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: 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: 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: An autonomous vehicle is described herein. The autonomous vehicle includes several different types of sensor systems, such as image, lidar, and radar. The autonomous vehicle additionally includes a computing system that executes several different object classifier modules, wherein the object classifier modules are configured to identify types of objects that are in proximity to the autonomous vehicle based upon outputs of the sensor systems. The computing system additionally executes a Bayesian object classifier system that is configured to receive outputs of the object classifier modules and assign labels to objects captured in sensor signals based upon the outputs of the object classifier modules.

Abstract: An autonomous vehicle is described herein. The autonomous vehicle includes several different types of sensor systems, such as image, lidar, and radar. The autonomous vehicle additionally includes a computing system that executes several different object classifier modules, wherein the object classifier modules are configured to identify types of objects that are in proximity to the autonomous vehicle based upon outputs of the sensor systems. The computing system additionally executes a Bayesian object classifier system that is configured to receive outputs of the object classifier modules and assign labels to objects captured in sensor signals based upon the outputs of the object classifier modules.

Abstract: Systems and method are provided for controlling a vehicle. In one embodiment, an obstacle management method includes receiving, via one or more sensing devices, sensor data relating to an environment associated with a vehicle, wherein the vehicle is substantially stationary and has an intended path corresponding to entering a traffic flow of a first lane. The method includes determining the presence of an obstacle that at least partially occludes a view of the first lane by a first sensing device of the one or more sensing devices, and positioning, with a processor, the vehicle with respect to the obstacle to improve the view of the first lane by the first sensing device without significantly obstructing the first lane. The first lane is then monitored via the first sensing device to determine when the intended path clear.

Abstract: Systems and method are provided for controlling an autonomous vehicle. A camera configured to capture an image, and a controller can execute an autonomous driving system (ADS) that classify that image. The ADS comprises a classification system for classifying objects in an environment within a driveable area of the autonomous vehicle. The classification system comprises a processor configured to execute a region proposal generator module and an image classification module. The region proposal generator module generates a set of bounding box region proposals for the image. The bounding box region proposals are selected areas of the image that include objects to be classified. The image classification module classifies, via a neural network executed by the processor, the objects from the image that are within one of the bounding box region proposals.

Abstract: Systems and method are provided for controlling a vehicle. In one embodiment, an obstacle management method includes receiving, via sensing devices, sensor data relating to an environment associated with a vehicle, and determining, with the sensor data, the presence of an obstacle that at least partially obstructs an intended path of the vehicle in a first lane adjacent to a second lane and at least partially occludes a view of the second lane by a first sensing device. The method further includes positioning the vehicle with respect to the obstacle to improve the view of the second lane by the first sensing device without significantly obstructing the second lane, determining a second path that circumnavigates the obstacle and rejoins the intended path, and monitoring the opposing lane via the first sensing device to determine when the second path is clear.

Abstract: Systems and method are provided for controlling a vehicle. In one embodiment, an obstacle management method includes receiving, via sensing devices, sensor data relating to an environment associated with a vehicle, and determining, with the sensor data, the presence of an obstacle that at least partially obstructs an intended path of the vehicle in a first lane adjacent to a second lane and at least partially occludes a view of the second lane by a first sensing device. The method further includes positioning the vehicle with respect to the obstacle to improve the view of the second lane by the first sensing device without significantly obstructing the second lane, determining a second path that circumnavigates the obstacle and rejoins the intended path, and monitoring the opposing lane via the first sensing device to determine when the second path is clear.

Abstract: Systems and method are provided for controlling a vehicle. In one embodiment, an obstacle management method includes receiving, via one or more sensing devices, sensor data relating to an environment associated with a vehicle, wherein the vehicle is substantially stationary and has an intended path corresponding to entering a traffic flow of a first lane. The method includes determining the presence of an obstacle that at least partially occludes a view of the first lane by a first sensing device of the one or more sensing devices, and positioning, with a processor, the vehicle with respect to the obstacle to improve the view of the first lane by the first sensing device without significantly obstructing the first lane. The first lane is then monitored via the first sensing device to determine when the intended path clear.

Abstract: Systems and method are provided for controlling an autonomous vehicle. A camera configured to capture an image, and a controller can execute an autonomous driving system (ADS) that classify that image. The ADS comprises a classification system for classifying objects in an environment within a driveable area of the autonomous vehicle. The classification system comprises a processor configured to execute a region proposal generator module and an image classification module. The region proposal generator module generates a set of bounding box region proposals for the image. The bounding box region proposals are selected areas of the image that include objects to be classified.