Abstract: Provided is a control apparatus for a vehicle configured to perform driving support control when a driving support operation state is an on state, the control apparatus being further configured to, in a case where the driving support operation state is the on state and a driving operation switching request for changing the driving support operation state to an off state is issued, calculate a target speed at a driving operation switching time point at which the driving support operation state is changed from the on state to the off state, and perform deceleration control for decelerating the vehicle such that a speed of the vehicle matches the target speed at the driving operation switching time point.

Abstract: A rotorcraft including a pilot control having a sensor that generates pilot control position data, a flight control that controls a flight characteristic of the rotorcraft, a trim system connected to the pilot control and configured to move the pilot control, and a flight control computer (FCC) configured to receive the pilot control position data from the sensor. The FCC executes a first flight control process and generates, according to the first flight control commands, a trim signal indicating a target position for the pilot control and to send the trim signal to the trim system to cause the trim system to attempt to move the pilot control to the target position to reflect a position of the flight control, and to monitor a working state of the trim system and execute a retrim process in response to determining that the trim system has failed.

Abstract: Disclosed are an autonomous driving apparatus and method for an ego vehicle that autonomously travels. The autonomous driving apparatus includes a first sensor to detect a nearby vehicle nearby an ego vehicle, a memory to store map information, and a processor including a driving trajectory generator to generate a first driving trajectory of the ego vehicle and a second driving trajectory of the nearby vehicle based on the map information stored in the memory and a control processor configured to control autonomous driving of the ego vehicle based on the first and second driving trajectories generated by the driving trajectory generator.

Abstract: While operating a vehicle, a candidate marker is detected via first image data from a first image sensor. Upon failing to identify the candidate marker, vehicle exterior lighting is actuated to illuminate the candidate marker. Then the candidate marker is determined to be one of a real marker or a projected marker based on determining whether the candidate marker is detected via second image data from the first image sensor. Upon determining the candidate marker is the real marker, the vehicle is operated based on the real marker.

Abstract: A system and method for generating a predicted vehicle trajectory includes a generative adversarial network configured to receive a trajectory vector of a target vehicle and generate a set of latent state vectors using the received trajectory vector and an artificial neural network. The latent state vectors each comprise a high-level sub-vector, ZH. The GAN enforces ZH to be correlated to an annotation coding representing semantic categories of vehicle trajectories. The GAN selects a subset, from the set of latent state vectors, using farthest point sampling and generates a predicted vehicle trajectory based on the selected subset of latent state vectors.

Abstract: A driving assistance system includes a sensor set, a data storage device and an output device. The sensor set detects a set of road users and, for each road user, a current state including a current speed and a current position. The data storage device includes a finite plurality of behavioral models. The data processor assigns a behavioral model to each road user, probabilistically estimates, for each road user, a belief state comprising a set of alternative subsequent states and corresponding probabilities, each alternative subsequent state including a speed and a position, according to the behavioral model assigned to each road user, and determines a risk of collision of the road vehicle with a road user, based on the probabilistically estimated future state of each road user. The output device outputs a driver warning signal or executes an avoidance action if the risk of collision exceeds a predetermined threshold.

Abstract: A vehicle includes a sensing device disposed at the vehicle so as to have an external field of view of the vehicle, configured to detect a target vehicle moving from the external field of view to a parking space and a plurality of stationary parked vehicles; a controller configured to obtain a first distance that is a width between the plurality of parked vehicles and a second distance that is a width between one parked vehicle of the plurality of parked vehicles and the target vehicle adjacent to the one parked vehicle; and a warner configured to output a warning signal based on a control command of the controller.

Abstract: A method of recognizing a median strip and predicting risk of a collision through analysis of an image includes acquiring an image of the road ahead including a median strip and a road bottom surface through a camera of a moving vehicle (S110), generating a Hough space by detecting an edge from the image (S120), recognizing an upper straight line of the median strip from the Hough space (S130), generating a region of interest (ROI) of the median strip using information on the upper straight line of the median strip and a lane (S140), detecting an object from an internal part of the ROI of the median strip through a labeling scheme (S150), and determining a tracking-point set of the objects that satisfy a specific condition (S160).

Abstract: System, method, and device for controlling a vehicle. In one example, the system includes an electronic processor configured to capture, via a camera, a first image, determine, within the first image, a road traffic factor, and generate, based on sensor information from one or more sensors of the vehicle, a second image depicting an environment surrounding the vehicle. The second image includes the road traffic factor. The electronic processor is also configured to, determine, based on the detected road traffic factor and the second image, a predicted trajectory of a traffic participant proximate to the vehicle, and generate a steering command for the vehicle based on the predicted trajectory.

Abstract: System, methods, and other embodiments described herein relate to lane keeping in a vehicle. In one embodiment, a method includes determining lane boundaries according to at least the sensor data and a map. The method includes defining a reference system over a lane defined by the lane boundaries. The method includes evaluating vehicle boundary points within the reference system as a cost in optimizing a trajectory of the vehicle and using an R-function that defines geometric relationships between the vehicle boundary points and the reference system. The method includes providing an indicator about the trajectory to control the vehicle.

Abstract: A system and method operate an autonomous vehicle. A sensor senses a road and an object. A processor determines, in a Cartesian reference frame, a representation of the road and a source point representative of the object, samples a first waypoint and a second waypoint from the representation of the road, determines a linear projection of the source point to a line connecting the first waypoint and the second waypoint, determines a first estimate of a longitudinal component of the source point in a road-based reference frame based on the linear projection, the first estimate being on a curve representing the road between the first waypoint and the second waypoint, determines a second estimate of the longitudinal component from the first estimate, determines a coordinate of the source point in the road-based reference frame from the second estimate and operates the vehicle with respect to the object using the coordinate.

Abstract: An information processing apparatus includes: a point group data acquisition unit configured to acquire, based on information from a sensor configured to detect an object existing in surroundings of a vehicle, point group data related to a plurality of points representing the object; a movement amount estimation unit configured to estimate a movement amount of the vehicle; a storage unit configured to store, as a point group map recorded in association with position information including a latitude and a longitude, relative positions of the plurality of points relative to a first reference position that is a place on a travel path of the vehicle; and a position estimation unit configured to estimate a position of the vehicle based on the point group map, the point group data, and the movement amount.

Abstract: A system and method for providing online multi-LiDAR dynamic occupancy mapping that include receiving LiDAR data from each of a plurality of LiDAR sensors. The system and method also include processing a region of interest grid to compute a static occupancy map of a surrounding environment of the ego vehicle and processing a dynamic occupancy map. The system and method further include controlling the ego vehicle to be operated based on the dynamic occupancy map.

Abstract: An arithmetic model generation system includes a sensor information acquisition unit, a tire force calculator, and an arithmetic model update unit. The sensor information acquisition unit acquires acceleration of a tire. The tire force calculator includes an arithmetic model for calculating tire force F based on the acceleration, and calculates the tire force F by inputting the acceleration acquired by the sensor information acquisition unit. The arithmetic model update unit compares tire axial force measured by the tire and the tire force F calculated by the tire force calculator, and updates the arithmetic model.

Abstract: A trajectory determination method for a vehicle is provided. A target vehicle trajectory is determined from among multiple candidate vehicle trajectories by considering, for each of the candidate vehicle trajectories, presence or absence of a front obstacle, presence or absence of a potentially-colliding obstacle, and a condition related to lane change, so as to enhance driving safety of the vehicle.

Abstract: Provided is an Artificial Intelligence (AI) system for simulating a human brain's functions, such as recognition, decision, etc., by using a machine learning algorithm such as deep learning, etc. and applications of the AI system. Provided is an electronic device including: a camera configured to capture an outside image of a vehicle, and a processor configured to execute one or more instructions stored in a memory, wherein the processor executes the one or more instructions to: determine, from the captured image, at least one object for estimating lane information; estimate, from the image, lane information of a road on which the vehicle is traveling, based on a distance between the determined at least one object and the vehicle and a vanishing point of the image; and output guide information for guiding driving of the vehicle based on the estimated lane information.

Abstract: A wind data estimating apparatus includes one or more processors configured to collect vehicle information including a first acceleration, an amount of driving operation performed by a driver of a vehicle, and position information, which are obtained by sensors installed in the vehicle; classify the collected vehicle information by an area of a plurality of areas according to the position information; and estimate a wind velocity and a wind direction for the area and for a time range when the vehicle information is obtained, on the basis of an acceleration obtained from subtracting a second acceleration caused by the amount of driving operation from the first acceleration included in the vehicle information classified by the area.

Abstract: A method for the remote control of at least one function of a motor vehicle by means of a mobile controller involves a remote control command transmitted to the motor vehicle and executed by the motor vehicle if an approval has been issued. The method provides a remote control system that has a particularly high level of reliability, which is achieved by detecting an image of the motor vehicle by an optical detection device in the mobile controller is transmitted to the motor vehicle, and the approval is then issued by a checking device in the motor vehicle if the motor vehicle has been identified in the transmitted image by the checking device.

Abstract: A tracking device can monitor a movement of an object. The tracking device can detect a deviation in the movement of the object and can generate a data set based on detecting the deviation in the movement of the object. The data set can relate to a location of the object. The tracking device can update a model with the data set. The model can provide an estimated path of the movement of the object over a time period based on a plurality of data sets. The tracking device can transmit the model to a device. The device can use the model to determine a particular location of the object during the time period.

Abstract: A multimodal sensing system for agricultural machines may include an agricultural machine operable in a first operating mode and a second operating mode and a sensor which is movable to adjust its field-of-view between a first field-of-view and a second field-of-view depending on the operating mode of the agricultural machine. The sensor may be configured to generate sensor data associated with the first field-of-view when the agricultural machine is operating in the first operating mode and generate sensor data associated with the second field-of-view when the agricultural machine is operating in the second operating mode. A controller may analyze the sensor data generated when the sensor has the first and second field-of-views so as to provide respective first and second output signals associated with the first and second operating modes, respectively, of the agricultural machine.