Systems, vehicles and methods for determining wrong direction driving are disclosed. In one embodiment, a system for determining a vehicle traveling in a wrong direction includes one or more sensors that produce sensor data, one or more processors, and one or more non-transitory computer-readable medium storing computer readable-instructions. When the computer-readable instructions are executed by the one or more processors, the computer-readable instructions cause the one or more processors to determine one or more lanes within a roadway using the sensor data, determine a direction of travel of the one or more lanes using the sensor data, and identify a non-compliant vehicle traveling in a direction in the one or more lanes that is different from the determined direction of travel in the one or more lanes.

A vehicular control system includes a sensor disposed at a vehicle and capturing sensor data. The system, as the vehicle is approaching an intersection and responsive to processing by a processor of sensor data captured by the sensor, detects a cross-traffic threat approaching the intersection and maintains a buffer to store a trajectory of the cross-traffic threat. The system, using the trajectory, determines an intersection point between the vehicle and the cross-traffic threat, determines an arrival time at the intersection point for both the vehicle and the cross-traffic threat, and determines a difference between the arrival time of the vehicle at the intersection and the cross-traffic threat. The system, responsive to determining that the difference between the arrival time of the equipped vehicle at the intersection and the arrival time of the cross-traffic threat is less than a threshold amount, controls a safety system of the vehicle.

A method for determining a lane change, performed by an apparatus for determining a lane change of an object located around a driving vehicle with which is equipped a sensor, the method including, detecting a plurality of objects located around the driving vehicle using scanning information obtained repeatedly at every predetermined period of time by the sensor scanning surroundings of the driving vehicle, selecting at least one candidate object estimated to change lanes among the plurality of objects based on previously detected lane edge information and determining whether the candidate object changes lanes based on information on movement of the candidate object.

A coordinated virtual scene for an autonomous vehicle is disclosed. A real route for an autonomous vehicle can be determined. This autonomous vehicle route can be employed to determine a virtualized route in a virtual environment. The autonomous and virtualized routes can be coordinated. A level of coordination can be selectable via one or more selectable congruence values. Generally, a greater congruence value can correspond to a greater coherence between the autonomous route and the virtualized route. A congruence value can be selectable, which can allow an occupant to indicate a level of coherence that is acceptable to an occupant. A virtual environment can be selectable to enable the virtualized routes to be more relevant to an autonomous vehicle occupant, passenger, operator, etc. Moreover, selection of a virtual environment can be predicated on a selection rule having been satisfied. Virtual environments can be non-fiction, fiction, or both.

The present invention relates to a method of cooperatively coordinating future driving maneuvers of a vehicle with fellow maneuvers of at least one fellow vehicle, wherein trajectories for the vehicle are rated with an effort value each, trajectories and fellow trajectories of the fellow vehicle are combined into tuples, the trajectory and the associated effort value of a collision-free tuple are selected as reference trajectory and reference effort value, trajectories with a lower effort value than the reference effort value are classified as demand trajectories, trajectories with higher effort value than the reference effort value are classified as alternative trajectories, and a data packet having a trajectory set consisting of the reference trajectory and the associated reference effort value as well as at least one trajectory from a group comprising the demand trajectories and the alternative trajectories as well as the respective effort values is transmitted to the fellow vehicle.

A control method for an autonomous vehicle is used in an autonomous vehicle including an engine, and a controller that controls an operation of the engine. In the control method, required driving force is set in accordance with an intervehicular distance between an own vehicle and a preceding vehicle when there is the preceding vehicle in front of the own vehicle. Also, when there is the preceding vehicle, a behavior of the preceding vehicle is predicted from a situation in front of the preceding vehicle. Further, when there is the preceding vehicle, sailing stop is executed based on the required driving force and the predicted behavior of the preceding vehicle. The sailing stop causes the engine to stop automatically while the own vehicle is traveling at vehicle speed equal to or higher than given vehicle speed.

Among other things, techniques are described for predicting how an agent (e.g., a vehicle, bicycle, pedestrian, etc.) will move in an environment based on prior movement, the road network, the surrounding objects and/or other relevant environmental factors. One trajectory prediction technique involves generating a probability map for an agent's movement. Another trajectory prediction technique involves generating a trajectory lattice, for an agent's movement. In addition, a different trajectory prediction technique involves multi-modal regression where a classifier (e.g., a neural network) is trained to classify the probability of a number of (learned) modes such that each model produces a trajectory based on the current input.

Provided herein is a system of a vehicle that comprises one or more sensors, one or more processors, and memory storing instructions that, when executed by the one or more processors, causes the system to perform: selecting a trajectory along a route of the vehicle; predicting a trajectory of another object along the route; adjusting the selected trajectory based on a predicted change, in response to adjusting the selected trajectory, to the predicted trajectory of the another object, the predicted change to the predicted trajectory of the another object being stored in a model; determining an actual change, in response to adjusting the selected trajectory, to a trajectory of the another object, in response to an interaction between the vehicle and the another object; updating the model based on the determined actual change to the trajectory of the another object; and selecting a future trajectory based on the updated model.

A blind spot assist device includes a first optical element and a second optical element. The first optical element reflects a part of incident light having a first angle of incidence at different second angles such that light is incident at the first angle on the first optical element from an outdoor view and reflected at the second angles on the first optical element. The second optical element is positioned to face the first optical element and reflects incident lights having different angles of incidence at a third angle such that lights reflected by the first optical element are incident at the second angles on the second optical element and reflected at the third angle on the second optical element toward a user.

The disclosure includes embodiments for improving an operation of a vehicular micro cloud by increasing a continuity of a vehicular micro cloud service provided by the vehicular micro cloud. A method includes maintaining a data structure of stored driving maneuver shapes, wherein each stored driving maneuver shape includes a set of driving maneuvers. The method includes sensing a candidate vehicle and a driving maneuver shape of the candidate vehicle. The method includes determining a matching driving maneuver shape from the stored driving maneuver shapes based on a match between the driving maneuver shape of the candidate vehicle and at least a portion of the matching driving maneuver shape. The method includes estimating a next driving maneuver of the candidate vehicle. The method includes assigning a role to the candidate vehicle in the vehicular micro cloud based on the next driving maneuver of the candidate vehicle.