The present disclosure provides a method and apparatus for automatically producing map data. The method includes: performing track rectification on crowdsourcing tracks based on corresponding standard tracks, and locating each map element included, based on depth information of track point images included in the rectified crowdsourcing tracks; comparing a latest map element obtained based on the rectified crowdsourcing tracks locating and an old map element at a corresponding locating position using a pre-built entity semantic map; determining, in response to a change in the latest map element compared to the old map element, a target processing method according to a processing standard of a changed map element pre-abstracted from a map element update specification; and processing the latest map element according to the target processing method to obtain a processed latest map.

An information processing apparatus includes: a shape information acquiring unit 204 configured to acquire shape information of a surrounding environment of a moving body measured by a sensor mounted in the moving body; a position and posture acquiring unit configured to acquire position and posture information of the sensor; a correction state acquiring unit configured to acquire a performance state relating to a process of correcting the position and posture information; a priority level determining unit configured to determine a priority level of an area for generating a map; and a map generating unit configured to generate the map on the basis of the shape information and the position and posture information acquired at the time of acquisition of the shape information, in which the map generating unit generates the map in order from an area of which the priority level is high in accordance with the performance state.

An apparatus for updating a map detects the positions of reference points corresponding to a feature on a road being traveled by a vehicle from surrounding data representing features around the vehicle, and updates probability distributions associated with the respective reference points so that the probabilities of existence of the reference points at the detected positions of the reference points increase. Each of the probability distributions indicates the probability of existence of the corresponding reference point as a function of position.

An apparatus, method and computer program product are provided for providing signature reduction for a vehicle. For example, the apparatus receives a destination for a vehicle as input, selects a subset from a plurality of road segments as a route from a location to the destination, and outputs the route or a portion thereof. The subset is selected based on association of each of the plurality of road segments with respect to a source, and the source is capable of acquiring vehicle signature information.

Systems and methods for updating an electronic map of a facility are disclosed. The electronic map includes a set of map nodes. Each map node has a stored image data associated with a position within the facility. The method includes collecting image data at a current position of a self-driving material-transport vehicle; searching the electronic map for at least one of a map node associated with the current position and one or more neighboring map nodes within a neighbor threshold to the current position; comparing the collected image data with the stored image data of the at least one of the map node and the one or more neighboring map nodes to determine a dissimilarity level. The electronic map may be updated based at least on the collected image data and the dissimilarity level. The image data represents one or more features observable from the current position.

In one embodiment, a method is provided. The method comprises determining a first value of a coefficient of an edge-determining algorithm in response to a spatial resolution of a first image acquired with an image capture device onboard a vehicle, a spatial resolution of a second image, and a second value of the coefficient in response to which the edge-determining algorithm generated a second edge map corresponding to the second image. The method further comprises determining, with the edge-determining algorithm in response to the coefficient having the first value, at least one edge of at least one object in the first image. The method further comprises generating, in response to the determined at least one edge, a first edge map corresponding to the first image. The method further comprises determining at least one navigation parameter of the vehicle in response to the first and second edge maps.

A voice system of a moving machine is a voice system of a moving machine driven by a driver who is exposed to an outside of the moving machine and includes: a noise estimating section which estimates a future noise state based on information related to a noise generation factor; and a voice control section which changes an attribute of voice in accordance with the estimated noise state, the voice being voice to be output to the driver.

System, methods, and other embodiments described herein relate to training a depth model for joint depth completion and prediction. In one arrangement, a method includes generating depth features from sparse depth data according to a sparse auxiliary network (SAN) of a depth model. The method includes generating a first depth map from a monocular image and a second depth map from the monocular image and the depth features using the depth model. The method includes generating a depth loss from the second depth map and the sparse depth data and an image loss from the first depth map and the sparse depth data. The method includes updating the depth model including the SAN using the depth loss and the image loss.

Techniques for determining distortion in a map caused by measurement errors are discussed herein. For example, such techniques may include implementing a model to estimate map distortion between the map frame and the inertial frame. Data such as sensor data, map data, and vehicle state data may be input into the model. A map distortion value output from the model may be used to compensate vehicle operations in a local region by approximating the distortion as linearly varying about the region. A vehicle, such as an autonomous vehicle, can be controlled to traverse an environment based on the trajectory.

In a computer-implemented method of assessing driving performance using route scoring, driving data indicative of operation of a vehicle while the vehicle was driven on a driving route may be received. Road infrastructure data indicative of one or more features of the driving route may also be received. A route score for the driving route may be calculated using the road infrastructure data, and a driving performance score for a driver of the vehicle may be calculated using the driving data and the route score for the driving route. Data may be sent to a client device via a network to cause the client device to display the driving performance score and/or a ranking based on the driving performance score, and/or the driving performance score may be used to determine a risk rating for the driver of the vehicle.