In one aspect, an operating method of an intelligence vehicle driving control system is provided that comprises: a collecting step of collecting big data including a wheel torque and a speed for every vehicle type and traffic information; a torque calculating step of learning the big data using a predetermined machine learning model and inputs a specific desired speed profile to the machine learning model to calculate a motor torque of a driving vehicle; and an optimal speed profile deriving step of calculating an energy consumption required to generate the calculated motor torque using a predetermined dynamic programming method and a reverse vehicle dynamic model and deriving an optimal speed profile in which the energy consumption is minimized.

The present disclosure provides a data acquisition method, apparatus, device and computer readable storage medium. According to the embodiments of the present disclosure, determination is made as to whether a preset stable driving condition is met based on acquired driving scene data of the driving scene where the vehicle is currently located, and acquired driving behavior data; if the preset stable driving condition is met, the driving scene data and the driving behavior data are acquired at a frequency lower than a preset sampling frequency. As a result, it is possible to reduce data redundancy in similar scenes and similar diving modes, reduce the amount of data, reduce occupation of the storage resources and transmission resources and facilitate subsequent analysis, and it is possible to implement the dynamic acquisition of driving scene data and driving behavior data by scenes and modes.

A method for determining a transportation mode acquires magnetometer and speed data from a mobile device, correlates the magnetometer to the speed data in groupings, and performs spectral analysis on the groups of magnetometer data. Energy calculated for each of a set of frequency components obtained from the spectral analysis is compared to a baseline value to generate a difference, and a transportation mode type is assigned to the vehicle based on the difference.

Techniques are described for dynamically selecting vehicles to perform road friction probing maneuvers and estimating road friction based on sensor data collected while a vehicle performs the road friction probing maneuvers. In one example, a computing system is configured to select, from a plurality of vehicles, based on an amount of elapsed time since each respective vehicle of the plurality of vehicles has performed a road friction probing maneuver, a vehicle to perform the road friction probing maneuver within a road segment of a roadway, and responsive to selecting the vehicle, output, to the vehicle, a command causing the vehicle to perform the road friction probing maneuver within the road segment.

Technologies and techniques for carrying out an assisted lane change onto a deceleration lane, during which an intervention in an acceleration device may be carried out in an at least partially assisted manner as a function of a driver input on a driving lane of the roadway. The lane change from the driving lane onto the deceleration lane may be carried out in an at least partially assisted manner, wherein swarm data are received from at least one further motor vehicle, which carried out a lane change onto the deceleration lane at a crossing, and a change position for the lane change of the motor vehicle is determined as a function of the received crossing position. The lane change is carried out at the determined change position. Other aspects relate to a computer program product and to an assistance system.

Systems and methods for vehicle-based weather detection are disclosed herein. The systems and methods can include selecting one or more vehicles from a plurality of vehicles based on one or more network membership parameters. One or more data acquisition networks can be formed using the one or more selected vehicles. Sensor data can be received from the one or more data acquisition networks. One or more weather conditions can be predicted using the sensor data. One or more environmental elements can be updated based on the predicted weather conditions.

A control apparatus includes a reception control unit configured to perform control to receive, when a movable object is located at a first point, presence information of a risk area including a plurality of position coordinates specified through image recognition by another movable object, a determination unit configured to determine whether the movable object is in a vicinity of the risk area based on the plurality of position coordinates included in the presence information of the risk area for which the reception control unit has performed the control to receive, a transmission control unit configured to perform control to transmit, when the determination unit determines that the movable object is in the vicinity of the risk area, information related to presence of the movable object, and a control unit configured to execute control of the movable object.

A method for processing friction data for vehicle tires on road segments, implemented by a processing system including at least one computer and an interface for remote communication with a plurality of vehicles, the method including: acquiring, from the plurality of vehicles, friction data for tires of the vehicles on a plurality of road segments, each friction datum including at least: a maximum coefficient of friction available to the vehicle on the road segment, and information relating to the road segment; establishing, for each road segment, a distribution of the friction data obtained from the plurality of vehicles for the road segment; and determining a plurality of road types, each road type comprising a set of road segments, from a measurement of similarity between the distributions of friction data obtained for each road segment.

An information processing server is configured to acquire target vehicle data having traveling conditions of target vehicles and position information of the target vehicles on a map, recognize, based on the target vehicle data, an unstable behavior position on the map where at least one of the target vehicles exhibits unstable behavior, measure an occurrence count of the unstable behavior positions within a predetermined period in a mesh preset on the map, enlarge the mesh to include the unstable behavior positions within the predetermined period such that the occurrence count of the unstable behavior positions is equal to or larger than a first threshold, and store the mesh and the unstable behavior positions of the mesh in a storage database related to each other.

A method for determining road surface conditions in a system with at least one vehicle and a data processing device. The vehicle exchanges data with the data processing device wirelessly. The vehicle has at least one sensor for determining measured values describing a road surface friction coefficient, and a computing unit. The data processing device includes a database, containing a road map having a plurality of route sections. The method includes determining measured values for a route section by the sensor, determining a first friction coefficient for the route section by the vehicle's computing unit, sending a data record, containing measured values and/or the first determined friction coefficient and a piece of information identifying the route section, to the data processing device, determining an average friction coefficient for the route section, sending the average friction coefficient determined for the route section to the vehicle, and determining the road surface condition.