Abstract: Prioritizing essential information for display within a vehicle notification system includes detecting a change in a traffic scene. The traffic scene encompasses a portion of a vehicle transportation network. In response to detecting the change in the traffic scene, it is determined that the traffic scene requires more than a defined level of operator engagement. In response to determining that the traffic scene requires more than the defined level of operator engagement, the traffic scene is stored to a traffic scene storage location. Further, it is determined that an operator is not aware of the traffic scene. Responsive to determining that the operator is not aware of the traffic scene, a control system of a vehicle notifies the operator that the traffic scene requires more than the defined level of operator engagement.

Abstract: Vehicle forgotten item detection using depth aided image background removal includes determining a first image of an interior of a vehicle for a start of a time-period and a second image for the interior of the vehicle for an end of the time-period. The first image and the second image include a depth metric. One or more elements present in both the first image and the second image are removed from the second image. One or more remaining elements are determined to exist within the second image after removal, which are categorized. An occupant of the vehicle is selectively notified based on the categorization.

Abstract: A visibility grid is determined for at least one external object within an operational environment of an autonomous vehicle. The visibility grid represents areas visible and areas obstructed for the external object. When the autonomous vehicle is positioned within an occluded region relative to the visibility grid of the external object is identified. Driving parameters of the autonomous vehicle are altered in response to being in the occluded region to avoid collision with the external object.

Abstract: A video of a driver of a vehicle is obtained. Based on subset of frames of the video, a series of body poses are identified. The series of body poses are identified by detecting landmark points associated with respective body parts of the driver. The landmark points correspond to coordinates of locations of pixels that represent at least one or more joints of the respective body parts of the driver in the subset of frames. A driver behavior is identified based on the series of the body poses. An assistive vehicle control action for the vehicle is output based on the driver behavior.

Abstract: 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.

Abstract: 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.

Abstract: A system and method of transitioning control of a vehicle from an autonomous control state to a manual control state is provided. A transition point is determined at which control of the vehicle changes from the autonomous control state to the manual control state. At least one task is presented to a driver in advance of the transition point. Whether the response of the driver to the at least one task indicates preparedness of the driver is determined. Control of the vehicle is transitioned from the autonomous control state to the manual control state when the driver is determined to be prepared. The transition from the autonomous control state to the manual control state is prevented when the driver is determined to not be prepared.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Road feel is created in a vehicle that has a steer-by-wire system using image data obtained from one or more cameras on the vehicle and image data obtained from a light detection and ranging (LIDAR) sensor. A generative adversarial network (GAN) machine learning (ML) model is used to determine a road surface based on the image data from the one or more cameras and the image data from the LIDAR sensor. Haptic vibrations are generated based on the determined road surface to create the road feel using one or more motors on various vehicle components.

Abstract: A method of delivering data to a vehicle including transmitting a request for the data from a vehicle to a database. The data stored in the database is tagged with location information. A location of the vehicle is determined. The data in the database tagged with location information within a predetermined distance of the determined location of the vehicle is determined. The data is delivered to the vehicle based on the determined location of the vehicle. The delivered data is displayed on a display disposed in the vehicle.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A method of displaying road information in a host vehicle includes receiving with the host vehicle the road information acquired by another vehicle. The road information includes information about a portion of a road ahead of a current portion of the road upon which the host vehicle is traveling. An information presentation time is determined at which the road information is configured to be presented in the host vehicle. The road information is presented in the host vehicle at the determined information presentation time.

Abstract: 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.

Abstract: 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.

Abstract: 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.