Disclosed herein are system, method, and computer program product aspects for enabling an autonomous vehicle (AV) to react to objects posing a risk to the AV. The system can monitor an object within a vicinity of the AV. A plurality of trajectories predicting paths the object will take can be generated, the plurality of trajectories being based on a plurality of inputs indicating current and past characteristics of the object. Using a learned model, a forecasted position of the object at an instance in time can be generated. An error value representing how accurate the forecasted position is versus an observed position of the object can be stored. Error values can be accumulated over a period of time. A risk factor can be assigned for the object based on the accumulated error values. A maneuver for the AV can be performed based on the risk factor.

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.

A vehicle control device includes a recognizer configured to recognize a surrounding environment including a structure of a road near a vehicle and another vehicle, a deriver configured to derive a predicted probability that the other vehicle will travel in the future along each of routes which are assumed when a plurality of routes along which the other vehicle is able to travel are assumed on a road on which the other vehicle recognized by the recognizer travels, and a travel controller configured to control behavior of the vehicle based on the predicted probability derived by the deriver.

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.

The present technology pertains to predicting trajectories of objects near an autonomous vehicle. The predictions may be obtained as output from a trajectory prediction machine learning model. The inputs to the trajectory prediction machine learning model may be based on a first map of an area surrounding an autonomous vehicle, and a second map of an area around an object within the first area. The second map may have a smaller area and a higher resolution relative to the first map.

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 computer-implemented method and system for classifying traffic situations of a virtual test. The method comprises concatenating a plurality of determined data segments of the lateral and longitudinal behavior of the ego vehicle to identify vehicle actions and classifying traffic situations by linking a subset of the determined data segments of the lateral and longitudinal behavior of the ego vehicle with the identified vehicle actions. The invention further comprises a computer-implemented method for providing a trained machine learning algorithm for classifying traffic situations of a virtual test.

Systems and methods navigate a vehicle by determining a free space region in which the vehicle can travel. In one implementation, a system may include at least one processor programmed to receive from an image capture device, a plurality of images associated with the environment of a vehicle, analyze at least one of the plurality of images to identify a first free space boundary on a driver side of the vehicle and extending forward of the vehicle, a second free space boundary on a passenger side of the vehicle and extending forward of the vehicle, and a forward free space boundary forward of the vehicle and extending between the first free space boundary and the second free space boundary. The first free space boundary, the second free space boundary, and the forward free space boundary may define a free space region forward of the vehicle. The at least one processor of the system may be further programmed to determine a navigational path for the vehicle through the free space region and cause the vehicle to travel on at least a portion of the determined navigational path within the free space region forward of the vehicle.

Techniques for determining that a first vehicle is associated with a reverse state, and controlling a second vehicle based on the reverse state, are described herein. In some examples, the first vehicle may provide an indication that the first vehicle will be executing a reverse maneuver, such as with reverse lights on the vehicle or by positioning at an angle relative to a road or parking space to allow for the reverse maneuver into a desired location. A planning system of the second vehicle (such as an autonomous vehicle) may receive sensor data and determine a variety of these indications to determine a probability that the vehicle is going to execute a reverse maneuver. The second vehicle can further determine a likely trajectory of the reverse maneuver and can provide appropriate accommodations (e.g., time and/or space) to allow the second vehicle to execute the maneuver safely and efficiently.

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.