Abstract: Among other things, we describe techniques for detecting objects in the environment surrounding a vehicle. A computer system is configured to receive a set of measurements from a sensor of a vehicle. The set of measurements includes a plurality of data points that represent a plurality of objects in a 3D space surrounding the vehicle. The system divides the 3D space into a plurality of pillars. The system then assigns each data point of the plurality of data points to a pillar in the plurality of pillars. The system generates a pseudo-image based on the plurality of pillars. The pseudo-image includes, for each pillar of the plurality of pillars, a corresponding feature representation of data points assigned to the pillar. The system detects the plurality of objects based on an analysis of the pseudo-image. The system then operates the vehicle based upon the detecting of the objects.

Abstract: The present disclosure relates to devices and methods for determining new virtual evidence to use with a deep probabilistic logic module. The devices and methods may receive output from a deep probabilistic logic module in response to running an initial set of virtual evidence through the deep probabilistic logic module. The devices and methods may use the output to automatically propose at least one factor as new virtual evidence for use with the deep probabilistic logic module. The devices and methods may add the new virtual evidence to the deep probabilistic logic module.

Abstract: The present disclosure relates to devices and methods for determining new virtual evidence to use with a deep probabilistic logic module. The devices and methods may receive output from a deep probabilistic logic module in response to running an initial set of virtual evidence through the deep probabilistic logic module. The devices and methods may use the output to automatically propose at least one factor as new virtual evidence for use with the deep probabilistic logic module. The devices and methods may add the new virtual evidence to the deep probabilistic logic module.

Abstract: The present disclosure relates to devices and methods for determining new virtual evidence to use with a deep probabilistic logic module. The devices and methods may receive output from a deep probabilistic logic module in response to running an initial set of virtual evidence through the deep probabilistic logic module. The devices and methods may use the output to automatically propose at least one factor as new virtual evidence for use with the deep probabilistic logic module. The devices and methods may add the new virtual evidence to the deep probabilistic logic module.

Abstract: Techniques are provided for improving a perception processing pipeline for object detection that fuses image segmentation data (e.g., segmentation scores) with LiDAR points. The disclosed techniques are implemented using an architecture that accepts point clouds and images as input and estimates oriented 3D bounding boxes for all relevant object classes. In an embodiment, a method comprises: matching temporally, using one or more processors of a vehicle, points in a three-dimensional (3D) point cloud with an image; generating, using an image-based neural network, semantic data for the image; decorating, using the one or more processors, the points in the 3D point cloud with the semantic data; and estimating, using a 3D object detector with the decorated points as input, oriented 3D bounding boxes for the one or more objects.

Abstract: Among other things, we describe techniques for detecting objects in the environment surrounding a vehicle. A computer system is configured to receive a set of measurements from a sensor of a vehicle. The set of measurements includes a plurality of data points that represent a plurality of objects in a 3D space surrounding the vehicle. The system divides the 3D space into a plurality of pillars. The system then assigns each data point of the plurality of data points to a pillar in the plurality of pillars. The system generates a pseudo-image based on the plurality of pillars. The pseudo-image includes, for each pillar of the plurality of pillars, a corresponding feature representation of data points assigned to the pillar. The system detects the plurality of objects based on an analysis of the pseudo-image. The system then operates the vehicle based upon the detecting of the objects.

Abstract: In an embodiment, a method includes: receiving ambient sound; determining if the ambient sound includes a siren; in accordance with determining that the ambient sound includes a siren, determining a first location associated with the siren; receiving a camera image; determining if the camera image includes a flashing light; in accordance with determining that the camera image includes a flashing light, determining a second location associated with the flashing light; 3D data; determining if the 3D data includes an object; in accordance with determining that the 3D data includes an object, determining a third location associated with the object; determining a presence of an emergency vehicle based on the siren, detected flashing light and detected object; determining an estimated location of the emergency vehicle based on the first, second and third locations; and initiating an action related to the vehicle based on the determined presence and location.

Abstract: Among other things, we describe techniques for detecting objects in the environment surrounding a vehicle. A computer system is configured to receive a set of measurements from a sensor of a vehicle. The set of measurements includes a plurality of data points that represent a plurality of objects in a 3D space surrounding the vehicle. The system divides the 3D space into a plurality of pillars. The system then assigns each data point of the plurality of data points to a pillar in the plurality of pillars. The system generates a pseudo-image based on the plurality of pillars. The pseudo-image includes, for each pillar of the plurality of pillars, a corresponding feature representation of data points assigned to the pillar. The system detects the plurality of objects based on an analysis of the pseudo-image. The system then operates the vehicle based upon the detecting of the objects.

Abstract: In an embodiment, a method comprises: receiving ambient sound; determining if the ambient sound includes a siren; in accordance with determining that the ambient sound includes a siren, determining a first location associated with the siren; receiving a camera image; determining if the camera image includes a flashing light; in accordance with determining that the camera image includes a flashing light, determining a second location associated with the flashing light; 3D data; determining if the 3D data includes an object; in accordance with determining that the 3D data includes an object, determining a third location associated with the object; determining a presence of an emergency vehicle based on the siren, detected flashing light and detected object; determining an estimated location of the emergency vehicle based on the first, second and third locations; and initiating an action related to the vehicle based on the determined presence and location.

Abstract: Techniques are provided for improving a perception processing pipeline for object detection that fuses image segmentation data (e.g., segmentation scores) with LiDAR points. The disclosed techniques are implemented using an architecture that accepts point clouds and images as input and estimates oriented 3D bounding boxes for all relevant object classes. In an embodiment, a method comprises: matching temporally, using one or more processors of a vehicle, points in a three-dimensional (3D) point cloud with an image; generating, using an image-based neural network, semantic data for the image; decorating, using the one or more processors, the points in the 3D point cloud with the semantic data; and estimating, using a 3D object detector with the decorated points as input, oriented 3D bounding boxes for the one or more objects.

Abstract: Techniques are provided for improving a perception processing pipeline for object detection that fuses image segmentation data (e.g., segmentation scores) with LiDAR points. The disclosed techniques are implemented using an architecture that accepts point clouds and images as input and estimates oriented 3D bounding boxes for all relevant object classes. In an embodiment, a method comprises: matching temporally, using one or more processors of a vehicle, points in a three-dimensional (3D) point cloud with an image; generating, using an image-based neural network, semantic data for the image; decorating, using the one or more processors, the points in the 3D point cloud with the semantic data; and estimating, using a 3D object detector with the decorated points as input, oriented 3D bounding boxes for the one or more objects.

Abstract: The present disclosure relates to devices and methods for determining new virtual evidence to use with a deep probabilistic logic module. The devices and methods may receive output from a deep probabilistic logic module in response to running an initial set of virtual evidence through the deep probabilistic logic module. The devices and methods may use the output to automatically propose at least one factor as new virtual evidence for use with the deep probabilistic logic module. The devices and methods may add the new virtual evidence to the deep probabilistic logic module.

Abstract: Techniques are provided for improving a perception processing pipeline for object detection that fuses image segmentation data (e.g., segmentation scores) with LiDAR points. The disclosed techniques are implemented using an architecture that accepts point clouds and images as input and estimates oriented 3D bounding boxes for all relevant object classes. In an embodiment, a method comprises: matching temporally, using one or more processors of a vehicle, points in a three-dimensional (3D) point cloud with an image; generating, using an image-based neural network, semantic data for the image; decorating, using the one or more processors, the points in the 3D point cloud with the semantic data; and estimating, using a 3D object detector with the decorated points as input, oriented 3D bounding boxes for the one or more objects.

Abstract: Among other things, we describe techniques for detecting objects in the environment surrounding a vehicle. A computer system is configured to receive a set of measurements from a sensor of a vehicle. The set of measurements includes a plurality of data points that represent a plurality of objects in a 3D space surrounding the vehicle. The system divides the 3D space into a plurality of pillars. The system then assigns each data point of the plurality of data points to a pillar in the plurality of pillars. The system generates a pseudo-image based on the plurality of pillars. The pseudo-image includes, for each pillar of the plurality of pillars, a corresponding feature representation of data points assigned to the pillar. The system detects the plurality of objects based on an analysis of the pseudo-image. The system then operates the vehicle based upon the detecting of the objects.