Abstract: The present technique relates to an information processing apparatus, an information processing method, and a program which enable a prediction result that contributes toward improving a pick-up ratio to be presented. The information processing apparatus includes: a display control portion configured to divide a business area of a business vehicle into a plurality of areas and cause a display portion to display, as a prediction result, a movement direction and a movement distance of passengers in an attention area being the area of attention among the plurality of areas for which a pick-up demand has been predicted per area. For example, the present technique can be applied to an information processing apparatus or the like for displaying a prediction result of a pick-up demand of a taxi.

Abstract: An autonomous vehicle configured to autonomously pass a cyclist includes an imaging device and processing circuitry configured to receive information from the imaging device. Additionally, the processing circuitry of the autonomous vehicle is configured to identify a cyclist passing situation based on the information received from the imaging device, and plan a path of an autonomous vehicle based on the cyclist passing situation. The autonomous vehicle also includes a positioning system and the processing circuitry is further configured to receive information from the positioning system, determine if the cyclist passing situation is sufficiently identified, and identify the cyclist passing situation based on the information from the imaging device and the positioning system when the cyclist passing situation is not sufficiently identified based on the information received from the imaging device.

Abstract: A method for determining a collision distance includes receiving a series of data points from a second vehicle that were during the second vehicle driving on a historical path, the data points contain path information and steering wheel angle, the path information contains position information; determining whether the first vehicle is located within a preset area range corresponding to the second vehicle using a series of positions of the second vehicle and the current position of the first vehicle; if the first vehicle is currently located within the preset area range, determining a lane change state of the second vehicle in the historical path according to the steering wheel angle, the lane change state represents the second vehicle changing lanes; and determining the collision distance between the vehicles according to the lane change state, the current position of the first vehicle, and the series of data points.

Abstract: A brake system and a method of monitoring and control. The method may include obtaining data indicative of temperature of a plurality of brake assemblies and determining at least one correlation value based on the temperature data. A mean temperature comparison may be conducted when a correlation value is less than a threshold correlation value.

Abstract: An environmental safety system may comprise a plurality of first sensors each located at a predetermined physical location of a traffic intersection and with a predetermined orientation. The system may have a memory storing executable instructions. The system may have one or more processors in communication with the plurality of first sensors and the memory. The one or more processors may be programmed by the executable instructions. The system may receive first sensor data captured at a time point and by the plurality of first sensors. The system may determine values of one or more parameters of an object of interest within a threshold distance of the traffic intersection using the first sensor data. The system may generate an information object comprising the values of the one or more parameters of the object of interest, the time point, and a signature of the information object. The system may transmit, via a communication network, the information object.

Abstract: Systems and methods for controlling the motion of an autonomous are provided. In one example embodiment, a computer implemented method includes obtaining, by one or more computing devices on-board an autonomous vehicle, data associated with one or more objects that are proximate to the autonomous vehicle. The data includes a predicted path of each respective object. The method includes identifying at least one object as an object of interest based at least in part on the data associated with the object of interest. The method includes generating cost data associated with the object of interest. The method includes determining a motion plan for the autonomous vehicle based at least in part on the cost data associated with the object of interest. The method includes providing data indicative of the motion plan to one or more vehicle control systems to implement the motion plan for the autonomous vehicle.

Abstract: Aspects of the disclosure provide for controlling an autonomous vehicle. For instance, a first probability distribution may be generated for the vehicle at a first future point in time using a generative model for predicting expected behaviors of objects and a set of characteristics for the vehicle at an initial time expected to be perceived by an observer. Planning system software of the vehicle may be used to generate a trajectory for the vehicle to follow. A second probability distribution may be generated for a second future point in time using the generative model based on the trajectory and a set of characteristics for the vehicle at the first future point expected to be perceived by the observer. A surprise assessment may be generated by comparing the first probability distribution to the second probability distribution. The vehicle may be controlled based on the surprise assessment.

Abstract: A hybrid deterministic override to cloud based probabilistic advanced driver assistance systems. Under default driving conditions, an ego vehicle is controlled by a probabilistic controller in a cloud. An overall gap between the ego vehicle and a leading vehicle is divided into an emergency collision gap and a driver specified gap. The vehicle sensors monitor the overall gap. When the gap between the ego vehicle and the leading vehicle is less than or equal to the emergency collision gap, a deterministic controller of the ego vehicle overrides the cloud based probabilistic controller to control the braking and acceleration of the ego vehicle.

Abstract: A plant tracking apparatus, system, and method for tracking planting locations of a plurality of plants configured to be planted in a ground surface using a planting utensil having a handle, a blade, and a shaft defined between the handle and the blade is disclosed herein. The plant tracking apparatus includes a housing configured to be coupled to the planting utensil. The plant track apparatus further includes a Global Navigation Satellite System (GNSS) sensor, a planting sensor, and a controller positioned within or coupled to the housing. The controller is configured to record location data from the GNSS sensor in response to a planting signal from the planting sensor. In certain embodiments, the controller may determine the planting signal by comparing data from the planting sensor to a predetermined motion profile.

Abstract: Systems and methods to fuse aviation-related data systems for comprehensive aircraft system health monitoring are provided. One example method includes obtaining, by one or more computing devices, fault data indicative of a plurality of fault indications provided by a first plurality of components of an aircraft. The method includes obtaining, by the one or more computing devices, condition indicators describing the respective operational conditions of a second plurality of components of the aircraft. The method includes fusing, by the one or more computing devices, the fault data with the condition indicators to form a comprehensive data set. The method includes identifying, by the one or more computing devices, one or more causes of the plurality of fault conditions based at least in part on the comprehensive data set. One example system includes a data fuser, a cause identifier, and an alert generator.

Abstract: A collision avoidance assist apparatus executes an emergency steering control including processes to determine a target steering torque used to change a steering angle to avoid a collision of a vehicle with an obstacle so as not to move the vehicle out of a moving lane when the vehicle has a high probability of colliding with the obstacle, and the moving lane is a straight lane, and applies a steering torque corresponding to the determined target steering torque to a steering mechanism. The collision avoidance assist apparatus stops executing the emergency steering control when determining that the moving lane is a curved lane, based on only one of left and right lane markings at an avoidance side of the obstacle which is the left or the right side of the obstacle which the vehicle passes over while executing the emergency steering control.

Abstract: Aspects of the present disclosure describe systems, methods, and devices for vehicle mode detection based on acceleration data and location data during a trip detected by mobile devices in a vehicle. According to some examples, location data will be compared to known routeway data to detect a vehicle mode. In other examples acceleration data will be used to calculate the consistency of vehicular velocity during a trip to detect a vehicle mode. In some additional aspects, multiple analyses will be performed on the same data sets to check for false positives with vehicle mode detection.

Abstract: A method for calibrating a height control system for an implement of an agricultural work vehicle can include providing an input signal to the height control system to adjust a height of the implement relative to the ground surface; monitoring the height of the implement relative to the ground surface; adjusting at least one gain of the height control system; and determining a maximum stability gain of the height control system based on the at least one gain and the monitored height. The maximum stability gain can correspond with a stability point of the height control system at which the height control system transitions from stable to unstable. The method can include setting gain(s) of the height control system based on the maximum stability gain.

Abstract: The invention relates to a method for checking an AI-based information processing system used in the partially automated or fully automated control of a vehicle, wherein at least one sensor of the vehicle provides sensor data, the captured sensor data are evaluated by an AI-based information processing system arranged in a first control circuit of the vehicle and, from the evaluated sensor data, at least one output for controlling the vehicle is generated. The AI-based information processing system is checked by a testing circuit arranged in a second control circuit of the vehicle using at least one testing method, and wherein a test result of the at least one testing method is stored, with a reference to the tested AI-based information processing system and to the at least one testing method used, in a multi-dimensional data structure in a database arranged in the vehicle.

Abstract: The disclosure relates to a steering system and a method for controlling the same. According to an embodiment, a steering system comprises an electric power steering (EPS) steering motor connected to a first inverter and a second inverter, an additional motor connected to the second inverter and providing a steering-related additional function, a main electric control unit (ECU) including the first inverter and controlling the EPS steering motor through the first inverter, and a sub ECU including the second inverter and controlling at least one of the EPS steering motor or the additional motor based on at least one of main ECU state information or vehicle driving state information.

Abstract: A computer-implemented method is provided for creating a velocity grid using a simulated environment for use by an autonomous vehicle. The method may include simulating a road scenario with simulated objects. The method may also include recording image data collected from a camera sensor, the image data comprising a first 2D image frame comprising the simulated objects made up of a plurality of pixels. The method may also include identifying a first 3D point on the first simulated object in a 3D view of the simulated road scenario, wherein the first 3D point corresponds to the first pixel in the first 2D image frame. The method may also include generating a velocity of the first point based upon a velocity of the first simulated object, and projecting the velocity back into the first 2D image frame.

Abstract: A computer-implemented method is provided for training a machine-learning (ML) algorithm that contributes to piloting an autonomous vehicle using a velocity grid generated from a simulation. The method may include simulating a scene. The scene includes a simulated autonomous vehicle and at least one simulated object moving in the scene. The method may also include predicting a velocity of at least one point on the simulated object as it moves in the simulated scene using a ML algorithm. The method may also include comparing the predicted velocity of the at least one point on the simulated object with velocities in the velocity grid generated from a simulation. The method may further include adjusting the ML algorithm to more accurately predict velocity of at least one point on the simulated object as it moves in the scene based on a difference in the predicted velocity compared to the velocity grid, which yields a trained ML algorithm.

Abstract: A control architecture for a vehicle connects a first control unit to a first vehicle communication network. A second control unit is connected to a second vehicle communication network. Commands are received by a plurality of commanded units from the first control unit and/or the second control unit over communication lines. An interlink communication line is connected between the first control unit to the second control unit.

Abstract: A method of determining whether a landing event of an aircraft is hard may comprise: receiving, by a controller via a stroke position sensor, a stroke profile as a function of time for a shock strut; receiving, by the controller via a gas pressure sensor, a gas pressure in a gas chamber of the shock strut; receiving, by the controller via a wheel speed sensor, a wheel speed of a tire in a landing gear assembly; calculating, by the controller, multiple time dependent functions based on the stroke profile of the shock strut, based on the gas pressure, a shock strut temperature, and the wheel speed; and comparing, by the controller, the multiple time dependent functions to respective predetermined thresholds to determine whether the landing event is hard.

Abstract: Techniques described are related to determining when a discrepancy between data of multiple sensors (e.g., IMUs) might be attributable to a sensor error, as opposed to operating conditions, such as sensor bias or noise. For example, the sensor data is passed through one or more filters (e.g., bandpass filter) that model the bias or noise, and the filtered data may then be compared for consistency. In some examples, consistency may be based on residuals or some other metric describing discrepancy among the filtered sensor data.