Abstract: Provided are systems and methods for a deep learning based beam control. Sensor data associated with the environment and the corresponding detected objects from a perception system are obtained. Object features and image features are extracted. The extracted object features and image features are fused into fused features. A beam control status is predicted according to the fused features, wherein the beam control status indicates a high beam illumination intensity or a low beam illumination intensity of a light emitting device.

Abstract: Provided are systems and methods for a deep learning based beam control. Sensor data associated with the environment and the corresponding detected objects from a perception system are obtained. Object features and image features are extracted. The extracted object features and image features are fused into fused features. A beam control status is predicted according to the fused features, wherein the beam control status indicates a high beam illumination intensity or a low beam illumination intensity of a light emitting device.

Abstract: Provided are methods for object detection using radar and lidar fusion, which can include generating clusters combining clusters of point clouds for radar and lidar, respectively, from which fused features are determined using a deep learning model. Systems and computer program products are also provided.

Abstract: In an example method, a perception system receives first data representing a point cloud having a plurality of points, and clusters the points into a plurality of clusters. The clusters include a first cluster representing a first portion of an object and a second cluster representing a second portion of the object. Further, the perception system generates a first bounding box enclosing at least the first cluster and the second cluster, and generates a second bounding box enclosing at least the first cluster and the second cluster. The perception system selects either the first bounding box or the second bounding box, and outputs second data representing the object. The second data includes an indication of the selected bounding box and an indication of the object.

Abstract: Among other things, a method includes receiving first LiDAR point cloud information from a first LiDAR device and second LiDAR point cloud information from a second LiDAR device, generating third point cloud information according to merging the first and second LiDAR point cloud information, and operating the vehicle based upon the third LiDAR point cloud information.

Abstract: Among other things, a method includes receiving first LiDAR point cloud information from a first LiDAR device and second LiDAR point cloud information from a second LiDAR device, generating third point cloud information according to merging the first and second LiDAR point cloud information, and operating the vehicle based upon the third LiDAR point cloud information.

Abstract: Among other things, a method includes receiving first LiDAR point cloud information from a first LiDAR device and second LiDAR point cloud information from a second LiDAR device, generating third point cloud information according to merging the first and second LiDAR point cloud information, and operating the vehicle based upon the third LiDAR point cloud information.