Gm Cruise Holdings LLC

Michael Frank Schmidt

Samuel Harrison Glidden

Chase Brian Kaufman

Erik Birk Nielsen

Jeffrey Brandon

Sriram Salem Kannan

Adam Mandel-Senft

Kenneth Ramon Ferguson

Maximilian Gerrit Euchenhofer

Tracy Cheung

Kyle Vogt

Divya Thakur

Daniel Henry Curzon

Jason Lee

Rajat Basu

Daniel Kan

Darren Hau

Robert Tweedy

Justin Henry Leder

A fleet management system dispatches autonomous electric vehicles (AEVs) as on-demand power sources. The fleet management system receives a request for a power source including a location and data describing the amount of power requested. The fleet management system selects an AEV of the fleet to service the request based on the relative locations of the AEVs to the requested location, and based on the amount of power requested. The fleet management system instructs the selected AEV to drive to the location and supply power. The fleet management system instructs the selected AEV to disconnect and return to the charging station, and may instruct another AEV to continue fulfilling the request if additional power is needed.

Centrally dispatched power supply using autonomous electric vehicle fleet

Systems and methods for dynamically suppressing noise at electric vehicle charging sites. In particular, systems and methods are provided for measuring the ambient noise at a charging site and adjusting electric vehicle chargers to reduce noise pollution. In some examples, electric vehicle charger cooling fans potentially generate a high level of noise, and the power output of a charger can be decreased to decrease the heat generated by the chargers, thereby reducing the need for fans. In various examples, reduction of noise pollution can be especially important in specified geographies (e.g., residential neighborhoods) and/or during selected timeframes (e.g., overnight). In various examples, the system interfaces with a central computer service (e.g., a dispatch service) to intelligently route autonomous vehicles to charging sites based on noise levels at various available charging sites.

System and method to dynamically suppress noise at electric vehicle charging sites

Systems and methods are provided for automated detection and disinfection of microbes in autonomous vehicles, to prevent possible transmission of diseases or illness to subsequent users. Automated detection and disinfection of a vehicle includes autonomously scanning various parts of the autonomous vehicle for microbes, selecting a disinfectant based on detected microbes, and autonomously disinfecting at least a portion of the autonomous vehicle.

System and method for automated detection and disinfection of a fleet of autonomous vehicles

A light inspection system positions an autonomous vehicle (AV) in a field of view of a camera such that the camera captures an image of a light of the AV. The light inspection system instructs a camera to capture an image of the light while the light is switched on. The light inspection system receives the captured image, determines a luminance of the light based on the image, and determines to service the light in response to the luminance of the light being below a threshold luminance.

Automated inspection of autonomous vehicle lights

The present disclosure provides a method comprising receiving a rideshare request from a user, the rideshare request including a destination; transporting the passenger to the destination using an autonomous vehicle (AV) having a plurality of windows comprising electrically switchable smart glass; during the transporting, monitoring a metric related to arrival of the AV at the destination; and untinting the windows when the monitored metric has a prescribed relationship to a threshold value.

Adjustable automatic window tinting for autonomous vehicles

Sensors coupled to a first vehicle are used to measure sensor measurements. Based on the sensor measurements, the first vehicle identifies a risk to the first vehicle and/or to a second vehicle near the first vehicle, for example based on a distance between the first and second vehicle being unsafely low. The first vehicle generates an alert based on the risk and outputs the alert toward the second vehicle, for example visually through a screen and/or audibly using speakers, to warn an operator of the second vehicle about the risk.

Dynamic inter-vehicle communication regarding risk detected based on vehicle sensor measurements

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

An equipment inspection system receives data captured by a sensor of an autonomous vehicle (AV). The captured data describes a current state of equipment for servicing the AV. The equipment inspection system compares the captured data to a model describing an expected state of the equipment. The equipment inspection system determines, based on the comparison, that the equipment differs from the expected state. The equipment inspection system may transmit data describing the current state of the equipment to an equipment manager. The equipment manager may schedule maintenance for the equipment based on the current state of the equipment.

Automated inspection of autonomous vehicle equipment

A method for communication between autonomous vehicles and personnel can include receiving a signal from a remote computing system, the signal based upon a position of an autonomous vehicle and a first position of a mobile device, wherein the signal identifies that the mobile device is located in a path of the autonomous vehicle, responsive to receipt of the signal from the remote computing system, stopping the autonomous vehicle, receiving an updated signal from the remote computing system, the updated signal based upon a second position of the mobile device, the second position effective to facilitate a determination that the mobile device is no longer within the path of the autonomous vehicle, and responsive to receiving the updated position of the mobile device, resuming operation of the autonomous vehicle along the path. An autonomous vehicle and non-transitory computer-readable medium is also provided.

Communication between autonomous vehicles and operations personnel

Predictive maintenance and diagnostics for an electronic module of an autonomous vehicle using modular condition monitoring is described herein. A computing system receives a signal from a data logger which monitors a condition of the electronic module of the autonomous vehicle, wherein the signal is indicative of damage accumulation information thereof. The computing system identifies a type of the electronic module and a damage accumulation threshold for the type of the electronic module to generate a predicted maintenance schedule for the electronic module of the autonomous vehicle. The damage accumulation information can be stored in a data store to define the damage accumulation threshold for the type of the electronic module.

Predictive maintenance and diagnostics using modular condition monitoring

Described herein is a server computing system that controls an autonomous vehicle to perform an operation to at least one of measure or isolate an effect of a variable on an actual power consumption by the autonomous vehicle. Data indicative of the actual power consumption, which is generated based on the operation, and data indicative of a projected power consumption, which is accumulated based on prior execution of the operation by a same or different autonomous vehicle, is received by the server computing system to determine whether an energy efficiency of the autonomous vehicle is degraded. The operation may be performed to identify a degraded vehicle system or component of the autonomous vehicle or to identify an autonomous vehicle in a fleet of autonomous vehicles for which further analysis is desirable. An output is generated by the server computing system that is indicative of the energy efficiency prognostics.

Growing community of inventors

Scottsdale, AZ, United States of America

Michael Frank Schmidt