Abstract: Systems and methods for detecting objects and predicting their motion are provided. In particular, a computing system can obtain a plurality of sensor sweeps. The computing system can determine movement data associated with movement of the autonomous vehicle. For each sensor sweep, the computing system can generate an image associated with the sensor sweep. The computing system can extract, using the respective image as input to one or more machine-learned models, feature data from the respective image. The computing system can transform the feature data into a coordinate frame associated with a next time step. The computing system can generate a fused image. The computing system can generate a final fused image. The computing system can predict, based, at least in part, on the final fused representation of the plurality of sensors sweeps from the plurality of sensor sweeps, movement associated with the feature data at one or more time steps in the future.

Abstract: Systems and methods for detecting objects and predicting their motion are provided. In particular, a computing system can obtain a plurality of sensor sweeps. The computing system can determine movement data associated with movement of the autonomous vehicle. For each sensor sweep, the computing system can generate an image associated with the sensor sweep. The computing system can extract, using the respective image as input to one or more machine-learned models, feature data from the respective image. The computing system can transform the feature data into a coordinate frame associated with a next time step. The computing system can generate a fused image. The computing system can generate a final fused image. The computing system can predict, based, at least in part, on the final fused representation of the plurality of sensors sweeps from the plurality of sensor sweeps, movement associated with the feature data at one or more time steps in the future.

Abstract: A method and non-transitory computer-readable medium capture an image of bulk grain and apply a feature extractor to the image to determine a feature of the bulk grain in the image. For each of a plurality of different sampling locations in the image, based upon the feature of the bulk grain at the sampling location, a determination is made regarding a classification score for the presence of a classification of material at the sampling location. A quality of the bulk grain of the image is determined based upon an aggregation of the classification scores for the presence of the classification of material at the sampling locations.

Abstract: Systems and methods for implementing a low-latency braking action for an autonomous vehicle are provided. A computing system can include a vehicle autonomy system comprising one or more processors configured to determine a motion plan for an autonomous vehicle based at least in part on sensor data from one or more sensors of the autonomous vehicle. The computing system can further include a low-latency braking system comprising one or more processors configured to determine that the autonomous vehicle has a likelihood of colliding with an object in a surrounding environment based at least in part on a previously-determined motion plan obtained from the vehicle autonomy system. In response to determining that the autonomous vehicle has a likelihood of colliding with the object in the surrounding environment, the low-latency braking system can further be configured to implement a braking action for the autonomous vehicle.

Abstract: Systems and methods for implementing a low-latency braking action for an autonomous vehicle are provided. A computing system can include a vehicle autonomy system comprising one or more processors configured to determine a motion plan for an autonomous vehicle based at least in part on sensor data from one or more sensors of the autonomous vehicle. The computing system can further include a low-latency braking system comprising one or more processors configured to determine that the autonomous vehicle has a likelihood of colliding with an object in a surrounding environment based at least in part on a previously-determined motion plan obtained from the vehicle autonomy system. In response to determining that the autonomous vehicle has a likelihood of colliding with the object in the surrounding environment, the low-latency braking system can further be configured to implement a braking action for the autonomous vehicle.

Abstract: Image capture devices can include an image sensor, a color filter array, and an image processor. The image sensor can include an array of sensor elements configured to detect incoming light provided incident to a surface of the image sensor. The color filter array can be positioned adjacent to the image sensor for filtering the incoming light provided incident to the surface of the image sensor. The color filter array can include an array of filter elements including a plurality of clear filter elements and a plurality of color-sensitive filter elements. The plurality of color-sensitive filter elements can include at least one first color-sensitive filter element sensitive to a first band of the visible color spectrum and at least one second color-sensitive filter element sensitive to a second band of the visible color spectrum different than the first band.

Abstract: A method and non-transitory computer-readable medium capture an image of bulk grain and apply a feature extractor to the image to determine a feature of the bulk grain in the image. For each of a plurality of different sampling locations in the image, based upon the feature of the bulk grain at the sampling location, a determination is made regarding a classification score for the presence of a classification of material at the sampling location. A quality of the bulk grain of the image is determined based upon an aggregation of the classification scores for the presence of the classification of material at the sampling locations.

Abstract: A method and non-transitory computer-readable medium capture an image of bulk grain and apply a feature extractor to the image to determine a feature of the bulk grain in the image. For each of a plurality of different sampling locations in the image, based upon the feature of the bulk grain at the sampling location, a determination is made regarding a classification score for the presence of a classification of material at the sampling location. A quality of the bulk grain of the image is determined based upon an aggregation of the classification scores for the presence of the classification of material at the sampling locations.

Abstract: A method and non-transitory computer-readable medium capture an image of bulk grain and apply a feature extractor to the image to determine a feature of the bulk grain in the image. For each of a plurality of different sampling locations in the image, based upon the feature of the bulk grain at the sampling location, a determination is made regarding a classification score for the presence of a classification of material at the sampling location. A quality of the bulk grain of the image is determined based upon an aggregation of the classification scores for the presence of the classification of material at the sampling locations.

Abstract: A method and system for detecting an obstacle comprises a terrain estimator for estimating a local terrain surface map based on at least one of range data points, color data, and infrared data gathered by electromagnetic perception focused in front of a vehicle. The map is composed of a series of terrain cells. An analyzer estimates at least one of predicted roll data, predicted pitch data, predicted ground clearance data, and predicted friction coefficient data based on the estimated terrain map for respective terrain cells and vehicular constrain data. A local planner determines predicted vehicle control data for terrain cells within the terrain along a planned path of the vehicle. One or more vehicle sensors sense at least one of actual roll data, actual pitch data, actual ground clearance data, and actual friction coefficient data for the terrain cells when the vehicle is coextensively positioned with the corresponding terrain cell.