Abstract: The subject disclosure relates to techniques for managing the configuration of vehicles. A process of the disclosed technology can include steps of receiving a first vehicle identifier for a first vehicle, wherein the first vehicle is associated with a first vehicle platform, identifying a first configuration for the first vehicle platform based on the first vehicle identifier, receiving a second vehicle identifier for a second vehicle, wherein the second vehicle is associated with a second vehicle platform, and wherein the first vehicle platform is different from the second vehicle platform, and identifying a second configuration for the second vehicle platform based on the second vehicle identifier, wherein the first vehicle and the second vehicle run on an identical software stack. Systems and machine-readable media are also provided.

Abstract: The subject disclosure relates to ways to identify self-hit data collected by autonomous vehicle (AV) sensors. In some aspects, a method of the disclosed technology includes steps for generating a geometric model of an autonomous vehicle (AV), wherein the geometric model specifies physical boundaries of the AV in three-dimensional (3D) space, collecting sensor data for an environment around the AV, and identifying one or more data points, from among the collected sensor data, that correspond with a surface of the AV. Systems and machine-readable media are also provided.

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: Distance measurements are received from one or more distance measurement sensors, which may be coupled to a vehicle. A three-dimensional (3D) point cloud are generated based on the distance measurements. In some cases, 3D point clouds corresponding to distance measurements from different distance measurement sensors may be combined into one 3D point cloud. A voxelized model is generated based on the 3D point cloud. An object may be detected within the voxelized model, and in some cases may be classified by object type. If the distance measurement sensors are coupled to a vehicle, the vehicle may avoid the detected object.

Abstract: Methods and systems are provided for dynamically adjusting at least one setting of a lidar. In some aspects, a process can include steps for receiving, at an autonomous vehicle system, road condition data associated with an autonomous vehicle, generating, by the autonomous vehicle system, instructions to adjust the at least one setting of the lidar of the autonomous vehicle based on the road condition data, and providing, by the autonomous vehicle system, the instructions to adjust the at least one setting of the lidar to the lidar, the instructions including updating firmware of the lidar to adjust the at least one setting of the lidar.

Abstract: The subject disclosure relates to ways to identify self-hit data collected by autonomous vehicle (AV) sensors. In some aspects, a method of the disclosed technology includes steps for generating a geometric model of an autonomous vehicle (AV), wherein the geometric model specifies physical boundaries of the AV in three-dimensional (3D) space, collecting sensor data for an environment around the AV, and identifying one or more data points, from among the collected sensor data, that correspond with a surface of the AV. Systems and machine-readable media are also provided.

Abstract: The present technology is directed to generating augmented data that are used for training a machine-learning (ML) algorithm to recognize objects under different weather conditions. The present technology may include receiving, by one or more processors, data of an environment including objects in a first geographical location. The data of the environment may be received from sensors on a vehicle moving on a road under a first weather condition. The present technology may also include receiving reference data that represent a second weather condition. The second weather condition may include a precipitation type. The present technology may also include generating augmented data including a subset of the reference data superimposed on the data of the environment. The augmented data simulates the environment under the second weather condition to simulate the environment under the second weather condition.

Abstract: The disclosed technology provides solutions for finding samples from image data that are similar to failure cases, by constructing N-dimensional vectors of the failure cases. The vectors of failure cases are compared to other image data, with the objective of identifying groups of images that can be labeled. The labeled images are then used to retrain a model. Systems and machine-readable media are also provided.

Abstract: The subject disclosure relates to techniques for managing the configuration of vehicles. A process of the disclosed technology can include steps of receiving a first vehicle identifier for a first vehicle, wherein the first vehicle is associated with a first vehicle platform, identifying a first configuration for the first vehicle platform based on the first vehicle identifier, receiving a second vehicle identifier for a second vehicle, wherein the second vehicle is associated with a second vehicle platform, and wherein the first vehicle platform is different from the second vehicle platform, and identifying a second configuration for the second vehicle platform based on the second vehicle identifier, wherein the first vehicle and the second vehicle run on an identical software stack. Systems and machine-readable media are also provided.

Abstract: Autonomous Vehicles (AVs) can navigate roadways without a human driver by using sensors, such as Lidar sensors, positioned around the AV. Systems, apparatuses, methods, computer readable medium, and circuits are provided for emulating a Lidar point cloud of an evaluation Lidar to be evaluated by transforming historical data received from a reference Lidar in order to determine a performance difference between the evaluation Lidar and the reference Lidar.

Abstract: Techniques to improve the accuracy and speed for detection and remediation of difficult to test nodes in a circuit design netlist. The techniques utilize improved netlist representations, test point insertion, and trained neural networks.

Abstract: Distance measurements are received from one or more distance measurement sensors, which may be coupled to a vehicle. A three-dimensional (3D) point cloud are generated based on the distance measurements. In some cases, 3D point clouds corresponding to distance measurements from different distance measurement sensors may be combined into one 3D point cloud. A voxelized model is generated based on the 3D point cloud. An object may be detected within the voxelized model, and in some cases may be classified by object type. If the distance measurement sensors are coupled to a vehicle, the vehicle may avoid the detected object.

Abstract: The subject disclosure relates to ways to identify self-hit data collected by autonomous vehicle (AV) sensors. In some aspects, a method of the disclosed technology includes steps for generating a geometric model of an autonomous vehicle (AV), wherein the geometric model specifies physical boundaries of the AV in three-dimensional (3D) space, collecting sensor data for an environment around the AV, and identifying one or more data points, from among the collected sensor data, that correspond with a surface of the AV. Systems and machine-readable media are also provided.

Abstract: Techniques to improve the accuracy and speed for detection and remediation of difficult to test nodes in a circuit design netlist. The techniques utilize improved netlist representations, test point insertion, and trained neural networks.

Abstract: Distance measurements are received from one or more distance measurement sensors, which may be coupled to a vehicle. A three-dimensional (3D) point cloud are generated based on the distance measurements. In some cases, 3D point clouds corresponding to distance measurements from different distance measurement sensors may be combined into one 3D point cloud. A voxelized model is generated based on the 3D point cloud. An object may be detected within the voxelized model, and in some cases may be classified by object type. If the distance measurement sensors are coupled to a vehicle, the vehicle may avoid the detected object.

Abstract: Distance measurements are received from one or more distance measurement sensors, which may be coupled to a vehicle. A three-dimensional (3D) point cloud are generated based on the distance measurements. In some cases, 3D point clouds corresponding to distance measurements from different distance measurement sensors may be combined into one 3D point cloud. A voxelized model is generated based on the 3D point cloud. An object may be detected within the voxelized model, and in some cases may be classified by object type. If the distance measurement sensors are coupled to a vehicle, the vehicle may avoid the detected object.

Abstract: Techniques to improve the accuracy and speed for detection and remediation of difficult to test nodes in a circuit design netlist. The techniques utilize improved netlist representations, test point insertion, and trained neural networks.

Abstract: Techniques to improve the accuracy and speed for detection and remediation of difficult to test nodes in a circuit design netlist. The techniques utilize improved netlist representations, test point insertion, and trained neural networks.