Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: The present technology pertains to generating hybrid scenario information for use during closed course testing of an autonomous vehicle (AV). Such hybrid scenario information may be generated by combining sensor data from an AV, simulated environment information, simulated object information, and information obtained using perceivable marker information of a perceivable marker perceived by the AV in the closed course environment. The hybrid scenario information may be provided to the AV during the closed course testing. The AV may respond to the hybrid scenario information by performing one or more AV control actions.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: The subject disclosure relates to techniques for a logistical support autonomous vehicle. A process of the disclosed technology can include receiving, at an autonomous vehicle (AV), a support request, in response to the follow-along routing preference specified by the user, determining a location of the user, and navigating the AV so that the AV remains positioned within a threshold distance of the user. Systems and machine-readable media are also provided.

Abstract: The disclosed technology provides solutions for performing disaster and emergency surveillance and in particular, for maintaining a Common Operating Picture (COP) using a fleet of distributed robots. A COP maintenance process can include steps for receiving a disaster notification identifying an affected area, and deploying autonomous robots to a map location corresponding with the disaster location. In some aspects, the process can further include steps for receiving updated map information from the one or more autonomous robots. Systems and machine-readable media are also provided.

Abstract: 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.

Abstract: 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.

Abstract: 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.