Abstract: In one embodiment, a method includes determining an initial cost volume associated with a plurality of potential trajectories of a vehicle in an environment based on a set of movement restrictions of the vehicle, generating a delta cost volume using the initial cost volume and environment data associated with the environment, wherein the delta cost volume is generated by determining adjustments to the initial cost volume that incorporate observed driving behavior, and scoring a trajectory of the plurality of potential trajectories for the vehicle based on the initial cost volume and the delta cost volume.

Abstract: In one embodiment, a computing system of a vehicle may receive vehicle driving data associated with a vehicle driving in an environment and detected environment data associated with the environment. The system may generate a reference trajectory of the vehicle driving in the environment based on the vehicle driving data. The system may determine driving constraints associated with the environment based on the detected environmental data. The system may generate a trajectory of the vehicle based on the driving constraints. The system may determine a difference in at least one parameter associated with the trajectory relative to at least one corresponding parameter associated with the reference trajectory. The system may adjust weight values associated with cost functions of the trajectory based on the difference between the at least one parameter associated with the trajectory and the corresponding parameter associated with the reference trajectory.

Abstract: In one embodiment, a method includes, by a computing system associated with a vehicle, receiving sensor data from one or more sensors of the vehicle, wherein the sensor data is based on an environment of the vehicle, identifying, based on the sensor data, one or more objects in the environment, generating, based on the one or more objects, a set of points that represent the environment, wherein each object has one or more corresponding points in the set of points, and each of the points is associated with one or more features associated with the corresponding object, generating a prediction for at least one of the objects in the environment or the vehicle by processing the set of points using a machine-learning model, and causing the vehicle to perform one or more operations based on the prediction.

Abstract: In one embodiment, a computing system of a vehicle may receive vehicle driving data associated with a vehicle driving in an environment and detected environment data associated with the environment. The system may generate a reference trajectory of the vehicle driving in the environment based on the vehicle driving data. The system may determine driving constraints associated with the environment based on the detected environmental data. The system may generate a trajectory of the vehicle based on the driving constraints. The system may determine a difference in at least one parameter associated with the trajectory relative to at least one corresponding parameter associated with the reference trajectory. The system may adjust weight values associated with cost functions of the trajectory based on the difference between the at least one parameter associated with the trajectory and the corresponding parameter associated with the reference trajectory.

Abstract: In one embodiment, a method includes determining an initial cost volume associated with a plurality of potential trajectories of a vehicle in an environment based on a set of movement restrictions of the vehicle, generating a delta cost volume using the initial cost volume and environment data associated with the environment, wherein the delta cost volume is generated by determining adjustments to the initial cost volume that incorporate observed driving behavior, and scoring a trajectory of the plurality of potential trajectories for the vehicle based on t the initial cost volume and the delta cost volume.

Abstract: In one embodiment, a method includes accessing contextual data associated with a vehicle, the contextual data being captured using one or more sensors associated with the vehicle and including perception data, generating, based on at least a portion of the perception data, one or more representations of an environment of the vehicle, determining a predicted risk score by processing the one or more representations of the environment of the vehicle using a machine-learning model, wherein the machine-learning model has been trained using human-driven vehicle risk observations and corresponding representations of environments associated with the observations, determining that one or more vehicle operations are to be performed based on a comparison of the predicted risk score to a threshold risk score, and causing the vehicle to perform the one or more vehicle operations based on the predicted risk score and the threshold risk score.

Abstract: In one embodiment, a method includes, by a computing system associated with a vehicle, receiving sensor data of an environment of the vehicle, the sensor data being captured by one or more sensors associated with the vehicle, generating, based on the sensor data, one or more representations of the environment of the vehicle, determining a target speed for the vehicle by processing the one or more representations of the environment of the vehicle using a machine-learning model that has been trained using human-driven vehicle speed observations and corresponding representations of environments associated with the observations, determining a trajectory plan and a planned speed for the vehicle based on at least the target speed, and causing the vehicle to perform one or more operations based on the trajectory plan and the planned speed.

Abstract: Examples disclosed herein may involve (i) generating a set of candidate trajectories for a vehicle that each comprise a respective series of planned states for the vehicle, (ii) scoring the candidate trajectories in the generated set of candidate trajectories using one or more reference models that are each configured to (a) receive input values for a respective set of feature variables that are correlated to a respective scoring parameter and (b) output a value for the respective scoring parameter that is reflective of human-driving behavior, (iii) based at least in part on the scoring, selecting a candidate trajectory from the generated set of candidate trajectories to serve as a planned trajectory for vehicle, and (iv) using the selected candidate trajectory as the planned trajectory for the vehicle.