Gm Cruise Holdings LLC

Dogan Gidon

Diego Plascencia-Vega

Nestor Grace

Mohamed Mostafa Elshenawy

Martin Sehr

Hooman Barekatain

Srishti Iyer Koduvayur

Pradeep Bhatta

Rushil Goradia

Clifton Trigg Hutchinson, Jr

Systems and techniques are provided for adjusting the planned path of an autonomous vehicle to converge with the actual path. An example method can include determining a first pose error associated with a first pose component from one or more pose components, wherein the first pose error is based on a first difference between an actual pose of an autonomous vehicle and a planned pose of the autonomous vehicle; determining that the first pose error associated with the first pose component exceeds a pose error threshold corresponding to the first pose component; and generating a new planned pose of the autonomous vehicle that yields a reduced first pose error associated with the first pose component, wherein the reduced first pose error is based on a second difference between the actual pose of the autonomous vehicle and the new planned pose of the autonomous vehicle.

Modification of the planned path of an autonomous vehicle to converge to the actual position

A method includes using sensory data produced by at least one onboard sensor of a witness vehicle to predict an imminent collision in an environment of the witness vehicle, the predicted imminent collision involving at least one participant vehicle; and alerting the least one participant vehicle to the predicted imminent collision, wherein the alerting is performed by the witness vehicle and prompts the at least one participant vehicle to perform at least one action to mitigate the predicted imminent collision.

Coordinated vehicle collision avoidance system

A method is described and includes subsequent to an autonomous vehicle becoming immobilized, initiating a local assistance request; subsequent to the initiating, receiving local assistance input from a passenger of the autonomous vehicle; and using the local assistance input to determine an action to be taken by the autonomous vehicle to mobilize the autonomous vehicle.

Local assistance for autonomous vehicle-enabled rideshare service

Systems, methods, and computer-readable media are provided for analyzing a traffic infrastructure element, determining reflectivity information of the traffic infrastructure element based on the analyzing of the traffic infrastructure element, comparing the reflectivity information of the traffic infrastructure element with semantic map information of the traffic infrastructure element, and providing instructions to an autonomous vehicle based on the comparing of the reflectivity information of the traffic infrastructure element with the semantic map information of the traffic infrastructure element.

Utilization of reflectivity to determine changes to traffic infrastructure elements

The subject disclosure relates to solutions for reducing or eliminating motion sickness experienced by a vehicle occupant/passenger. In some aspects, a process of the disclosed technology includes steps for collecting motion data associated with a vehicle using one or more environmental sensors, tracking eye movements of a user within a cabin of the vehicle, processing the motion data and the eye movements to identify a motion event, and generating a motion compensation signal based on the motion event. Systems and machine-readable media are also provided.

Adaptive adjustments to visual media to reduce motion sickness

The disclosed technology provides solutions for implementing dynamic autonomous vehicle (AV) dispatch services. A process of the disclosed technology can include steps for receiving a dispatch request from a mobile device associated with a user, wherein the dispatch request comprises a first pick-up location associated with the user, dispatching a first autonomous vehicle (AV) to the first pick-up location in response to the dispatch request, and automatically receiving an updated location of the mobile device associated with the user. In some aspects, the process can further include steps for determining a second pick-up location based on a change between the first pick-up location and the updated location, and transmitting an updated dispatch request based on the updated location of the mobile device. Systems and machine-readable media are also provided.

Adaptive dispatch and routing of autonomous vehicles based on threshold distances

Particular embodiments described herein provide for a system and method to help enable a towing vehicle. In an example, the system can identify a location of the disabled vehicle, deploy the towing vehicle to the location of the disabled vehicle, where the towing vehicle uses sensor data from a sensor suite and a perception module to navigate to the location of the disabled vehicle, and use the towing vehicle to tow the disabled vehicle to a new location.

System and method to help enable autonomous towing vehicle

An autonomous vehicle (AV) uses an authentication system to verify that a person on the street attempting to control behavior of the AV is authorized to do so. An authorized person (e.g., a construction worker or a police officer) has an associated beacon that provides a beacon signal, such as a visual symbol or wireless signal. The AV perceives the beacon signal and transmits it to the authentication system. The authentication system looks up the beacon signal in a database and retrieves conditions associated with the beacon signal. If the authentication system and/or AV confirms that the beacon signal is in the database, and the conditions under which the person associated with the beacon signal is authorized to control the AV are met, the AV follows instructions from the person.

Verifying authorized personnel for interaction with autonomous vehicles

The disclosed technology provides solutions for improving the operational efficiency and safety of autonomous vehicles (AVs). In some implementations, the disclosed technology encompasses methods for performing vehicle clustering or platooning, for example, that can include steps for computing navigation routes for each of a plurality of autonomous vehicles (AVs), identifying a route overlap for a first AV and a second AV selected from among the plurality of AVs, and transmitting a clustering instruction to the first AV and the second AV based on the route overlap, wherein the clustering instruction is configured to cause the first AV and the second AV to initiate a clustering configuration. Systems and machine-readable media are also provided.

Coordinated multi-vehicle routing

Collaborative perception is based on recognition that a fleet of AVs and stationary infrastructure objects equipped with sensors may be configured to communicate with one another in sharing their sensor data, thus benefiting from collaborative perception, rather than being limited to their individual perception. Three specific scenarios of collaborative perception are disclosed. The first scenario relates to two AVs in the vicinity of one another exchanging complexity scores indicative of their respective environments. The second scenario relates to an AV detecting that it has a blind spot and seeking other AVs or infrastructure objects to provide information indicative of the environment in the blind spot. The third scenario relates to providing infrastructure objects equipped with sensors in appropriate locations so that, when an AV is in the vicinity of such objects, the AV may receive information from their sensors.

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Berkeley, CA, United States of America

Dogan Gidon