Abstract: The subject disclosure relates to techniques for increasing a quality of predicted trajectories output from a trained prediction algorithm. A process of the disclosed technology can include receiving by a trained prediction algorithm, information about objects in an environment as perceived by sensors of an autonomous vehicle, receiving by the trained prediction algorithm, information about a location of the autonomous vehicle in the environment, generating by the trained prediction algorithm, a predicted trajectory for an object among the objects in the environment, wherein the predicted trajectory being anchored by a path through a tree of paths including at least two modes, wherein each mode creates a node in the tree, wherein a mode is a semantic classification of a portion of the predicted trajectory, and outputting by the trained prediction algorithm, the predicted trajectory for the object.

Abstract: The present technology is directed to training and the use of a machine learning model to measure the safety of an autonomous vehicle (AV) driving in simulation. An AV management system can run a simulation of an AV autonomously piloting itself and collect simulation driving data. Further, the AV management system can parse the simulation driving data into kinematic and semantic environmental features and output a simulation safety score of the simulation based on the kinematic and semantic environmental features. The simulation safety score indicates a probability of a safety critical event such as a collision or a near-miss between the AV and the at least one simulated object in the simulation.

Abstract: Examples provide a method that includes providing electric power at a first voltage level for a first auxiliary low voltage bus. The method further includes providing electric power at a second voltage level for a second auxiliary low voltage bus, the first voltage level differing from the second voltage level. The method further includes sensing, by a controller, a first power consumption at the first auxiliary low voltage bus. The method further includes sensing, by the controller, a second power consumption at the second auxiliary low voltage bus. The method further includes calculating, by the controller, a difference between the first power consumption and the second power consumption. The method further includes managing a load in one of the first auxiliary low voltage bus or the second auxiliary low voltage bus based at least in part on the difference between the first power consumption and the second power consumption.

Abstract: A system for providing information to an occupant of a vehicle includes at least one vehicle sensor for determining information about an environment surrounding the vehicle, a display for providing information to the occupant, and a controller in electrical communication with the at least one vehicle sensor and the display. The controller is programmed to identify at least one driving rule associated with at least one portion of the environment surrounding the vehicle using the at least one vehicle sensor, determine an applicability state of the at least one driving rule, where the applicability state includes an applicable state and a non-applicable state, and display a graphic using the display in response to determining that the applicability state of the at least one driving rule is the applicable state.

Abstract: The present disclosure provides a method comprising detecting an event in connection with an autonomous vehicle, wherein the detected event comprises at least one of a current state of the autonomous vehicle, an impending state of the autonomous vehicle, and an intent of the autonomous vehicle; generating an image in connection with the detected event, wherein the image comprises at least one of text information and graphics information; and displaying the generated image on at least one window of the autonomous vehicle for conveying information about the detected event to at least one user located outside the autonomous vehicle.

Abstract: Systems and methods for a vehicle are provided. In one embodiment, a method includes: receiving image data defining a plurality of images associated with an environment of the vehicle; determining, by a processor, feature points within at least one image of the plurality of images; selecting, by the processor, a subset of the feature points as ground points based on a fixed two dimensional image road mask and a three dimensional region; determining, by the processor, a ground plane based on the subset of feature points; determining, by the processor, a ground normal vector from the ground plane; determining, by the processor, a camera to ground alignment value based on the ground normal vector; and generating, by the processor, second image data based on the camera to ground alignment value.

Abstract: Multi-frequency microDoppler fusion is described. An example of a method includes performing a 2D Fourier transform of raw Radar sensor data and raw LiDAR sensor data to generate Radar range-Doppler map data and LiDAR range-Doppler map data; performing a Stationary Wavelet Transform (SWT) calculation of the Radar and LiDAR sensor range-Doppler map data to generate a Radar SWT product and a LiDAR SWT product; performing an SWT product thresholding operation to extract signatures that are common to the Radar SWT product and the LiDAR SWT product; and performing a microDoppler calculation on the extracted common signatures to generate Radar-LIDAR joint fused micro-Doppler signatures.

Abstract: Methods and systems are provided for controlling an autonomous vehicle. In one embodiment, a method includes: identifying, by a processor, at least one constraint on a longitudinal dimension of an upcoming road; defining, by the processor, constraint activation logic based on a type of the at least one constraint; performing, by the processor, the constraint activation logic to determine a state of the constraint to be at least one of active and inactive; when the state of the constraint is active, validating, by the processor, a motion plan of the autonomous vehicle based on the constraint; and selectively controlling the autonomous vehicle based on the validating of the motion plan.

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 method for early detection of a stochastic preignition (SPI) in an internal combustion engine includes monitoring sensor data from a sensor that is coupled to the internal combustion engine of a vehicle, determining whether a SPI event will occur using the sensor data and a Hankel Alternative View of Koopman (HAVOK) model, and commanding the vehicle to take a corrective action before the SPI event occurs to prevent the SPI event from happening in response to determining that the SPI event will occur.

Abstract: A system for transmitting an audio signal in a vehicle includes a stabilizer located at a source position in the vehicle. At least one speaker is operatively connected to the stabilizer. The speaker is adapted to transmit an audio signal to a target position for at least partially supporting a spatial audio representation. The target position and the source position are independently movable. The stabilizer is adapted to direct the audio signal such that a trajectory between the target position and the source position is maintained regardless of respective motion of the target position and the source position. The trajectory includes a first segment between the source position and a deflecting surface and a second segment between the deflecting surface and the target position.

Abstract: Methods and systems for a vehicle are provided. In one embodiment, the method includes: receiving image data defining a plurality of images associated with an environment of the vehicle; determining, by a processor, feature points within at least one image of the plurality of images; selecting, by the processor, a subset of the feature points as ground points; determining, by the processor, a ground plane based on the subset of feature points; determining, by the processor, a ground normal vector from the ground plane; determining, by the processor, the ground normal vector based on a sliding widow method; determining, by the processor, a camera to ground alignment value based on the ground normal vector; and generating, by the processor, second image data based on the camera to ground alignment value.

Abstract: A fleet management system coordinates multiple autonomous vehicles (AVs) to transport a group of passengers from one location to another. A single user can submit a request to the fleet management system to provide a ride for the group, the request specifying the number of passengers and the pickup location. The fleet management system determines a number of AVs to transport the group based on the number passengers, and dispatches this number of AVs to the pickup location. The AVs pick up the passengers at the pickup location, drive the passengers to a destination, and drop the passengers off at the destination.