Abstract: Activating an engine of a HEV to charge a battery includes obtaining a first GPS trace from a first trip of the HEV along a first route, where the first GPS trace includes first trace metadata; obtaining a second GPS trace from a second trip, where the second GPS trace includes second trace metadata; adding, to a navigation map, an aggregation of the first trace metadata and the second trace metadata for edges of the navigation map; using the navigation map to obtain an activation action of the engine, where the activation action is selected from a set that includes a first activation action of turning the engine on and a second activation action of turning the engine off; and activating the engine according to the activation action, where activating the engine using the first activation action causes the engine to turn on to charge the battery of the HEV.

Abstract: Route planning for a hybrid electric vehicle (HEV) includes obtaining respective engine activation actions for at least some road segments of a route between an origin and a destination by optimizing for at least one of a noise level or energy consumption of an engine of the HEV that is used to charge a battery of the HEV. The HEV is then controlled to follow the at least some of the road segments of the route and to activate the engine according to the respective engine activation actions. Controlling the HEV to follow the at least some of the road segments includes masking at least one of the respective engine activation actions for a current road segment by increasing a volume of an entertainment system of the HEV.

Abstract: A method for planning an activation action for an engine of a vehicle is disclosed. The method includes planning, according to a model, an activation action of an engine of a vehicle, and activating the engine according to the activation action. The model includes a state space comprising a current charge level of the battery and whether the engine is currently on or off. The activation action is selected from a set comprising a first action to turn on the engine to charge the battery and a second action to turn off the engine.

Abstract: Route planning for a hybrid electric vehicle (HEV) includes obtaining a route between an origin and a destination, where the route is optimized for at least one of a noise level or energy consumption of an engine of the HEV that is used to charge a battery of the HEV, and where the route comprises respective engine activation actions for at least some segments of the route; and controlling the HEV to follow the segments of the route and to activate the engine according to the respective engine activation actions.

Abstract: A system includes a battery, an engine, and a processor. The processor is configured to plan, according to a model, an activation action of the engine of a vehicle for a next road segment subsequent to a current road segment; and activate, for the next road segment, the engine according to the activation action. The model includes a state space that includes a navigation map, which includes the current road segment of the vehicle, a current charge level of the battery, and whether the engine is currently on or off. The activation action is selected from a set comprising a first action to turn on the engine to charge the battery and a second action to turn off the engine.

Abstract: Route planning for a hybrid electric vehicle (HEV) includes obtaining a route between an origin and a destination, where the route is optimized for at least one of a noise level or energy consumption of an engine of the HEV that is used to charge a battery of the HEV, and where the route comprises respective engine activation actions for at least some segments of the route; and controlling the HEV to follow the segments of the route and to activate the engine according to the respective engine activation actions.

Abstract: Activating an engine of a HEV to charge a battery includes obtaining a first GPS trace from a first trip of the HEV along a first route, where the first GPS trace includes first trace metadata; obtaining a second GPS trace from a second trip, where the second GPS trace includes second trace metadata; adding, to a navigation map, an aggregation of the first trace metadata and the second trace metadata for edges of the navigation map; using the navigation map to obtain an activation action of the engine, where the activation action is selected from a set that includes a first activation action of turning the engine on and a second activation action of turning the engine off; and activating the engine according to the activation action, where activating the engine using the first activation action causes the engine to turn on to charge the battery of the HEV.

Abstract: A system includes a battery, an engine, and a processor. The processor is configured to plan, according to a model, an activation action of the engine of a vehicle for a next road segment subsequent to a current road segment; and activate, for the next road segment, the engine according to the activation action. The model includes a state space that includes a navigation map, which includes the current road segment of the vehicle, a current charge level of the battery, and whether the engine is currently on or off. The activation action is selected from a set comprising a first action to turn on the engine to charge the battery and a second action to turn off the engine.

Abstract: An autonomous vehicle notification system comprises a sensor onboard the autonomous vehicle, a messaging system onboard the autonomous vehicle, and a controller. The sensor is configured to sense a condition external to the autonomous vehicle. The controller is configured to control communication with a remote manager to enable the remote manager to control the autonomous vehicle based on the condition, and is configured to control the messaging system to provide a message at the autonomous vehicle pertaining to the control by the remote manger.

Abstract: Autonomous driving simulation using recorded driving data is disclosed. A method of simulating autonomous driving includes receiving recorded driving data from a recorded autonomous vehicle (AV), the recorded driving data includes decision-making data generated using a first decision-making algorithm, sensing data, and movement data including positions of the recorded AV; obtaining simulation data from the recorded driving data by excluding the decision-making data from the recorded driving data; and simulating, by a simulation AV, a second decision-making algorithm using the simulation data. The simulating the second decision-making algorithm includes determining a first position of the simulation AV at a first time and adjusting a playback speed of the simulation data based on a difference between the first position and a second position of the positions of the recorded AV at the first time.

Abstract: Autonomous driving simulation using recorded driving data is disclosed. A method of simulating autonomous driving includes receiving recorded driving data from a recorded autonomous vehicle (AV), the recorded driving data includes decision-making data generated using a first decision-making algorithm, sensing data, and movement data including positions of the recorded AV; obtaining simulation data from the recorded driving data by excluding the decision-making data from the recorded driving data; and simulating, by a simulation AV, a second decision-making algorithm using the simulation data. The simulating the second decision-making algorithm includes determining a first position of the simulation AV at a first time and adjusting a playback speed of the simulation data based on a difference between the first position and a second position of the positions of the recorded AV at the first time.

Abstract: A networked multimedia system comprises a plurality of workstations and at least one storage server. A signal path interconnects the workstations and the storage server. Each workstation includes video and audio reproduction capabilities, as well as video and audio capture capabilities. Any given storage server comprises a set of storage cells that operate under the direction of a storage cell manager. A storage cell may include one or more encoding and/or transcoding converters configured to convert or transform audio and video signals originating at a workstation into a form suitable for storage. A storage cell may further include one or more decoding converters configured to convert stored signals into a form suitable for audio and video signal reproduction at a workstation. Each storage cell additionally includes at least one storage device and storage device controller capable of storing, for later retrieval, signals generated by one or more converters.

Abstract: A video storage environment for a networked multimedia system comprising a plurality of workstations, each with audio and video reproduction and capture capabilities. The video storage environmental comprises one or more storage servers. In one embodiment, each video storage server comprises at least one storage cell. Each storage cell internally comprises at least one storage disk, at least one converter, and at least one storage disk, at least one converter, and at least one storage cell manager. The invention provides for various methods of resource allocation under various conditions and policies where there are pluralities of like entities. The invention also provides for transfers of video files among storage disks anywhere in the storage environment, and provides for the same video file to be used simultaneously by multiple users and types of applications.