Abstract: An autonomous vehicle (AV) system includes an AV control system that resolves unique situations encountered while operating as a ride hail provider. The AV control system includes an artificial intelligence (AI) agent programmed to receive a situation data set as an input from vehicle sensors or from another AI agent. The situation data set can include a generic representation of information describing and characterizing the circumstances and details of the current situation. The system may access a knowledge library having catalogued prior situation data sets describing situations encountered by the AV or other vehicles in a fleet. The AV control system may search the knowledge library for similar situations encountered by a fleet vehicle. Once a similar situation is identified, the AV control system may identify one or more remedies that were executed to handle the prior situation, and execute the remedy to address the current situation.

Abstract: Systems and methods navigating a digital map are provided herein. A method includes displaying a plurality of clusters. Each cluster includes a plurality of page icons and each page icon linking to a page. The method includes receiving a control input to move a page icon from a first one of the clusters to a second one of the clusters.

Abstract: Systems and methods navigating a digital map are provided herein. A method includes displaying a plurality of clusters. Each cluster includes a plurality of page icons and each page icon linking to a page. The method includes receiving a control input to move a page icon from a first one of the clusters to a second one of the clusters.

Abstract: An autonomous vehicle (AV) system includes an AV control system that resolves unique situations encountered while operating as a ride hail provider. The AV control system includes an artificial intelligence (AI) agent programmed to receive a situation data set as an input from vehicle sensors or from another AI agent. The situation data set can include a generic representation of information describing and characterizing the circumstances and details of the current situation. The system may access a knowledge library having catalogued prior situation data sets that describe situations encountered by the AV or other vehicles in a fleet. The AV control system may search the knowledge library for a similar situation encountered by a fleet vehicle. Once a similar situation is identified, the AV control system may identify one or more remedies that were executed to handle the prior situation, and execute the remedy to address the current situation.

Abstract: The present disclosure extends to methods, systems, and computer program products for handling rider service at autonomous vehicles. Aspects of the disclosure use a task planning artificial intelligence (AI) framework to improve rider services provided at autonomous vehicles (AV). The AI framework uses tasking priorities and historical data-based machine learning to provide improved services, such as, passenger pickup, passenger drop off, etc. at an autonomous vehicle. A vehicle service is modeled as a Virtual Chauffer Agent (VCA) that acts independently and reacts in an environment to pursue delegated goals. The VCA can interoperate with a Virtual Driving System (VDS) to control an autonomous vehicle and transport a rider between locations. The VCA can interact with other agents (e.g., weather, traffic, map, etc.) to address rider service issues.

Abstract: A computing device receives a first digital signal from a data acquisition system. It is determined that a first value includes in the first signal indicates an event. A second digital signal is received from the data acquisition system. It is determined that a second value included in the second signal includes a value that indicates the event, and that the event has occurred based at least in part on the first value, the second value, and a passage of a predetermined duration of time between a time of the first digital signal and a time of the second digital signal.

Abstract: The present disclosure extends to methods, systems, and computer program products for handling rider service at autonomous vehicles. Aspects of the disclosure use a task planning artificial intelligence (AI) framework to improve rider services provided at autonomous vehicles (AV). The AI framework uses tasking priorities and historical data-based machine learning to provide improved services, such as, passenger pickup, passenger drop off, etc. at an autonomous vehicle. A vehicle service is modeled as a Virtual Chauffer Agent (VCA) that acts independently and reacts in an environment to pursue delegated goals. The VCA can interoperate with a Virtual Driving System (VDS) to control an autonomous vehicle and transport a rider between locations. The VCA can interact with other agents (e.g., weather, traffic, map, etc.) to address rider service issues.

Abstract: A computing device receives a first digital signal from a data acquisition system. It is determined that a first value includes in the first signal indicates an event. A second digital signal is received from the data acquisition system. It is determined that a second value included in the second signal includes a value that indicates the event, and that the event has occurred based at least in part on the first value, the second value, and a passage of a predetermined duration of time between a time of the first digital signal and a time of the second digital signal.