The disclosure generally pertains to systems and methods to classify a road based on a level of support offered by the road for autonomous driving operations. An example method may involve a computer receiving sensor data from a vehicle, the sensor data containing information about a current functional condition of a road. The computer may predict a future functional condition of the road by using a deterioration model to evaluate the sensor data. The computer may then determine a level of support offered by the road for autonomous driving operations based on the future functional condition of the road, and assign a classification to the road based on the level of support offered by the road for autonomous driving operations. The level of support offered by the road for autonomous driving operations may also be based on items such as road markings, traffic signs, traffic signals, and/or infrastructure elements.

An in-vehicle device includes a vehicle detection unit that detects behavior of a user's own vehicle that is traveling, a neighboring vehicle detection unit that detects behavior of a neighboring vehicle preceding the user's vehicle, a neighboring vehicle following the user's vehicle, or both of neighboring vehicles, a road determination unit that determines the condition of a road on which the user's vehicle is traveling on the basis of behavior of the user's vehicle detected by the user's vehicle detection unit and behavior of a neighboring vehicle detected by the neighboring vehicle detection unit, and a transmission information creation unit and a transmission unit that transmit road information including a determination result by the road determination unit.

An apparatus including a capture device and a processor. The capture device may be configured to generate pixel data corresponding to an exterior view from a vehicle. The processor may be configured to generate video frames from the pixel data, perform computer vision operations on the video frames to detect objects in the video frames and determine characteristics of the objects, detect a change in orientation of the vehicle at a first time, analyze the characteristics of the objects at a second time to determine a cause of the change in orientation of the vehicle and generate annotations for the video frames that comprise the objects determined to have caused the change in orientation of the vehicle. The second time may be earlier than the first time.

A travel route generation device (4) includes a travel route acquisition unit (4) configured to acquire position information (Pc) about a travel route (H) for a moving object (M) to perform autonomous travel or remotely controlled travel, a surrounding environment information acquisition unit (22) configured to acquire surrounding environment information, a dangerous location detection unit (23) configured to detect dangerous locations (Pd1 to Pd3) where there is a risk of an accident from the surrounding environment information, and an information processing unit (24) configured to add dangerous location information including first information indicating the dangerous locations (Pd1 to Pd3) to the position information (Pc) corresponding to the dangerous locations (Pd1 to Pd3).

The present disclosure relates to a method for driving on the basis of characteristics of a driving surface, and a robot for implementing the same, and a method for driving on the basis of characteristics of a driving surface, according to one embodiment of the present disclosure, comprises the steps in which: a sensing module of the robot senses an adjacent driving surface to generate characteristic information of the driving surface, and a control unit of the robot stores position and characteristic information of the driving surface in a map storage of the robot; the controller of the robot sets a function to be applied to the driving surface in response to the characteristic information of the driving surface, or generates a movement path selectively including the driving surface corresponding to start and end points of the robot; and the controller controls a moving unit and a functional unit of the robot according to the set function or the movement path.

Mappings of keys to actions can automate various vehicle systems. Some automations can provide lane departure warnings. Keys for lane departure mappings can specify vibration patterns expected when a vehicle drives over lane delineators. These vibration-based mappings can include keys with vibration patterns, e.g., defining vibration frequencies or vibration locations. Keys for emergency light mappings can be based on conditions such as (1) the vehicle being on the road, stopped, not in traffic, and not at a stop signal; (2) components of the vehicle having failed; or (3) weather conditions.

A vehicle compelling force detection apparatus includes one or more processors configured to: obtain, from a first sensor, a road surface disturbance force received from a road surface on which the vehicle drives via a wheel of the vehicle, and store the road surface disturbance force in one or more memories, the first sensor being disposed below a damper supporting the wheel in a direction of gravity; obtain, from a second sensor, a body disturbance force applied to the vehicle, and store the body disturbance force in the one or more memories, the second sensor being disposed above the damper in the direction of gravity; and detect the compelling force due to the wind disturbance to which the vehicle is subjected, based on the body disturbance force applied to the vehicle and the road surface disturbance force, which are stored in the one or more memories.

A method of determining a traveling state of a vehicle, such as passing over a speed bump, occurrence of wheel slip, or traveling on a slope, is determined in real time to prevent degradations in wheel slip control performance and to avoid unnecessarily malfunctions in a traction control system without compromise of wheel slip control performance. The method includes steps of: determining a torque command of a drive unit to apply torque to a drive wheel in accordance with vehicle driving information collected during traveling of the vehicle; determining an acceleration error in accordance with the determined torque command and information regarding a measured longitudinal acceleration of the vehicle measured by a first sensor; determining an acceleration disturbance rate in accordance with the determined torque command; and determining a current traveling state of the vehicle in accordance with the determined acceleration error and the determined acceleration disturbance rate.

A method for improving the ride comfort of a transportation vehicle including planning a first driving route by a navigation system; automatically detecting at least one road parameter of the first driving route by a sensor system of the transportation vehicle; automatically evaluating the first driving route in view of the ride comfort of the first driving route by taking into account the road parameter; and in response thereto using the first driving route or planning an alternative driving route.

A vehicle includes a controller programed to: collect a set of data related to a driver of the vehicle; predict a driving setting for the driver using the set of data and an initial student-T process (STP) machine learning (ML) model; generate an updated STP ML model based on the prediction of the driving setting as to the set of vehicle data; transmit incremental learning related to the updated STP ML model to a server; and receive, from the server, a personalized driving setting for the driver output from a cloud STP ML model trained by the incremental learning.