A parking management system includes a guide robot disposed in a parking space provided with a plurality of parking areas, the guide robot being configured to be matched with a vehicle entering the parking space and to be driven ahead of the matched vehicle to guide the matched vehicle to an allocated parking area, and a management server configured to recognize the vehicle entering the parking space, to match the recognized vehicle with the guide robot, to monitor the parking space to identify a parking status of each of the parking areas, to allocate a parking area to the matched vehicle based on the identified parking status, and to control the guide robot to be driven to the allocated parking area.

Methods and systems are provided for controlling an autonomous vehicle. In one embodiment, a method includes: identifying, by a processor, at least one constraint on a longitudinal dimension of an upcoming road; defining, by the processor, constraint activation logic based on a type of the at least one constraint; performing, by the processor, the constraint activation logic to determine a state of the constraint to be at least one of active and inactive; when the state of the constraint is active, validating, by the processor, a motion plan of the autonomous vehicle based on the constraint; and selectively controlling the autonomous vehicle based on the validating of the motion plan.

Provided are methods for scalable and realistic camera blockage dataset generation, which can include generating synthetic images depicting a blockage on or near an imaging sensor. The synthetic images may be created by combining one or more chroma key-extracted partial blockage image with one or more background images, the combination of which can provide a scalable blockage dataset. Metadata for each synthetic image can be generated along with the synthetic image, by annotating the portion of the synthetic image represented by the chroma key-extracted partial blockage image as constituting blockage. The synthetic images can be used to increase the accuracy of machine learning models trained to identify blockage by increasing the volume of data available for such training.

The offline map generation process may collect multiple point cloud data of the same area. A perception algorithm may operate on the point cloud data to detect static objects, which may be fixed road features that do not change among the point cloud data, allowing the perception algorithm to more accurately detect the static objects. During online operation of the ADV through the area, the ADV may trim regions-of-interest (ROI) of the area to exclude the predefined static objects. The perception algorithm may execute the sensor data of the ROI in real-time to detect objects in the ROI. The may be added back to the output of the perception algorithm to complete the perception output.

A distance measuring method and device using image tracking for autonomous driving are proposed. The distance measuring method using image tracking for autonomous driving performed in a computing device includes recognizing a grid matching part marked on a road photographed by a camera; generating a virtual grid applied to the road using the grid matching part; and calculating a distance to a target object using the virtual grid.

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.

To provide a technology that makes it possible to recognize a target object quickly and accurately. An information processing apparatus according to the present technology includes a controller. The controller recognizes a target object on the basis of event information that is detected by an event-based sensor, and transmits a result of the recognition to a sensor apparatus that includes a sensor section that is capable of acquiring information regarding the target object.

A vehicle communication, power, and guidance system for use in guiding and communicating with a land vehicle along a roadway, the system comprising: one or more reference devices positioned along the roadway, each of the reference devices further comprising: a memory device for storing fixed values for one or more vehicle and traffic related parameters; and one or more transmission modules for transmitting said fixed values for one or more vehicle and traffic related parameters; a vehicle mounted device comprising: a receiving module for receiving transmitted signals from the transmission module of said reference devices; and a processing module for processing the received signals and a transmitter module to transmitting signals to the reference device to communicate with one or more controllers of the vehicle to guide and control movement of the vehicle along the roadway.

A rover or semi-autonomous or autonomous vehicle may use an image classifier to determine a terrain class of regions of an image of the terrain ahead of the rover or vehicle. The regions of the images are used to estimate the slope of the terrain for the different regions. The terrain class and slope are used to predict an amount of slip the rover will experience when traversing the terrain of the different regions. A heuristic mapping for the terrain class may be applied to the predicted slip amount to determine a hazard level for the rover or vehicle traversing the terrain.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using a surfel map to generate a prediction for a state of an environment. One of the methods includes obtaining surfel data comprising a plurality of surfels, wherein each surfel corresponds to a respective different location in an environment, and each surfel has associated data that comprises an uncertainty measure; obtaining sensor data for one or more locations in the environment, the sensor data having been captured by one or more sensors of a first vehicle; determining one or more particular surfels corresponding to respective locations of the obtained sensor data; and combining the surfel data and the sensor data to generate a respective object prediction for each of the one or more locations of the obtained sensor data.