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: System and methods for estimating a centerline of a road that separates traffic moving in opposite directions include aggregating a data set from each of a plurality of vehicles traversing the road over a period of time as telemetry data. Each data set of the telemetry data indicates a location and a heading. The method includes clustering the data sets of the telemetry data based on the heading indicated by each data set, and identifying a separator to separate the data sets indicating a first heading from the data sets indicating a second heading, opposite to the first heading. The centerline is estimated based on applying a spatial smoothing to the separator.

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 vehicle control device includes a failure detection unit configured to detect a failure of a vehicle, and a vehicle controller configured to control the vehicle. The vehicle controller changes the control of the vehicle depending on a failure level when the failure detection unit detects a failure of an on-vehicle component other than the vehicle control device.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a simulation of an environment that is being interacted with by a plurality of agents over a plurality of time steps, wherein the simulation comprises a respective simulation state for each time step that specifies a respective state of each agent at the time step. In one aspect, a method comprises, for each time step: obtaining a current simulation state for the current time step; generating a plurality of candidate next simulation states for a next time step; determining, for each candidate next simulation state, a discriminative score characterizing a likelihood that the candidate next simulation state is a realistic simulation state; and selecting a candidate next simulation state as the simulation state for the next time step based on the discriminative scores for the candidate next simulation states.

Abstract: [Problem to be Solved] The object is to provide a device, a method and the like which, when a fault occurs, particularly when a fault occurs in an operation of some of rotary wings, can control a flight depending on the fault, or which can be at least used for control when a fault occurs, or which can detect the fault.

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: Ground truth data may be too sparse to supervise training of a machine-learned (ML) model enough to achieve an ML model with sufficient accuracy/recall. For example, in some cases, ground truth data may only be available for every third, tenth, or hundredth frame of raw data. Training an ML model to detect a velocity of an object when ground truth data for training is sparse may comprise training the ML model to predict a future position of the object based at least in part on image, radar, and/or lidar data (e.g., for which no ground truth may be available). The ML model may be altered based at least in part on a difference between ground truth data associated with a future time and the future position.

Abstract: A vehicle control device comprises a unit to detect a distance to an obstacle; a unit to detect a speed of the vehicle; and a reduction support unit to, based on the speed and the distance, perform support for reduction of a speed of the vehicle. The reduction support unit changes the distance at which the reduction support is to be performed between a case where driving during parking and a case where not driving during parking. The distance at which the reduction support is to be performed is, for a first speed range, longer in a case where driving during parking than in a case where not driving during parking, and, for a second speed range, longer in a case where not driving during parking than in a case where driving during parking.

Abstract: A steering apparatus includes an actuator and an actuator controller. The actuator is configured to generate an output for turning a steerable wheel of a vehicle. The actuator controller is configured to control the actuator in accordance with a steering input. The actuator controller includes a filtering processor configured to perform filtering processing for reducing or cutting a component in a predetermined frequency band from the steering input, and the actuator controller is configured to control the actuator in accordance with the steering input after the filtering processing.

Abstract: An autonomous driving system includes an electronic control unit. The electronic control unit is configured to suggest to a driver that a host vehicle overtake a preceding vehicle during autonomous driving, recognize whether the driver accepts a suggestion for the overtaking, when the electronic control unit recognizes that the driver accepts the suggestion, cause the host vehicle to overtake the preceding vehicle, and, when the electronic control unit recognizes that the driver rejects the suggestion, not cause the host vehicle to overtake the preceding vehicle, set a first prohibition duration, and, when the electronic control unit suggests to the driver that the host vehicle overtake the preceding vehicle and recognizes that the driver rejects the suggestion, extend the first prohibition duration as compared to when the electronic control unit suggests to the driver that the host vehicle overtake the preceding vehicle and recognizes that the driver accepts the suggestion.

Abstract: In one embodiment, a method for monitoring a localization function in an autonomous driving vehicle (ADV) can use known static objects as ground truths to determine when the localization function encounter errors. The known static objects are marked on a high definition (HD) map for the real-time driving environment. When the ADV detects one or more known static objects, the ADV can use sensor data, locations of the one or more static objects, and one or more error tolerance parameters to create a localization error tolerance area surrounding a current location of the ADV. The ADV can project the tolerance area on the HD map, performs a localization operation to generate an expected location of the ADV on the HD map, and determines whether the generated location falls within the projected tolerance area.

Abstract: Included are: a timing determining unit for determining timing for preparing for transfer of driving authority to a driver on the basis of current position information of the vehicle, route information of the vehicle, and vehicle information of the vehicle; an awakening level calculating unit for calculating an awakening level of an occupant on the basis of occupant information; an authority transfer determining unit for determining whether the transfer of the driving authority is allowed on the basis of the calculated awakening level; and a vehicle state controlling unit for performing control to change a state of the vehicle before switching to the manual driving is announced to the occupant of the vehicle when it is determined that the transfer of the driving authority is not allowed.

Abstract: A suspension control system includes: a first electric current setting unit configured to set a first electric current based on an actual damping speed; a second electric current setting unit configured to set a second electric current based on a model damping speed; a weight coefficient setting unit configured to set a weight coefficient based on the actual damping speed; and a target electric current setting unit configured to set a sum of a first value and a second value as a target electric current of the damper, the first value being obtained by multiplying the second electric current by the weight coefficient, the second value being obtained by multiplying the first electric current by a value obtained by subtracting the weight coefficient from one. The first electric current setting unit is configured to make the first electric current smaller than the second electric current in a prescribed case.

Abstract: Concerning a partial area image that constitutes a wide area image, to control a flying body in accordance with a flight altitude at a past point of time of image capturing, an information processing apparatus includes a wide area image generator that extracts, from a flying body video obtained when a flying body captures a ground area spreading below while moving, a plurality of video frame images and combines the video frame images, thereby generating a captured image in a wide area, an image capturing altitude acquirer that acquires a flight altitude at a point of time of image capturing by the flying body for each of the plurality of video frame images, and an image capturing altitude output unit that outputs a difference of the flight altitude for each video frame image.

Abstract: An apparatus for controlling a vehicle capable of performing autonomous driving is provided. The apparatus includes an autonomous driving device that executes the autonomous driving and generates a transition demand when it is impossible to execute the autonomous driving. A driving controller performs a minimum risk maneuver (MRM) of applying a deceleration pattern differently depending on a driving environment of the vehicle, when the transition demand is generated, but when driving manipulation by a driver does not occur. A subsequent safety ensuring function is performed according to the MRM for the driver to recognize the MRM, and a drive mode of the vehicle is changed to a drive mode with a rapid response speed to acceleration or steering.

Abstract: A vehicle running control method includes: calculating, by a controller, a lateral velocity of an adjacent vehicle that travels in a lane adjacent to a traveling lane in which an autonomous vehicle travels in the road-width direction, and a longitudinal velocity of the adjacent vehicle in the direction in which the adjacent lane extends; specifying, by the controller, a predetermined road section based on the longitudinal velocity and calculating a first path on the assumption that an offset distance of the adjacent vehicle in the adjacent lane in the road-width direction is maintained within the road section; and applying, by the controller, the lateral velocity to the first path to calculate a second path corresponding to a predicted traveling path of the adjacent vehicle.

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: A technique relates to autonomous obstacle avoidance. A vehicle is controlled on a route to a destination, the route being a three-dimensional route. An obstruction is determined on the global route. A local route including alternate points to avoid the obstruction is autonomously determined. The local route is autonomously converged back to the global route in response to avoiding the obstruction.