An autonomous vehicle (AV) includes features that allows the AV to comply with applicable regulations and statues for performing safe driving operation. An example method for operating an AV includes receiving, from a sensor located on the AV, sensor data that captures a road sign located at a distance from the AV that is operating on a roadway; obtaining, from the sensor data, roadway information indicated by the road sign that corresponds to a segment of the roadway associated with the road sign that is ahead of a current position of the AV on the roadway; determining trajectory-related information for the AV for the distance that is based on the roadway information obtained from the sensor data; and causing the AV to travel in accordance with the trajectory-related information until a determination that the AV has arrived within the segment of the roadway associated with the road sign.

An evacuation running assistance system includes a road shoulder evacuation possibility determiner to determine if an own vehicle can be evacuated to a road shoulder; an own vehicle situation determiner to determine a current situation of an own vehicle in accordance with a time limit and the road shoulder evacuation possibility, a controller to control an own vehicle in accordance with the situation of the own vehicle; and a road shoulder evacuation possibility road determiner to acquire evacuation space information from a past running history of the own vehicle. The own vehicle situation determiner determines that the own vehicle is in the situation to be controlled to perform the on-lane stopping when the road shoulder evacuation possibility road determiner does not determine within the provisional time that the evacuation of the own vehicle to the road shoulder is possible.

A vehicle includes a position recognition module that creates position information, a road information combining module configured to create first precise map information including a driving route of the vehicle, an object combining module that creates second precise map information including a driving route of a surrounding vehicle around the present vehicle, a lane link determination module that selects, from the second precise map information, a lane link at which a first lane and a second lane intersect or join each other, a target determination module configured to determine a target vehicle, a pass priority determination module that determines a pass priority at which each of the present vehicle and the target vehicle passes through the lane link, an object route creation module that creates a driving route of the target vehicle, and an adaptive route determination module that determines an adaptive driving route of the present vehicle.

Systems and methods for predicting a trajectory of a moving object are disclosed herein. One embodiment downloads, to a robot, a probabilistic hybrid discrete-continuous automaton (PHA) model learned as a deep neural network; uses the deep neural network to infer a sequence of high-level discrete modes and a set of associated low-level samples, wherein the high-level discrete modes correspond to candidate maneuvers for the moving object and the low-level samples are candidate trajectories; uses the sequence of high-level discrete modes and the set of associated low-level samples, via a learned proposal distribution in the deep neural network, to adaptively sample the sequence of high-level discrete modes to produce a reduced set of low-level samples; applies a sample selection technique to the reduced set of low-level samples to select a predicted trajectory for the moving object; and controls operation of the robot based, at least in part, on the predicted trajectory.

Disclosed are an apparatus for controlling a vehicle, a system including the apparatus, and a method for controlling the apparatus. The apparatus includes: a reference lane calculator to calculate a reference lane based on a traveling condition of the vehicle; a target determining device to determine a target of interest based on the reference lane and a predicted path of an object around the vehicle; and a control parameter calculating device to calculate a control parameter of the vehicle based on a traveling state of the target of interest.

The technology relates to detecting and responding to processions. For instance, sensor data identifying two or more objects in an environment of a vehicle may be received. The two or more objects may be determined to be disobeying a predetermined rule in a same way. Based on the determination that the two or more objects are disobeying a predetermined rule, that the two or more objects are involved in a procession may be determined. The vehicle may then be controlled autonomously in order to respond to the procession based on the determination that the two or more objects are involved in a procession.

A parking assistance method is provided. The method includes recognizing image features of a traffic line corresponding to a parking position of a vehicle. A category of the parking position of the vehicle is determined based on the image features of the traffic line. Once the vehicle is determined to be parking illegally based on the category of the parking position of the vehicle; a warning is issued.

A device (16) for determining a discrete representation (30) of a road section ahead of a vehicle (12) includes an input interface (22) for receiving sensor data (20) of a sensor (14) with information about the road section ahead of the vehicle, a setting unit (24) for ascertaining a control distance at which a property of the road section ahead of the vehicle that is relevant for an open-loop control of the vehicle changes based on the sensor data and for setting a support point in a discrete representation of the road section corresponding to the control distance. The setting unit is configured for setting a lower predefined second number (n2) of support points based on a predefined first number (n1) of support points. The device also includes an output interface (26) for outputting the lower predefined second number of support points to an optimizer (52) in order to determine a profile of at least one control parameter for the open-loop control of an open-loop system, a vehicle function based on the second number (n2) of support points.

A processor unit (3) for model-based predictive control of a vehicle (1) taking into account an arrival time factor is configured to calculate a trajectory for the vehicle (1) based at least in part on at least one arrival time factor, with the trajectory including an entire route (20) to a specified destination (19) at which the vehicle (1) is to arrive, and with the at least one arrival time factor influencing an arrival time of the vehicle (1) at the specified destination (19). Additionally, the processor unit (3) is configured to optimize a section of the trajectory for the vehicle (1) for a sliding prediction horizon by executing a model-based predictive control (MPC) algorithm (13), where the MPC algorithm (13) includes a longitudinal dynamic model (14) of a drive train (7) of the vehicle (1) and a cost function (15) to be minimized.

A method for controlling a vehicle including: based on map information including information of an installation position of a traffic light and information of a lane controlled by the traffic light and a range of the angle of view of a camera mounted on the own vehicle, calculating an imaging-enabled area in which an image of the traffic light can be captured on the lane by the camera; determining whether or not the own vehicle is positioned in the imaging-enabled area; and when the own vehicle is positioned in the imaging-enabled area, controlling the own vehicle in such a way that the traffic light is not shielded from the range of the angle of view of the camera by a preceding vehicle of the own vehicle.