Abstract: Systems and methods are provided for implementing personalized adaptive cruise control techniques in connection with, but not necessarily, autonomous and semi-autonomous vehicles. In accordance with one embodiment, a method comprises receiving first vehicle operating data and associated first environmental data of a plurality of vehicles; classifying the first vehicle operating data and the first environmental data into a plurality of driver type classifications; training a control policy model for each driver type classification based on the first vehicle operating data and the first environmental data; receiving a real-time classification of a target vehicle based on second vehicle operating data and associated second environmental data of the target vehicle; and output a trained control policy model the to target vehicle based on the real-time classification of the vehicle, wherein the target vehicle is controlled according to the trained control policy model.

Abstract: A method may include receiving a request for drone-assisted media capture from a vehicle located at a first location, the request specifying one or more user preferences, selecting an unmanned aerial vehicle that is able to perform the drone-assisted media capture based on the user preferences, causing the selected unmanned aerial vehicle to travel to the first location, and causing the selected unmanned aerial vehicle to capture media at the first location based on the user preferences.

Abstract: Systems and methods are provided for utilizing sensor data from sensors of different modalities and from different vehicles to generate a combined image of an environment. Sensor data, such as a point cloud, generated by a LiDAR sensor on a first vehicle may be combined with sensor data, such as image data, generated by a camera on a second vehicle. The point cloud and image data may be combined to provide benefits over either data individually and processed to provide an improved image of the environment of the first and second vehicles. Either vehicle can perform this processing when receiving the sensor data from the other vehicle. An external system can also do the processing when receiving the sensor data from both vehicles. The improved image can then be used by one or both of the vehicles to improve, for example, automated travel through or obstacle identification in the environment.

Abstract: A method comprises determining a vectorized representation of positions of road agents and road geometry based on sensor data from a vehicle, inputting the vectorized representation of the positions of the road agents and the road geometry into a trained transformer network, predicting one or more road agent trajectories at one or more future time steps based on an output of the transformer network, predicting an acceleration of the vehicle at the one or more future time steps based on the predicted one or more road agent trajectories at the one or more future time steps, and causing the vehicle to perform the predicted acceleration at the one or more future time steps.

Abstract: Systems and methods for distributed cooperative localization in a connected vehicular platform are disclosed herein. At a first sensor-rich vehicle, one embodiment receives, from a second sensor-rich vehicle via direct vehicle-to-vehicle (V2V) communication, an estimated location of the first sensor-rich vehicle and an associated confidence level; estimates a location of the second sensor-rich vehicle based on first sensor data and assigns a confidence level to the estimated location; transmits to the second sensor-rich vehicle via direct V2V communication, the estimated location of the second sensor-rich vehicle and the assigned confidence level; accepts, based on communication between the first and second sensor-rich vehicles and predetermined acceptance criteria, an assignment to perform localization for a legacy vehicle; estimates a location of the legacy vehicle based on second sensor data; and transmits, to the legacy vehicle, the estimated location of the legacy vehicle.

Abstract: A method comprises receiving driving data from a plurality of connected vehicles approaching an intersection, the driving data comprising a speed and position of a connected vehicle, determining estimated times of arrival that each of the connected vehicles will arrive at the intersection based on the driving data, scheduling the connected vehicles to cross the intersection in a particular order based on the estimated times of arrival, and transmitting the scheduled order to the connected vehicles.

Abstract: A server includes a controller programmed to obtain information about a first perception model installed in a vehicle. The controller is further programmed to determine a value of updating the first perception model. The controller is further programmed to determine whether the first perception model needs to be updated to a second perception model based on the value of updating the first perception model. The controller is further programmed to transmit the second perception model to the vehicle in response to determining that the first perception model needs to be updated to the second perception model.

Abstract: A method comprises receiving an image of a road captured by a vehicle driving on the road, receiving a map of the road, the map comprising a road geometry of the road, obtaining an edge map of the road based on the image of the road, inputting the image, the map of the road, and the edge map into a trained regressor neural network, determining an estimated snow depth for each of one or more lanes of the road based on an output of the regressor neural network, and transmitting the estimated snow depth to an edge computing device.

Abstract: A method comprises receiving, at a vehicle system of a vehicle, sensor data from one or more sensors; detecting an object based on the sensor data; determining whether a dynamic map maintained by a remote computing device includes the detected object; upon determination that the dynamic map includes the detected object, determining a remaining time-to-live associated with the detected object based on data associated with the dynamic map; and transmitting data about the detected object to the remote computing device if the determined remaining time-to-live associated with the detected object is less than a predetermined threshold time.

Abstract: A vehicle for interpreting a traffic scene is provided. The vehicle includes one or more sensors configured to capture an image of an external view of the vehicle, and a controller. The controller is configured to obtain the captured image of the external view of the vehicle from the one or more sensors, segment a plurality of instances from the captured image, determine relational information among the plurality of instances, and generate a hyper graph including a plurality of nodes representing the plurality of instances and a plurality of edges representing the relational information among the plurality of instances.

Abstract: Candidate routes, from an origination to a destination, can be produced. Information can be obtained. The information can be of likelihoods of encounters, along the candidate routes, with types of driving situations demonstrated to cause changes in a degree of confidence, of an occupant of an autonomous vehicle, that a controller of the autonomous vehicle will be able to control the autonomous vehicle so that a result of the encounters is not a collision. Take-over probabilities can be obtained. The take-over probabilities can be of likelihoods that the occupant, in response to the encounters, will cause control of the autonomous vehicle to be transferred to the occupant. Based on the information and take-over probabilities, a route from the origination to the destination can be selected from the candidate routes. The autonomous vehicle can be caused to be configured to commence to move in a direction in accordance with the route.

Abstract: In accordance with one embodiment of the present disclosure, a system for generating surrounding views includes a controller. The controller may be programmed to perform operations including receiving a status information from a remote source, receiving a surround view system (SVS) request having a set of requirements from a vehicle, identifying one or more remote sources based on the status information of the one or more remote sources and the set of requirements of the SVS request, and directing the one or more remote sources to stream SVS data to the vehicle.

Abstract: System, methods, and embodiments described herein relate to dynamically generating a wide field-of-view three-dimensional pseudo point cloud of an environment around a vehicle. A disclosed method may include capturing, via a camera, a first view in a first image, determining a first depth map based on the first image, obtaining, from an external system, a second image of a second view that overlaps the first view and a second depth map based on the second image, inputting the first image and second image into a self-supervised homograph network that is trained to output a homographic transformation matrix between the first image and the second image, and generating a three-dimensional pseudo point cloud that combines the first depth map and the second depth map based on the homographic transformation matrix.

Abstract: Connected vehicles and methods described herein provide for message sender identification. Connected vehicles and methods disclosed herein can include generating a message based hyper-graph based on the positions of connected elements of a road traffic network as communicated to the connected vehicle by the connected elements. Connected vehicles and methods disclosed herein can include generating, a perception based hyper-graph based on the perceived positions of at least a subset of the elements of the road traffic network as determined by a sensor of the vehicle. Connected vehicles and methods disclosed herein can include matching a node of the message-based hyper-graph to a corresponding node of the perception-based hyper-graph to determine the sender of a received message.

Abstract: Methods, vehicles, and systems described herein generate alerts based on a generated macro state level indicative of a traffic hazard risk in view of a set of parameters which may lead driver to make various sub-conscious decisions. The set of parameters can include at least one of a vehicle parameter, driver state parameter, or external parameter.

Abstract: In one embodiment, a vehicle merge control system generates sensor data that indicate a detected vehicle in an environment of the ego vehicle, identifies a conflict based at least in part on the sensor data indicating that the detected vehicle and the ego vehicle are traveling: 1) in adjacent lanes that will merge into a single lane, and 2) at respective speeds that will result in the detected vehicle entering within a threshold range of the ego vehicle, determines, upon identification of the conflict, merge position assignments for the ego vehicle and the detected vehicle based at least in part on a game theory cost function applied to game theory actions that result in the merge position assignments exclusively being either a lead vehicle or a follower vehicle, and outputs an acceleration rate to achieve the merge position assignment for the ego vehicle.

Abstract: Described are systems and methods for substituting missing graph information caused during the transmission of graph information from one entity to another. In one example, a system includes a processor and a memory with machine-readable instructions that cause the processor to receive an incoming graph stream comprising a plurality of graphs having detected object information and at least one missing graph, transform the plurality of graphs into incoming embedded vectors having values that represent detected object information of the incoming graph stream, provide the embedded vector that corresponds to the at least one missing graph with a missing graph value, and substitute the missing graph value with a substitute value using a recurrent neural network that interpolates the substitute value using the incoming embedded vectors that are not equal to the missing graph value.

Abstract: Systems and methods for maintaining a service session in an automotive edge computing environment are disclosed herein. One embodiment computes a first estimation result pertaining to computation resources of a current edge server with which a vehicle is in communication; computes a second estimation result pertaining to link quality; computes a third estimation result pertaining to service quality; compares the first, second, and third estimation results with first, second, and third predetermined thresholds, respectively; selects a new edge server with which to maintain the service session and initiates a service migration request, when the first predetermined threshold is triggered; initiates a wireless access handover request, when only the second predetermined threshold is triggered; and selects a new edge server with which to maintain the service session and initiates a service migration request, when only the third predetermined threshold is triggered and one of two predetermined conditions is met.

Abstract: Systems and methods for protecting a vehicle at an intersection are disclosed herein. One embodiment detects that the vehicle is stopped at the intersection at one of a first position and a second position; detects that a driver of the vehicle is pressing on an accelerator pedal of the vehicle; delays acceleration of the vehicle automatically by a first predetermined period, when the vehicle has been detected at the first position; and delays acceleration of the vehicle automatically by a second predetermined period, when the vehicle has been detected at the second position. Delaying acceleration of the vehicle automatically prevents the vehicle from being struck by a cross-traffic vehicle that has proceeded through the intersection against a first traffic signal in a red-light state.

Abstract: Driver classification systems and methods are disclosed herein. The driver classification method includes collecting first vehicle driving data from a first vehicle, processing the first vehicle driving data using a driver classification learning model including a machine learning algorithm at one of an edge server and the first vehicle to assign a driver classification to the first vehicle, updating the driver classification learning model based on additional driver classification learning models received from a plurality of additional vehicles, sending the driver classification to an insurance provider, receiving an insurance rate for the first vehicle from the insurance provider based on the driver classification of the first vehicle, and providing the insurance rate to the first vehicle.