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 vehicle, system and a method of operating the vehicle is disclosed. The system includes a first sensor, a second sensor and a processor. The first sensor captures an image of a traffic control device. The second sensor identifies a lane within a road that is occupied by the vehicle. The processor is configured to determine an attribute of the lane occupied by the vehicle, associate a semantic meaning to a motion control signal of the traffic control device, determine a maneuver for the vehicle based on the semantic meaning of the motion control signal and the attribute of the lane occupied by the vehicle, and perform the maneuver at the vehicle.

Systems, methods and computer-readable media for selecting a trajectory for an autonomous vehicle are disclosed that include computing a current vehicle state for the autonomous vehicle based on observations by a sensing system; computing respective collision probability scores for a plurality of candidate trajectories based on the current vehicle state; computing respective information gain scores for the plurality of candidate trajectories based on the current vehicle state, the information gain score for each candidate trajectory indicating an respective information gain for a next planning horizon interval that is subsequent to the current planning horizon interval; and selecting a planned trajectory from the plurality of candidate trajectories based on the respective collision probability scores and respective information gain scores.

A travel path data generation apparatus for generating travel path data inside an intersections for automated driving includes a travel path data generator that generates the data of the travel path in such a way that, using an absolute trajectory of actual traveling of a vehicle inside the intersection, the travel path data generator fits an estimated trajectory of the actual traveling of the vehicle inside the intersection into data of lane network connected to the intersection.

A vehicle configured to operate in an autonomous mode can obtain sensor data from one or more sensors observing one or more aspects of an environment of the vehicle. At least one aspect of the environment of the vehicle that is not observed by the one or more sensors could be inferred based on the sensor data. The vehicle could be controlled in the autonomous mode based on the at least one inferred aspect of the environment of the vehicle.

A behavior control method for controlling a behavior of a vehicle comprising: specifying a blind-spot region as blind-spot of an environment recognition portion along a travel route for the vehicle; determining a jump-out possibility of a moving object to the travel route from the blind-spot region; performing a possibility reduction behavior to lower the jump-out possibility, in response to that the jump-out possibility is confirmed; and performing a travel behavior compliant with the travel route after starting the possibility reduction behavior.

A method for use in traversing a vehicle transportation network by an autonomous vehicle (AV) includes traversing, by the AV, the vehicle transportation network. Traversing the vehicle transportation network includes identifying a distinct vehicle operational scenario; instantiating a first decision component instance; receiving a first set of candidate vehicle control actions from the first decision component instance; selecting an action; and controlling the AV to traverse a portion of the vehicle transportation network based on the action. The first decision component instance is an instance of a first decision component modeling the distinct vehicle operational scenario.

Provided is a vehicle braking support device. The braking support device includes: detection units for detecting a state around a host vehicle; a braking support control unit for executing braking support by a braking device according to the detected state; and a vehicle stop control unit for maintaining a stopped state of a host vehicle after the host vehicle is stopped by the braking support control unit, and for releasing the stopped state of the host vehicle after a predetermined period has elapsed. The vehicle stop control unit, in a case where by using the detected state it is determined that it is desirable to maintain the stopped state of the host vehicle beyond the predetermined period, the vehicle stop control unit does not release the stopped state of the host vehicle until an operation by a driver is detected.

A travel assistance method and a travel assistance device for a vehicle is capable of avoiding any risk that may arise. The method includes obtaining a risk potential of an object detected by the vehicle, associating the risk potential of the object with an encounter location at which the object is encountered, accumulating the risk potential at the encounter location, and using the accumulated risk potential to obtain a primary estimated risk potential of the object predicted to be encountered at the encounter location. The primary estimated risk potential is lower than the risk potential obtained when detecting the object. The method further includes obtaining a secondary estimated risk potential using a predicted travel movement of another vehicle that avoids a risk due to the primary estimated risk potential, and when traveling at the encounter location again, autonomously controlling travel of the vehicle using the secondary estimated risk potential.

A method for determining passage of an autonomous vehicle includes: acquiring information about an intersection on a driving route of the autonomous vehicle, wherein the information about the intersection comprises lane data; acquiring a historical trajectory of an obstacle in the intersection within a specific time; acquiring, by matching the historical trajectory of the obstacle with center lines of respective lanes in the lane data, a lane with smallest matching error; and determining that the lane with the smallest matching error is in a passable state, wherein the lane with the smallest matching error is a lane where the obstacle is located.