An autonomous driving control system, comprising a main control system and a backup control system. The main control system comprises a main control module and main execution modules, and the backup control system comprises a backup control module and backup execution modules; the main control module monitors an operating status of the main control system in real time; the main control module further sends, when detecting that a failure occurs in the main control system, a failure notification to the backup control module, and sends a response termination control instruction to each of the main execution modules, the response termination control instruction being a control instruction for instructing each of the main execution modules not to respond to any control over a vehicle; and the backup control module controls, after receiving the failure notification, the backup execution modules to start to execute a backup control instruction.

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.

The present disclosure provides a route processing method and apparatus. The solution includes: acquiring an initial traveling route, which includes a plurality of track points, corresponding to a vehicle; determining a vehicle traveling area, which includes an area where the vehicle is located when traveling to the track point, corresponding to each track point; determining at least one target track point in the plurality of track points according to the vehicle traveling area, where a first obstacle exists in the vehicle traveling area; performing updating processing on a position of each target track point in the initial traveling route respectively according to the position of the each target track point and a position of the first obstacle, and obtaining a target traveling route according to the target track point for which the updating processing has been performed; and controlling the vehicle to travel according to the target traveling route.

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.

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.

A vehicle navigation apparatus includes a vehicle navigation unit and a route guidance control unit. The vehicle navigation unit includes a route setting unit and a navigation control unit. The route setting unit sets a route to a destination point on the basis of information on a position of a vehicle, information on a destination point, and first map information stored in a storage. The navigation control unit performs guidance on the route and controls the form of displaying the route on a display. The route guidance control unit includes at least one processor determining the driving entity of a vehicle. In a case where the at least one processor determines that the driving entity of the vehicle is the vehicle itself and the vehicle deviates from the route, the at least one processor stops the guidance until the vehicle reaches a next waypoint of the route.

Provided are methods for vehicle scenario mining for machine learning methods, which can include determining a set of attributes associated with an untested scenario for which a machine learning model of an autonomous vehicle is to make planned movements. The method includes searching a scenario database for the untested scenario based on the set of attributes. The scenario database includes a plurality of datasets representative of data received from an autonomous vehicle sensor system in which the plurality of datasets is marked with at least one attribute of the set of attributes. The method further includes obtaining the untested scenario from the scenario database for inputting into the machine learning model for training the machine learning model. The machine learning model is configured to make the planned movements for the autonomous vehicle. Systems and computer program products are also provided.

A computer system obtains a plurality of road images captured by one or more cameras attached to one or more vehicles. The one or more vehicles execute a model that facilitates driving of the one or more vehicles. For each road image of the plurality of road images, the computer system determines, in the road image, a fraction of pixels having an ambiguous lane marker classification. Based on the fraction of pixels, the computer system determines whether the road image is an ambiguous image for lane marker classification. In accordance with a determination that the road image is an ambiguous image for lane marker classification, the computer system enables labeling of the image and adds the labeled image into a corpus of training images for retraining the model.

Methods and systems for controlling navigation of a vehicle are disclosed. The system will first identify a plurality of goal points corresponding to a drivable area that a vehicle is traversing or will traverse, where the plurality of goal points are potential targets that an uncertain road user (URU) within the drivable area can use to exit the drivable area. The system will then receive perception information relating to the URU within the drivable area, and identify a target exit point from the plurality goal points based on a score. The score is computed based on the received perception information and a loss function. The system will generate a trajectory of the URU from a current position of the URU to the target exit point, and control navigation of the vehicle to avoid collision with the URU.