Abstract: The disclosed technology provides methods for training of a convolutional neural network (CNN) to identify or predict its own errors, and then those errors are used as inputs with feature visualization to generate images of scenes associated with those errors. This allows adjustment of a set of labeled training images, and then the adjusted set of labeled training images are used to retrain or further train the CNN. Systems and machine-readable media are also provided.

Abstract: Apparatus and methods of the present disclosure provide a solution to quickly sort through and identify relevant data sets from large amounts of data collected by numerous vehicles. This process is a form of data mining where a processor executes instructions out of a memory to evaluate sensor data collected by vehicles over time. These vehicles may be driven by person's such that data collected by those sensors would be representative of how a person drives. After data has been acquired, it may be evaluated to identify portions of data where one vehicle encounters another vehicle along a roadway. Data may be classified as being associated with particular types of driving maneuvers and organized into data sets with labels consistent with the classification or driving maneuver. Classifications could correspond to different typed of driving maneuvers, “turning left across traffic,” “all way stop,” “merging onto a highway,” or “lane change”.

Abstract: Systems and methods for collision imminent detection are provided. A method includes collecting sensor data generated by one or more sensors of a vehicle; detecting, by a fallback control system of the vehicle based on the sensor data, an imminent collision between the vehicle and an object in an environment of the vehicle using a first process different from a second process of a primary control system of the vehicle, wherein the first process and the second process are associated with at least one of a perception or a prediction; and transmitting, by the fallback control system to the primary control system, an indication of the detected imminent collision.

Abstract: Particular embodiments described herein provide for a system and method for determining an intent of a third-party autonomous vehicle by an autonomous vehicle. The method can include identifying the third-party autonomous vehicle, identifying a third-party autonomous vehicle behavior signature associated with the third-party autonomous vehicle, capturing sensor data related to the third-party autonomous vehicle, analyzing the captured sensor data to determine behaviors of the third-party autonomous vehicle, and using a third-party autonomous vehicle behavior signature to determine if the behaviors of the third-party autonomous vehicle indicate a specific intent of the third-party autonomous vehicle. The third-party autonomous vehicle behavior signature can be created by identifying behaviors of the third-party autonomous vehicle and associating the behaviors with an intent of the third-party autonomous vehicle.

Abstract: Disclosed are embodiments for facilitating a backup system for observability, communication, and control of autonomous systems. In some aspects, an embodiment includes initializing an instance of a backup tool to provide observability, communication, and control of a fleet of autonomous vehicles (AVs) responsive to an outage of primary fleet tool services; storing AV data received from the fleet of AVs in in-memory storage of the instance of the backup tool; populating a cache of the backup tool with semi-static data corresponding to the fleet of AVs; authenticating a user requesting access to the instance, the authenticating to utilize credentials of the user established with the primary fleet tool services; and facilitating the issuance of one or more control commands to the fleet of AVs based on the AV data stored in the in-memory storage and based on the semi-static data stored in the cache.

Abstract: The present technology pertains to validating a virtual camera in a simulation environment by utilizing an improved camera model to provide quantitative measurements of the model's performance. A method of validating a virtual camera in a simulation environment comprises presenting at least one reference chart in the simulated environment and capturing images of the reference chart in the simulated environment using a virtual camera. The method further includes interrupting an image pipeline of the virtual camera after at least one simulated process in the image pipeline to extract a RAW image. The method analyzes the RAW image to derive measurements of metrics to characterize the virtual camera. The measured metrics are compared to metrics of a calibrated real-world camera to verify that the metrics are within a threshold delta that is indicative that the virtual camera sufficiently approximates the real-world camera.

Abstract: A system for filtering RADAR data includes one or more graphical processing units (GPUs) for performing steps of a filtering process in parallel. For example, a first GPU or first portion of GPU circuitry calculates a threshold parabola for a tensor of RADAR data. In parallel, a second GPU or second portion of GPU circuitry separately reduces and indexes the tensor for comparison to the threshold parabola. The threshold parabola is compared to reduced and indexed data to filter the RADAR data. Importance sampling can also be used to reduce data, e.g., if the RADAR data includes four dimensions (range, Doppler, azimuth, and elevation).

Abstract: A radar apparatus at a vehicle may transmit a set of radar energy that is reflected off an object after which the reflected radar energy may be received at the radar apparatus. When relative motion of a radar apparatus and an object include movement in two different directions, power of the reflected radar signals may be spread out in a manner that makes data associated with the reflected radar signals difficult to interpret. This spreading out of the radar signals can make mappings of the received radar data appear to be smeared or distorted. To compensate for this smearing effect, mappings of this smeared radar data may be compared with curves from which compensation factors may be identified. These compensation factors may allow a processor to perform calculations to generate updated mappings of the radar data that may allow the processor to more accurately identify characteristics of the object.

Abstract: A computer-aided method of comparing legal requirements, including: comparing a first legal requirement from a first jurisdiction to a second legal requirement from a second jurisdiction, including applying a natural language processing (NLP) algorithm to the first and second legal requirements; computing a distance between the first and second legal requirements; determining from the distance that the second legal requirement belongs to a same subject matter category as the first legal requirement; and providing a user interface (UI) to display the first and second legal requirements to a user, with the computed distance.

Abstract: The subject disclosure relates to features that improve safety for autonomous vehicle (AV) driving maneuvers. In some aspects, a process of the disclosed technology includes steps for detecting an unprotected maneuver, navigating an AV into an intersection, and detecting a wheel safety angle relative to the intersection. In some aspects, the process further includes steps for automatically adjusting a wheel angle of the AV based on the wheel safety angle. Systems and machine-readable media are also provided.

Abstract: System, methods, and computer-readable media for configuring an autonomous vehicle based on safety scores determined by a safety score prediction algorithm, and an associated training technique, to output a safety score for a predicted and/or actual path. The safety score is a value indicating a likelihood of risky events per distance. The safety score prediction algorithm is trained with historical human driving datasets associated with paths (predicted or actual) taken by one or more AVs during human driving.

Abstract: Systems and techniques are provided for communicating using an automotive audio bus. An example method can include receiving, by a first automotive audio bus manager, a request from a first automotive audio bus client for a first data transmission to a second automotive audio bus client, wherein a first size of the first data transmission exceeds a maximum data frame size; parsing the first data transmission into a first plurality of data frames based on the first size of the first data transmission and the maximum data frame size; sending a first header frame to a second automotive audio bus manager associated with the second automotive audio bus client, wherein the first header frame includes a first indication of a first incoming long message transmission and the first size of the first data transmission; and sending the first plurality of data frames to the second automotive audio bus manager.

Abstract: Approaches for top speed dependent operational parameter configuration are disclosed. An operational parameter to be used by an autonomous vehicle is received. Operational modes and corresponding operational regions are determined based on the received operational parameter. A first set of autonomous vehicle parameters are determined and set based on the received operational parameter. A second set of autonomous vehicle parameters are determined and set based on the first set of autonomous vehicle parameters.

Abstract: A method includes using sensory data produced by at least one onboard sensor of a witness vehicle to predict an imminent collision in an environment of the witness vehicle, the predicted imminent collision involving at least one participant vehicle; and alerting the least one participant vehicle to the predicted imminent collision, wherein the alerting is performed by the witness vehicle and prompts the at least one participant vehicle to perform at least one action to mitigate the predicted imminent collision.

Abstract: A vehicle (AV) includes a radar sensor and a hardware logic component. The radar sensor receives a radar return from a driving environment of the vehicle and outputs radar data that is indicative of the return to the hardware logic component. The hardware logic component further receives data indicative of a velocity of the vehicle from a sensor mounted on the vehicle. The hardware logic component is configured to employ synthetic aperture radar (SAR) techniques to compute a three-dimensional position of a point on a surface of an object in the driving environment of the vehicle based upon the radar data and the velocity of the vehicle.

Abstract: Systems and techniques are described for Light Detection and Ranging (LiDAR) noise modeling using machine learning (ML). An example method can include collecting, using one or more sensors, a first set of data for a simulation environment and generating, using the first set of data, a point cloud that represents and/or describes the simulation environment. The method can further include collecting, using the one or more sensors, a second set of data for a real-world environment and generating a noise model using the second set of data and a neural network. The method can also include generating, using the noise model and the point cloud, a noisy point cloud that represents and/or describes the real-world environment.

Abstract: System, methods, and computer-readable media for a calibration check process of sensors on an autonomous vehicle (AV) via a drive-through environment mapped with a series of targets. The targets are used to check the calibration of the sensors on the AV as a quality check before the AV is determined fit to drive autonomously. Supervising a calibration process that collects data from the sensors on the AV based on exposure to a series of targets or augmented camera targets. The collected data may be used for extrinsic calibration aimed to determine that extrinsic parameters that define the rigid relationship between sensors are in set checker bounds.

Abstract: Aspects of the disclosed technology provide solutions for generating synthetic driving scenarios using text-based inputs that describe an intended operating goal. A process of the disclosed technology can include steps for generating a first synthetic scene for testing an autonomous vehicle (AV), providing the first synthetic scene to a first machine-learning model to generate a first text description of the synthetic scene, and providing the first text description to a second machine-learning model to determine if the first text description aligns with the predetermined operating goal for the AV. In some aspects, the process can further include generating a second text description for the synthetic scene, if the first text description does not align with the predetermined operating goal for the AV and providing the second text description to a third machine-learning model to generate a second synthetic scene. Systems and machine-readable media are also provided.

Abstract: A VNLS is described and includes stack recorders for recording data from active data sources comprising at least one topic and having associated therewith a logstream comprising a plurality of active data source data elements; edge recorders for recording data from passive data sources and having associated therewith a second logstream comprising a plurality of passive data source data elements; a staging area for storing the first and second logstreams; and a library for storing snapshots associated with events, wherein the snapshots are created when triggers associated with the events are received at the VNLS and wherein the snapshots include links to a first subset of the plurality of data elements; wherein a second subset of the plurality of data elements that are outside a rolling hold time window and that are not in the first subset of the plurality of data elements are periodically deleted from the VNLS.

Abstract: The present technology is directed to determining an accuracy of an infrared (IR) camera, and more particularly, validating a distortion accuracy of an IR camera. The present technology can receive one or more images of a calibration harp captured by the IR camera, wherein the one or more images include a plurality of lines corresponding to a plurality of strings of the calibration harp. The present technology can further determine a degree of distortion of the plurality of lines based on a distortion error coefficient, wherein the distortion error coefficient is computed based on edge points of the plurality of lines on the one or more images.