This document discloses system, method, and computer program product embodiments for operating a robot. For example, the method includes: detecting an object in proximity to the robot; generating a path of travel for the object that is defined by a plurality of first data points representing different object locations in an area; generating cost curves respectively associated with the plurality of first data points; generating a polyline comprising a plurality of second data points associated with displacements of the cost curves from a particular location in the area; defining a predicted path of travel for the object based on the polyline; and using the predicted path of travel for the object to facilitate at least one autonomous operation of the robot.

An autonomous vehicle (AV) includes features that allows the AV to comply with applicable regulations and statutes for performing safe driving operation. Example embodiments relate to an autonomous vehicle having a trailer coupled to a rear thereof. An example method includes continuously predicting a trailer trajectory that is distinct from a planned trajectory of the autonomous vehicle. The method further includes determining that the predicted trailer trajectory is within a minimum avoidance distance away from a stationary vehicle located on a roadway on which the autonomous vehicle is located. The method further includes modifying the planned trajectory of the autonomous vehicle such that the predicted trailer trajectory satisfies the minimum avoidance distance. The method further includes causing the autonomous vehicle to navigate along the modified trajectory based on transmitting instructions to one or more subsystems of the autonomous vehicle.

A trajectory prediction method and apparatus, a storage medium, and an electronic device are provided. In embodiments of this disclosure, according to a historical trajectory of a designated target and a historical trajectory of each obstacle, a historical interaction feature between the designated target and each obstacle is determined, and a motion trajectory of the designated target is predicted to obtain an initial predicted trajectory. A future interaction feature between each obstacle and the designated target is then determined according to the initial predicted trajectory and a planned trajectory of each obstacle. According to the future interaction feature, a final predicted trajectory of the designated target is obtained.

A vehicle is disclosed. The vehicle may comprise a plurality of ground engaging members, and a frame supported by the plurality of ground engaging members. The frame may include an operator area having a platform sized and shaped to provide a location for a standing operator and at least one upstanding frame member rearward of the operator area. The vehicle may further comprise a steering input operatively coupled to at least one of the plurality of ground engaging members to steer the vehicle; and at least one communication device supported by the at least one upstanding frame member rearward of the operator area, the at least one communication member being positioned higher than the steering input.

Technologies and techniques for determining a trajectory of an assisted-operated motor vehicle. At least one object is detected in an environment of the motor vehicle and at least one uncertainty with respect to the object is determined. A future environment with the object is predicted via an electronic computing device, as a function of the detected environment and the detected object, wherein a risk value for a planned trajectory is determined on the basis of a collision probability. A most probable impact constellation and accident severity for the most probable impact constellation is determined, wherein the collision probability and the accident severity is weighted in a risk value, and wherein the trajectory is determined as a function of the determined risk value.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium that determine yield behavior for an autonomous vehicle, and can include identifying an agent that is in a vicinity of an autonomous vehicle navigating through a scene at a current time point. Scene features can be obtained and can include features of (i) the agent and (ii) the autonomous vehicle. An input that can include the scene features can be processed using a first machine learning model that is configured to generate (i) a crossing intent prediction that includes a crossing intent score that represents a likelihood that the agent intends to cross a roadway in a future time window after the current time, and (ii) a crossing action prediction that includes a crossing action score that represents a likelihood that the agent will cross the roadway in the future time window after the current time.

Provided are methods for selection of optimal minimal risk maneuver, which can include receiving at least one first parameter associated with a characteristic of a vehicle and at least one second parameter associated with at least one object external to the vehicle, generating at least one future state for at least one of the first and second parameters, selecting at least one maneuver from a plurality of maneuvers based on the generated future state, determining at least one reward value associated with the selected maneuver, updating the selected maneuver based on the determined reward value to generate an updated maneuver, and operating the vehicle based on the updated maneuver. Systems and computer program products are also provided.

Provided are methods for predicting motion of hypothetical agents, which can include receiving sensor data, generating a segmentation mask indicative of at least one occluded area, generating at least one hypothetical agent trajectory, determining at least one agent generation point, determining whether a threshold distance from the at least one agent generation point to the vehicle is satisfied, generating at least one agent, planning a path of the vehicle and controlling the vehicle according to the planned path. Systems and computer program products are also provided.

The described aspects and implementations enable vehicle light classification in autonomous vehicle (AV) applications. In one implementation, disclosed is a method and a system to perform the method that includes, obtaining, by a processing device, first image data characterizing a driving environment of an autonomous vehicle (AV). The processing device may identify, based on the image data, a vehicle within the driving environment. The processing device may process the image data using one or more trained machine-learning models (MLMs) to determine a state of one or more lights of the vehicle and cause an update to a driving path of the AV based on the determined state of the lights.

A method and an electronic apparatus for predicting a path based on an object interaction relationship are provided. The method includes the following. A video including a plurality of image frames is received. Object recognition is performed on a certain image frame among the plurality of image frames to recognize at least one object in the certain image frame. Preset interactive relationship information associated with the at least one object is obtained from an interactive relationship database based on the at least one object. A first trajectory for navigating the first vehicle is determined based on the preset interactive relationship information.