Methods of determining relevance of objects that a vehicle's perception system detects are disclosed. A system on or in communication with the vehicle will identify a time horizon, and a look-ahead lane based on a lane in which the vehicle is currently traveling. The system defines a region of interest (ROI) that includes one or more lane segments within the look-ahead lane. The system identifies a first subset that includes objects located within the ROI, but not objects not located within the ROI. The system identifies a second subset that includes objects located within the ROI that may interact with the vehicle during the time horizon, but not excludes actors that may not interact with the vehicle during the time horizon. The system classifies any object that is in the first subset, the second subset or both subsets as a priority relevant object.

Real-time decision-making for a vehicle using belief state determination is described. Operational environment data is received while the vehicle is traversing a vehicle transportation network, where the data includes data associated with an external object. An operational environment monitor establishes an observation that relates the object to a distinct vehicle operation scenario. A belief state model of the monitor computes a belief state for the observation directly from the operational environment data. The monitor provides the computed belief state to a decision component implementing a policy that maps a respective belief state for the object within the distinct vehicle operation scenario to a respective candidate vehicle control action. A candidate vehicle control action is received from the policy of the decision component, and a vehicle control action is selected for traversing the vehicle transportation from any available candidate vehicle control actions.

An autonomous vehicle control system is provided. The control system may include a plurality of cameras to acquire a plurality of images of an area in a vicinity of a vehicle; and at least one processing device configured to: recognize a curve to be navigated based on map data and vehicle position information; determine an initial target velocity for the vehicle based on at least one characteristic of the curve as reflected in the map data; adjust a velocity of the vehicle to the initial target velocity; determine, based on the plurality of images, observed characteristics of the curve; determine an updated target velocity based on the observed characteristics of the curve; and adjust the velocity of the vehicle to the updated target velocity.

An electronic control device including a sensor fusion processing unit that integrates a plurality of pieces of sensor information having been input from a plurality of sensors. The electronic control device further including a behavior prediction processing unit that obtains a future value in which a future behavior of a target object is predicted based on joint information integrated by the sensor fusion processing unit. The electronic control device further including a comparison unit that compares a future value predicted by the behavior prediction processing unit with output information of each sensor of the sensor fusion processing unit at a predicted time.

Embodiments of the present disclosure disclose a method and apparatus for avoidance control of a vehicle, an electronic device, and a storage medium, where the method includes: obtaining behavior information of a moving obstacle, where the moving obstacle is located in a direction of movement of the vehicle and a distance between the moving obstacle and the vehicle satisfies a preset avoidance distance; determining an avoidance strategy for the vehicle based on the behavior information of the moving obstacle; and controlling movement of the vehicle based on the avoidance strategy. The method in the embodiments of the present disclosure considers the behavior information of the moving obstacle in the process of avoidance control of the vehicle, and the vehicle can be controlled to travel when the moving obstacle takes an avoidance action, thereby improving the accuracy of avoidance for the vehicle and the practicality of intelligent driving.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium that generates path prediction data for agents in the vicinity of an autonomous vehicle using one or more machine learning models. One of the methods includes identifying an agent in a vicinity of an autonomous vehicle navigating through an environment and determining that the agent is within a vicinity of a crossing zone across a roadway. The crossing zone can be a marked crossing zone or an unmarked crossing zone. For example, the crossing zone can be an unmarked crossing zone that has been identified based on previous observations of agents crossing the roadway. In response to determining that the agent is within a vicinity of a crossing zone: (i) features of the agent and of the crossing zone can be obtained; (ii) a first input that includes the features can be processed using a first machine learning model that is configured to generate a first crossing prediction that characterizes future crossing behavior of the agent, and (iii) a predicted path for the agent for crossing the roadway can be determined from at least the first crossing prediction.

The present disclosure provides a method and device for predicting a motion track of an obstacle and an autonomous vehicle, and relates to the technical field of autonomous driving, so as to at least solve the technical problem of low prediction precision of a motion track of an obstacle in an interaction scene. A specific implementation solution includes: environment information in a target scene, historical state information of a target obstacle and track planning information of a target vehicle are obtained, and the target obstacle is a potential interaction object of the target vehicle; and a motion track of the target obstacle is predicted based on the environment information, the historical state information and the track planning information.

Systems and methods for operating an autonomous vehicle (AV) are provided. The method includes detecting one or more objects in an environment, predicting a first set of predicted object trajectories comprising one or more trajectories for each of the detected one or more objects, generating a plurality of candidate AV trajectories for the AV, scoring each of the candidate AV trajectories according to a cost function, using the scoring to select a final AV trajectory for execution, determining which of the predicted object trajectories affected the final AV trajectory and which did not do so, adding the predicted object trajectories that affected the final AV trajectory to a persisted prediction cache, excluding from the persisted prediction cache any predicted object trajectories that did not affect the final AV trajectory, and executing the final AV trajectory to cause the AV to move along the final AV trajectory.

Techniques for determining occupancy using unobstructed sensor emissions. For instance, a vehicle may receive sensor data from one or more sensors. The sensor data may represent at least locations to points within an environment. Using the sensor data, the vehicle may determine areas within the environment that are obstructed by objects (e.g., locations where objects are located). The vehicle may also use the sensor data to determine areas within the environment that are unobstructed by objects (e.g., locations where objects are not located). In some examples, the vehicle determines the unobstructed areas as including areas that are between the vehicle and the identified objects. This is because sensor emissions from the sensor(s) passed through these areas and then reflected off of objects located farther distances from the vehicle. The vehicle may then generate a map indicating at least the obstructed areas and the unobstructed areas within the environment.

A method for planning the motion of a vehicle includes: determining a nominal trajectory for the vehicle based on a desired maneuver to be carried out in a traffic space, on a current state of movement of the vehicle and on a detected state of a surrounding of the vehicle, and determining an abort trajectory branching off from the nominal trajectory and guiding the vehicle to a safe condition regardless of the desired maneuver, wherein the nominal trajectory and the abort trajectory are determined simultaneously using a single optimization process.