Nissan North America, Inc.

Renault S.a.s.

Richard Lui

Kyle Hollins Wray

Liam Pedersen

Christopher Ostafew

Stefan Witwicki

Mauro Della Penna

Ali Mortazavi

David Ilstrup

Engine activation planning in a hybrid electric vehicle (HEV) is disclosed. Historical driving data of the HEV are analyzed to identify recurring driving scenarios and patterns specific to a driver of the HEV. Future driving scenarios are predicted based on the historical driving data. An engine activation policy is generated for the HEV. The engine activation policy optimizes battery charging and usage in response to the future driving scenarios. The engine activation policy is used to control activation of a gasoline engine in the HEV where a control decision is dynamically adjusted based on a comparison of real-time driving conditions with the future driving scenarios.

Navigation map learning for intelligent hybrid-electric vehicle planning

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.

Intelligent engine activation planner

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.

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.

Route planner optimization for hybrid-electric vehicles

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.

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.

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Sunnyvale, CA, United States of America

Richard Lui