Abstract: In one embodiment, a system for correcting parallax error is provided. An image is received from a camera and a plurality of points is received from a LiDAR sensor. The points are placed on the image based on coordinates associated with each point. The image is divided into a plurality of cells by placing a grid over the image. For each cell, a minimum distance between the points in the cell and the camera is determined. For each cell, a margin is calculated based on the determined minimum distance. For each cell, points that have a distance from the camera that is greater than the minimum distance plus the margin are removed or deleted. The image and/or the remaining points are then used to provide one or more vehicle functions.

Abstract: Systems and methods to systematically identify and collect operational vehicle data for selective transmission to a non-local storage location for further analysis and use in training autonomous and semi-autonomous vehicles are provided. The systems and methods provided overcome limitations in storage and transmission of collected data by selectively archiving only that collected driving data warranting further analysis.

Abstract: A multi-part steering apparatus includes an outer rim and an inner hub. The outer rim rotates relative to the inner hub with an adjustable rotation resistance. A rotatable mechanical interface connected the outer rim to the inner hub, and a damper connects to the rotatable mechanical interface. The damper is configured to change the rotation resistance between the outer rim and inner hub. The rotatable mechanical interface may include a gear track disposed on an inside portion of the outer rim and a gear system attached to the inner hub and positioned to mesh with the gear track. The damper may include an electric motor connected to the rotatable mechanical interface and an adjustable load connected to terminals of the electric motor.

Abstract: System, methods, and other embodiments described herein relate to selectively intervening in manual control of a vehicle by a driver. In one embodiment, a method includes predicting a future state of the vehicle according to at least a current state and a control input. The current state defines at least one attribute of a current trajectory of the vehicle, and the control input defines at least one driver input for controlling the vehicle. The method includes comparing the future state with a state constraint indicating a range within which a target path of the vehicle is acceptable. The target path defines a subsequent trajectory for the vehicle. The method includes selectively modifying the target path according to whether the future state violates the state constraint. The method includes controlling the vehicle according to the target path.

Abstract: A method and system for generating and outputting a targeting warning in association with a road agent is provided. The method includes detecting, by a sensor operating in conjunction with a computing device of a vehicle, a road agent at a distance from the vehicle, analyzing, by the computing device of the vehicle, one or more characteristics of the road agent at the distance, generating, by the computing device, a targeted warning based on analyzing the one or more characteristics of the road agent at the distance, and outputting, by a component operating in conjunction with the computing device of the vehicle, the targeted warning in association with the road agent at the distance.

Abstract: A method for an online mapping system includes localizing a location of an ego vehicle relative to an offline feature map. The method also includes querying surrounding features of the ego vehicle based on the offline feature map. The method further includes generating a probabilistic map regarding the surrounding features of the ego vehicle queried from the offline feature map.

Abstract: The systems and methods described herein disclose detecting events in a vehicular environment using vehicle behavior. As described here, vehicles, either manual or autonomous, that detect an event in the environment will operate to respond to the event. As such, those movements can be used to determine if an event has occurred, even if the event cannot be determined directly. The systems and methods can include collecting detection data about a vehicle behaviors in a vehicular environment. Event behaviors can then be selected from the vehicle behaviors. A predicted event can be formulated based on the event behaviors. The predicted event and an event location can be associated in the vehicular environment. A guidance input can then be formulated for a recipient vehicle. Finally, a recipient vehicle can be navigated using the guidance input.

Abstract: A method for performing vehicle taillight recognition is described. The method includes extracting spatial features from a sequence of images of a real-world traffic scene during operation of an ego vehicle. The method includes selectively focusing a convolutional neural network (CNN) of a CNN-long short-term memory (CNN-LSTM) framework on a selected region of the sequence of images according to a spatial attention model for a vehicle taillight recognition task. The method includes selecting, by an LSTM network of the CNN-LSTM framework, frames within the selected region of the sequence of images according to a temporal attention model for the vehicle taillight recognition task. The method includes inferring, according to the selected frames within the selected region of the sequence of images, an intent of an ado vehicle according to a taillight state. The method includes planning a trajectory of the ego vehicle from the intent inferred from the ado vehicle.

Abstract: Systems, methods, and vehicle for controlling a vehicle's adaptive window transparency with respect to its autonomous operation are disclosed. In one embodiment, a method of controlling adaptive window transparency includes calculating a buffer period having a start time and an end time. The method also includes adjusting transparency on a set of windows based on the start time of the buffer period and transferring control of the vehicle to the vehicle or driver based on the end time. In response to detecting an emergency, the method further includes removing all tinting on the set of windows. In another embodiment, a method of controlling adaptive window transparency includes calculating a buffer period based on a navigation route.

Abstract: Systems and methods for tracking objects are disclosed herein. In one embodiment, a system having a processor merges features of detected objects extracted from a point cloud and a corresponding image to generate fused features for the detected objects, generates a learned distance metric for the detected objects using the fused features, determines matched detected objects and unmatched detected objects, applies prior tracking identifiers of the detected objects at the prior time to the matched detected objects, determines a confidence score for the fused features of the unmatched detected objects, and applies new tracking identifiers to the unmatched detected objects based on the confidence score.

Abstract: System, methods, and other embodiments described herein relate to improving clustering of points within a point cloud. In one embodiment, a method includes grouping the points into cells of a grid. The grid divides an observed region of a surrounding environment associated with the point cloud into the cells. The method includes computing feature vectors for the cells that use cell features to characterize the points in the cells and relationships between the cells. The method includes analyzing the feature vectors according to a clustering model to identify clusters for the cells. The clustering model evaluates the cells to identify which of the cells belong to common entities. The method includes providing the clusters as assignments of the points to the entities depicted in the point cloud.

Abstract: A method for controlling a robotic device includes monitor an action of an operator of the robotic device. The method also includes inferring an intended target based on the monitored action. The method further includes determining an intended action for the intended target. The method still further includes controlling the robotic device to perform the intended action.

Abstract: Systems and methods for generating a requested image view are disclosed. Exemplary implementations may: electronically store map information and contextual information for an area; receive a query for the requested image view; determine, based on the parameter values specified by the query and the map information, values of the physics-based metric; translate the contextual information to a translated representation of the contextual information; encode, based on the translated representation of the contextual information and the values of the physics-based metric, an image file that defines the requested image view such that the translated representation of the contextual information and the values of the physics-based metric are combined; and generate the requested image view by decoding the image file.

Abstract: A method for controlling a robotic device based on observed object locations is presented. The method includes observing objects in an environment. The method also includes generating a probability distribution for locations of the observed objects. The method further includes controlling the robotic device to perform an action in the environment based on the generated probability distribution.

Abstract: A method for controlling a robotic device is presented. The method includes capturing an image corresponding to a current view of the robotic device. The method also includes identifying a keyframe image comprising a first set of pixels matching a second set of pixels of the image. The method further includes performing, by the robotic device, a task corresponding to the keyframe image.

Abstract: A method for controlling a robotic device includes observing a first object associated with an object type at one or more first locations in an environment over a period of time prior to a current time. The method also includes generating a probability distribution associated with the one or more first locations based on observing the first object over the period of time. The method further includes observing, at the current time, a second object associated with the object type at a second location in the environment. The method still further includes determining a probability of the second object being at the second location based on observing the second object at the second location. The probability is based on the probability distribution associated with the one or more first locations. The method also includes controlling the robotic device to perform an action based on the probability being less than a threshold.

Abstract: System, methods, and other embodiments described herein relate to improving the training of a driver during automated driving system mode. In one embodiment, a method includes generating, in association with a vehicle takeover and a maneuver by the driver, an automated motion plan associated with the maneuver. The method also includes determining if a difference parameter satisfies a threshold, wherein the difference parameter indicates a disparity between the maneuver by the driver in relation to the automated motion plan associated with the maneuver. The method also includes notifying, if the difference parameter does not satisfy the threshold, the driver that the vehicle takeover and the maneuver by the driver were unnecessary.

Abstract: A method for controlling a vehicle based on a driver-centric model is presented. The method includes generating a trajectory for the vehicle and receiving an input from a driver. The method also includes generating a velocity profile and an acceleration profile based on a combination of the trajectory and the input. The method further includes controlling the vehicle based on the velocity profile and the acceleration profile.

Abstract: A method is presented. The method includes estimating an ego-motion of an agent based on a current image from a sequence of images and at least one previous image from the sequence of images. Each image in the sequence of images may be a two-dimensional (2D) image. The method also includes estimating a depth of the current image based the at least one previous image. The estimated depth accounts for a depth uncertainty measurement in the current image and the at least one previous image. The method further includes generating a three-dimensional (3D) reconstruction of the current image based on the estimated ego-motion and the estimated depth. The method still further includes controlling an action of the agent based on the three-dimensional reconstruction.

Abstract: A vehicle system includes a steering wheel configured to output an output based on an input from a user, and a controller configured to: determine a target orientation of front wheels of a vehicle based on vehicle environment information, determine whether an orientation of the front wheels of the vehicle based on the output from the steering wheel deviates from the target orientation, disengage the steering wheel from the front wheels in response to determination that the orientation of the front wheels deviates from the target orientation, adjust the orientation of the front wheels to the target orientation and provide a feedback in response to adjusting the orientation of the front wheels to the target orientation.