A method for predicting a direction of movement of a target object, a method for training a neural network, a smart vehicle control method, a device, an electronic apparatus, a computer readable storage medium, and a computer program. The method for predicting a direction of movement of a target object comprises: acquiring an apparent orientation of a target object in an image captured by a camera device, and acquiring a relative position relationship of the target object in the image and the camera device in three-dimensional space (S100); and determining, according to the apparent orientation of the target object and the relative position relationship, a direction of movement of the target object relative to a traveling direction of the camera device (S110).

A computer-implemented method for controlling a vehicle comprises: receiving tracking data associated with a surrounding environment of the vehicle; detecting, based upon the tracking data, an object in the surrounding environment of the vehicle; determining a location of the object; determining, based on navigation assistance data, whether the location of the object is at least partially within a classified area in the surrounding environment; and configuring a control system of the vehicle to: initiate, based upon determining that the location of the object is not at least partially within the classified area, a first collision avoidance response procedure for responding to the object; and initiate, based upon determining that the location of the object is at least partially within the classified area, a second collision avoidance response procedure for responding to the object, the second collision avoidance response procedure different from the first collision avoidance response procedure.

Vehicle behavioral monitoring includes determining a measure of distraction of the operator of a target vehicle, characterizing the type or category of distraction, determining level of risk that the target vehicle poses, and invoking various responses including host vehicle notifications and evasive actions and external notification and information sharing.

According to one aspect, systems and techniques for lane selection may include receiving a current state of an ego vehicle and a traffic participant vehicle, and a goal position, projecting the ego vehicle and the traffic participant vehicle onto a graph network, where nodes of the graph network may be indicative of discretized space within an operating environment, determining a current node for the ego vehicle within the graph network, and determining a subsequent node for the ego vehicle based on identifying adjacent nodes which may be adjacent to the current node, calculating travel times associated with each of the adjacent nodes, calculating step costs associated with each of the adjacent nodes, calculating heuristic costs associated with each of the adjacent nodes, and predicting a position of the traffic participant vehicle.

An information processing device (20) includes a route acquisition unit (202) and an automatic driving section determination unit (204). The route acquisition unit (202) acquires route information indicating a moving route of a mobile body. The automatic driving section determination unit (204) acquires adaptation coefficients for a plurality of sections included in the moving route indicated by the route information, with reference to an adaptation coefficient storage unit (206) that stores automatic driving adaptation coefficients set for the respective sections. Further, the automatic driving section determination unit (204) determines the automatic driving sections of the mobile body in the moving route, based on the acquired adaptation coefficients.

A vehicle traveling control system according to the example in the present disclosure communicates with an automatic operation control system which drafts a traveling plan of the vehicle, and performs an automatic traveling control for automatically running the vehicle along the traveling plan received from the automatic operation control system. The vehicle traveling control system predicts a risk based on information about surrounding environment of the vehicle, and performs, when the risk is predicted, a risk avoidance control to intervene in the automatic traveling control in order to avoid the risk. When the risk avoidance control is executed, the vehicle traveling control system transmits information on the risk avoidance control to the automatic operation control system.

Systems and methods for curiosity development in an agent located in an uncertain environment are provided. In one embodiment, the system includes a goal state module, a curiosity module, and a planning module. The goal module is configured to calculate a goal state of a goal associated with the environment. The curiosity module is configured to determine an uncertainty value for the environment and calculate a curiosity reward based on the uncertainty value. The planning module is configured to update a motion plan based on the goal state and the curiosity reward.

Systems and methods for controlling an autonomous vehicle are provided. In one example embodiment, a computer-implemented method includes obtaining sensor data indicative of a surrounding environment of the autonomous vehicle, the surrounding environment including one or more occluded sensor zones. The method includes determining that a first occluded sensor zone of the occluded sensor zone(s) is occupied based at least in part on the sensor data. The method includes, in response to determining that the first occluded sensor zone is occupied, controlling the autonomous vehicle to travel clear of the first occluded sensor zone.

Systems and methods are provided for predicting blind spot incursions for a host vehicle. In one implementation, a navigation system for a host vehicle may comprise a processor. The processor may be programmed to receive, from an image capture device located on a rear of the host vehicle, at least one image representative of an environment of the host vehicle. The processor may be programmed to analyze the at least one image to identify an object in the environment of the host vehicle and to determine kinematic information associated with the object. The processor may further be programmed to predict, based on the kinematic information, that the object will travel in a region outside of a field of view of the image capture device and perform a control action based on the prediction.

A security system includes: an autonomous vehicle; a camera installed in the autonomous vehicle; and a crime determination unit that makes a determination regarding a crime on the basis of an image captured by the camera. The autonomous vehicle is a shared car shared by residents within a region; in response to a request, the autonomous vehicle automatically picks up at the departure point of the residents and automatically moves to the destination of the residents; and the camera take pictures of the moving section to the starting point and the moving section from the starting point to the destination; and the crime determination unit determines a crime based on the images taken in the moving section to the departure point and the moving section from the starting point to the destination.