Abstract: A computer-implemented method for combining a plurality of road graphs that partially overlap with an adjacent area, the method comprising: an acquisition step of acquiring a plurality of road graphs; a labeling step of grouping the plurality of road graphs into groups, and assigning a label to each of the road graphs in each of the groups; and a merging step of selecting one label and executing, in parallel for the groups, a process of merging a first road graph to which the selected label is assigned and a second road graph adjacent to the first road graph, wherein the merging step is repeated, with selecting a different label in each repetition, until all of the plurality of road graphs are merged.

Abstract: A computer-implemented method for merging a first road graph corresponding to a first area and a second road graph corresponding to a second area that partially overlaps with the first area, the method comprising: a selection step of selecting one edge from a plurality of edges overlapping each other in the first road graph and the second road graph and deleting non-selected edge; and a merging step of determine whether or not to merge a first node, that is a node connected to the deleted edges, and in response to determination to merge the first node, selecting a second node to be merged with the first node, and merge the first node and the second node.

Abstract: An intersection area extraction method comprising acquiring a mask image; determining whether a target point selected from the mask image is an intersection point; and obtaining an intersection area by applying a clustering algorithm to intersection points, which are obtained by performing said determining step for a plurality of points in the mask image, and determining a resulting cluster area as the intersection area. The determination of a target point being an intersection point comprises: selecting the target point from the mask image; calculating a distance from the target point to a road boundary for a plurality of directions; and determining the target point is the intersection point if a graph, whose horizontal axis is the direction and whose vertical axis is the distance to the road boundary, has more than two peaks, and the target point is not the intersection point otherwise.

Abstract: A method comprising: selecting a position corresponding to a road from a mask image of road area, estimating road information at the first point; selecting a second point that is advanced a predetermined distance from the first point; and storing a link between the first point and the second point as a road link if it is determined that the link between the first point and the second point does not intersect any other road link already stored, wherein if it is determined that the link between the first point and the second point intersect a road link already stored, the second point is set as a new first point and the above processes are performed again.

Abstract: System, methods, and other embodiments described herein relate to implementing a mapping management system. In one embodiment, a method includes receiving trace data; processing the trace data to provide a group of passes within a lane segment; determining error measurements for the group of passes within the lane segment; receiving a criterion; and determining an estimated number of passes to satisfy the criterion based on the error measurements.

Abstract: Systems and methods disclosed herein include generating weather data and location data using a sensor provided on a vehicle, determining a confidence level of the weather data and the location data, compiling the weather data and the location data into a weather map by selectively integrating the weather data based on the confidence level of the weather data, and transmitting the weather map to the vehicle for display to a user of the vehicle.

Abstract: Methods and systems are presented for sharing detected changes in roads using blockchains in a network. The network includes vehicles operable to detect a change in a road, a plurality of local blockchains, and a global blockchain. The vehicles are divided into groups of local vehicles. The plurality of local blockchains includes local blocks of changes, wherein after a first vehicle publishes a change of the road among a group of local vehicles, a block of the change is integrated into a local blockchain when at least a local threshold number of the local vehicles endorse the change. The global blockchain includes blocks of changes, wherein a block of the change is integrated into the global blockchain when at least a global threshold number of the vehicles endorse the change. A token is awarded to the first vehicle.

Abstract: A system for aiding an individual to cause a vehicle to make a turn correctly can include a processor and a memory. The memory can store a communications module and a controller module. The communications module can cause the processor to obtain information about a specific side of a road on which the vehicle is to be operated. The communications module can cause the processor to obtain information about a degree of familiarity of the individual in operating the vehicle on the specific side. The communications module can cause the processor to obtain information about a direction of the turn. The controller module can cause the processor to cause, in response to the degree of familiarity being less than a threshold and based on the information about the direction, a visual aid to be possibly presented to aid the individual to cause the vehicle to make the turn correctly.

Abstract: Provided are a method, system, and device for evaluating a machine learning (ML) model. The method may include: receiving, by a requirements management layer, at least one requirement obtained from a storage layer; interpreting, by the requirements management layer, the at least one requirement; and transmitting, by the requirements management layer, instructions to perform an ML evaluation process to an execution layer based on the interpreted requirements, wherein the execution layer transmits an output signal with the results of the ML evaluation process upon completing the ML evaluation process.

Abstract: A method of optimizing execution of a control function on a control system including a plurality of hardware components includes: determining a processing capability and a communication capability of each of the plurality of hardware components; generating a plurality of solutions for executing the control function using the plurality of hardware components based on a processing capability and a communication capability of each of the plurality of hardware components; scoring the plurality of generated solutions based on a desirability of each solution; selecting a solution having a highest desirability score; and controlling the control system to execute the control function based on the selected solution.

Abstract: Systems, methods, and other embodiments described herein relate to recommending road infrastructure maintenance tasks based on vehicle sensor data and infrastructure map data. In one embodiment, a system includes a processor and a memory storing machine-readable instructions. The instructions, when executed by the processor, cause the processor to infer a perceived infrastructure element by a motorist within an environment based on sensor data collected from a sensor system of a vehicle. The instructions, when executed by the processor, also cause the processor to retrieve map data associated with the environment. The map data indicates a mapped infrastructure element. The instructions, when executed by the processor, also cause the processor to recommend a maintenance task to be performed based on the map data and the sensor data.

Abstract: A vehicle occupant gaze detection system includes a non-transitory computer readable medium configured to store instructions thereon; and a processor connected to the non-transitory computer readable medium. The processor is configured to execute the instructions for receiving gaze data from a vehicle. The gaze data includes a viewing direction of an occupant of the vehicle, vehicle information, and time stamp information. The processor is configured to execute the instructions for generating a gridmap including an array of lattice points, and each lattice point of the array of lattice points corresponds to a location. The processor is configured to execute the instructions for generating a histogram including information related to viewing of at least one location by the occupant. The processor is configured to execute the instructions for determining an effectiveness of an object based on the histogram; and transmitting a recommendation based on the effectiveness of the object.

Abstract: A position estimating device includes a processor configured to detect a first position of a vehicle traveling a predetermined road section at a first time, based on a fixed sensor installed on or near the predetermined road section, estimate a second position of the vehicle at a second time, based on the first position of the vehicle at the first time, the second time being a time when a vehicle-captured image representing a predetermined feature is generated by a vehicle-mounted camera mounted on the vehicle, and estimate a real-space position of the predetermined feature, based on the estimated second position.

Abstract: A feature identification device identifies a type of lane separation equipment, including at least separation poles and separation wire ropes, for separating a road by travel direction. When the type of lane separation equipment is not associated with a predetermined position on the road, the device identifies the type of the lane separation equipment based on an image in which the lane separation equipment is captured at a predetermined position by an algorithm selected from a first algorithm and a second algorithm based on whether the predetermined position is included in the two-way traffic section. When the predetermined position is included in the two-way traffic section, the device identifies the type of the lane separation equipment using the second algorithm which is capable of identifying separation poles and separation wire ropes with higher certainty than the first algorithm.

Abstract: The present disclosure is to provide an annotation verification method. In the annotation verification method, a first result which is a verified result of the annotation for a first image sequence included in the image sequence is acquired. Next, first reference information regarding a position of the specified target object range in each image included in the first image sequence is acquired based on the first result. Next, a position of the target object range in a target image is predicted based on reference information including the first reference information. Next, an actually-specified position of the target object range in the target image is acquired based on the result of the annotation for the target image. Then, the result of the annotation for the target image is verified by comparing the target object range at the predicted position and the target object range at the actually-specified position.

Abstract: A vehicle controller includes a memory configured to store trajectory distribution information representing distribution of trajectories for each lane in a predetermined section of a road; and a processor configured to set a target trajectory of a vehicle by referring to the trajectory distribution information of a second lane different from a first lane being traveled by the vehicle, when the vehicle travels the predetermined section, and make the vehicle travel along the target trajectory.

Abstract: System, methods, and other embodiments described herein relate to estimating lane elevation using headings for generating three-dimensional (3D) maps. In one embodiment, a method includes augmenting positions of detected lane boundaries with headings of a vehicle from sensor data. The method also includes adding the headings to vertices of two-dimensional (2D) lines using direction vectors between the vertices, wherein the 2D lines are directed towards the detected lane boundaries. The method also includes estimating elevations of the vertices including surrounding lane boundaries using a weighted average and vehicle poses from the sensor data, the surrounding lane boundaries having locations different than the positions and the weighted average factors directional differences between the vertices. The method also includes generating a 3D map of driving lanes having stacked roads identified using the elevations.

Abstract: A data collecting device includes a processor configured to determine whether snow lies around a vehicle, set a type of feature to be detected, based on the result of determination of the presence or absence of the snow, detect a feature of the set type from an image representing an area around the vehicle generated by a camera mounted on the vehicle, and generate probe data representing the detected feature.

Abstract: A trajectory information collecting device includes a processor configured to determine whether a trajectory along which a host vehicle has traveled through a predetermined section is within a predetermined standard travel area, generate trajectory information by including individual position coordinates on the trajectory in the trajectory information when the trajectory is within the standard travel area, generate the trajectory information by including individual position coordinates on the trajectory and operation information indicating how much the host vehicle is operated by a driver in the predetermined section in the trajectory information when the trajectory deviates from the standard travel area, and transmit the generated trajectory information to a server via a communication device mounted on the host vehicle.

Abstract: Systems, methods, and other embodiments described herein relate to improving the generation multiple vehicle traces for a roadway. In one embodiment, a method includes acquiring sensor data about a roadway, including at least imaging data of an overhead view of the roadway. The method includes generating complementary traces of the roadway using a trace model that iteratively generates the complementary traces according to learned perturbations that imitate information acquired from probe vehicles traversing the roadway, including variations between separate traversals. The method includes providing the complementary traces that include multiple vehicle traces and associated detections about attributes of the roadway.