Abstract: An automobile vehicle visual wireless-based positioning system, incudes an automobile vehicle having a radio receiver. A map contains candidate locations of access-points (APs) and access-point corresponding media-access-control (MAC) IDs. A wireless range sensor determines different ranges of various detected APs visible to the automobile vehicle. An image collection feature identifies image data visible to the automobile vehicle. A real-time feature matching element matching features identified by image collection feature with data from the map. A filter receives an output from the real-time feature matching element to generate an automobile vehicle pose.

Abstract: A system and method of gamified learning for a vehicle is provided. The system includes a perception model configured to receive perception data from a camera, execute a perception task to obtain a task result for the object, determine the perception confidence level of the task result is below a predetermined perception confidence threshold, show an image of the object to an on-scene user, request the on-scene user to annotate the object, and reward the on-scene user in response to receiving the annotation from the on-scene user. The system further includes a server configured to aggregate a plurality of annotations from a plurality of users, apply a probabilistic model to determine a ground truth annotation, and input the ground truth annotation and an image to a machine learning model to update the perception model.

Abstract: A method generating planning-based attention signals includes receiving driving-scene data. The driving-scene data is indicative of a driving scene around a host vehicle. The driving-scene data includes map data and localization data. The driving scene includes a plurality of actors. The method further includes converting the driving-scene data into a scene-graph. The method further includes inputting the scene-graph into a deep neural network (DNN). Further, the method includes determining an attention score for each of the plurality of actors using the DNN and the scene-graph. The attention score of each of the plurality of actors represents a priority given to each of the plurality of actors in the driving scene. The method further includes commanding the host vehicle to autonomously drive according to a trajectory determined by taking into account the attention score of each of the plurality of actors.

Abstract: A scene creation system for an autonomous vehicle includes one or more controllers executing instructions to receive perception data and map data of a roadway the autonomous vehicle is traveling along, and identify a plurality of lane segments of the roadway that the autonomous vehicle travels along based on the perception data and the map data. The one or more controllers connect the plurality of lane segments together based on a spatial relationship between the plurality of lane segments to create a lane graph. The one or more controllers classify each of the plurality of lane segments of the lane graph to one or more lane attributes and reassemble the plurality of lane segments to create a representation of the roadway. The one or more controllers recreate a scene of an environment surrounding the autonomous vehicle based on the representation of the lanes that are part of the roadway.

Abstract: A method for generating garage parking notifications includes receiving current parked-vehicle data indicating that a vehicle has been parked inside a garage. The current parked-vehicle data includes the current location of the vehicle parked inside the garage. The method further includes determining whether the current location of the vehicle inside the garage is within a warning zone. The warning zone is an area inside the garage where the vehicle has not been frequently parked. The warning zone area is determined using historical parked-vehicle data. The method further includes generating a notification to warn an operator of the vehicle that the vehicle should be moved in response to determining that the current location of the vehicle inside the garage is within the warning zone.

Abstract: A method for efficient autonomous driving planning includes receiving a current driving-scene data and a predicted driving-scene data. The current driving-scene data is indicative of a current driving scene around a host vehicle. The predicted driving-scene data is indicative of a predicted driving scene around the host vehicle. The predicted driving scene around the host vehicle is different from the current driving scene around the host vehicle. The method further includes converting the current driving-scene data and the predicted driving-scene data into a first scene-graph and a second scene-graph, respectively. The method further includes determining a plurality of scene change metrics using the first scene-graph and the second scene-graph. The method further includes selecting between a first trajectory planning process and a second trajectory planning process based on the plurality of scene change metrics.

Abstract: A monocular camera system for a vehicle includes a front mono camera, a side mono camera, and one or more controllers in electronic communication with the front mono camera and the side mono camera. The one or more controllers execute instructions to determine an ideal lagged distance the vehicle travels between a current time step and a previous time step as two asynchronous camera frames are captured by the front mono camera and the side mono camera, determine a number of delayed frames captured by either the front mono camera or the side mono camera between the current time step and the previous time step based on the ideal lagged distance. The controllers determine a direction of travel of the vehicle that indicates which mono camera is selected to provide a previous camera frame captured at the previous time step.

Abstract: A monocular camera system for a vehicle includes a front mono camera, a side mono camera, and one or more controllers in electronic communication with the front mono camera and the side mono camera. The one or more controllers execute instructions to determine an ideal lagged distance the vehicle travels between a current time step and a previous time step as two asynchronous camera frames are captured by the front mono camera and the side mono camera, determine a number of delayed frames captured by either the front mono camera or the side mono camera between the current time step and the previous time step based on the ideal lagged distance. The controllers determine a direction of travel of the vehicle that indicates which mono camera is selected to provide a previous camera frame captured at the previous time step.

Abstract: A crowd-sourced road sign interpretation system includes one or more central computers in wireless communication with a plurality of vehicles and a network that transmits map data. The one or more central computers execute instructions to perform a statistical hypothesis test of telemetry data collected at one or more trajectory segments including a specific allowed maneuver and the presence of the one or more unidentified road signs, and the telemetry data collected at one or more trajectory segments including the specific allowed maneuver without the presence of the one or more unidentified road sign to determine a statistical impact of the presence of the one or more unidentified road signs upon the specific allowed maneuver.

Abstract: A method can be used to provide smart notifications to avoid collisions while the vehicle maneuvers in a tight structural environment, such as a home garage or an underground parking lot. The method includes receiving historical vehicle-trajectory data. The historical vehicle-trajectory data includes the location and the heading of the vehicle for each of the plurality of historical trajectories along the structure. The method further includes clustering the plurality of historical trajectories of the vehicle along the structure by types of maneuvers to generate a plurality of trajectory clusters. The method also includes creating a probability distribution bitmap using the plurality of trajectory clusters and creating a topographic map based on the probability distribution bitmap.

Abstract: A road sign interpretation system includes a perceptual cache storing perceptual hash values that each correspond to image data representing a road sign. Each road sign that corresponds to one of the perceptual hash values stored in the perceptual cache is associated with a road sign identifier. The road sign interpretation system includes one or more controllers in electronic communication with the perceptual cache execute instructions to compute a perceptual hash of a detected road sign within a cropped image frame based on a perceptual hash function and identify a near match between the perceptual hash of the detected road sign and a selected perceptual hash value stored in the perceptual cache. In response to identifying the near match, the controllers interpret the detected road sign represented by the perceptual hash based on the road sign identifier associated with the selected perceptual hash value stored in the perceptual cache.

Abstract: A system of a motor vehicle includes a radar unit for generating a radar signal associated with a target positioned about the vehicle. The system further includes a tracker generating a tracker signal associated with a radar heading and a doppler based on the radar signal. The system further includes a wheel speed sensor for generating a wheel speed signal associated with a velocity of the vehicle. The system further includes a gyroscope for generating a gyro signal associated with a measured yaw rate. The system further includes a computer having a processor and a computer readable medium. The processor is programmed to determine a gyro drift and a corrected yaw rate while the vehicle is in motion, with the gyro drift and the corrected yaw rate being based on at least the radar heading, the doppler effect, and the velocity of the vehicle, and the measured yaw rate.

Abstract: A map updating system for a vehicle includes one or more input devices. The input device generates an input signal associated with data indicative of multiple landmark points relative to multiple road semantic features. The system further includes a computer having one or more processors that receive the input signal. The computer further includes a non-transitory computer readable storage medium for storing instructions. The processor is programmed to build a local map including the road semantic features and the landmark points. The processor is further programmed to determine a radius of road curvature associated with each road semantic feature and compare the radius of road curvature to a maximum radius of curvature threshold. The processor is further programmed to transmit an update signal to a cloud server, in response the processor determining that the radius of road curvature is less than the maximum radius of curvature threshold.

Abstract: A method for generating garage parking notifications includes receiving current parked-vehicle data indicating that a vehicle has been parked inside a garage. The current parked-vehicle data includes the current location of the vehicle parked inside the garage. The method further includes determining whether the current location of the vehicle inside the garage is within a warning zone. The warning zone is an area inside the garage where the vehicle has not been frequently parked. The warning zone area is determined using historical parked-vehicle data. The method further includes generating a notification to warn an operator of the vehicle that the vehicle should be moved in response to determining that the current location of the vehicle inside the garage is within the warning zone.

Abstract: A method for quantifying map errors includes receiving first map data and second map data. The method includes receiving a road topographic map. The method further includes dividing the road into road segments. The method further includes creating a plurality of bounding boxes for each of the plurality of road segments. The method includes creating a first map tile and a second map tile by filtering out the bounding boxes. The method includes executing point cloud registration to align the plurality of first data points in the first map tile with the plurality of second data points in the second map tile to determine a plurality of absolute offsets between the plurality of first data points and the plurality of second data points. The method includes determining a relative map error between the first map and the second map based on the absolute offsets.

Abstract: A perception and map edge association system for an autonomous vehicle includes one or more controllers executing instructions to receive a plurality of perception lane edge candidates that are each representative of a perception lane edge and a plurality of map lane edge candidates that are each representative of a map lane edge. The one or more controllers identify a discrepancy between a selected perception lane edge candidate and a best fit map edge candidate for a lane characteristic based on a posterior association probability matrix and a discrepancy flag. The one or more controllers segment the pair of inconsistent perception and map lane edges into one or more matching segments. The one or more matching segments indicate where the perception lane edge and the map lane edge match one another based on the lane characteristics. The perception and map edge association system improves localization and scene creation performance.

Abstract: A lanelet classification system for an autonomous vehicle includes one or more controllers including a classifier having a neural network that classifies lanelets of a lane graph structure based on one or more lane attributes. In one embodiment, the one or more controllers execute instructions to build the neural network. In another embodiment, the one or more controllers train the neural network to classify each lanelet of the lane graph structure.

Abstract: A lane edge fusion system for an autonomous vehicle includes one or more controllers executing instructions to receive perception data and map data of a roadway the autonomous vehicle is traveling along. The one or more controllers derive a plurality of map lane edge points from the map data and a plurality of perception lane edge points from the perception data and select an evaluation point based on the plurality of map lane edge points and the plurality of perception lane edge points. The one or more controllers fit an implicit function for the evaluation point based on an implicit moving least squares approach. The one or more controllers build a fused lane edge by setting a point on the implicit curve as one of a plurality fused lane edge points.

Abstract: A scene creation system for an autonomous vehicle includes one or more controllers executing instructions to receive perception data and map data of a roadway the autonomous vehicle is traveling along, and identify a plurality of lane segments of the roadway that the autonomous vehicle travels along based on the perception data and the map data. The one or more controllers connect the plurality of lane segments together based on a spatial relationship between the plurality of lane segments to create a lane graph. The one or more controllers classify each of the plurality of lane segments of the lane graph to one or more lane attributes and reassemble the plurality of lane segments to create a representation of the roadway. The one or more controllers recreate a scene of an environment surrounding the autonomous vehicle based on the representation of the lanes that are part of the roadway.

Abstract: A method generating planning-based attention signals includes receiving driving-scene data. The driving-scene data is indicative of a driving scene around a host vehicle. The driving-scene data includes map data and localization data. The driving scene includes a plurality of actors. The method further includes converting the driving-scene data into a scene-graph. The method further includes inputting the scene-graph into a deep neural network (DNN). Further, the method includes determining an attention score for each of the plurality of actors using the DNN and the scene-graph. The attention score of each of the plurality of actors represents a priority given to each of the plurality of actors in the driving scene. The method further includes commanding the host vehicle to autonomously drive according to a trajectory determined by taking into account the attention score of each of the plurality of actors.