A method for operating a vehicle platoon with a plurality of motor vehicles includes recording of operating data of each motor vehicle of the vehicle platoon, transmitting the recorded operating data of at least one following motor vehicle of the vehicle platoon to the first motor vehicle of the vehicle platoon, evaluating the operating data to determine a control signal data set for controlling a component of each of the motor vehicles of the vehicle platoon, transmitting the respective control signal data sets to at least one following motor vehicle, and controlling components of one or all motor vehicles according to the respective control signal data sets.

An autonomous vehicle, a control system for remotely controlling the same, and a method thereof may include an autonomous driving control apparatus including a processor for determining whether remote control of the autonomous vehicle is required according to at least one of occurrence of failure of the vehicle during autonomous driving thereof, occurrence of an accident, a region where the autonomous driving is not possible, reliability of positioning data of the vehicle, a stopping time of the vehicle, or a response point of a hand signal, and for requesting the remote control to a control system when the remote control of the vehicle is required.

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.

Systems, apparatuses, and methods disclosed provide for receiving internal information, external static information, and external dynamic information of a hybrid vehicle, and selectively enable or disable a stop/start function for the engine of the hybrid vehicle based on the internal hybrid vehicle information, external static information, and external dynamic information. The stop/start function controls selective activation and deactivation of the engine during operation of the hybrid vehicle.

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 vehicle control method implemented by a first vehicle configured with at least one driver assistance system. The method includes activating a driver assistance system to an active state, applying a vehicle speed range to the first vehicle in response to the driver assistance system being in the active state, obtaining first information of a moving object near the first vehicle, determining a first parameter based on the first information, and further terminating, based on the first parameter, the active state, and setting, based on the first parameter, the first vehicle to run at a first vehicle speed beyond the vehicle speed range.

Technologies and techniques for assigning the lane in which a vehicle is currently driven, wherein input variables of at least one data source are detected to determine the lane currently being driven in, and wherein at least one data source includes signals from an environmental sensor system of the vehicle. In cases in which the possibility of unequivocal assignment of the lane in which a vehicle is currently driven is improved, it is provided that at least one data source includes a C2X communication.

A control apparatus includes a reception control unit configured to perform control to receive, when a movable object is located at a first point, presence information of a risk area including a plurality of position coordinates specified through image recognition by another movable object, a determination unit configured to determine whether the movable object is in a vicinity of the risk area based on the plurality of position coordinates included in the presence information of the risk area for which the reception control unit has performed the control to receive, a transmission control unit configured to perform control to transmit, when the determination unit determines that the movable object is in the vicinity of the risk area, information related to presence of the movable object, and a control unit configured to execute control of the movable object.

A method for controlling a driving behavior performed by a first agent includes navigating, by the first agent, according to a trajectory and a velocity. The method also includes receiving, from a second agent, a risk identification message identifying a third agent as a potential risk based on the third agent performing a behavior associated with a probability that is less than a threshold. The method further includes autonomously engaging a defensive driving mode in response to receiving the risk identification message. The method still further includes adjusting one or both of the trajectory or the velocity in response to autonomously engaging the defensive driving mode.

Among other things, we describe techniques for an interface board for harmonizing performance of different autonomous vehicles in a fleet. In an embodiment, the interface system includes a printed circuit board installed in a host vehicle that includes an interface controller and interface circuitry coupled to the interface controller. The interface circuitry includes a plurality of relays configured by the interface controller to route/pass sensor signals received from actuators of the host vehicle to the interface controller, and route/pass control signals from the interface controller to actuator controllers of the host vehicle that control the actuators in accordance with an operating mode. Signal conditioning circuitry in the interface controller conditions the sensor signals and control signals to ensure that the electrical characteristics of the sensor signals are compatible with the interface controller, and that the electrical characteristics of the control signals are compatible with the actuator controllers.