Nissan North America, Inc.

University of Massachusetts

Renault S.a.s.

United States of America As Represented by the Administrator of Nasa

Stefan Witwicki

Kyle Hollins Wray

Shlomo Zilberstein

Liam Pedersen

Christopher Ostafew

Omar Bentahar

Erik St Gray

Kuniaki Noda

Najamuddin Mirza Baig

Atsuhide Kobashi

Astha Vagadia

Arec Jamgochian

David Ilstrup

Nikhil Gowda

Erik Vinkhuyzen

Laura Cesafsky

Sparsh Sharma

Mark Bailey

Connor Basich

Kevin Kochever

Julius S Gyorfi

Melissa Cefkin

Ali Mortazavi

Viju James

Richard Lui

Erik Stayton

Marcell Vazquez-Chanlatte

Justin Svegliato

Luis Lorenzo Bill Clark

Hillary Abraham

Luis Lorenzo Bill

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

A vehicle includes a vehicle pedal, a vehicle engine and an electronic control unit. The vehicle engine generates a torque output of the vehicle in accordance with an operation of the vehicle pedal. The electronic control unit controls the vehicle pedal between a normal state, an increased resistance state and a decreased resistance state. The vehicle pedal is less sensitive to driver pressure in the increased resistance state. The vehicle pedal is more sensitive to driver pressure in the decreased resistance state.

Temporary torque control system

Systems and methods for adjusting vehicle components based on audible commands are disclosed herein. In an embodiment, the system includes a plurality of adjustable vehicle components, an audio device, and a controller. The audio device is configured to receive an audible command and generate corresponding command data. The controller is programmed to generate at least one confidence score based on the command data and (i) cause a first response to be output from the audio device after determining that the at least one confidence score does not meet a first threshold, (ii) cause a second response to be output from the audio device after determining that the at least one confidence score does not meet a second threshold, and (iii) cause an adjustment of at least one adjustable vehicle component after determining that the at least one confidence score meets the second threshold.

Systems and methods for responding to audible commands and/or adjusting vehicle components based thereon

Autonomous vehicle operations use learned constraints over beliefs to traverse a vehicle transportation network. Sensor data and user demonstration data are used to determine a belief path using a partially observable Markov decision process (POMDP) model. The belief path can be updated based on the learned constraints. Candidate actions are determined based on the POMDP model. The candidate actions are constrained by the updated belief path. An action is selected, and the vehicle traverses the vehicle network using the selected action.

Learning constraints over beliefs in autonomous vehicle operations

A system may detect a sound associated with a vehicle. The sound may be detected using a set of one or more microphones. The system may use a machine learning model to detect at least one of a squeak or a rattle based on the sound. The machine learning model may be trained using a plurality of sounds emitted in relation to one or more vehicles. The system may determine, based on detecting at least one of the squeak or the rattle, a location of the vehicle associated with at least one of the squeak or the rattle. In some implementations, the location may be determined based on detecting different sound levels between a first microphone and a second microphone, or detecting a time difference between the first and the second microphone, or by correlating the sound to a sound signature associated with a part of the vehicle.

Determining a location of a squeak or rattle associated with a vehicle

A first method includes detecting, based on sensor data, an environment state; selecting an action based on the environment state; determining an autonomy level associated with the environment state and the action; and performing the action according to the autonomy level. The autonomy level can be selected based at least on an autonomy model and a feedback model. A second method includes calculating, by solving an extended Stochastic Shortest Path (SSP) problem, a policy for solving a task. The policy can map environment states and autonomy levels to actions and autonomy levels. Calculating the policy can include generating plans that operate across multiple levels of autonomy.

Online state space refinement for competence-aware systems

Vehicle control using map-based braking includes receiving, while a driver is operating the vehicle to traverse a vehicle transportation network, vehicle operation information including at least a current speed of the vehicle, retrieving, from a planned path of an in-vehicle navigation system, an upcoming turn in a current road when the driver is using the in-vehicle navigation system, and retrieving from map data, the upcoming turn in the current road when the driver is not using tin-vehicle navigation system. Thereafter, it is determined whether, during the upcoming turn, wheels of the vehicle will maintain contact with a road surface at the current speed. A braking instruction is issued to a control system of the vehicle responsive to whether the wheels of the vehicle will maintain contact with the road surface at the current speed.

Map-based braking

An occlusion is identified in a vehicle transportation network. A visibility grid is identified on a second side of the occlusion for a vehicle that is on a first side of the occlusion. The visibility grid is identified with respect to a region of interest that is at least a predefined distance above ground. The visibility grid is used to identify first portions of roads sensed by a sensor positioned on the vehicle and second portions of the roads that are not sensed by the sensor. A driving behavior of the vehicle is altered based on the visibility grid.

3D occlusion reasoning for accident avoidance

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

A vehicle includes an on-board satellite navigation device, a telematics control unit, a non-transitory computer readable medium, an electronic display device, and a processor. The on-board satellite navigation device is in communication with a global positioning system unit to acquire real-time information regarding conditions near the vehicle's vicinity. The telematics control unit is in wireless communications to at least one of a cloud services and a vehicle network to upload and receive crowdsourced information regarding conditions near the vehicle's vicinity. The non-transitory computer readable medium stores predetermined information regarding conditions near the vehicle vicinity. The processor is programmed to control the electronic display device to display notification data regarding the vehicle vicinity based on one or more of the real-time information, the crowdsourced information and the predetermined information.

Vehicle notification system

An automated vehicle system includes an information system, data storage, an electronic controller and a notification device. The information system is configured to receive information related to an environment of the automated vehicle. The data storage has a predetermined socially acceptable behavior parameter stored therein. The electronic controller is configured to compare the information to the predetermined socially acceptable behavior parameter and determine vehicle behavior based on the information. The notification device is configured to present a notification of a course of action based on the determined vehicle behavior.

Automated vehicle system

A first distinct vehicle operational scenario is identified for an autonomous vehicle (AV). A first set of candidate vehicle control actions are received from a model that provides a first solution to the first distinct vehicle operational scenario. An action is selected from the first set of candidate vehicle control actions. The AV is controlled based on the action. The first solution is obtained offline in a first idealized situation that is decoupled from a current context of the AV.

Risk-aware executor with action set recommendations

Lane-level route planning includes obtaining lane-level information of a road, where the road includes a first lane and a second lane and the lane-level information includes first lane information related to the first lane and second lane information related to the second lane; converting the lane-level information to probabilities for a state transition function; receiving a destination; and obtaining a policy as a solution to a model that uses the state transition function.

Learning in lane-level route planner

Providing explanations in route planning includes determining a route based on at least two objectives received from a user, where a second objective of the at least two objectives is constrained to within a slack value of a first objective of the at least two objectives; receiving, from the user, a request for an explanation as to an action along the route; and providing the explanation to the user. The explanation describes an extent of violating the slack value.

Explainability and interface design for lane-level route planner

Real-time decision-making for a vehicle using belief state determination is described. Operational environment data is received while the vehicle is traversing a vehicle transportation network, where the data includes data associated with an external object. An operational environment monitor establishes an observation that relates the object to a distinct vehicle operation scenario. A belief state model of the monitor computes a belief state for the observation directly from the operational environment data. The monitor provides the computed belief state to a decision component implementing a policy that maps a respective belief state for the object within the distinct vehicle operation scenario to a respective candidate vehicle control action. A candidate vehicle control action is received from the policy of the decision component, and a vehicle control action is selected for traversing the vehicle transportation from any available candidate vehicle control actions.

Growing community of inventors

San Carlos, CA, United States of America

Stefan Witwicki