Abstract: A system for route guidance, the system comprising a driving controller, wherein the driving controller is configured to receive sensor output from at least one sensor, wherein, based on the received sensor output, the driving controller determines, for a common time interval, a first set of route actions and a second set of route actions; and a vehicle motion controller configured to receive, from the driving controller, the first set of route actions and the second set of route actions, and wherein, during the common time interval, the first set of route actions are executed when the vehicle is in a first state and the second set of route actions are executed when the vehicle is in a different second state.

Abstract: A system for route guidance, the system comprising a driving controller, wherein the driving controller is configured to receive sensor output from at least one sensor, wherein, based on the received sensor output, the driving controller determines, for a common time interval, a first set of route actions and a second set of route actions; and a vehicle motion controller configured to receive, from the driving controller, the first set of route actions and the second set of route actions, and wherein, during the common time interval, the first set of route actions are executed when the vehicle is in a first state and the second set of route actions are executed when the vehicle is in a different second state.

Abstract: A device for controlling an autonomous vehicle includes a processor and a memory including instructions that, when executed by the processor, cause the processor to detect that the autonomous vehicle has one or more malfunctioning components and to determine, in response to the detection, a likelihood that the autonomous vehicle is able to reach a desired stop location based on one or more factors associated with the one or more malfunctioning components. The device determines at least one maneuver for the autonomous vehicle based on the likelihood, and causes the autonomous vehicle to perform the at least one maneuver.

Abstract: An autonomous vehicle or self-driving vehicle can protect persons from accidents. When the occupants of a vehicle exit the vehicle, those occupants can be endangered by the driving environment. Thus, while the occupants are outside the vehicle, the vehicle can continue to monitor the environment and provide warnings or intercept other vehicles or objects that may impact the occupants. Once all occupants have exited the vehicle, the vehicle's systems can continue to track each person around the vehicle and may engage, in limited maneuvers, to shield the pedestrians around the vehicle.

Abstract: An autonomous vehicle or self-driving vehicle can protect persons from accidents. When the occupants of a vehicle exit the vehicle, those occupants can be endangered by the driving environment. Thus, while the occupants are outside the vehicle, the vehicle can continue to monitor the environment and provide warnings or intercept other vehicles or objects that may impact the occupants. Once all occupants have exited the vehicle, the vehicle's systems can continue to track each person around the vehicle and may engage, in limited maneuvers, to shield the pedestrians around the vehicle.

Abstract: A device for controlling an autonomous vehicle includes a processor and a memory including instructions that, when executed by the processor, cause the processor to detect that the autonomous vehicle has one or more malfunctioning components and to determine, in response to the detection, a likelihood that the autonomous vehicle is able to reach a desired stop location based on one or more factors associated with the one or more malfunctioning components. The device determines at least one maneuver for the autonomous vehicle based on the likelihood, and causes the autonomous vehicle to perform the at least one maneuver.

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