A control system for controlling a motion of a vehicle to a target driving goal uses a decision-maker configured to determine a sequence of intermediate goals leading to the next target goal by optimizing the motion of the vehicle subject to a first model and tightened driving constraints formed by tightening driving constraints by a safety margin, and uses a motion planner configured to determine a motion trajectory of the vehicle tracking the sequence of intermediate goals by optimizing the motion of the vehicle subject to the second model. The driving constraints include mixed logical inequalities of temporal logic formulae specified by traffic rules to define an area where the temporal logic formulae are satisfied, while the tightened driving constraints shrink the area by the safety margin, which is a function of a difference between the second model and the first model approximating the second model.

The present invention improves the accuracy of predicting rarely occurring behavior of moving bodies, without reducing the accuracy of predicting commonly occurring behavior of moving bodies. A vehicle 101 is provided with a moving body behavior prediction device 10. The moving body behavior prediction device 10 is provided with a first behavior prediction unit 203 and a second behavior prediction unit 207. The first behavior prediction unit 203 learns first predicted behavior 204 so as to minimize the error between behavior prediction results for moving bodies and behavior recognition results for the moving bodies after a prediction time has elapsed. The second behavior prediction unit 207 learns future second predicted behavior 208 of the moving bodies around the vehicle 101 so that the vehicle 101 does not drive in an unsafe manner.

An autonomous driving control method carried out by an autonomous driving control system having an autonomous driving control unit that executes an autonomous driving control for causing a host vehicle to travel along a target travel route generated on a map, comprising setting one or a plurality of target passage gates through which the host vehicle is scheduled to pass during passage through a toll plaza, determining the presence or absence of a preceding vehicle that has the predicted passage gate that matches the target passage gate of the host vehicle from among a plurality of preceding vehicles, and carrying out following travel using the preceding vehicle that has the predicted passage gate that matches the target passage gate as a follow target.

Disclosed are a short cut-in target identification apparatus and an identification method thereof. The short cut-in target identification apparatus includes an occupancy distance map (ODM) information calculator configured to calculate ODM information based on subject vehicle and surrounding object information, a track information calculator configured to calculate track information based on the subject vehicle and surrounding object information, and a short cut-in target selector configured to select a short cut-in target based on the ODM information and the track information.

A vehicle controller includes a processor configured to detect an object region including another vehicle near a vehicle from each of time series images obtained by a camera mounted on the vehicle; detect a predetermined action taken by the other vehicle, based on a trajectory of the other vehicle estimated from the object region of each image; identify the state of a signal light of the other vehicle, based on characteristics obtained from pixel values of the object region of each image; extract information indicating characteristics of an action of the other vehicle or the state of a signal light at the predetermined action taken by the other vehicle, based on the predetermined action detected in a tracking period and the state of the signal light related to the predetermined action; and predict behavior of the other vehicle, using the extracted information.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for identifying high-priority agents in the vicinity of a vehicle. The high-priority agents can be identified based on a set of mutual importance scores in which each mutual importance score indicates an estimated mutual relevance between the vehicle and a different agent from a set of agents on planning decisions of the other. The mutual importance scores can be calculated based on importance scores assessed from the perspectives of both the vehicle and the agents.

Disclosed herein are systems and method including a method for managing an autonomous vehicle. The method includes providing input associated with an autonomous vehicle to a machine learning model, wherein the machine learning model is trained to predict what a planning stack of the autonomous vehicle will choose with respect to selecting a low cost branch of a tree structure in which a plurality of branches of the tree structure are evaluated to determine the low cost branch associated with a future route for the autonomous vehicle. The method further includes generating an output of the machine learning model to predict an output of the planning stack and inputting the output of the machine learning model into the planning stack. The planning stack can traverse a tree structure of possible routes more efficiently with a predicted outcome based on the output of the machine learning model.

Disclosed are systems and techniques for managing an autonomous vehicle (AV). In some aspects, an AV may predict a first predicted position of an object perceived by one or more sensors of the autonomous vehicle, wherein the first predicted position of the object is associated with an uncertainty metric. The AV may determine that a first error between a first actual position of the object and the first predicted position of the object is greater than the uncertainty metric. The AV may increase the uncertainty metric corresponding to a second predicted position of the object based on the first error to result in a revised uncertainty metric. The AV may provide the revised uncertainty metric to a planning stack for maneuvering the AV.

A method and an electronic device for generating a trajectory of a Self-Driving Car (SDC) are provided. The method comprises: determining a presence of at least one third-party object around the SDC; generating a plurality of predicted trajectories for the third-party object, where at least one of the plurality of trajectories includes a maneuver executable, by the third-party object, at a future third-party object location; calculating, for the at least one of the plurality of trajectories including the a respective braking profile associated with the third-party object; in response to the respective braking profile being above a pre-determined threshold, eliminating an associated one of the at least one of the plurality of trajectories from future processing; determining an SDC trajectory based on remaining ones of the plurality of predicted trajectories for the third-party.

During the autonomous driving, the movement trails or moving history of obstacles, as well as, an autonomous driving vehicle (ADV) may be maintained in a corresponding buffer. For the obstacles and the ADV, the vehicle states at different points in time are maintained and stored in one or more buffers. The vehicle states representing the moving trails or moving history of the obstacles and the ADV may be utilized to reconstruct a history trajectory of the obstacles and the ADV, which may be used for a variety of purposes. For example, the moving trails or history of obstacles may be utilized to determine lane configuration of one or more lanes of a road, particularly, in a rural area where the lane markings are unclear. The moving history of the obstacles may also be utilized predict the future movement of the obstacles, tailgate an obstacle, and infer a lane line.