Nissan North America, Inc.

United States of America As Represented by the Administrator of Nasa

Renault S.a.s.

University of Massachusetts

Florida Institute for Human and Machine Cognition, Inc.

Liam Pedersen

Maarten Sierhuis

Mauro Della Penna

Kyle Hollins Wray

Hans Utz

Mark Allan

Eric Schafer

Christopher Ostafew

Ali Mortazavi

Terrence Fong

Maria Bualat

Stefan Witwicki

Najamuddin Mirza Baig

Richard Lui

Fang-Chieh Chou

Viju James

Shlomo Zilberstein

Atsuhide Kobashi

Jeffrey M Bradshaw

Matthew Johnson

Lawrence K Bunch

Michael Vignati

Siddharth Thakur

David Ilstrup

Armelle Guerin

Abdul Rahman Kreidieh

Qizhan Tam

Simon Tien

John Deniston

Jen Hamon

Sannidhi Jalukar

David Lees

Richard Enlow

Dawn Wheeler

Andrea Angquist

Richard Salloum

Stephen Wu

Ben Christel

Shane Hogan

Yue Zhao

Erik Vinkhuyzen

Therese Cypher-Plissart

Katsuhiko Hirayama

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 system receives GPS data and on-board sensor data from several connected vehicles indicating locations of nearby vehicles and objects. The system processes the data to create a shared-world model that includes locations and velocities of the connected vehicles and nearby vehicles and objects, and the system determines whether driving hazards exist, such as potential collisions. The system may transmit an alert to at least one of the connected vehicles, to cause the connected vehicle or a mobile device to present a warning message to a driver, such as a visual, audio, or haptic message, or to cause the connected vehicle to implement an action to avoid the driving hazard, such as activating emergency braking or altering course. The system may create, and transmit to a connected vehicle or mobile device, a lane-level traffic model indicating traffic density, traffic speed, and traffic throughput.

Real-time traffic condition warning system

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

A control signal related to a road segment is received by a processor of a vehicle from a remote server. Using at least a portion of the control signal, a cruise control parameter for controlling the vehicle is obtained. Responsive to determining that a cruise control module of the vehicle is enabled, the cruise control module is configured to use the cruise control parameter. The cruise control module operates to maintain the cruise control parameter for the vehicle.

Congestion management with cruise control

A server accesses vehicle data from each connected vehicle (CV) of a subset of a plurality of CVs on a roadway portion, the vehicle data comprising at least one of: a position, a velocity, or a headway. The server generates, based on the accessed vehicle data, a long-term shared world model. The server generates, using the long-term shared world model, a data structure representing predicted future velocities on the roadway portion by position and time by applying a traffic flow model to the long-term shared world model. The server transmits, to a connected autonomous vehicle (CAV), a control signal for controlling operation of the CAV based on the generated data structure.

Generation and transmission of control commands for connected vehicles based on predicted future flow, average velocity, or future density of traffic

Vehicle guidance with systemic optimization may include traversing, by a current vehicle, a vehicle transportation network, by obtaining, by the current vehicle, systemic-utility vehicle guidance data for a current portion of the vehicle transportation network and traversing, by the current vehicle, the current portion of the vehicle transportation network in accordance with the systemic-utility vehicle guidance data. Obtaining the systemic-utility vehicle guidance data may include obtaining vehicle operational data for a region of a vehicle transportation network, wherein the vehicle operational data includes current operational data for a plurality of vehicles operating in the region, operating a systemic-utility vehicle guidance model for the region, obtaining systemic-utility vehicle guidance data for the region from the systemic-utility vehicle guidance model in response to the vehicle operational data, and outputting the systemic-utility vehicle guidance data to the current vehicle.

Vehicle guidance with systemic optimization

Intersection collision avoidance may use a cellular transceiver for a cellular network and a processor located at a cellular access point for multi-access edge computing. The processor includes a shared world model and a conflict detection module. The shared world model is configured to receive, by the cellular transceiver, signals relating to at least two road users in proximity to an intersection within a vehicle transportation network, wherein the at least two road users include an ego vehicle, and the signals conform to a standards-based communication protocol. The conflict detection module is configured to receive object information from the shared world model, determine a potential future collision between the ego vehicle and an other road user of the at least two road users, and transmit a notification of the potential future collision to the ego vehicle over the cellular network.

System and method for intersection collision avoidance

An intersection collision avoidance system determines, for an ego vehicle, a direction indicated by its turn signal, its destination setting, or both, generates, where the direction is determined, a possible intended path relative to an intersection using a high-definition (HD) map and the direction, and generates, where the destination setting is determined, a possible intended path for using the HD map and the destination setting. Where the direction and the destination setting are both determined, the direction indicated by the turn signal is compared to the possible intended path generated using the destination setting, and one of the possible intended paths is selected based on the comparison. The system transmits, to a conflict detection module, a set of drive goals for the ego vehicle relative to the intersection that conforms to the intended path. The module can determine a possible collision with another road user using the drive goals.

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 vehicle operational management may include traversing, by an autonomous vehicle, a vehicle transportation network. Traversing the vehicle transportation network may include operating a scenario-specific operational control evaluation module instance, wherein the scenario-specific operational control evaluation module instance is an instance of a scenario-specific operational control evaluation module, wherein the scenario-specific operational control evaluation module implements a partially observable Markov decision process. Traversing the vehicle transportation network may include receiving a candidate vehicle control action from the scenario-specific operational control evaluation module instance, and traversing a portion of the vehicle transportation network based on the candidate vehicle control action.

Autonomous vehicle operational management including operating a partially observable Markov decision process model instance

Batch routing of autonomous vehicles is disclosed. A method may include maintaining a plurality of vehicle state data object instances that respectively correspond to a plurality of autonomous vehicles. The method includes obtaining a traffic condition location and determining a group of autonomous vehicles from the plurality of autonomous vehicles where the current route of each autonomous vehicle intersects the traffic condition location. The method also includes determining two or more subgroups of autonomous vehicles, and for each subgroup, determining a respective avoidance waypoint corresponding to the subgroup that is not on the current route of the autonomous vehicle. For each autonomous vehicle in the subgroup, the method includes determining an updated route based on the current route of the autonomous vehicle and the respective avoidance waypoint, generating a control instruction based on the updated route, and transmitting the control instruction to the autonomous vehicle.

Computing framework for batch routing of autonomous vehicles

Methods and systems for remote support of autonomous operation of vehicles have been disclosed. State indicators are generated by a first state display based on state data from a portion of vehicles assigned to a respective first level control station. A second state display is generated for a second control station and displays state indicators for the state data of the vehicles. A remote support interface including the first state display and image data received from a first vehicle of the vehicles is generated. Instruction data to the first vehicle is transmitted using the remote support interface and based on an indication that the first vehicle needs remote support, the instruction data modifying the autonomous operation of the first vehicle. A workload between the first level control stations is allocated by assigning the vehicles using the state indicators of the second state display.

Autonomous vehicle remote support mapping interface

A remote system for an autonomous vehicle, includes a receiver, a controller, and a display device. The receiver is configured to receive road information. The controller is programmed to receive input related to the road information and create a supervision zone when the road information impacts road drivability. The display device is disposed at a control center area and configured to display a visual indication on a map of the supervision zone.

Remote system for an autonomous vehicle

Growing community of inventors

San Francisco, CA, United States of America

Liam Pedersen