Data-driven driving line estimation and mapping

Abstract

A data-driven driving line map is determined using road-level map data and driving line data for road users. The map data includes way data containing one or more ways, where each way contains a series of nodes, and the driving line data includes driving lines, where each driving line is a series of attitudes representing road users. A first section of the way data is identified as an intersection, and a second section is matched with the driving line data to generate multiple waivers, each waiver comprising one or more attitudes and one node. Waivers are classified as either constant or variable based on the lanes counted therein. Consecutive waivers are grouped into waiver sections based on the classification and lane count, and the map is generated using the waiver sections and the first section. The vehicle operates using the map as input to the control system.

Description

Technical Field

[0001] The present application relates to maps and kinematic prediction for vehicles, and more particularly to data-based travel line estimation and mapping.

Background Art

[0002] For safe and highly reliable operation, at least some subsystems in a vehicle may include inherent self-monitoring functions, problem detection functions, and, where possible, repair functions.

[0003] Autonomous vehicles (or more broadly, autonomous driving) provide passengers with the convenience of efficient and safe transportation from one place to another. In the absence of real-time perception information of a part of a vehicle traffic network, an autonomous vehicle may plan a trajectory for passing through the part of the vehicle traffic network based on a lane-level map.

Summary of Invention

[0004] Constructing an accurate lane-level map is expensive and time-consuming. A lane-level map may be formed by estimating a geometric centerline with respect to lane markings. However, a lane-level map defined by a geometric centerline may not represent how people actually drive.

[0005] The teaching in the present specification describes combining a road-level map and observed travel lines of road users in the real world to generate a data-based travel line map having lane-level details. Such a map may be used for improved determination of vehicle trajectories and improved operation of vehicles.

[0006] A first aspect of the disclosed embodiment includes the step of receiving road-level map data and travel line data for at least one road user observed in a vehicle traffic network. The road-level map data includes way data comprising one or more ways, one of the one or more ways comprising a set of nodes, and the travel line data includes one or more travel lines, one of the one or more travel lines comprising a set of attitudes representing the road user as the road user travels through the vehicle traffic network. The method also includes the steps of identifying a first section of the way data as an intersection in the vehicle traffic network, and matching a second section of the way data with the travel line data to generate a plurality of waivers, one of the plurality of waivers comprising one or more attitudes of the set of attitudes from each travel line and comprising one of the set of nodes, the second section being different from the first section. The method also includes the steps of classifying each way bar as either constant or variable based on the number of lanes counted therein, and when each way bar is classified as constant, assigning a lane count representing the number of lanes to each way bar; grouping consecutive way bars among the plurality of way bars into a way bar section based on the classification of each way bar and the lane count of each way bar; and generating a data-based driving line map using the way bar section and the first section. The method also includes the step of operating the vehicle by using the data-based driving line map as input to the vehicle's control system.

[0007] A second aspect of the disclosed embodiment is an apparatus comprising memory and a processor. The processor is configured to execute instructions stored in the memory to receive road-level map data and travel line data for at least one road user observed in a vehicle traffic network, wherein the road-level map data includes way data comprising one or more ways, one of the ways comprising a series of nodes, the travel line data includes one or more travel lines, one of the travel lines comprising a series of attitudes representing the road user as the road user travels through the vehicle traffic network, a first section of the way data is identified as an intersection in the vehicle traffic network, and a second section of the way data is matched with the travel line data to generate a plurality of waivers, one of the plurality of waivers being the series of attitudes The system is configured to include one or more attitudes of a force from each driving line and one node of the series of nodes, wherein the second section is different from the first section, and to classify each waiver as either constant or variable based on the number of lanes counted therein, wherein when each waiver is classified as constant, a lane count representing the number of lanes is assigned to each waiver, to group consecutive waivers among the plurality of waivers into a waiver section based on the classification of each waiver and the lane count of each waiver, to generate a data-based driving line map using the waiver section and the first section, and to operate the vehicle using the data-based driving line map as input to the vehicle control system.

[0008] A third aspect of the disclosed embodiment is a non-temporary computer-readable medium for storing instructions operable to cause one or more processors to perform an operation, wherein the instructions include receiving road-level map data and travel line data for at least one road user observed in a vehicle traffic network, the road-level map data including way data including one or more ways, one of the ways including a set of nodes, the travel line data including one or more travel lines, one of the travel lines including a set of attitudes representing the road user as the road user travels through the vehicle traffic network, identifying a first section of the way data as an intersection in the vehicle traffic network, and matching a second section of the way data with the travel line data to generate a plurality of waivers, the plurality The method includes: one waiver among a number of waivers includes one or more attitudes of the set of attitudes from their respective driving lines and includes one node of the set of nodes, the second section being different from the first section; classifying each waiver as either constant or variable based on the number of lanes counted therein, where when each waiver is classified as constant, a lane count representing the number of lanes is assigned to each waiver; grouping consecutive waivers among the plurality of waivers into a waiver section based on the classification of each waiver and the lane count of each waiver; generating a data-based driving line map using the waiver section and the first section; and operating the vehicle using the data-based driving line map as input to the vehicle control system.

[0009] Modifications of these and other aspects, features, elements, implementations, and embodiments of the methods, apparatus, procedures, and algorithms disclosed herein are described in further detail below. [Brief explanation of the drawing]

[0010] The disclosed technology is best understood by reading the following detailed description in conjunction with the attached drawings. It should be emphasized that, according to common practice, various features in the drawings may not be to scale. Conversely, the dimensions of various features may be enlarged or reduced as appropriate for clarity. Furthermore, unless otherwise noted, similar reference numbers refer to the same elements throughout the drawings.

[0011] [Figure 1] This figure shows an example of a vehicle portion in which the embodiments, features, and elements disclosed herein may be implemented.

[0012] [Figure 2] This figure shows an example of a part of a vehicle transport and communication system in which embodiments, features, and elements disclosed herein may be implemented.

[0013] [Figure 3A] This figure shows an example of a road-level map.

[0014] [Figure 3B] This figure shows an example of observed driving lines in a portion of a vehicle traffic network.

[0015] [Figure 3C] This figure shows examples of observed driving lines and ways within a portion of a vehicle traffic network.

[0016] [Figure 4] This is a flowchart illustrating an example of a process for generating a data-driven route map.

[0017] [Figure 5] Figures 5A to 5C illustrate examples of identifying intersections and matching road-level map data with observed driving line data. Figure 5A shows a portion of road-level map data where multiple ways intersect, Figure 5B shows how waivers relate to the ways, and Figure 5C shows the orientation of driving lines relative to the ways from the observed driving line data.

[0018] [Figure 6] It is a diagram illustrating an example of lane number estimation.

[0019] [Figure 7A] It is a diagram illustrating an example of assigning link nodes.

[0020] [Figure 7B] It is a diagram illustrating an example of estimating travel line connectivity.

Mode for Carrying Out the Invention

[0021] A vehicle (which may also be referred to as a host vehicle in the present specification) may be an autonomous vehicle (AV) or a semi-autonomous vehicle, for example a vehicle including an advanced driving assistance system (ADAS), and may autonomously travel through a part of a vehicle traffic network. These vehicles may be collectively referred to as autonomous vehicles.

[0022] The host vehicle may include one or more sensors. Traveling through the vehicle traffic network may include the sensors generating or capturing sensor data, such as data corresponding to the operating environment of the vehicle or a part thereof. For example, sensor data may include data corresponding to one or more external objects (or simply objects), and the external objects may include other (i.e., other than the host vehicle itself) road users (other vehicles, bicycles, motorcycles, trucks, etc.), which may also be traveling through the vehicle traffic network.

[0023] The trajectory may be planned based on scene understanding (e.g., by the host vehicle's controller). The scene may include static and dynamic objects, and may also include external objects detected using the host vehicle's sensors (e.g., other road users). The scene may include data available in a road-level map. The road-level map may include way data. Way data may consist of one or more ways, and a way may be a lane line such that the longitudinal axis of road users traveling in the lane is expected to align with the way. A way may also include nodes, each node constituting a point along the way.

[0024] Furthermore, the scene may also include observed driving line data from at least several other road users. Observed driving line data includes one or more driving lines. A driving line represents a line recorded as having been traveled by the road user while they were traveling through the traffic network. A driving line has a set of attitudes, where an attitude represents a specific position along the driving line, including the direction the road user was facing at the time the attitude was recorded. Thus, scene understanding may include way data available in a road-level map and observed driving line data from other road users.

[0025] Poor or inaccurate lane-level maps may cause a vehicle controller to plan a trajectory that is not optimal or safe for the host vehicle. Inaccurate lane-level maps can occur in several situations. For example, inaccurate lane-level maps can occur when the data in the road-level map is inaccurate or incomplete. For example, even if the data in the road-level map is accurate, inaccurate lane-level maps can occur if road users drive in a manner that does not conform to the data in the road-level map.

[0026] For illustrative purposes and without loss of generality, left-turn lanes at intersections may be precisely mapped. However, most road users may travel past the mapped lanes before turning left in the interaction. It should be noted that there can be considerable variation in how drivers perform their turns (or how they deal with any other driving conditions or lanes).

[0027] Although described herein with reference to an autonomous host vehicle, the technologies and devices described herein may be implemented in any vehicle capable of autonomous or semi-autonomous operation. The methods and devices described herein may be used within a vehicle traffic network that may include any area in which a host vehicle can travel.

[0028] To describe in more detail some embodiments of the teachings in this specification, we first refer to an environment in which this disclosure may be implemented.

[0029] Figure 1 is a diagram of an example of a portion of a vehicle 100, in which embodiments, features, and elements disclosed herein may be implemented. The vehicle 100 comprises a chassis 102, a powertrain 104, a controller 114, and wheels 132 / 134 / 136 / 138, and may also comprise any other elements or combinations of elements of the vehicle. For simplicity, the vehicle 100 is shown as having four wheels 132 / 134 / 136 / 138, but may also have any other propulsion device such as a propeller or tracks. In Figure 1, the lines interconnecting elements such as the powertrain 104, the controller 114, and the wheels 132 / 134 / 136 / 138 indicate that information such as data or control signals, power such as electricity or torque, or both information and power may be transmitted between the corresponding elements. For example, the controller 114 may receive power from the powertrain 104 and communicate with the powertrain 104, the wheels 132 / 134 / 136 / 138, or both, to control the vehicle 100, which may include accelerating, decelerating, steering, or otherwise controlling the vehicle 100.

[0030] The powertrain 104 comprises a power source 106, a transmission 108, a steering unit 110, and a vehicle actuator 112, and may also include any other elements or combinations of elements of the powertrain, such as a suspension, drive shafts, axles, or exhaust system. Wheels 132 / 134 / 136 / 138, as shown separately, may also be included in the powertrain 104.

[0031] The power source 106 may be any device or combination of devices capable of supplying energy such as electrical energy, thermal energy, or kinetic energy. For example, the power source 106 may comprise an engine such as an internal combustion engine, an electric motor, or a combination of an internal combustion engine and an electric motor, and be capable of supplying kinetic energy as propulsion to one or more of the wheels 132 / 134 / 136 / 138. In some embodiments, the power source 106 may comprise a potential energy unit such as one or more dry cell batteries such as nickel-cadmium (NiCd), nickel-zinc (NiZn), nickel-metal hydride (NiMH), or lithium-ion (Li-ion), a solar cell, a fuel cell, or any other device capable of supplying energy.

[0032] The transmission 108 receives energy such as kinetic energy from the power source 106 and transmits that energy to the wheels 132 / 134 / 136 / 138 to provide propulsion. The transmission 108 may be controlled by the controller 114, the vehicle actuator 112, or both. The steering unit 110 may be controlled by the controller 114, the vehicle actuator 112, or both, and controls the wheels 132 / 134 / 136 / 138 to steer the vehicle. The vehicle actuator 112 may receive signals from the controller 114 and may actuate or control the power source 106, the transmission 108, the steering unit 110, or any combination thereof, to operate the vehicle 100.

[0033] In the illustrated embodiment, the controller 114 comprises a location unit 116, an electronic communication unit 118, a processor 120, a memory 122, a user interface 124, a sensor 126, and an electronic communication interface 128. Although shown as a single unit, any one or more elements of the controller 114 may be integrated into any number of separate physical units. For example, the user interface 124 and the processor 120 may be integrated into a first physical unit, and the memory 122 may be integrated into a second physical unit. Although not shown in Figure 1, the controller 114 may include a power source such as a battery. Although shown as separate elements, the location unit 116, the electronic communication unit 118, the processor 120, the memory 122, the user interface 124, the sensor 126, the electronic communication interface 128, or any combination thereof, may be integrated into one or more electronic units, circuits, or chips.

[0034] In some embodiments, the processor 120 may comprise any existing or future-developed device or combination of devices capable of manipulating or processing signals or other information, such as an optical processor, a quantum processor, a molecular processor, or a combination thereof. For example, the processor 120 may comprise one or more dedicated processors, one or more digital signal processors, one or more microprocessors, one or more controllers, one or more microcontrollers, one or more integrated circuits, one or more application-specific integrated circuits, one or more field-programmable gate arrays, one or more programmable logic arrays, one or more programmable logic controllers, one or more state machines, or any combination thereof. The processor 120 may be operably coupled with a location unit 116, a memory 122, an electronic communication interface 128, an electronic communication unit 118, a user interface 124, a sensor 126, a powertrain 104, or any combination thereof. For example, the processor may be operably coupled with the memory 122 via a communication bus 130.

[0035] The processor 120 may be configured to execute instructions. Such instructions may include instructions for remote operation, which may be used to operate the vehicle 100 from a remote location, including an operation center. Instructions for remote operation may be stored in the vehicle 100 or received from an external source such as a traffic management center or a server computing device, the server computing device may include a cloud-type server computing device.

[0036] Memory 122 may include any tangible non-temporary computer-usable or computer-readable medium capable of containing, storing, communicating, or transporting machine-readable instructions or any related information for use by or in connection with the processor 120. Memory 122 may include, for example, one or more solid-state drives, one or more memory cards, one or more removable media, one or more read-only memories (ROMs), one or more random-access memories (RAMs), one or more registers, one or more low-power double data-rate (LPDDR) memories, one or more cache memories, one or more disks (including hard disks, floppy disks, or optical disks), magnetic cards or optical cards, or any type of non-temporary medium suitable for storing electronic information, or any combination thereof.

[0037] The electronic communication interface 128 may be a wireless antenna, a wired communication port, an optical communication port, or any other wired or wireless unit capable of interfacing with any other wired or wireless electronic communication medium 140, as shown in the figure.

[0038] The electronic communication unit 118 may be configured to transmit or receive signals via a wired or wireless electronic communication medium 140, such as via an electronic communication interface 128. Although not explicitly shown in Figure 1, the electronic communication unit 118 may be configured to transmit, receive, or both, via any wired or wireless communication medium, such as radio frequency (RF), ultraviolet (UV), visible light, optical fiber, wired line, or a combination thereof. Figure 1 shows one electronic communication unit 118 and one electronic communication interface 128, but any number of communication units and any number of communication interfaces may be used. In some embodiments, the electronic communication unit 118 may include a dedicated narrow-range communication (DSRC) unit, a wireless safety unit (WSU), IEEE 802.11p (WiFi-P), or a combination thereof.

[0039] The positioning unit 116 may determine geographical location information of the vehicle 100, including but not limited to longitude, latitude, altitude, direction of travel, or speed. For example, the positioning unit may include a Global Positioning System (GPS) unit such as a Wide Area Augmentation System (WAAS) compatible National Marine Electronics Association (NMEA) unit, a radio triangulation unit, or a combination thereof. The positioning unit 116 may be used, for example, to obtain information representing the current bearing of the vehicle 100, the current position of the vehicle 100 in two or three dimensions, the current angular direction of the vehicle 100, or a combination thereof.

[0040] The user interface 124 may include any unit that can be used as an interface by a person, including any virtual keypad, physical keypad, touchpad, display, touchscreen, speaker, microphone, video camera, sensor, and printer. The user interface 124 may be operably coupled with the processor 120 as shown in the figure, or operably coupled with any other element of the controller 114. Although shown as a single unit, the user interface 124 may comprise one or more physical units. For example, the user interface 124 may include a voice interface for performing voice communication with a person, and a touch display for performing visual and touch-based communication with a person.

[0041] Sensor 126 may comprise one or more sensors, such as a sensor array, that are operable to provide information that can be used to control the vehicle. Sensor 126 may provide information about the vehicle's current operating characteristics or its surroundings. Sensor 126 may comprise, for example, a speed sensor, an acceleration sensor, a steering angle sensor, a traction-related sensor, a braking-related sensor, or any sensor or combination of sensors that are operable to report information about a certain aspect of the vehicle 100's current dynamic condition.

[0042] In some embodiments, the sensor 126 comprises sensors that can operate to acquire information about the physical environment surrounding the vehicle 100. For example, one or more sensors may detect road shape and obstacles such as fixed obstacles, vehicles, cyclists, and pedestrians. The sensor 126 may be one or more video cameras, laser sensing systems, infrared sensing systems, acoustic sensing systems, or any other suitable type of in-vehicle environment sensing device or combination of devices that are currently known or to be developed, or may comprise them. The sensor 126 and the positioning unit 116 may be combined.

[0043] Although not shown separately, the vehicle 100 may include a track controller. For example, controller 114 may include a track controller. The track controller may be operable to acquire information describing the current state of the vehicle 100 and a planned route for the vehicle 100, and may determine and optimize a track for the vehicle 100 based on this information. In some embodiments, the track controller outputs a signal that allows the vehicle 100 to be controlled to follow a track determined by the track controller. For example, the output of the track controller may be an optimized track which may be supplied to the powertrain 104, wheels 132 / 134 / 136 / 138, or both. The optimized track may be a control input such as a set of steering angles, where each steering angle corresponds to a time or position. The optimized track may be one or more paths, lines, curves, or a combination thereof.

[0044] One or more of the wheels 132 / 134 / 136 / 138 may be steered wheels that are turned to a steering angle under the control of the steering unit 110, or driven wheels to which torque is applied to propel the vehicle 100 under the control of the transmission 108, or they may be steering wheels and drive wheels that steer and propel the vehicle 100.

[0045] The vehicle may also include units or elements not shown in Figure 1, such as a housing, a Bluetooth(R) module, a frequency modulation (FM) radio unit, a near-field communication (NFC) module, a liquid crystal display (LCD) display unit, an organic light-emitting diode (OLED) display unit, a speaker, or any combination thereof.

[0046] Figure 2 is a diagram illustrating an example of a portion of a vehicle transport and communication system 200 in which embodiments, features, and elements disclosed herein may be implemented. The vehicle transport and communication system 200 comprises a vehicle 202, such as the vehicle 100 shown in Figure 1, and one or more external objects, such as an external object 206, which may include any form of transport, such as the vehicle 100 shown in Figure 1, pedestrians, cyclists, and any form of structure, such as a building. The vehicle 202 may travel through one or more portions of the transport network 208 and may communicate with the external object 206 via one or more electronic communication networks 212. Although not explicitly shown in Figure 2, the vehicle may travel through areas not explicitly or entirely included in the transport network, such as off-road areas. In some embodiments, the transport network 208 may comprise one or more vehicle detection sensors 210, such as guided loop sensors, which may be used to detect the movement of vehicles on the transport network 208.

[0047] The electronic communication network 212 may be a multiple access system that provides communication such as voice communication, data communication, video communication, message communication, or a combination thereof between the vehicle 202, the external object 206, and the operation center 230. For example, the vehicle 202 or the external object 206 may receive information such as information representing the transport network 208 from the operation center 230 via the electronic communication network 212.

[0048] The operation center 230 includes a control device 232, which has some or all of the features of the controller 114 shown in Figure 1. The control device 232 can monitor and adjust the movement of vehicles, including autonomous vehicles. The control device 232 may monitor the state or conditions of vehicles, such as vehicle 202, and external objects, such as external object 206. The control device 232 can receive vehicle data and infrastructure data, which may include any of the following: vehicle speed, vehicle position, vehicle operating state, vehicle destination, vehicle route, vehicle sensor data, external object speed, external object position, external object operating state, external object destination, external object route, and external object sensor data.

[0049] Furthermore, the control device 232 can establish remote control of one or more vehicles, such as vehicle 202, or external objects, such as external object 206. In this way, the control device 232 may remotely operate those vehicles or external objects from a remote location. The control device 232 may exchange (transmit or receive) status data with vehicles, external objects, or computing devices, such as vehicle 202, external object 206, or server computing device 234, via wireless communication links, such as wireless communication link 226, or wired communication links, such as wired communication link 228.

[0050] The server computing device 234 may comprise one or more server computing devices and may exchange (transmit or receive) status signal data with one or more vehicles or computing devices, including the vehicle 202, external object 206, or operation center 230, via the electronic communication network 212.

[0051] In some embodiments, the vehicle 202 or external object 206 communicates via a wired communication link 228, wireless communication links 214 / 216 / 224, or any combination of any number or types of wired or wireless communication links. For example, as shown, the vehicle 202 or external object 206 communicates via a terrestrial wireless communication link 214, via a non-terrestrial wireless communication link 216, or a combination thereof. In some implementations, the terrestrial wireless communication link 214 includes an Ethernet link, a serial link, a Bluetooth link, an infrared (IR) link, an ultraviolet (UV) link, or any link capable of electronic communication.

[0052] A vehicle, such as vehicle 202, or an external object, such as external object 206, may communicate with another vehicle, external object, or operation center 230. For example, a host vehicle or target vehicle 202 may receive one or more vehicle-to-vehicle messages, such as basic safety messages (BSMs), from operation center 230 via direct communication link 224 or via electronic communication network 212. For example, operation center 230 may broadcast the messages to host vehicles within a defined broadcast range, such as 300 meters, or to a defined geographical area. In some embodiments, vehicle 202 receives messages via a third party, such as a signal repeater (not shown) or another remote vehicle (not shown). In some embodiments, vehicle 202 or external object 206 periodically transmits one or more vehicle-to-vehicle messages based on a defined interval, such as 100 milliseconds.

[0053] Vehicle 202 may communicate with the electronic communications network 212 via access point 218. Access point 218 may include computing devices and be configured to communicate with vehicle 202, the electronic communications network 212, the operation center 230, or a combination thereof, via wired or wireless links 214 / 220. For example, access point 218 is a base station, base transceiver station (BTS), Node-B, extended Node-B (eNode-B), home Node-B (HNode-B), wireless router, wired router, hub, relay, switch, or any similar wired or wireless device. Although shown as a single unit, access point may comprise any number of interconnected elements.

[0054] Vehicle 202 may communicate with the electronic communications network 212 via satellite 222 or other non-terrestrial communications devices. Satellite 222, which may have computing devices, may be configured to communicate with vehicle 202, the electronic communications network 212, the operation center 230, or a combination thereof, via one or more communications links 216 / 236. Although shown as a single unit, the satellite may comprise any number of interconnected elements.

[0055] The electronic communication network 212 may be any type of network configured to provide voice, data, or any other type of electronic communication. For example, the electronic communication network 212 may include a local area network (LAN), a wide area network (WAN), a virtual private network (VPN), a mobile phone network or cell phone network, the Internet, or any other electronic communication system. The electronic communication network 212 may use communication protocols such as the Transmission Control Protocol (TCP), User Datagram Protocol (UDP), Internet Protocol (IP), Real-time Transport Protocol (RTP), Hypertext Transfer Protocol (HTTP), or a combination thereof. Although shown as a single unit, the electronic communication network may comprise any number of interconnected elements.

[0056] In some embodiments, the vehicle 202 communicates with the operation center 230 via an electronic communication network 212, an access point 218, or a satellite 222. The operation center 230 may include one or more computing devices that can exchange (transmit or receive) data from vehicles such as the vehicle 202 with data from external objects, including external objects 206, or data from computing devices such as a server computing device 234.

[0057] In some embodiments, the vehicle 202 identifies a portion or state of the transport network 208. For example, the vehicle 202 may be equipped with one or more on-board sensors 204, such as the sensor 126 shown in Figure 1, which include speed sensors, wheel speed sensors, cameras, gyroscopes, optical sensors, laser sensors, radar sensors, acoustic sensors, or any other sensors or devices or combinations thereof capable of determining or identifying a portion or state of the transport network 208.

[0058] Vehicle 202 may travel through one or more parts of the transport network 208 using information communicated via the electronic communication network 212, such as information representing the transport network 208, information identified by one or more onboard sensors 204, or a combination thereof. External objects 206 may be capable of performing all or part of the communications and operations described above with respect to vehicle 202.

[0059] For simplicity, Figure 2 shows the vehicle 202 as the host vehicle, along with external objects 206, a transport network 208, an electronic communications network 212, and an operation center 230. However, any number of vehicles, networks, or computing devices may be used. In some embodiments, the vehicle transport and communications system 200 may include devices, units, or elements not shown in Figure 2.

[0060] Although the vehicle 202 is shown communicating with the operation center 230 via the electronic communication network 212, the vehicle 202 (and external objects 206) may communicate with the operation center 230 via any number of direct or indirect communication links. For example, the vehicle 202 or external object 206 may communicate with the operation center 230 via a direct communication link such as a Bluetooth communication link. Also, for simplicity, Figure 2 shows one of the transport networks 208 and one of the electronic communication networks 212, but any number of networks or communication devices may be used.

[0061] The external object 206 is shown as a second remote vehicle in Figure 2. The external object is not limited to other vehicles. The external object may be any infrastructure element, such as a fence, sign, or building, which has the ability to transmit data to the operation center 230. The data may be, for example, sensor data from the infrastructure element.

[0062] As initially stated, the observed driving lines may be used in conjunction with available map data (e.g., road-level data) to create a data-based driving line map with lane-level details. Next, we will describe the data used to create the data-based driving line map, as well as the process or method for creating and using the data-based driving line map.

[0063] Figure 3A shows an example of map data according to this disclosure. An example of road-level map data is shown. The road-level map data shows a portion 302 of the mapped area. In the road-level map data, roads 304 are mapped at the road level. However, for illustrative purposes, a lane-level mapping based on geometric centerlines is shown in portion 302.

[0064] Figure 3B shows an example of observed traffic lines in part 306 of the vehicle traffic network, and Figure 3C shows an example of observed traffic lines and ways in part 308 of the vehicle traffic network.

[0065] In Figure 3B, a portion 306 shows multiple observed vehicle travel lines 316, such as vehicle 202 in Figure 2, collected while the vehicle was turning within the traffic network. A travel line comprises a series of attitudes, which represent the specific point position and direction the vehicle was facing as it traveled through the traffic network. Travel line data includes one or more travel lines.

[0066] In Figure 3C, a portion 308 shows the travel lines 318 of multiple observed vehicles, such as vehicle 202 in Figure 2, collected while vehicles travel along residential streets within a traffic network. Road-level map data includes way data, which contains one or more ways. A way represents a lane or road. Each way includes a set of nodes, each node representing a specific point location and orientation of that way. The example in Figure 3C shows a bidirectional way without lane markings. Point 328 in Figure 3C is shown on the geometric centerline for each direction of travel. As can be seen by comparing the travel lines 318 with point 328, drivers rarely travel on the geometric centerline for each direction of travel.

[0067] Figure 4 includes a flowchart of a method or process 400 for generating a data-based driving line map according to the present disclosure. Process 400 includes operations 402 to 416, which are described below. Process 400 may be stored in memory (e.g., memory 122 in Figure 1) as instructions that can be executed by the processor (e.g., processor 120 in Figure 1) of the AV (e.g., vehicle 100 in Figure 1). In some implementations, some or all of the operations of process 400 may be executed in a remote support center for the vehicle, for example, by a control device 232 in an operation center 230.

[0068] In operation 402, process 400 receives as input road-level map data as described above with respect to the road-level map data in Figure 3A, and observed driving line data as described above with respect to the observed driving line data in Figures 3B and 3C. The road-level map data may be received from an existing source such as OpenStreetMap (OSM) or another similar source. Note that some roads in the road-level map data may have lane-level data.

[0069] The observed driving line data may be received from another vehicle traveling through the traffic network, such as vehicle 202 in Figure 2, or the observed driving line data may be generated by the host vehicle. The observed driving line data may also be received from an operation center 230 where data has been stored by one or more vehicles that have previously traveled through the traffic network.

[0070] In operation 404, process 400 identifies a first segment of way data received with road-level map data as an intersection. An identified intersection is a portion of a traffic network where multiple ways of road-level data intersect or intersect. An intersection may be identified by nodes belonging to multiple ways. Each individual way in the road-level map data includes a set of nodes, as shown in Figure 3C above. However, each node may belong to multiple ways. A segment of way data is identified as an intersection when it includes overlapping nodes or nodes belonging to multiple ways.

[0071] In operation 406, process 400 generates a waiver by matching a second segment of way data with received driving line data. The first segment is different from the second segment. More specifically, simultaneously with or after determining which segments of way data are classified as intersections, process 400 may generate a waiver by matching the observed driving line data with road-level map data. A waiver is a grouping of attitudes from separate driving lines in the driving line data, associated with way nodes contained in the way data of the road-level map data. Way nodes may be actual nodes in the way data, or nodes may be interpolated from the way data. An interpolated node may be interpolated from two actual nodes on either side of the interpolated node. For each pair of consecutive actual nodes, there may be a number of interpolated nodes. Each interpolated node is associated with the same way as the actual node from which it was interpolated. For example, a waiver is a cross-section of way and travel line data, where each attitude within the waiver is associated with a different travel line, and each node within the waiver is associated with a single way.

[0072] Figures 5A to 5C illustrate an example of identifying an intersection and matching road-level map data with observed driving line data. This example shows a portion of a vehicle traffic network, namely road 502, way 504 which is included in the way data of the road-level map data, node 506 which is part of a series of nodes of the way, and driving line 508 which is included in the observed driving line data.

[0073] Figure 5A shows a portion of road-level map data where multiple ways 504 intersect. Intersection 512 is identified by overlapping nodes 506, as well as nodes 506 belonging to multiple ways 504. Intersection 512 may also be one of the intersections identified in operation 404 of process 400.

[0074] Figure 5B shows how waiver 522 relates to way 504 and its node 506. Waiver 522 is oriented perpendicular to way 504, so that waiver 522 may represent the width or spread of the corresponding lane in the traffic network. Waiver 522 is a set of attitudes associated with the travel line in the observed travel line data. Furthermore, waiver 522 includes node 506 associated with way 504 from the way data. Node 506, as well as the set of attitudes contained within waiver 522, are all associated with the same location in the longitudinal direction along the road lanes in the traffic network.

[0075] Figure 5C shows the orientation of a travel line 508 relative to way 504 from observed travel line data, as well as a set of nodes 506 contained within the travel line 508. For example, multiple travel lines 508 may be observed for a given way 504. Way 504 may have multiple nodes 506 contained within a set of nodes for that way 504. A travel line 508 includes a set of attitudes 532. Attitudes 532 correspond to nodes 506 of way 504. These corresponding attitudes 532 and nodes 506 are grouped together to generate a waiver 522.

[0076] Furthermore, although three travel lines 508 are shown in Figure 5C for the first waiver 534 and the second waiver 536, there may be more or fewer attitudes associated with more or fewer travel lines 508. In addition, the travel lines 508 may be spaced at various intervals or offsets from the node 506 contained within the waiver 522.

[0077] Figures 5B and 5C together show that, as described with respect to operation 406, a waiver is generated by matching a second section of way data with the received travel line data.

[0078] Referring again to Figure 4, in operation 408, the waiver is classified based on the cardinality (e.g., number) of the lanes counted within the waiver. That is, for a waiver generated as part of operation 406, operation 408 determines the number of lanes represented by the waiver. The number of lanes may be counted based on one or more attitudes contained within the waiver, and the distance from each attitude within the waiver to the next nearest attitude.

[0079] For operation 408 to determine the number of lanes, one or more attitudes in the waiver are first grouped according to the direction of that attitude. For example, each attitude in the waiver corresponds to a point position and includes a direction of travel. The direction of travel is used to determine the direction of that attitude. Attitudes having the same direction of travel are grouped together. For example, an attitude including a direction of travel indicating east is grouped separately from an attitude including a direction of travel indicating west.

[0080] After the orientations are grouped according to their orientation, an offset may be defined for each orientation. The offset represents the distance between the orientation and the node in the waiver. After the offsets are determined, the orientations within each group are sorted based on their offsets. For example, a group of orientations all having an east orientation may contain three nodes. For each of the three nodes in the group, an offset is defined based on the distance of that orientation from the node. Specifically, if the node is at position 0 in the waiver and the first orientation is at position 0.3, then the offset for the first orientation is 0.3. Similarly, if the distance between the second orientation and the node is 0.2, then the offset for the second orientation is 0.2. This same process is repeated for the remaining orientations in the waiver. In this example, the first, second, and third orientations have offsets of 0.3, 0.2, and 0.35, respectively. The orientations are then sorted based on their offsets. The resulting order of the orientations in this example is the second orientation, the first orientation, and the third orientation.

[0081] After the poses are sorted based on a defined offset, the distances between the poses are determined based on the sort order. Continuing with the example where the poses are ordered as second pose, first pose, and third pose, the first distance between the second pose and the first pose is calculated, and then the second distance between the first pose and the third pose is calculated.

[0082] Next, the calculated distance may be compared with the new lane threshold. If the first distance is greater than the new lane threshold, the lane count may be incremented. Furthermore, if the first distance is less than the new lane threshold and greater than the same lane threshold, the lane count may be characterized as unknown. This process may then be repeated for each distance thus calculated until all distances have been evaluated against the new lane threshold and the same lane threshold.

[0083] Figure 6 shows an example of how lane counts are calculated. Figure 6 shows way 602, node 604, driving line 606 indicating the way direction, waiver 608, and attitude 610. Furthermore, the first waiver 612 shows a case where the lane count is classified as unknown. For example, the first waiver 612 contains multiple attitudes. The attitudes are grouped according to direction and sorted based on a defined offset of attitudes, for example, by using any appropriate clustering technique. In the upper group of attitudes, there is no defined grouping of attitudes. In other words, the distance between each of the attitudes is greater than the same-lane threshold but less than the new lane threshold. Therefore, the lane count is classified as unknown. In this example, the same-lane threshold is equal to 0.55 meters and the new lane threshold is equal to 2.2 meters. However, the thresholds may be set to any appropriate value.

[0084] The first waiver 612 indicates two distinct groups based on direction, and it can be estimated that at least two lanes exist. However, the fact that all calculated distances between attitudes included within the direction grouping lie between the new lane threshold and the same lane threshold suggests that there is most likely to be a change in the number of lanes for this waiver. For example, the waiver may represent a transition from a two-lane road to a three-lane road, or vice versa. Since the total number of lanes cannot be determined, the lane count is classified as unknown for the entire first waiver 612.

[0085] Furthermore, the second waiver 614 indicates the case in which the lane count is determined to be 3. The attitudes are grouped according to direction and sorted based on the attitude offset. In contrast to the first waiver 614, both the upper and lower groups have clearly defined attitude groupings. That is, the calculated distances between attitudes included in the direction-based group are all calculated to be either less than the same lane threshold or greater than the new lane threshold. This allows process 400 to determine that the total lane count for the second waiver 614 is 3.

[0086] Referring again to Figure 4, process 400 groups consecutive waivers into waiver sections based on the lane counts assigned in operation 410. That is, each consecutive waiver is compared with an adjacent waiver. For example, the lane count of a waiver shown in a horizontal alignment is compared with the waivers to its left and right, while a waiver shown in a vertical alignment may be compared with the lane counts of the waivers above and below it. Although waivers are shown in a horizontal or vertical alignment, they may be aligned in any orientation as long as it is perpendicular to the corresponding way to which the waiver is associated.

[0087] In a further example, referring again to Figure 6, the waiver 608 shown may be grouped into three segments. The first segment includes waiver 608 with a lane count of 2, shown on the left side of the image. The second segment includes waiver 608 classified as having an unknown lane count (like the example of the first waiver 612), shown in the center of the image. The third segment includes waiver 608 with a lane count equal to 3 (like the example of the second waiver 614), shown on the right side of the image.

[0088] Referring again to Figure 4, in operation 412, a data-based travel line map is generated using the waiver section and the first section. Link nodes may be assigned to the beginning and end of the waiver section. When link nodes are assigned to the beginning and end of the waiver section, travel line nodes corresponding to the relevant travel lines of attitudes included in the first and last waivers of each section are assigned.

[0089] Link nodes assigned to waiver intervals may be used to determine how the first waiver interval may connect to the second waiver interval and / or the third waiver interval. Waiver intervals may be connected using link nodes and / or the first interval (i.e., intersections).

[0090] Figures 7A and 7B may be used to illustrate operation 412.

[0091] Figure 7A is an example of assigning link nodes. This example shows a portion of a vehicle traffic network, namely road 702, as well as way 704 and waiver 706. Link nodes 712 link different sections of the road (e.g., sections with changing lane numbers, sections with constant lanes, and intersections) using, for example, way start / end bars. For example, link nodes 712 may be assigned to the beginning and end of waiver sections with respect to the way direction (see also Figure 6). For example, if a vehicle is trying to navigate from LN6 (i.e., the rightmost end of Figure 7A) to LN1 (i.e., the bottommost end of Figure 7A), the sequence of link nodes would be LN6→LN5→LN2→LN1. Or, if a vehicle is trying to navigate from LN1 (i.e., the bottommost end of view 700) to LN4 (i.e., the leftmost end of view 700), the sequence of nodes would be LN1→LN2→LN3→LN4. Therefore, any route through the vehicle traffic network can be represented as a series of link nodes 712.

[0092] Once the sequence of link nodes 712 is defined, the travel line nodes 722 in Figure 7B are used to determine the actual routes (i.e., routes, courses, etc.) used to travel through that portion of the vehicle traffic network. This is also called travel line connectivity estimation. The travel line nodes 722 are created in bars that have link nodes 712 and link the travel lines 724 in each travel line direction.

[0093] For example, continuing the above example, if a vehicle is navigating from LN6 to LN1, the sequence of link nodes 712 may be LN6→LN5→LN2→LN1. The travel line navigating this sequence of link nodes 712 follows the sequence of travel line nodes 722 labeled DN19→DN16→DN7→DN3. This is just one possible mode of travel through the vehicle traffic network to reach LN1 from LN6. For example, another sequence of travel line nodes navigating this sequence of link nodes 712 is labeled DN19→DN16→DN8→DN4. The remaining route may be similarly inferred from travel line 724.

[0094] Referring again to Figure 4, the data-based driving line map is converted to a lane-level map in operation 414. Additional map characteristics may be useful when using the data-based driving line map to plan the trajectory for a vehicle, for example, vehicle 100 in Figure 1. Characteristics such as speed limits and lane characteristics are useful when planning the trajectory for a vehicle. Lane characteristics may be estimated based on observed driving line data from driving line node to driving line node, such as driving line node 722 in Figure 7. Speed ​​limit data may be obtained from road-level map data received in operation 402. For example, the way type in OSM may be used to set the speed initially. Speed ​​limit data may come from any other source. For example, vehicle 100 may receive speed limit data as part of the road-level map data 302 in Figure 3. Alternatively, speed limit data may be received from the operation center 230 or server computing device 234 via the electronic communication network 212 in Figure 2. Lane relationships (following / leading) may be estimated from the sequence of driving line nodes. Parallel / opposing lanes may be defined according to the relative position of the lanes. Intersecting lanes and / or intersection points for intersections may be identified.

[0095] In operation 416, process 400 operates the vehicle using a data-based travel line map as input to the vehicle's control system. The vehicle may be vehicle 100. The control system may be implemented using the controller 114 shown in Figure 1. For example, the data-based travel line map may be received by the controller 114 in response to a request to move from a first location to a second location. The controller 114 may use the data-based travel line map to efficiently determine the best route for moving from the first location to the second location. More specifically, the controller may use the data-based travel line map to determine a sequence of link nodes 712, and then a sequence of travel line nodes 722 shown in Figure 7. After the sequence of travel line nodes 722 has been determined, the controller 114 may plan the trajectory for the vehicle to travel through the vehicle traffic network. The data-based travel line map may also be used as input to a specific control system. For example, the map may be used in a trajectory controller for braking assistance, steering assistance, or a combination thereof.

[0096] For the sake of brevity, the techniques described herein are illustrated and described as a series of actions. However, the actions described herein may occur in various orders and / or simultaneously. Furthermore, other steps or actions not shown or described herein may be used. Moreover, not all illustrated actions are necessarily required to implement the techniques described herein.

[0097] In this specification, the terms “driver” or “operator” may be used interchangeably. In this specification, the terms “brake” or “deceleration” may be used interchangeably. In this specification, the terms “computer” or “computer device” include any unit or combination of units capable of performing any method or any part thereof disclosed herein.

[0098] In this specification, the term “instruction” may include instructions or expressions for performing any method or any part thereof disclosed herein, and may be implemented in hardware, software, or any combination thereof. For example, an instruction may be implemented as information such as a computer program stored in memory, which may be executed by a processor to perform any method, algorithm, aspect, or combination thereof as described herein. In some implementations, an instruction or a part thereof may be implemented as a dedicated processor or circuit which may include specialized hardware for performing any method, algorithm, aspect, or combination thereof as described herein. In some implementations, a part of an instruction may be distributed across multiple processors on a single device or across multiple devices, which may communicate over a network such as a local area network, wide area network, the Internet, or a combination thereof, or directly without a network.

[0099] In this specification, the terms “example,” “embodiment,” “implementation,” “aspect,” “feature,” or “element” indicate that they are examples, illustrations, or illustrative. Unless expressly indicated otherwise, any example, embodiment, implementation, aspect, feature, or element is independent of any other example, embodiment, implementation, aspect, feature, or element and may be used in combination with any other example, embodiment, implementation, aspect, feature, or element.

[0100] As used herein, the terms “determine” and “identify,” or any variation thereof, include selecting, verifying, calculating, referencing, receiving, determining, establishing, obtaining, or otherwise identifying or determining using one or more devices as shown and described herein.

[0101] As used herein, the term “or” is intended to mean inclusive, not exclusive. That is, unless otherwise specified or it is clear from the context, “X contains A or B” is intended to indicate any of its natural inclusive combinations. Whether X contains A, X contains B, or X contains both A and B, “X contains A or B” is satisfied in all of the above cases. Furthermore, the articles ("a" and "an") used in this application and the attached claims should generally be interpreted as meaning “one or more” unless otherwise specified or it is clear from the context that they refer to a singular form.

[0102] Furthermore, for the sake of brevity, the drawings and descriptions herein may include a series or sequence of actions or steps, but the elements of the methods disclosed herein may occur in various orders or simultaneously. Furthermore, the elements of the methods disclosed herein may occur together with other elements not expressly shown and described herein. Furthermore, not all elements of the methods described herein are necessary to implement the methods according to this disclosure. While aspects, features, and elements are described herein in specific combinations, each aspect, feature, or element may be used independently or in various combinations with or without other aspects, features, and / or elements.

[0103] Although the disclosed technology is described in relation to specific embodiments, the disclosed technology should not be limited to the disclosed embodiments, but rather is intended to encompass a variety of modifications and equivalent configurations that fall within the scope of the appended claims, and such scope should be given the broadest possible interpretation permitted by law to encompass all such modifications and equivalent configurations.

Claims

1. A step of receiving road-level map data and driving line data for at least one road user observed in a vehicle traffic network, wherein the road-level map data includes way data including one or more ways, one of the one or more ways includes a series of nodes, and the driving line data includes one or more driving lines, one of the one or more driving lines includes a series of attitudes representing the road user when the road user is traveling through the vehicle traffic network, The steps include identifying the first section of the way data as an intersection of the vehicle traffic network, A step of generating a plurality of waivers by matching a second section of the way data with the travel line data, wherein one of the plurality of waivers comprises one or more attitudes of the series of attitudes from each travel line and includes one node of the series of nodes, and the second section is different from the first section. A step of classifying each waiver as either constant or variable based on the number of lanes counted therein, wherein when each waiver is classified as constant, a lane count representing the number of lanes is assigned to each waiver; The steps include: grouping consecutive waivers among the plurality of waivers into a waiver section based on the classification and lane count of each of the waivers; A step of generating a data-based travel line map using the waiver section and the first section, The steps include: using the driving line map based on the aforementioned data as input to the vehicle's control system to operate the vehicle; Methods that include...

2. The method according to claim 1, wherein the intersection is identified by identifying nodes corresponding to multiple ways.

3. The number of lanes to be counted is determined through a first process, The first process described above is Grouping one or more of the aforementioned attitudes of the waber according to direction, Defining one or more attitude offsets from the aforementioned one node, Sort the offset within the group identified in the grouping of one or more postures. Determining the distance between the offsets ordered by the sort of one or more of the above postures, and The number of lanes is determined based on the aforementioned distance. The method according to claim 1, including the method described in claim 1.

4. The first process described above is The method according to claim 3, further comprising increasing the lane count in response to the distance being greater than the new lane threshold.

5. The first process described above is The method according to claim 3, further comprising characterizing the lane count as unknown in response that the distance is greater than the same lane threshold and less than the new lane threshold.

6. The step of generating a driving line map based on the aforementioned data is: Assigning link nodes to the beginning and end of the aforementioned waiver interval, To determine the first link node of the first waiver section of the waiver section that connects to the second link node of the second waiver section of the waiver section, Connecting a first travel line among the one or more travel lines to a second travel line among the one or more travel lines, wherein the first travel line is associated with the first link node and the second travel line is associated with the second link node. The method according to claim 1, including the method described in claim 1.

7. The method further includes the step of converting the driving line map based on the aforementioned data into a lane-level map by setting map characteristics for the driving line map based on the aforementioned data, The method according to claim 1, wherein the map characteristics include speed limits and lane relationships.

8. The method according to claim 1, wherein the aforementioned driving line data is obtained from a sensor attached to an autonomous vehicle.

9. Memory and Processor and A device comprising the processor, wherein the processor executes instructions stored in the memory, Receiving road-level map data and travel line data for at least one road user observed in a vehicle traffic network, wherein the road-level map data includes way data comprising one or more ways, one of the one or more ways comprising a series of nodes, and the travel line data includes one or more travel lines, one of the one or more travel lines comprising a series of attitudes representing the road user when the road user travels through the vehicle traffic network. Identifying the first section of the way data as an intersection of the vehicle traffic network, The method involves matching a second section of the way data with the travel line data to generate a plurality of waivers, wherein one of the plurality of waivers comprises one or more attitudes from the series of attitudes from each travel line and includes one node from the series of nodes, and the second section is different from the first section. Each waiver is classified as either constant or variable based on the number of lanes counted within it, and when each waiver is classified as constant, a lane count representing the number of lanes is assigned to each waiver. Based on the classification and lane count of each of the aforementioned waivers, consecutive waivers among the plurality of waivers are grouped into a waiver section. Using the aforementioned waiver section and the first section, generate a data-based travel line map, and The vehicle is operated by using the driving line map based on the aforementioned data as input to the vehicle's control system. A device configured to perform the following actions.

10. The apparatus according to claim 9, wherein the intersection is identified by identifying nodes corresponding to multiple ways.

11. The number of lanes to be counted is determined through a first process, The first process described above is Grouping one or more of the aforementioned attitudes of the waber according to direction, Defining one or more attitude offsets from the aforementioned one node, Sort the offset within the group identified in the grouping of one or more postures. Determining the distance between the offsets ordered by the sort of one or more of the above postures, and The number of lanes is determined based on the aforementioned distance. The apparatus according to claim 9, including the apparatus described in claim 9.

12. The first process described above is In response to the distance being greater than the new lane threshold, increase the lane count, and Characterizing the lane count as unknown in response to the aforementioned distance being greater than the same lane threshold and less than the new lane threshold. The apparatus according to claim 11, further comprising:

13. Generating a driving line map based on the aforementioned data is: Assigning link nodes to the beginning and end of the aforementioned waiver interval, To determine the first link node of the first waiver section of the waiver section that is connected to the second link node of the second waiver section of the waiver section, and Connecting a first running line of the one or more running lines to a second running line of the one or more running lines, wherein the first running line is associated with the first link node and the second running line is associated with the second link node. The apparatus according to claim 9, including the apparatus described in claim 9.

14. The aforementioned processor, The system is further configured to convert the driving line map based on the aforementioned data into a lane-level map by setting map characteristics for the driving line map based on the aforementioned data. The apparatus according to claim 9, wherein the map characteristics include speed limits and lane relationships.

15. The apparatus according to claim 9, wherein the aforementioned driving line data is acquired from a sensor attached to an autonomous vehicle.

16. A non-temporary computer-readable medium that stores instructions that can be used to cause one or more processors to perform an action, wherein the action is: Receiving road-level map data and travel line data for at least one road user observed in a vehicle traffic network, wherein the road-level map data includes way data comprising one or more ways, one of the one or more ways comprising a series of nodes, and the travel line data includes one or more travel lines, one of the one or more travel lines comprising a series of attitudes representing the road user when the road user travels through the vehicle traffic network. Identifying the first section of the way data as an intersection of the vehicle traffic network, The method involves matching a second section of the way data with the travel line data to generate a plurality of waivers, wherein one of the plurality of waivers comprises one or more attitudes from the series of attitudes from each travel line and includes one node from the series of nodes, and the second section is different from the first section. Each waiver is classified as either constant or variable based on the number of lanes counted within it, and when each waiver is classified as constant, a lane count representing the number of lanes is assigned to each waiver. Based on the classification and lane count of each of the aforementioned waivers, consecutive waivers among the plurality of waivers are grouped into a waiver section. Using the aforementioned waiver section and the first section, generate a data-based travel line map, and The vehicle is operated by using the driving line map based on the aforementioned data as input to the vehicle's control system. Non-temporary computer-readable media, including [specific examples of such media].

17. The number of lanes to be counted is determined through a first process, The first process described above is Grouping one or more of the aforementioned attitudes of the waber according to direction, Defining one or more attitude offsets from the aforementioned one node, Sort the offset within the group identified in the grouping of one or more postures. Determining the distance between the offsets ordered by the sort of one or more of the above postures, and The number of lanes is determined based on the aforementioned distance. A non-temporary computer-readable medium according to claim 16, including the following:

18. The first process described above is In response to the distance being greater than the new lane threshold, increase the lane count, and Characterizing the lane count as unknown in response to the aforementioned distance being greater than the same lane threshold and less than the new lane threshold. A non-temporary computer-readable medium according to claim 17, further comprising:

19. Generating a driving line map based on the aforementioned data is: Assigning link nodes to the beginning and end of the aforementioned waiver interval, To determine the first link node of the first waiver section of the waiver section that is connected to the second link node of the second waiver section of the waiver section, and Connecting a first running line of the one or more running lines to a second running line of the one or more running lines, wherein the first running line is associated with the first link node and the second running line is associated with the second link node. A non-temporary computer-readable medium according to claim 16, including the following:

20. The aforementioned operation is, The method further includes converting the driving line map based on the aforementioned data into a lane-level map by setting map characteristics for the driving line map based on the aforementioned data. The non-temporary computer-readable medium according to claim 16, wherein the map characteristics include speed limits and lane relationships.

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