A vehicle control system includes a map generating unit configured to generate a map of a surrounding area of a vehicle, and a road shoulder determining unit configured to determine whether the vehicle has entered a road shoulder on the map. The road shoulder determining unit is configured to identify an own lane on which the vehicle is traveling on the map, determine whether an adjacent lane adjacent to the own lane is present on the map, determine whether the vehicle has crossed an own delimiting line that delimits the own lane on the map, and determine that the vehicle has entered the road shoulder on the map upon determining that the adjacent lane is not present on one lateral side of the own lane and that the vehicle has crossed the own delimiting line on the one lateral side.

A vehicle travel route control system controls a plurality of vehicles having an autonomous driving function or a driving assist function, wherein a vehicle dispatch information data server creates a travel route map, in which driving mode switching position at which vehicles have switched over during travel to the manual driving mode from the autonomous driving mode, have been added to a road map. Based on the travel route map, a travel route that avoids the driving mode switching position is calculated from among a plurality of routes on which a dispatch vehicle candidate travels to a destination via user position. When a travel route that avoids the driving mode switching position is calculated, a dispatch vehicle candidate is set as a dispatch vehicle, and the calculated travel route information is transmitted to an on-board unit mounted in the dispatch vehicle.

A system and related method determines implicit lane boundaries by generating a bird's eye view of a portion of a road having a first lane and a second lane from environment data having information related to the road, overlaying a grid having cells onto the bird's eye view of the portion of the road, determining cells of the grid that form at least portions of the first lane of the road, determining cells of the grid that form at least portions of the second lane of the road, and determining a probability for one or more cells of the grid indicating a likelihood that a vehicle will travel upon portions of the road represented by the cells of the grid when traveling from the first lane to the second lane. The probability for one or more cells of the grid may be generated by a neural network trained with training data.

According to an aspect, there is provided a computer-implemented method for condition monitoring of a vehicle. The method comprises applying a dynamic model associated with a vehicle (800), the dynamic model having been determined by obtaining status information from at least one information bus of the vehicle, the status information providing real-time status information about the vehicle (200), obtaining, based on vehicle identity information, vehicle dynamics information (202), obtaining map data representing road characteristics of roads of a geographical area, the map data comprising two-dimensional road map data, three-dimensional road map associated with the roads, and road characteristics data (204), analyzing behavior of the vehicle based on the status information and the vehicle dynamics information (206), and computing a dynamic model for the vehicle by comparing the behavior of the vehicle to the map data (208); analyzing historical changes in at least one calibration parameter associated with the dynamic model of the vehicle (802); analyzing effects of the three-dimensional road map associated with the roads and the road characteristics data in at least one position on the behavior of the vehicle (804); and determining, based on the analyzed historical changes and the effects, at least one change in at least one vehicle characteristic (806).

A traffic information determination device includes a server configured to acquire driving environment information that may affect safety of traveling of a vehicle to be subjected to autonomous driving control, determine a first allowable area in which traveling of the vehicle is allowed, and in which traveling of the vehicle is not allowed when a person gets on the vehicle, based on the driving environment information, and determine a second allowable area in which traveling of the vehicle is allowed, and in which traveling of the vehicle is allowed even when the person gets on the vehicle, based on the driving environment information.

A road updating method for an electronic map is provided. The method includes acquiring a target road in the electronic map, the target road being at least one of an intrinsic upper road of an overpass or an intrinsic lower road of the overpass, acquiring a target node of the target road, the target node being at least one of a first node of the target road or a last node of the target road, acquiring visible points within a preset range of the target node, determining path lengths between the visible points and the target node, determining an extended road of the target node according to the path lengths between the visible points and the target node, and updating the electronic map based on the extended road.

An autonomous travel system includes a road surface image acquisition device, a storage unit, an estimation unit, a ground satellite signal reception antenna configured to receive a radio wave from a satellite, and a ground wireless communication device installed on a ground where the autonomous travel vehicle travels. The autonomous travel system includes a vehicle satellite signal reception antenna configured to receive the radio wave from the satellite, and a mapping unit configured to create the map image of the road surface with which the coordinates and the positional information are associated, from the image of the road surface acquired by the road surface image acquisition device based on the measurement information acquired through the ground satellite signal reception antenna and the measurement information acquired through the vehicle satellite signal reception antenna, in mapping outdoors.

Provided is an apparatus for providing obstacle information and reliability information to vehicle based on information received from roadside units. The apparatus includes a communication module that receives obstacle information from multiple roadside units, each roadside unit including multiple sensors for detecting obstacles within a predetermined field of view. A reliability judgement unit in the apparatus determines a reliability of the received obstacle information to output a reliability value based on a number of roadside units detecting a same obstacle, a number of sensor of one roadside unit detecting a same obstacle, and a difference value of detection of the same obstacle between different roadside units or different sensors.

Method and apparatus for generating a road map, electronic device, and non-transitory computer storage medium are disclosed, including: inputting a remote sensing image into a first neural network to extract first road feature information of multiple channels via the first neural network; inputting the first road feature information of multiple channels into a third neural network, to extract third road feature information of multiple channels via the third neural network, where the third neural network is a neural network trained by using road direction information as supervision information; fusing the first road feature information and the third road feature information; and generating a road map according to a fusion result.

A method estimates a course of a roadway in a vicinity of a vehicle based on a state function describing the course of the roadway, wherein the state function includes a clothoid spline. The method includes providing ambient measured data describing the course of the roadway at a current position of the vehicle, where the ambient measured data includes a polynomial function. The method also includes transforming the state function and the ambient measured data into a common coordinate system, and checking the ambient measured data for an error. If no error is detected, then the state function is adapted based on the ambient measured data in the common coordinate system. If an error is detected, then the error is stored.