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.

A method may include obtaining input information relating to an environment in which an autonomous vehicle (AV) operates, the input information describing at least one of: a state of the AV, an operation of the AV within the environment, a property of the environment, or an object included in the environment. The method may include identifying a first object in the vicinity of the AV based on the obtained input information. The method may include determining a first object rule corresponding to the first object, the first object rule indicating suggested driving behavior for interacting with the first object. The method may include determining a first decision that follows the first object rule and sending an instruction to a control system of the AV, the instruction describing a given operation of the AV responsive to the first object rule according to the first decision.

The subject matter of this specification can be implemented in, among other things, a system that includes a first light source to produce a pulsed beam and a second light source to produce a continuous beam, a modulator to impart a modulation to the second beam, and an optical interface subsystem to transmit the pulsed beam and the continuous beam to an outside environment and to detect a plurality of signals reflected from the outside environment. The system further includes one or more circuits configured to identify associations of various reflected pulsed signals, used to detect distance to various objects in the environment, with correct reflected continuous signals, used to detect velocities of the objects. The one or more circuits identify the associations based on the modulation of the detected continuous signals.

Techniques and methods for training and/or using a machine learned model that identifies unsafe events. For instance, computing device(s) may receive input data, such as vehicle data generated by one or more vehicles and/or simulation data representing a simulated environment. The computing device(s) may then analyze features represented by the input data using one or more criteria in order to identify potential unsafe events represented by the input data. Additionally, the computing device(s) may receive ground truth data classifying the identified events as unsafe events or safe events. The computing device(s) may then train the machine learned model using at least the input data representing the unsafe events and the classifications. Next, when the computing device(s) and/or vehicles receive input data, the computing device(s) and/or vehicles may use the machine learned model to determine if the input data represents unsafe events.

Systems and methods for automated vehicle sensor calibration and verification are provided. One example method involves monitoring a vehicle using one or more external sensors of a vehicle calibration facility. The sensor data may be indicative of a relative position of the vehicle in the vehicle calibration facility. The method also involves causing the vehicle to navigate in an autonomous driving mode, based on the sensor data, from a current position of the vehicle to a first calibration position in the vehicle calibration facility. The method also involves causing a first sensor of the vehicle to perform a first calibration measurement while the vehicle is at the first calibration position. The method also involves calibrating the first sensor based on at least the first calibration measurement.

This application describes systems and techniques for adjusting one or more setting(s) of a vehicle based on detected condition(s) to avoid damage due to contact of the tires with a body, chassis, or other components of the vehicle. In some instances, the vehicle may determine a ride height of the vehicle, determine a limited range of steering angles based at least in part on the ride height, and control operation of the steering system of the vehicle based at least in part on the limited range of steering angles. In some instances, the vehicle may determine a steering angle of the vehicle, determine a limited range of ride heights based at least in part on the steering angle, and control operation of the suspension system of the vehicle based at least in part on the limited range of ride heights.

A method and a system determine a change of state of an autonomous device, such as an autonomous vehicle. A plurality of performance parameter values obtained by monitoring at least one performance parameter during the autonomous operation of the device is received. A performance quantity quantifying the quality of autonomous operation of the device, in particular the quality of driving of the autonomous vehicle, is determined based on the obtained performance parameter values and information associated with a flux of software and/or hardware related to the autonomous operation of the device. Further, a change of state value for the device is determined based on the performance quantity.