Formation of a tire trace due to automated driving travel of an automated driving vehicle (10) is prevented. In an automated driving system (500), an in-vehicle apparatus (100) is mounted in the automated driving vehicle (10) traveling a road by automated driving. The in-vehicle apparatus (100) effects automated driving such that a vehicle position through which the automated driving vehicle (10) travels deviates from a center in a transverse direction of the road by an offset value (321) in the transverse direction of the road.

A mapping system creates feature data associated with a first section of a route, stores the feature data in the local storage system as stored feature data, fuses the feature data with the stored feature data to create map data associated with the first section when the vehicle has traversed the first section and collected the stored feature data at least a threshold number of times, stores the map data in the local storage system, identifies one or more route sections as being part of one or more commute routes, and when space in the local storage system is insufficient for storage of the map data or the feature data, enables the storage by removing stored map data from the location storage system based at least in part on a determination that the removed data is not associated with the one or more commute routes.

Provided are a method, an apparatus, and a computer program for generating road network data for an autonomous driving vehicle. The method of generating road network data for an autonomous driving vehicle, which is performed by a computing device, the method includes generating one or more roads by grouping a plurality of unit lanes, generating connection information about the one or more roads, and generating road network data including road graphs generated by graphing the one or more roads and reflecting the connection information on the graphed one or more roads.

This disclosure makes vehicle passage at a tollbooth on a vehicle road smoother. A plurality of candidate lanes are selected based on whether or not a first vehicle scheduled to pass through a tollbooth is an ETC vehicle, and the type of each lane at the tollbooth. Moreover, a predicted value in correlation with a required time predicted to be required for passing through the tollbooth is calculated for each candidate lane. Further, a recommended lane that is recommended for the first vehicle to travel is determined from the plurality of candidate lanes based on the predicted value for each candidate lane.

The subject disclosure relates to techniques for real-time lane validation. A process of the disclosed technologies can include steps for receiving a route from a remote computing system, the route indicating lanes on the route that an autonomous vehicle must traverse, traversing at least one lane of the lanes on the route, and sending sensor data to the remote computing system after traversing the at least one lane, the sensor data indicating whether the at least one lane is in accordance with a virtual lane of a virtual map, the virtual lane corresponding to the at least one lane.

A method for evaluating suitability route sections of a digital map storing landmarks for automated driving operation of a vehicle is provided. For each route section of the digital map a spatial density of landmarks is determined, an expected recognizability of the landmarks is determined by a vehicle sensor system under predetermined ambient conditions, a classification is performed based on the determined density and recognizability of the landmarks as to whether a vehicle can be located on the route section with a minimum accuracy required for a predetermined operating mode and/or for a predetermined driving maneuver, and a classification result is stored as a data record in a route attribute associated with the route section, the route attribute indicating for which of the predetermined operating modes and/or driving maneuvers requirements for the minimum accuracy of the landmark-based vehicle localization are met under which of the predetermined environmental conditions.

Systems and methods are provided for autonomous vehicle navigation. The systems and methods may map a lane mark, may map a directional arrow, selectively harvest road information based on data quality, map road segment free spaces, map traffic lights and determine traffic light relevancy, and map traffic lights and associated traffic light cycle times.

System and methods for detecting and obtaining lane level insight in unplanned incidents. Probe-based vehicles and lane-level insight using a lane-level map-matcher are used to acquire information. This information is aggregated and used to differentiate lane activity in terms of traffic and safe navigation. With the identification of probes per-lane and probe speeds per-lane, sudden reductions in probe speeds may be obtained at a lane-based level. This is used to verify or detect lane-level incident or hazard warnings and consequently alert a driver to safer navigation paths ahead of time, like maneuver to a different lane or to take an alternative route.

A point-to-point autonomous driving path generation and driving control method based on a navigation route and a high definition map, which generates a point-to-point autonomous driving path by utilizing both of a route made with a navigation map (SD map) and a lane centerline of a high definition map (HD map). The point-to-point autonomous driving path generation and driving control method based on the navigation route and the high definition map includes a step of acquiring navigation route data; and a step of generating autonomous driving trajectory data on the basis of lane information of the high definition map matched with the navigation route data.

Disclosed is a method of controlling a vehicle. The method includes: obtaining information on a first lane of a currently driving road from a foreground image of the vehicle, obtaining information on a second lane of the currently driving road from map data of a navigation, and updating the information on the second lane of the map data by sharing the information on the first lane obtained from the foreground image of the vehicle with the navigation.