A method for providing a positive decision signal for a vehicle which is about to perform a traffic scenario action. The method includes receiving information about at least one surrounding road user, which information is indicative of distance to the surrounding road user with respect to the vehicle and at least one of speed and acceleration of the surrounding road user; calculating a value based on the received information; providing the positive decision signal to perform the traffic scenario action when the calculated value is fulfilling a predetermined condition. The value is calculated based on an assumption that the surrounding road user will react on the traffic scenario action by changing its acceleration.

A method for safe at least semi-autonomous driving operation of an ego vehicle in case of sun glare is disclosed. The method involves checking, by a computing system, whether one or more vehicle sensors of the ego vehicle are dazzled by sun glare, and if yes, detecting environmental information by a detection system of the ego vehicle. The method further involves subsequently checking, by a computing system, the environmental information for a presence of at least one dynamic object for intercepting the sun glare during driving operation of the ego vehicle, and if yes, checking, by a computing system, whether the ego vehicle can execute a driving manoeuvre in such a way that the at least one dynamic object intercepts the sun glare during driving operation of the ego vehicle. If yes, then the driving manoeuvre is executed.

Provided is a system and method that can control a merge of an autonomous vehicle when other vehicles are present on the road. In one example, the method may include iteratively estimating a series of values associated with one or more vehicles in an adjacent lane with respect to an ego vehicle, identifying a trend associated with the one or more vehicles from the iteratively estimated series of values, determining merge intentions of the one or more vehicles with respect to the ego vehicle based on the identified trend over time, verifying the merge intentions against a simulated change in the trend, selecting a merge position of the ego vehicle with respect to the one or more vehicles within the lane based on the verified merge intentions, and executing an instruction to cause the ego vehicle to perform a merge operation based on the selected merge position.

Implementations described and claimed herein provide systems and methods for controlling an autonomous vehicle. In one implementation, the autonomous vehicle is navigated towards a flow of traffic with a first gap between first and second vehicles and a second gap following the second vehicle. A motion plan for directing the autonomous vehicle into the flow of traffic at an interaction zone is generated based on whether an ability of the autonomous vehicle to enter the interaction zone at the second gap exceeds a confidence threshold. The autonomous vehicle is autonomously navigated into the flow of traffic at the first gap when the confidence threshold is exceeded. The motion plan forgoes navigation of the autonomous vehicle into the flow of traffic at the first and second gaps when the ability of the autonomous vehicle to enter the interaction zone at the second gap does not exceed the confidence threshold.

A method may include obtaining one or more inputs in which each of the inputs describes at least one of: a state of an autonomous vehicle (AV) or a state of an object; and identifying a prediction context of the AV based on the inputs. The method may also include determining a relevancy of each object of a plurality of objects to the AV in relation to the prediction context; and outputting a set of relevant objects based on the relevancy determination for each of the plurality of objects. Another method may include obtaining a set of objects designated as relevant to operation of an AV; selecting a trajectory prediction approach for a given object based on context of the AV and characteristics of the given object; predicting a trajectory of the given object using the selected trajectory prediction approach; and outputting the given object and the predicted trajectory.

Method and systems for generating vehicle motion planning model simulation scenarios are disclosed. The method receives a base simulation scenario with features of a scene through which a vehicle may travel, defines an interaction zone in the scene, generates an augmentation element that includes an object and a behavior for the object, and adds the augmentation element to the base simulation scenario at the interaction zone to yield an augmented simulation scenario. The augmented simulation scenario is applied to a vehicle motion planning model to train the model.

Embodiments for operational envelope detection (OED) with situational assessment are disclosed. Embodiments herein relate to an operational envelope detector that is configured to receive, as inputs, information related to sensors of the system and information related to operational design domain (ODD) requirements. The OED then compares the information related to sensors of the system to the information related to the ODD requirements, and identifies whether the system is operating within its ODD or whether a remedial action is appropriate to adjust the ODD requirements based on the current sensor information. Other embodiments are described and/or claimed.

A method of classifying a driving maneuver performed by another vehicle in an environment of an ego-vehicle. In the method: a time series of a metrologically determined position of the other vehicle relative to the ego-vehicle that extends to a time step t is provided; spatial profiles of lanes in which the other vehicle may be located are provided; for a plurality of driving maneuvers from a predetermined catalog of possible driving maneuvers, conditional probabilities for the other vehicle to perform this driving maneuver at the time t are respectively determined with a predetermined model by using the time series of the position and the profiles of the lanes; by using these conditional probabilities, a most likely position and/or a probability distribution of positions of the other vehicle at the time step t is determined.

A vehicle controller includes a processor configured to: determine a driving plan when a lane change from a host vehicle lane to another lane is requested. The driving plan represents travel behavior of a vehicle until completion of the lane change and satisfies a safety condition that the vehicle will collide with none of objects around the vehicle detected from a sensor signal obtained by a sensor mounted on the vehicle. The processor is further configured to: set a completion condition indicating a position or time at which the completion of the lane change is required, determine whether the completion condition is satisfied when the vehicle is driven according to the driving plan, control the vehicle to make the lane change according to the driving plan when the completion condition is satisfied, and restrict execution of the lane change when the completion condition is not satisfied.

Embodiments of this application provides example methods, media, and apparatuses for predicting moving trajectory of a target object. One example method includes obtaining a candidate moving trajectory, where the candidate moving trajectory is a motion trajectory that is of the target object in future period and that is obtained by using a prediction method, and the target object is in a sensing range of an ego vehicle. Environmental information is obtained within the sensing range. A constraint barrier is generated based on the environmental information, where the constraint barrier is used to indicate an area through which the target object is constrained to pass. The candidate moving trajectory is processed based on the constraint barrier.