Abstract: In response to perceiving a moving object, one or more possible object paths of the moving object are determined based on the prior movement predictions of the moving object, for example, using a machine-learning model, which may be created based on a large amount of driving statistics of different vehicles. For each of the possible object paths, a set of trajectory candidates is generated based on a set of predetermined accelerations. Each of the trajectory candidates corresponds to one of the predetermined accelerations. A trajectory cost is calculated for each of the trajectory candidates using a predetermined cost function. One of the trajectory candidates having the lowest trajectory cost amongst the trajectory candidates is selected. An ADV path is planned to navigate the ADV to avoid collision with the moving object based on the lowest costs of the possible object paths of the moving object.

Abstract: According to various embodiments, systems and methods described in the disclosure combine mapped features with point cloud features to improve object detection precision of an autonomous driving vehicle (ADV). The map features and the point cloud features can be extracted from a perception area of the ADV within a particular angle view at each driving cycle based on a position of the ADV. The map features and the point cloud features can be concatenated and provided to a neutral network for object detections.

Abstract: In one embodiment, a method, apparatus, and system may predict behavior of environmental objects using machine learning at an autonomous driving vehicle (ADV). One or more yield/overtake decisions are made with respect to one or more objects in the ADV's surrounding environment using a data processing architecture comprising at least a first, a second, and a third neural networks, the first, the second, and the third neural networks having been trained with a training data set. Driving signals are generated based at least in part on the yield/overtake decisions to control operations of the ADV.

Abstract: In an embodiment, a method for representing a surrounding environment of an ego autonomous driving vehicle (ADV) is described. The method represents the surrounding environment using a first set of features from a definition (HD) map and a second set of features from a target object in the surrounding environment. The first set of features are extracted from the high definition map using a convolutional neural network (CNN), and the second set of features are handcrafted features from the target object during a predetermined number of past driving cycles of the ego ADV. The first set of features and the second set of features are concatenated and provided to a number of fully connected layers of the CNN to predict behaviors of the target object. In one embodiment, the operations in the method can be repeated for each driving cycle of the ego ADV.

Abstract: In one embodiment, a method, apparatus, and system may predict behavior of environmental objects using machine learning at an autonomous driving vehicle (ADV). A data processing architecture comprising at least a first neural network and a second neural network is generated, the first and the second neural networks having been trained with a training data set. Behavior of one or more objects in the ADV's environment is predicted using the data processing architecture comprising the trained neural networks. Driving signals are generated based at least in part on the predicted behavior of the one or more objects in the ADV's environment to control operations of the ADV.