environmental sensors, and calculating a mass of the vehicle based on the first pose and the second pose. Systems and machine-readable media are also provided.

Vehicle mass determination

A fleet management system implements a rewards-based feedback system to receive feedback from users of a rideshare service that provides rides using autonomous vehicles (AVs). The fleet management system maintains user accounts associated with each user and provides reward points to each user account. When the user is riding in an AV, the user can access a user interface to select a portion of the reward points and reward them to the AV. The fleet management system may analyze the point rewards to identify individual user preferences, to identify preferences across users, or to identify AVs for maintenance or other types of modification.

Reward system for autonomous rideshare vehicles

The present disclosure provides a method comprising, subsequent to arrival of a vehicle at a charging station, establishing communications between a vehicle side autonomous charging system associated with the vehicle and a charging station side autonomous charging system associated with the charging station; receiving by the charging station side autonomous charging system at least one signal from the vehicle side autonomous charging system, wherein the at least one signal is indicative of a location of a charging port of the vehicle; and using the received at least one signal to guide a robotic extension arm of the charging station to a location proximate the vehicle charging port.

System for automated charging of autonomous vehicles

Systems and methods are provided for remotely assisting an autonomous vehicle. The method includes: aggregating sensor data from the autonomous vehicle; identifying an assistance-desired scenario; generating an assistance request based on the sensor data; transmitting the assistance request to a remote assistance interface; and receiving and processing a response to the assistance request. The remote assistance interface includes a remote assistance interface that is used in generating the response to the assistance request.

System and method for remotely assisting autonomous vehicle operation

Growing community of inventors

San Francisco, CA, United States of America

Kyle Vogt