Abstract: Various embodiments of the present technology can include systems, methods, and non-transitory computer readable media configured to adaptively identify clutter points representing static objects from a sensor data scan. A plurality of sensor data scans are captured, by a sensor unit placed on a vehicle, at a plurality of consecutive time instants while the vehicle is traveling along a route. A set of target points from each of the plurality of sensor data scans is identified. A characteristic indicative of a trajectory pattern relating to one or more respective sets of target points is obtained from one or more sensor data scans taken at consecutive time instants. In response to determining that the characteristic satisfies a pre-defined condition, an indicator with the respective sets of target points is adopted as relating to one or more static objects in an environment at which the vehicle is situated.

Abstract: A method includes accessing perception data generated based on first sensor data and generating first prediction data using a prediction module based on the perception data. The method includes capturing second sensor data while the vehicle is operating according to a first planned trajectory based on the first prediction data and a path planning parameter. The method includes generating a simulation for evaluating the prediction module, including generating second prediction data based on the second sensor data and generating a second planned trajectory. The method includes, subsequent to determining that a difference between the first planned trajectory and the second planned trajectory fails to satisfy predetermined prediction criteria, identifying an object for which the prediction module underperforms relative to the predetermined prediction criteria and updating the prediction module based on the identified object and data associated with the second prediction data.

Abstract: A method includes accessing a training sample including an image of a scene, depth measurements of the scene, and a predetermined 3D position of an object in the scene. The method includes training a 3D-detection model for detecting 3D positions of objects based the depth measurements and the predetermined 3D position, and training a 2D-detection model for detecting 2D positions of objects within images. Training the 2D-detection model includes generating an estimated 2D position of the object by processing the image using the 2D-detection model, determining a subset of the depth measurements that correspond to the object based on the estimated 2D position and a viewpoint from which the image is captured, generating an estimated 3D position of the object based on the subset of the depth measurements, and updating the 2D-detection model based on a comparison between the estimated 3D position and the predetermined 3D position.

Abstract: Systems, methods, and non-transitory computer-readable media can determine contextual information associated with an environment including a vehicle and at least one agent for generating a computer simulation based on the environment. One or more behavior constraints for the at least one agent can be determined based on the contextual information. A set of trajectories can be generated based on the one or more behavior constraints. A trajectory can be selected from the set of trajectories based on determining that the trajectory satisfies one or more predetermined criteria. The computer simulation can be generated, wherein the computer simulation includes monitoring driving behavior of the vehicle in response to the vehicle interacting with the at least one agent based on the selected trajectory.

Abstract: In one embodiment, a method includes accessing sensor data generated by one or more sensors of the vehicle, determining that a first beam angle of a radar of the vehicle provides insufficient radar visibility of a current road condition according to one or more criteria based on the sensor data, determining an amount of adjustment needed to adjust the first beam angle of the radar, adjusting the first beam angle of the radar to a second beam angle based on the determined amount of adjustment, and detecting one or more objects based on the second beam angle of the radar.

Abstract: In one embodiment, a method includes, by a computing system associated with a vehicle, receiving sensor data from one or more sensors of the vehicle, wherein the sensor data is based on an environment of the vehicle, identifying, based on the sensor data, one or more objects in the environment, generating, based on the one or more objects, a set of points that represent the environment, wherein each object has one or more corresponding points in the set of points, and each of the points is associated with one or more features associated with the corresponding object, generating a prediction for at least one of the objects in the environment or the vehicle by processing the set of points using a machine-learning model, and causing the vehicle to perform one or more operations based on the prediction.

Abstract: A method includes generating a parameter of a trajectory associated with a scenario using a path planner. The parameter is generated based on a training dataset. The method includes comparing the parameter of the trajectory against a validation parameter associated with a validation dataset. The validation parameter is based on human-based vehicle driving trajectory data associated with scenarios that satisfy a level of similarity with the scenario. The method further includes determining a level of similarity between the parameter associated with the scenario and the validation parameter associated with the scenarios, and, subsequent to determining that the level of similarity fails to satisfy a similarity threshold, the method concludes with providing training data associated with the scenario to the training dataset so that a subsequent parameter of a subsequent trajectory generated by the path planner and associated with the scenario satisfies the level of similarity against the validation parameter.

Abstract: A method includes generating, while a vehicle is operating in an autonomous-driving mode, a planned trajectory associated with a computing system of the vehicle based on first sensor data capturing an environment of the vehicle. The method further includes, while the vehicle is operating according to the planned trajectory, receiving a disengagement instruction associated that causes the vehicle to disengage from operating in the autonomous-driving mode and switch to operating in a disengagement mode. Subsequent to the vehicle operating in the disengagement mode, the method further includes capturing second sensor data and generating a simulation of the environment. The simulation is based on sensor data associated with the environment and the planned trajectory. Additionally, subsequent to the vehicle operating in the disengagement mode, the method concludes with evaluating a performance of an autonomy system based on the simulation, and providing feedback based on the evaluation.

Abstract: In general, one or more loads on a vehicle can be connected to both a first voltage source on the vehicle and a backup vehicle power system on the vehicle. If the voltage provided by the first voltage source to the one or more loads satisfies a voltage threshold, the backup vehicle power system does not provide power to the one or more loads. However, if the voltage provided by the first voltage source to the one or more loads falls below the voltage threshold, the backup vehicle power system provides power to the one or more loads.

Abstract: In one embodiment, an apparatus includes a volatile memory module configured to store vehicle data, and a refrigeration module coupled to the volatile memory module. The refrigeration module includes one or more chambers containing one or more coolant materials. When the one or more chambers are exposed by an exigent situation associated with a vehicle, the one or more chambers are configured to release the one or more coolant materials to lower a temperature of the volatile memory module.

Abstract: Examples disclosed herein may involve (i) obtaining 2D image data and 3D sensor data that is representative of an area, (ii) identifying a first set of pixels associated with ephemeral objects detected in the area and a second set of pixels associated with non-ephemeral objects detected in the area, (iii) identifying a first set of ephemeral 3D data points associated with the detected ephemeral objects and a second set of non-ephemeral 3D data points associated with the detected non-ephemeral objects, (iv) mapping the first and second sets of 3D data points to a grid of voxels associated with the area, (v) making a determination that one or more voxels in the grid each contain a threshold extent of ephemeral data points, and (vi) based at least in part on the determination, filtering the 3D sensor data to remove the 3D data points contained within the one or more voxels.

Abstract: The present invention relates to a method and system for accurately predicting future trajectories of observed objects in dense and ever-changing city environments. More particularly, the present invention relates to substantially continuously tracking and estimating the future movements of an observed object. As an example, an observed object may be a moving vehicle, for example along a path or road. Aspects and/or embodiments seek to provide an end to end method and system for substantially continuously tracking and predicting future movements of a newly observed object, such as a vehicle, using motion prior data extracted from map data.

Abstract: The present disclosure relates to a method of generating a ground map. For instance, the present disclosure provides a method of determining relevant point cloud data for generating a surface topology over real-world geographical areas using sensor data, which may involve (i) receiving a first dataset from a first sensor, (ii) receiving a second dataset from a second sensor, (iii) classifying the first dataset by identifying characteristics of the first dataset that correspond to a ground surface, (iv) filtering the second dataset based on the classified first dataset, and (v) generating a ground map from the filtered second dataset.

Abstract: The present invention relates to a method of generating an overhead view image of an area. More particularly, the present invention relates to a method of generating a contextual multi-image based overhead view image of an area using ground map data and field of view image data. Various embodiments of the present technology can include methods, systems and non-transitory computer readable media and computer programs configured to determine a ground map of the geographical area; receiving a plurality of images of the geographical area; process the plurality of images to select a subset of images to generate the overhead view of the geographical area; divide the ground map into a plurality of sampling points of the geographical area; and determine a color of a plurality of patches of the overhead view image from the subset of images, each patch representing each sampling point of the geographical area.

Abstract: Systems, methods, and non-transitory computer-readable media can determine a raster representative of a surrounding environment of a vehicle, wherein the raster depicts one or more objects in the surrounding environment of the vehicle. A plurality of trajectory proposals are determined for a first object of the one or more objects. For each trajectory proposal of the plurality of trajectory proposals, a score indicative of a likelihood that the first object will take a trajectory consistent with the trajectory proposal, and an offset for modifying the trajectory proposal are generated. A predicted trajectory is determined for the first object based on the scores and the offsets for the plurality of trajectory proposals.

Abstract: In one embodiment, a computing system of a vehicle may access sensor data associated with a surrounding environment of a vehicle. The system may generate, based on the sensor data, a first trajectory having one or more first driving characteristics for navigating the vehicle in the surrounding environment. The system may generate a second trajectory having one or more second driving characteristics by modifying the one or more first driving characteristics of the first trajectory. The modifying may use adjustment parameters based on one or more human-driving characteristics of observed human-driven trajectories such that the one or more second driving characteristics satisfy a similarity threshold relative to the one or more human-driving characteristics. The system may determine, based on the second trajectory, vehicle operations to navigate the vehicle in the surrounding environment.

Abstract: In one embodiment, an apparatus includes a transmitter operable to transmit a first light beam from a light source. The apparatus also includes a receiver operable to receive a plurality of return light beams and direct the plurality of return light beams through a first beam splitter to an imaging sensor and a LiDAR sensor. The imaging sensor may be operable to process a first portion of the return light beams into image profile data, and the LiDAR sensor may be operable to process a second portion of the return light beams into depth profile data. In addition, the first and second portions of the return light beams may be received from a shared field of view.

Abstract: Examples disclosed herein may involve (i) obtaining an aggregated overhead view image of a geographical area that has been generated by a pipeline for generating aggregated overhead view images, the geographical area comprising a plurality of regions, where the aggregated overhead view image is generated from aggregating pixel values from a plurality of source images of the geographical area, (ii) generating one or more reprojection images of one or more of the regions of the geographic area from the aggregated overhead view image, (iii) identifying, from the plurality of source images, one or more source images that capture the one or more regions of the geographical area, (iv) calculating one or more differences between the identified one or more source images and the one or more reprojection images, and (v) determining one or more error corrections to be applied to the pipeline for generating overhead view images.

Abstract: Systems, methods, and non-transitory computer-readable media can receive a message transmitted over a robotics framework implemented on a vehicle. A determination can be made that the message satisfies criteria for multi-threaded hashing. The message can be divided into two or more message segments. A hash can be independently computed for each message segment of the two or more message segments to generate two or more message segment hashes. A message hash can be determined for the message based on the two or more message segment hashes.

Abstract: In one embodiment, a method includes receiving environment data associated with an environment detected by a vehicle, generating goal states of the environment for the vehicle by using observed driving data associated with the environment, wherein each goal state corresponds to a region that the vehicle is capable of navigating through in the environment, generating candidate trajectories for the vehicle based on at least the goal states of the environment, wherein each candidate trajectory is associated with at least one goal state, assigning candidate values to the candidate trajectories based on the observed driving data, and selecting a candidate trajectory associated with at least one goal state from the candidate trajectories for the vehicle to navigate through the environment based on the candidate values.