Nissan North America, Inc.

Renault S.a.s.

David Ilstrup

Christopher Ostafew

Kyle Hollins Wray

Sachin Hagaribommanahalli

Stefan Witwicki

Liam Pedersen

Richard Lui

Atsuhide Kobashi

Qizhan Tam

Therese Cypher-Plissart

Samer Nashed

Thiago Aguiar

Luis Lorenzo Bill

Luis Lorenzo Bill Clark

Decision-making for a vehicle uses a data determining interface between perception and decision-making. The interface receives, from at least two data sources, operational environment data representing objects external to the vehicle while the vehicle is traversing a vehicle transportation network. The operational environment data is modified to determine output data for data types needed to determine a candidate vehicle control action responsive to the distinct vehicle operation scenario identified using the operational environment data. The solution is the same candidate vehicle control action for the same conditions regardless of the data types available from the data sources. That is, even where different data sources are available, consistent output is provided to a decision-making system. Consistent decision-making results whether the vehicle has a high-definition map, a standard-definition map, or no map as a data source, for example.

Data determining interface for vehicle decision-making

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

Categorizing driving behaviors of other road users includes maintaining a first history of first lateral-offset values of a road user with respect to a center line of a lane of a road; determining a first pattern based on the first history of the first lateral-offset values; determining a driving behavior of the road user based on the first pattern; and autonomously performing, by a host vehicle, a driving maneuver based on the driving behavior of the road user. The first history can be maintained for a predetermined period of time. An apparatus includes a processor that is configured to track a trajectory history of a road user; determine, based on the trajectory history, a driving behavior of the road user; and transmit a notification of the driving behavior.

Road user categorization through monitoring

Detecting prediction errors includes detecting a road user; determining respective predicted data for the road user; storing, in a data structure, the respective predicted data; storing, in the data structure, actual data of the road user; obtaining an average prediction displacement error using at least one of the actual data and at least two corresponding respective predicted data; and determining a prediction accuracy based on the average prediction displacement error. Detecting map errors includes detecting a road user; storing, in a data structure, actual data of the road user; storing, in the data structure, map data corresponding to the actual data; obtaining an average map displacement error based on a comparison of at least some of the actual data and corresponding at least some map data; and determining a map accuracy based on the average map displacement error.

Autonomous vehicle control and map accuracy determination based on predicted and actual trajectories of surrounding objects and vehicles

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.

A vehicle receives sensor data from at least one of its sensors as it approaches an intersection and determines whether a traffic flow control device for the intersection is detected. When detected, a detected type, a detected state, or both of the traffic flow control device is determined. Using a type of the intersection, at least one of an existing type or an existing state of the traffic flow control device is determined, where the traffic flow control device is undetected or the detected type, the detected state, or both are determined with a detection confidence less than a defined level of detection confidence. The traffic flow control device is tagged with a label including its location and existing type, the existing state, or both within at least one control system for the vehicle. The vehicle is operated within vehicle transportation network using a control system that incorporates the label.

Annotation and mapping for vehicle operation in low-confidence object detection conditions

An apparatus for traveling through a transportation network performs a method including generating a display that includes a geographical area about a starting location for route assistance through the transportation network and a virtual vehicle at the starting location, and forming a virtual path for the route assistance using the virtual vehicle. The virtual path includes a first portion obtained from advancing the virtual vehicle from the starting location while an autonomous vehicle (AV) is at the starting location and a second portion obtained from, after the virtual vehicle departs from the starting location, extrapolating from the virtual vehicle through the geographical area to a stopping location or an ending location of the route assistance. Points along the virtual path are continually transmitted to a trajectory planner of the AV while the virtual vehicle advances through the geographical area, and the trajectory planner generates a route for the AV conforming to the virtual path.

Safety-assured remote driving for autonomous vehicles

Traversing a vehicle transportation network, by a vehicle, may include determining vehicle operational information, determining a metric location estimate for the vehicle using the vehicle operational information, determining operational environment information of a portion of the vehicle transportation network, determining a topological location estimate for the vehicle within the vehicle transportation network using the metric location estimate and the operational environment information, and traversing the vehicle transportation network based on the topological location estimate for the vehicle. The operational environment information can include sensor data of a portion of the vehicle transportation network that is observable to the vehicle. To determine the metric location estimate, a non-linear loss function with a Kalman filter may mitigate effects of un-modeled sensor error(s). Techniques using Hidden Markov Models and the Earth Mover's Distance to determine the topological location estimate are also described.

Growing community of inventors

Santa Cruz, CA, United States of America

David Ilstrup