Abstract: An autonomous vehicle can access map data comprising travel ways within a surrounding environment of the autonomous vehicle, and further access constraint data that define original navigational constraints within the map data. The vehicle can further receive constraint files comprising additional navigational constraints within the map data and modify the constraint data based on the constraint files. The vehicle can determine a travel route to a destination using composite constraint data and autonomously driver to the destination along the travel route accordingly.

Abstract: Disclosed herein are system, method, and computer program product embodiments for switching between local and remote guidance instructions for autonomous vehicles. For example, the method includes, in response to monitoring one or more actions of objects detected in a scene in which the autonomous robotic system is moving, causing the autonomous robotic system to slow or cease movement in the scene. The method includes detecting a trigger condition based on movement of the autonomous robotic system in the scene. In response to the one or more monitored actions and detecting the trigger condition, the method includes transmitting a remote guidance request to a remote server. After transmitting the remote guidance request, the method includes receiving remote guidance instructions from the remote server and causing the autonomous robotic system to begin operating according to the remote guidance instructions.

Abstract: Disclosed herein are system, method, and computer program product embodiments for switching between local and remote guidance instructions for autonomous vehicles. For example, the method includes, in response to monitoring one or more actions of objects detected in a scene in which the autonomous robotic system is moving, causing the autonomous robotic system to slow or cease movement in the scene. The method includes detecting a trigger condition based on movement of the autonomous robotic system in the scene. In response to the one or more monitored actions and detecting the trigger condition, the method includes transmitting a remote guidance request to a remote server. After transmitting the remote guidance request, the method includes receiving remote guidance instructions from the remote server and causing the autonomous robotic system to begin operating according to the remote guidance instructions.

Abstract: A computing system can generate a map constraint interface enabling a fleet operator to update map constraints for autonomous vehicles (AVs). The map constraint interface can comprise a unified document model enabling the fleet operator to configure a set of constraint layers of autonomy maps utilized by the AVs. Each constraint layer can include a toggle feature that enables the fleet operator to enable and disable the constraint layer. The system can receive, via the map constraint interface, a set of inputs configuring the set of constraint layers of the one or more autonomy maps, compile a set of updated map constraints, corresponding to the configured set of constraint layers, into a document container, and output the document container to a subset of the AVs to enable the subset of AVs to integrate the set of updated map constraints with the autonomy maps.

Abstract: An autonomous vehicle can access map data comprising travel ways within a surrounding environment of the autonomous vehicle, and further access constraint data that define original navigational constraints within the map data. The vehicle can further receive constraint files comprising additional navigational constraints within the map data and modify the constraint data based on the constraint files. The vehicle can determine a travel route to a destination using composite constraint data and autonomously driver to the destination along the travel route accordingly.

Abstract: Systems and methods for controlling the motion of an autonomous vehicle are provided. In one example embodiment, one or more computing devices on-board an autonomous vehicle receive one or more constraint files including constraint data descriptive of one or more geographic identifiers (e.g., a polygon) and an application type (e.g., partial inclusion, complete inclusion, partial exclusion, complete exclusion) associated with each of the one or more geographic identifiers. Map data descriptive of the identity and location of different travel ways within the surrounding environment of the autonomous vehicle is accessed. A travel route for navigating the autonomous vehicle is determined, wherein the travel route is determined at least in part from the map data evaluated relative to the constraint data. Motion of the autonomous vehicle can be controlled based at least in part on the determined travel route.

Abstract: A computing system can generate a map constraint interface enabling a fleet operator to update map constraints for autonomous vehicles (AVs). The map constraint interface can comprise a unified document model enabling the fleet operator to configure a set of constraint layers of autonomy maps utilized by the AVs. Each constraint layer can include a toggle feature that enables the fleet operator to enable and disable the constraint layer. The system can receive, via the map constraint interface, a set of inputs configuring the set of constraint layers of the one or more autonomy maps, compile a set of updated map constraints, corresponding to the configured set of constraint layers, into a document container, and output the document container to a subset of the AVs to enable the subset of AVs to integrate the set of updated map constraints with the autonomy maps.

Abstract: A system and methodologies for neuromorphic vision simulate conventional analog NM system functionality and generate digital NM image data that facilitate improved object detection, classification, and tracking.

Abstract: A system and methodologies for neuromorphic vision simulate conventional analog NM system functionality and generate digital NM image data that facilitate improved object detection, classification, and tracking.

Abstract: A system and methodologies for neuromorphic vision simulate conventional analog NM system functionality and generate digital NM image data that facilitate improved object detection, classification, and tracking so as to detect and predict movement of a vehicle occupant.

Abstract: A system and methodologies for neuromorphic vision simulate conventional analog NM system functionality and generate digital NM image data that facilitate improved object detection, classification, and tracking.

Abstract: A system and methodologies for neuromorphic vision simulate conventional analog NM system functionality and generate digital NM image data that facilitate improved object detection, classification, and tracking so as to detect and predict movement of a vehicle occupant.

Abstract: A system and methodologies for neuromorphic vision simulate conventional analog NM system functionality and generate digital NM image data that facilitate improved object detection, classification, and tracking.

Abstract: A system and methodologies for neuromorphic vision simulate conventional analog NM system functionality and generate digital NM image data that facilitate improved object detection, classification, and tracking so as to detect and predict movement of a vehicle occupant.

Abstract: A system and methodologies for neuromorphic vision simulate conventional analog NM system functionality and generate digital NM image data that facilitate improved object detection, classification, and tracking.

Abstract: A system and methodologies for neuromorphic vision simulate conventional analog NM system functionality and generate digital NM image data that facilitate improved object detection, classification, and tracking.

Abstract: Systems and methods for controlling the motion of an autonomous vehicle are provided. In one example embodiment, one or more computing devices on-board an autonomous vehicle receive one or more constraint files including constraint data descriptive of one or more geographic identifiers (e.g., a polygon) and an application type (e.g., partial inclusion, complete inclusion, partial exclusion, complete exclusion) associated with each of the one or more geographic identifiers. Map data descriptive of the identity and location of different travel ways within the surrounding environment of the autonomous vehicle is accessed. A travel route for navigating the autonomous vehicle is determined, wherein the travel route is determined at least in part from the map data evaluated relative to the constraint data. Motion of the autonomous vehicle can be controlled based at least in part on the determined travel route.

Abstract: A system and methodologies for neuromorphic vision simulate conventional analog NM system functionality and generate digital NM image data that facilitate improved object detection, classification, and tracking.

Abstract: A system and methodologies for neuromorphic vision simulate conventional analog NM system functionality and generate digital NM image data that facilitate improved object detection, classification, and tracking so as to detect and predict movement of a vehicle occupant.

Abstract: A system and methodologies for neuromorphic vision simulate conventional analog NM system functionality and generate digital NM image data that facilitate improved object detection, classification, and tracking.