Abstract: A system includes a first device that controls a moving body and a second device that remotely controls the moving body. The first device obtains information around the moving body, movement information of the moving body, and remote control information. The first device calculates a control amount for controlling movement of the moving body based on one of the information and the movement information, switches autonomous movement and remote control of the moving body based on the remote-control information, and transmits one of the information, the movement information, and the control amount to the second device. The second device receives one of the information, the movement information, and the control amount, transmits the remote-control information to the first device, determines autonomous mobility of the moving body based on second information stored remotely, and generates the remote-control information including a determination result.

Abstract: Systems and apparatuses for using machine learning to generate a safety output are provided. In some examples, data may be received from a plurality of sources, may be analyzed and one or more machine learning datasets may be generated based on the analyzed data. In some arrangements, data may be received from one or more vehicles. The vehicles may be autonomous, semi-autonomous, or non-autonomous, and/or configured to operate in one or more of those modes. The data may be evaluated based on the one or more machine learning datasets to determine a safety output associated with the data. The safety output may then be used to classify the data and/or to generate one or more instructions for operation of an autonomous vehicle. The instruction(s) may be transmitted to the autonomous vehicle and may modify operation of the vehicle (e.g., to improve safety associated with the vehicle).

Abstract: An image captured using a sensor on a vehicle is received and decomposed into a plurality of component images. Each component image of the plurality of component images is provided as a different input to a different layer of a plurality of layers of an artificial neural network to determine a result. The result of the artificial neural network is used to at least in part autonomously operate the vehicle.

Abstract: The present invention discloses a receiver and transmitter of enroute aircraft data (RATEAD) system and method of tracking missing aircraft by the system. The system comprising: a plurality of network enabled devices at a plurality of locations; and said plurality of network enabled devices are communicatively coupled to an aircraft passing within a pre-defined range, wherein a data of said in range aircraft is communicatively transmitted in real-time to a network device of said plurality of network enabled devices with which said aircraft is connected.

Abstract: A method for controlling a U-turn using a high-definition map may include recognizing a U-turn section in front of a vehicle on the basis of sensor information, detecting a first point in the U-turn section using a high-definition map, determining at least one candidate lane link on the high-definition map and selecting a target lane link from the at least one candidate lane link, determining a second point on the target lane link on the basis of the first point, and generating a dynamic curvature route based on the first point and the second point.

Abstract: A transport vehicle, such as a vision guided vehicle, can comprise a drive portion constructed to facilitate movement of the transport vehicle and a load portion constructed to engage an object of interest. The load portion can comprise an object engagement apparatus and at least one sensor coupled to or disposed within a distal end of the object engagement apparatus, wherein the sensor can be at least a 2D sensor. The engagement apparatus can comprise forks, at least one fork having sensor coupled to or disposed within a fork tip. The 2D sensor can comprise a scanning LIDAR sensor arranged to collect information to identify a pickable pallet, for example.

Abstract: A method predicts the trajectory of an ego vehicle travelling in a main lane. A lane change by the ego vehicle from the main lane to an adjacent lane is determined according to an estimate of the dynamic behavior of a group of vehicles travelling in the adjacent lane. The group of vehicles includes at least one main vehicle which is located near the ego vehicle and a secondary vehicle which is located behind the ego vehicle.

Abstract: A system includes a processor configured to determine a driver identity. The processor is also configured to receive a request for a change to a driving mode and responsive to the request, enable or deny the driving mode based on mode-correlation to one of a predefined set of permissible driving modes pre-associated with the driver identity.

Abstract: Systems and methods of using a common control scheme to autonomously controlling a vehicle during semi-autonomous and fully autonomous driving modes are provided. In particular, embodiments of the presently disclosed technology incorporate reference tracking for driving input and vehicle state into this common control scheme. In some embodiments, this common control scheme may be implemented using Model Predictive Control (MPC).

Abstract: A traffic monitoring apparatus (10) includes a vehicle information acquisition unit (11), a congestion determination unit (13), and a priority calculation unit (16). The vehicle information acquisition unit (11) acquires, from data received from each of a plurality of detection apparatuses (20), vehicle information regarding travelling states of vehicles that are present in the vicinity of a plurality of respective intersections. The congestion determination unit (13) determines, for each of the plurality of intersections, whether or not congestion is occurring based on the vehicle information and determines an intersection where the congestion has occurred to be a congested intersection. The priority calculation unit (16) calculates, for each of the plurality of continuous congestion paths, a priority level for implementing measures to eliminate congestion based on at least the vehicle travelling direction in the continuous congestion path.

Abstract: The present disclosure provides a method of estimating a weight of a vehicle, including determining whether a current state is an enable state in which a vehicle stoppage event due to brake occurs from vehicle driving information, by a weight estimating system, when the current state is the enable state, removing noise by filtering an acceleration signal input from an acceleration sensor at a moment when the vehicle is stopped, by the weight estimating system, determining a period value of an acceleration signal from the acceleration signal from which noise is removed, by the weight estimating system, and estimating the weight of the vehicle using information on the determined period value, by the weight estimating system.

Abstract: A method of managing operator take-over of autonomous vehicle. The method includes gathering information on an external surrounding of the autonomous vehicle; analyzing the gathered information on the external surrounding of the autonomous vehicle to determine an upcoming traffic pattern; gathering information on an operator of the autonomous vehicle; analyzing the gathered information on the operator of the autonomous vehicle to determine an operator behavior; predicting an operator action based on the determined upcoming traffic pattern and the determined operator behavior; and initiating a predetermined vehicle response based on the predicted operator action. The predicting the operator action includes comparing the determined upcoming traffic pattern with a similar historic traffic pattern and retrieving a historical operator action in response to the similar historical pattern.

Abstract: Systems and methods for implementing one or more autonomous features for autonomous and semi-autonomous control of one or more vehicles are provided. More specifically, image data is obtained from an image acquisition device and processed utilizing one or more machine learning models to identify, track, and extract one or more features of the image utilized in decision making processes for providing steering angle and/or acceleration/deceleration input to one or more vehicle controllers. In some instances, techniques are employed such that the autonomous and semi-autonomous control of a vehicle changes between lane follow and vehicle follow modes. Further, a vehicle may determine whether to continue to follow a vehicle after the followed vehicle moves in relation to the following vehicle.

Abstract: A control device to be mounted on a vehicle includes a memory configured to store information related to priority levels of a plurality of applications that implements driving assistance functions and a processor. The processor is configured to receive, from the applications, application requests and identifiers that identify the respective applications. The processor is configured to arbitrate a plurality of the application requests based on the information and the identifiers. The processor is configured to output a request to an actuator based on a result of arbitration of the application requests.

Abstract: Systems and methods for implementing one or more autonomous features for autonomous and semi-autonomous control of one or more vehicles are provided. More specifically, image data may be obtained from an image acquisition device and processed utilizing one or more machine learning models to identify, track, and extract one or more features of the image utilized in decision making processes for providing steering angle and/or acceleration/deceleration input to one or more vehicle controllers. In some instances, techniques may be employed such that the autonomous and semi-autonomous control of a vehicle may change between vehicle follow and lane follow modes. In some instances, at least a portion of the machine learning model may be updated based on one or more conditions.

Abstract: Systems and methods for implementing one or more autonomous features for autonomous and semi-autonomous control of one or more vehicles are provided. More specifically, image data may be obtained from an image acquisition device and processed utilizing one or more machine learning models to identify, track, and extract one or more features of the image utilized in decision making processes for providing steering angle and/or acceleration/deceleration input to one or more vehicle controllers. In some instances, techniques may be employed such that the autonomous and semi-autonomous control of a vehicle may change between vehicle follow and lane follow modes. In some instances, at least a portion of the machine learning model may be updated based on one or more conditions.

Abstract: This vehicle control device is provided with: an environment recognition unit for recognizing the surrounding environment of a vehicle; a parking route generation unit for generating a travel route to a parking position which is determined on the basis of the recognized surrounding environment; a signal input unit to which a parking command signal is input; and a travel control unit for causing the vehicle to travel along the travel route to the parking position on the basis of the input parking command signal. The vehicle control device drives the vehicle to travel to the parking position for automatic parking while behaving differently depending on whether the parking command signal input to the signal input unit is a first parking command signal or a second parking command signal.

Abstract: A projected recovery trajectory for an aircraft autopilot system is precomputed by providing a stored set of predefined recovery mode segments, including: a mode 1 segment that models the aircraft coasting; a mode 2 segment that models the aircraft executing a nose high recovery; a mode 3 segment that models the aircraft executing a nose low recovery; a mode 4 segment that models the aircraft executing a throttle only recovery; and a mode 5 segment that models the aircraft executing a terrain avoidance recovery. A processor generates at least one projected recovery trajectory based on a current state of the aircraft, where the processor selectively concatenates selected ones of the predefined recovery mode segments into a sequence and uses that sequence to generate the projected trajectory.

Abstract: In a localization apparatus for a vehicle, a landmark detector detects a landmark from camera information acquired from a camera mounted to the vehicle. An associator associates the landmark with a specific set of radar information by setting an error region in which the landmark may exist, selecting, from a radar observation point cluster formed of a set of radar observation points corresponding to a set of radar information acquired from a radar mounted to the vehicle, a radar observation point cluster formed of a set of radar observation points included in the error region, and associating the landmark with the specific set of radar information corresponding to the selected radar observation point cluster. A positional relationship calculator calculates a positional relationship between the landmark and the vehicle based on the specific set of radar information associated with the landmark.

Abstract: A vehicle computer system implements techniques to predict behavior of objects detected by a vehicle operating in the environment. The techniques include using a model to determine a first object trajectory for an object (e.g., a predicted object trajectory) and/or a potential object in an occluded area, as well as a second object trajectory for the object or potential object (e.g., an adverse object trajectory). The model is configured to use one or more algorithms, classifiers, and/or computational resources to predict candidate trajectories for the vehicle based on at least one of the first object trajectory or the second object trajectory. Based on the predicted behavior of the object (or potential object) and the predicted candidate trajectories for the vehicle, a vehicle computer system controls operation of the vehicle.