Navigation method and navigation system for autonomous vehicle

Abstract

The invention provides a navigation method for an autonomous vehicle. The navigation method comprises the following steps: acquiring a navigation destination; and planning a driving route from the current position to the navigation destination based on the attributes associated with the road infrastructure, the emotion of the driver, and / or the information reported by the vehicle. In addition, the invention also provides a navigation system for the autonomous vehicle. According to the invention, the road infrastructure, the emotion of the driver and / or the information reported by the vehicle can be considered during route planning, so that a feasible, safe, comfortable and efficient navigation route is provided for the automatic driving vehicle.

Description

Technical Field

[0001] The present invention relates to vehicles, and more particularly, to a navigation method and a navigation system for autonomous driving vehicles. Background Art

[0002] With the continuous development of navigation technology, people can use various navigation systems or various navigation software to plan a specific route to a destination when driving a vehicle. Generally speaking, various existing navigation systems or navigation software usually provide users with several options for selection, including but not limited to selecting the fastest route; selecting the shortest route; selecting a user-defined route; etc. These options are usually based on driving distance, driving time, and / or driving cost.

[0003] Today, autonomous driving technology is maturing, and vehicles with autonomous driving capabilities will be put into use on a large scale in the near future. However, existing navigation systems or navigation software do not consider the characteristics of autonomous driving vehicles when planning driving routes, resulting in the planned routes being suboptimal for autonomous driving vehicles.

[0004] Accordingly, there is a need in the art for an improved navigation system or navigation method suitable for an autonomous vehicle. Summary of the invention

[0005] This Summary is provided to introduce some concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.

[0006] In view of the deficiencies in the prior art, an object of the present invention is to provide an improved navigation system and navigation method for an autonomous driving vehicle.

[0007] According to one aspect of the present invention, a navigation method for an autonomous vehicle is provided, comprising: obtaining a navigation destination; and planning a driving route from a current location to the navigation destination based at least in part on attributes associated with road infrastructure, the driver's emotions, and / or information reported by the vehicle.

[0008] In one embodiment, the attributes associated with the road infrastructure may include attributes associated with one or more of: road classification, road infrastructure, and / or road users.

[0009] In one embodiment, road classification may include: roads dedicated to autonomous vehicles, highways, urban roads, and / or residential areas.

[0010] In one embodiment, the attributes associated with the road facilities may include: the number of intersections with traffic lights, the number of intersections without traffic lights, the number of irregular road sections, the number of non-motorized vehicle lanes, and / or the number of sidewalks.

[0011] In one embodiment, the attributes associated with road users may include: the number of vulnerable road users, and / or the number of two-wheeled vehicles.

[0012] In one embodiment, the driver's emotions may include positive emotions, negative emotions, high arousal, and / or low arousal.

[0013] In one embodiment, the information reported by the vehicle may include: the number of unidentified objects on the corresponding road, and / or the number of times a user took over the autonomous driving vehicle during previous travel along the corresponding road.

[0014] In one embodiment, the information reported by the vehicle can be obtained from a public cloud server.

[0015] In one embodiment, different priority weights may be assigned to attributes associated with road infrastructure, driver emotions, and / or vehicle reported information when planning a driving route.

[0016] In one embodiment, attributes associated with road infrastructure may be assigned the highest priority weight.

[0017] According to another aspect of the present invention, a navigation system for an autonomous vehicle is provided, comprising: a memory; a processor coupled to the memory, the processor being configured to: obtain a navigation destination; and plan a driving route from a current location to the navigation destination based at least in part on attributes associated with road infrastructure, the driver's emotions, and / or information reported by the vehicle.

[0018] In one embodiment, the processor may be further configured to assign different priority weights to attributes associated with road infrastructure, driver emotions, and / or vehicle reported information.

[0019] In one embodiment, attributes associated with road infrastructure may be assigned the highest priority weight.

[0020] According to yet another aspect of the present invention, a vehicle is provided, the vehicle comprising the navigation system according to the present invention.

[0021] By adopting the navigation method and navigation system of the present invention, road infrastructure, driver's emotions, and / or information reported by the vehicle can be taken into consideration when planning the route, thereby providing a feasible, safe, comfortable and efficient navigation route for the autonomous driving vehicle.

[0022] These and other features and advantages will become apparent by reading the following detailed description and by reference to the associated drawings.It is to be understood that the foregoing general description and the following detailed description are illustrative only and are not restrictive of the aspects of what is claimed. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to understand the manner in which the above features of the present invention are used in detail, the above briefly summarized contents can be described in more detail with reference to various embodiments, some of which are shown in the accompanying drawings. However, it should be noted that the accompanying drawings only show some typical aspects of the present invention and should not be considered to limit its scope, because the description may allow for other equally effective aspects.

[0024] Figure 1 A block diagram illustrating various components and / or systems implemented in an exemplary vehicle according to one embodiment of the present invention.

[0025] Figure 2 Illustrated is a diagram of an exemplary vehicle configured with various sensor and communication components and / or systems in accordance with one embodiment of the present invention.

[0026] Figure 3 A flow chart illustrating a navigation method for an autonomous vehicle according to one embodiment of the present invention.

[0027] Figure 4 A block diagram of an exemplary computing device is illustrated in accordance with one embodiment of the invention. DETAILED DESCRIPTION

[0028] In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the described exemplary embodiments. However, it will be apparent to one skilled in the art that the described embodiments may be practiced without some or all of these specific details. In other exemplary embodiments, well-known structures or processing steps are not described in detail to avoid unnecessarily obscuring the concepts of the present disclosure.

[0029] As mentioned above, in current navigation systems or navigation software, the driving route is generally planned based on the driving distance, driving time, and / or driving cost (e.g., tolls, etc.) to the destination. However, this approach does not take into account the characteristics of the autonomous driving vehicle, resulting in the planned route being suboptimal for the autonomous driving vehicle. According to the present invention, various factors may be taken into consideration when planning the driving route (such as road infrastructure, driver's emotions, and / or information reported by other vehicles, etc.) to provide a feasible, safe, comfortable and efficient navigation route, thereby significantly enhancing the user experience.

[0030] Figure 1 A block diagram illustrating various components and / or systems implemented in an exemplary vehicle 100 according to one embodiment of the present invention.

[0031] The vehicle 100 may include one or more cameras 135. The camera may include a camera sensor and a mounting assembly. Different mounting assemblies may be used for different cameras on the vehicle 100. For example, a front camera may be mounted in a front bumper, in a rod of a rearview mirror assembly, or in other front areas of the vehicle 100. A rear camera may be mounted in a rear bumper / fender, on a rear windshield, in a trunk, or on other rear areas of the vehicle. Side mirrors may be mounted on the sides of the vehicle 100, such as integrated into a mirror assembly or a door assembly. The camera may provide object detection and distance estimation (especially for objects of known size and / or shape), and may also provide information about rotational motion relative to the vehicle's axis (such as during a turn). When used in conjunction with other sensors, the camera may be calibrated to verify travel distance and angular orientation by using other systems (such as by using LIDAR, distance sensors, etc.). The camera may similarly be used to verify and calibrate other systems to verify that distance measurements are correct, and is also used to verify that object detection is accurately performed so that objects are mapped to the correct position relative to the vehicle by LIDAR and other systems accordingly. Similarly, when combined with, for example, an accelerometer, the time to impact with a road hazard can be estimated (eg, the time elapsed before hitting a pothole).

[0032] In one embodiment, the accelerometer, gyroscope and magnetometer 140 can be used to provide and / or verify motion and direction information. The accelerometer and gyroscope can be used to monitor wheel and driveline performance. In one embodiment, the accelerometer can also be used to verify the actual collision time of road hazard obstacles (such as potholes) relative to the predicted time based on existing braking and acceleration models and steering models. In one embodiment, the gyroscope and magnetometer can be used to measure the rotation state of the vehicle and the orientation relative to magnetic north, respectively, and to measure and calibrate the estimation and / or model of the turning radius at the current speed and / or the maneuverability metric at the current speed (especially when used in conjunction with measurements from other external and internal sensors (such as other sensors 145, such as speed sensors, and / or odometer measurements)).

[0033] The Light Detection and Ranging (LIDAR, also known as laser radar) 150 subsystem uses pulsed lasers to measure the distance to an object. While cameras can be used for object detection, LIDAR 150 provides a means to more confidently detect the distance (and orientation) of an object, especially with respect to objects of unknown size and shape. LIDAR 150 measurements can also be used to estimate travel rate, vector direction, relative positioning, and braking distance by providing accurate distance measurements and incremental distance measurements.

[0034] The memory 160 may be used with the processor 110 and / or the digital signal processor (DSP) 120, and the memory may include flash memory, RAM, ROM, disk drive, or flash memory card or other memory device, or various combinations thereof. In one embodiment, the memory 160 may include instructions for implementing the various methods described throughout this specification. In one embodiment, the memory may include instructions for the following operations: operating and calibrating sensors, and receiving maps, weather, vehicles (both vehicle 100 and surrounding vehicles) and other data, and determining driving parameters (such as relative positioning, absolute positioning, braking distance, acceleration and turning radius at current speed and / or maneuverability at current speed, vehicle distance, turn initiation / timing and execution, and initiation / timing of driving operations) using various internal and external sensor measurements and received data and measurements.

[0035] In one embodiment, the power and drive systems (generator, battery, transmission, engine) and related systems 175 and systems 155 (brakes, actuators, throttle control, steering and electrical) may be controlled by processor(s) and / or hardware or software or by an operator of the vehicle or by some combination thereof. The systems 155 (brakes, actuators, throttle control, steering and electrical, etc.) and power and drive or other systems 175 may be utilized in conjunction with performance parameters and operating parameters to enable autonomous (and manual, with respect to alarms and emergency overrides / brakes / stopping) safe and accurate driving and operation of the vehicle 100, such as to merge into traffic, park, accelerate and otherwise operate the vehicle 100 safely, effectively and efficiently. In one embodiment, inputs from various sensor systems (such as cameras 135, accelerometers, gyroscopes and magnetometers 140, LIDAR 150, GNSS receivers 170, radar 153, inputs, messaging and / or measurements from wireless transceiver(s) 130 and / or other sensors 145, or various combinations thereof) may be used by the processor 110 and / or DSP 120 or other processing system to control the power and drive system 175 as well as the systems (brakes, actuators, throttle control, steering and electrical) 155.

[0036] Global Navigation Satellite System (GNSS) receivers can be used to determine positioning relative to the ground (absolute positioning) and, when used with other information (such as measurements and / or mapping data from other objects), can be used to determine positioning relative to other objects (such as relative to other vehicles and / or relative to the road).

[0037] In one embodiment, the GNSS receiver 170 may support one or more GNSS constellations and other satellite-based navigation systems. For example, in one embodiment, the GNSS receiver 170 may support global navigation satellite systems such as the Global Positioning System (GPS), the Russian Global Navigation Satellite System (GLONASS), Galileo, and / or BeiDou, or any combination thereof. In one embodiment, the GNSS receiver 170 may support regional navigation satellite systems (such as NAVIC or QZSS or a combination thereof) and various augmentation systems (e.g., satellite-based augmentation systems (SBAS) or ground-based augmentation systems (GBAS)), such as Doppler orbit diagrams and radio positioning integrated by satellite (DORIS) or wide area augmentation system (WAAS) or European Geostationary Navigation Overlay Service (EGNOS) or Multi-Function Satellite Augmentation System (MSAS) or Local Area Augmentation System (LAAS). In one embodiment, the GNSS receiver(s) 130 and the antenna(s) 132 may support multiple frequency bands and sub-bands, such as GPS L1, L2, and L5 bands, Galileo E1, E5, and E6 bands, Compass (Beidou) B1, B3, and B2 bands, GLONASS G1, G2, and G3 bands, and QZSS L1C, L2C, and L5-Q bands.

[0038] The GNSS receiver 170 may be used to determine position and relative position that may be used for positioning, navigation, and for calibrating other sensors when appropriate, such as for determining the distance between two points in time under clear sky conditions and using the distance data to calibrate other sensors (such as odometers and / or LIDAR). In one embodiment, GNSS-based relative position based on, for example, Doppler and / or pseudorange measurements shared between vehicles may be used to determine a highly accurate distance between two vehicles, and when combined with vehicle information (such as shape and model information and GNSS antenna position) may be used to calibrate, verify, and / or influence the confidence level associated with information from LIDAR, cameras, radar, sonar, and other distance estimation techniques. GNSS Doppler measurements may also be used to determine the linear and rotational motion of a vehicle or vehicles relative to another vehicle, which may be used in conjunction with gyroscopes and / or magnetometers and other sensor systems to maintain calibration of those systems based on measured position data. Relative GNSS positioning data may also be combined with high confidence absolute position from roadside equipment (also referred to as roadside units or RSUs) to determine a high confidence absolute position of a vehicle. Additionally, during inclement weather that may obscure LIDAR and / or camera-based data sources, relative GNSS positioning data may be used to avoid other vehicles and remain within lanes or other assigned road areas. For example, using an RSU equipped with a GNSS receiver and V2X capabilities, GNSS measurement data may be provided to the vehicle, which, when provided along with the absolute position of the RSU, may be used to navigate the vehicle relative to a map, thereby keeping the vehicle in a lane and / or road despite lack of visibility.

[0039] Radio detection and ranging (radar 153) uses transmitted radio waves reflected from an object. The reflected radio waves are analyzed based on the time required for the reflection to arrive and other signal characteristics of the reflected waves to determine the location of nearby objects. Radar 153 can be used to detect the location of nearby cars, roadside objects (signs, other vehicles, pedestrians, etc.), and can usually detect objects even in the presence of ambiguous weather (such as snow, rain or hail). Thus, radar 153 can be used to supplement LIDAR 150 system and camera 135 when providing ranging information to other objects by providing ranging and distance measurements and information when vision-based systems usually fail. In addition, radar 153 can be used to calibrate and / or sanity check other systems (such as LIDAR 150 and camera 135). Ranging measurements from radar 153 can be used to determine / measure braking distance at current speed, acceleration, maneuverability at current speed, and / or turning radius at current speed and / or maneuverability metrics at current speed. In some systems, ground penetrating radar can also be used to track the road surface via, for example, radar reflection marks on the road surface or terrain features such as ditches.

[0040] Figure 2 Illustrated is a diagram of an exemplary vehicle 200 configured with various sensor and communication components and / or systems in accordance with one embodiment of the present invention.

[0041] like Figure 2 As shown in , in one embodiment, the vehicle 200 may have camera(s), such as a camera 260 mounted on a rearview mirror, a camera mounted on a front fender (not shown), a camera mounted on a side mirror (not shown), and a rear camera (not shown, but typically on a trunk, hatch, or rear bumper). The vehicle 200 may also have a LIDAR subsystem 204 for detecting objects and measuring distances to those objects; the LIDAR subsystem 204 is typically mounted on the roof, however, if there are multiple LIDAR units 204, they may be oriented around the front, rear, and sides of the vehicle. The vehicle 200 may have various other location-related systems, such as a GNSS receiver 170 (typically located in a shark fin unit at the rear of the roof), various wireless transceivers 202 (such as WAN, WLAN, V2X; typically but not necessarily located in a shark fin)), a radar system 208 (typically in the front bumper), and a sonar 210 (typically located on both sides of the vehicle, if present). There may also be various wheel 212 and driveline sensors, such as tire pressure sensors, accelerometers, gyroscopes, and wheel rotation detection and / or counters. In one embodiment, distance measurements and relative positions determined via various sensors (such as LIDAR, radar, camera, GNSS, and sonar) may be combined with vehicle size and shape information and information about sensor locations to determine distances and relative positions between surfaces of different vehicles so that the distance or vector from a sensor to another vehicle or between two different sensors (such as two GNSS receivers) is incrementally increased to account for the location of the sensors on each vehicle. Therefore, the accurate GNSS distance and vector between two GNSS receivers will need to be modified based on the relative position of each vehicle surface to the GNSS receiver. For example, when determining the distance between the front bumper of a rear vehicle and the rear bumper of a front vehicle, the distance will need to be adjusted based on the distance between the GNSS receiver on the rear vehicle and the front bumper and the distance between the GNSS receiver of the front vehicle and the rear bumper of the front vehicle. As an example, the distance between the rear bumper of the leading vehicle and the front bumper of the trailing vehicle is the relative distance between the two GNSS receivers minus the distance from the trailing vehicle's GNSS receiver to the front bumper, minus the distance from the leading vehicle's GNSS receiver to the rear bumper. It is recognized that this list is not intended to be limiting, and Figure 2 The present invention is intended to provide exemplary locations of various sensors in an embodiment of the vehicle 200. Additionally, further details regarding specific sensors are provided with respect to Figure 1 to describe.

[0042] Figure 3A flowchart of a navigation method 300 for an autonomous vehicle according to one embodiment of the present invention is illustrated. In one embodiment, Figure 3 The following references can be used Figure 4 In another embodiment, Figure 3 The method may be performed using any other suitable apparatus.

[0043] At block 310, method 300 may include obtaining a navigation destination. In one embodiment, the navigation destination may be manually input by a user. In another embodiment, the navigation destination may be input by voice recognition technology. It should be noted that the present invention is not limited to a specific manner of obtaining the navigation destination, but may employ any known or future developed technology to obtain the navigation destination.

[0044] In block 320, method 300 may include planning a driving route from the current location to the navigation destination based at least in part on attributes associated with the road infrastructure, the driver's emotions, and / or information reported by the vehicle. In other words, in addition to factors such as driving distance, driving time, and / or driving cost that are typically considered in the prior art, the present invention further considers various other factors when planning a driving route to the destination, such as attributes associated with the road infrastructure, the driver's emotions, and / or information reported by the vehicle.

[0045] In one embodiment, the attributes associated with the road infrastructure may include attributes associated with one or more of the following: road classification, road facilities, and / or road users. In one example, the road classification may include: dedicated roads for autonomous vehicles, highways, urban roads, and / or residential areas. In one example, the attributes associated with road facilities may include: the number of intersections with traffic lights, the number of intersections without traffic lights, the number of irregular sections, the number of non-motorized lanes, and / or the number of sidewalks. In one example, the attributes associated with road users may include: the number of vulnerable road users, and / or the number of two-wheeled vehicles. Thus, when planning a driving route to a destination, route planning may be implemented according to the attributes associated with the road infrastructure listed above, which may be obtained, for example, from map data, traffic flow data, or historical data. For example, it may be preferred to plan a route with more dedicated roads for autonomous vehicles, fewer intersections with traffic lights, fewer irregular sections, fewer non-motorized lanes and sidewalks, and / or fewer vulnerable road users, thereby providing passengers with an efficient, fast, safe, and comfortable user experience.

[0046] In one embodiment, the driver's emotions can also be taken into account when planning a route. In one example, the driver's emotions can include positive emotions, negative emotions, high arousal, and / or low arousal. For example, the driver can input his current emotions into the navigation system. Alternatively, other technologies such as facial expression recognition technology can also be used to identify the driver's emotions at this time. For example, when the driver's mood is good at this time (for example, positive emotions), a route with more landscapes or more people on both sides of the road can be selected; and when the driver's mood is not so good at this time (for example, negative emotions), a route with more flowers, trees and less people on both sides of the road can be selected. In addition or alternatively, the driving route can also be planned according to the driver's arousal at this time (for example, high arousal and / or low arousal) to match the corresponding arousal.

[0047] In one embodiment, the information reported by the vehicle can be further taken into account when planning the route. In one example, the information can include: the number of unidentified objects on the corresponding road, and / or the number of times the user took over the autonomous driving vehicle in the previous driving along the corresponding road. In one example, the information can be obtained from a public cloud server (e.g., a public cloud server managed by a vehicle management office or a vehicle manufacturer). Due to some reasons (e.g., road structure reasons), the autonomous driving vehicle may not identify objects that may exist on the road during driving (e.g., large potholes on the road), and the presence of these objects may pose a hidden danger to safe driving. In addition, for some special sections where the autonomous driving vehicle cannot travel, the driver needs to take over the autonomous driving vehicle (e.g., turn off the autonomous driving mode and switch to manual driving mode). Such information can be reported to the public cloud server by the vehicle when it passes the corresponding road for use by others, so that when planning the route, it can be avoided as much as possible to choose a route containing a large number of unidentified objects and / or a large number of takeovers.

[0048] In one embodiment, the same priority weight may be assigned to attributes associated with road infrastructure, the driver's emotions, and / or vehicle reported information when planning a driving route. In another embodiment, different priority weights may be assigned to attributes associated with road infrastructure, the driver's emotions, and / or vehicle reported information when planning a driving route. For example, the attributes associated with road infrastructure may be assigned the highest priority weight. Thus, the attributes associated with road infrastructure may be considered first when planning a driving route.

[0049] Figure 4 Illustrated is a block diagram of an exemplary computing device 400 according to one embodiment of the present invention, which is one example of a hardware device applicable to aspects of the present invention.

[0050] refer to Figure 4 Now, a computing device 400 will be described, which is an example of a hardware device (e.g., user equipment, cloud server, vehicle control unit, etc.) that can be applied to various aspects of the present invention. The computing device 400 can be any machine that can be configured to perform processing and / or calculations, and can be but is not limited to a workstation, server, desktop computer, laptop computer, tablet computer, personal digital assistant, smart phone, vehicle computer, or any combination thereof. The various methods / devices / servers / user equipment described above can be implemented in whole or in part by the computing device 400 or similar devices or systems.

[0051] The computing device 400 may include components that may be connected or communicated via one or more interfaces and a bus 402. For example, the computing device 400 may include a bus 402, one or more processors 404, one or more input devices 406, and one or more output devices 408. The one or more processors 404 may be any type of processor and may include, but are not limited to, one or more general purpose processors and / or one or more special purpose processors (e.g., a dedicated processing chip). The input device 406 may be any type of device capable of inputting information to the computing device and may include, but are not limited to, a mouse, a keyboard, a touch screen, a microphone, and / or a remote controller. The output device 408 may be any type of device capable of presenting information and may include, but are not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The computing device 400 may also include or be connected to a non-transient storage device 410, which may be any storage device that is non-transient and capable of data storage, and may include, but is not limited to, a disk drive, an optical storage device, a solid-state memory, a floppy disk, a floppy disk, a hard disk, a tape or any other magnetic medium, an optical disk or any other optical medium, a ROM (read-only memory), a RAM (random access memory), a cache memory, and / or any memory chip or cassette, and / or any other medium from which a computer can read data, instructions, and / or code. The non-transient storage device 410 may be detachable from the interface. The non-transient storage device 410 may have data / instructions / code for implementing the above methods and steps. The computing device 400 may also include a communication device 412. The communication device 412 can be any type of device or system that can communicate with internal devices and / or with a network and can include but is not limited to a modem, a network card, an infrared communication device, a wireless communication device and / or a chipset, such as a Bluetooth device, an IEEE 1302.11 device, a WiFi device, a WiMax device, a cellular communication device and / or the like.

[0052] When the computing device 400 is used as an in-vehicle device, it can also be connected to external devices (e.g., a GPS receiver, sensors for sensing different environmental data (such as an accelerometer, a wheel speed sensor, a gyroscope, etc.)). In this way, the computing device 400 can, for example, receive positioning data and sensor data indicating the driving conditions of the vehicle. When the computing device 400 is used as an in-vehicle device, it can also be connected to other devices for controlling the driving and operation of the vehicle (e.g., an engine system, a wiper, an anti-lock brake system, a light control system, etc.).

[0053] In addition, the non-transitory storage device 410 may have map information and software components so that the processor 404 can implement route guidance processing. In addition, the output device 406 may include a display for displaying a map, displaying a positioning mark of the vehicle, and displaying an image indicating the driving status of the vehicle. The output device 406 may also include a speaker or a headphone interface for audio guidance.

[0054] The bus 402 may include, but is not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MCA) bus, an Enhanced ISA (EISA) bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus. In particular, for in-vehicle devices, the bus 402 may also include a Controller Area Network (CAN) bus or other structures designed for automotive applications.

[0055] The computing device 400 may also include a working memory 414 , which may be any type of working memory capable of storing instructions and / or data that facilitate the operation of the processor 404 and may include, but is not limited to, a random access memory and / or a read-only storage device.

[0056] Software components may be located in the working memory 414, including but not limited to an operating system 416, one or more application programs 418, drivers and / or other data and codes. Instructions for implementing the above methods and steps may be included in the one or more application programs 418, and the modules / units / components of the aforementioned various devices / servers / user equipment may be implemented by the processor 404 reading and executing the instructions of the one or more application programs 418.

[0057] It should also be recognized that changes may be made according to specific needs. For example, custom hardware may also be used, and / or specific components may be implemented in hardware, software, firmware, middleware, microcode, hardware description language, or any combination thereof. In addition, connections to other computing devices, such as network input / output devices, etc. may be employed. For example, programming hardware (e.g., programmable logic circuits including field programmable gate arrays (FPGAs) and / or programmable logic arrays (PLAs)) with assembly language or hardware programming languages ​​(e.g., VERILOG, VHDL, C++) may be used to implement part or all of the disclosed methods and devices using logic and algorithms according to the present invention.

[0058] Although various aspects of the present invention have been described so far with reference to the accompanying drawings, the above-described methods, systems and devices are merely examples, and the scope of the present invention is not limited to these aspects, but is limited only by the appended claims and their equivalents. Various components may be omitted or replaced by equivalent components. In addition, the steps may be implemented in an order different from the order described in the present invention. In addition, various components may be combined in various ways. It is also important that, as technology develops, many of the components described may be replaced by equivalent components that appear later.

[0059] List of reference numerals:

[0060] 100 Vehicles

[0061] 110 Processor

[0062] 120DSP

[0063] 130(s) wireless transceiver

[0064] 135 Camera

[0065] 140Accelerometer, gyroscope and magnetometer

[0066] 145 Other sensors (speed sensor / speedometer, distance / odometer, weather, light, rain sensor, tire pressure sensor, tire traction sensor (anti-lock and / or traction control), etc.)

[0067] 150LIDAR

[0068] 153 Radar

[0069] 155 systems (brakes, actuators, throttle control, steering and electrical, etc.)

[0070] 160 Memory

[0071] 170GNSS transceiver

[0072] 175 Power and drive systems (generators, batteries, transmissions, engines) and related systems

[0073] 200 vehicles

[0074] 202GNSS wireless receiver, wireless communication transceiver

[0075] 204LIDAR

[0076] 206 Camera

[0077] 208 Radar

[0078] 210 Sonar Sensor

[0079] 212 Wheel sensor

[0080] 300 Methods

[0081] 310 Get navigation destination

[0082] 320 Planning a driving route from the current location to the navigation destination based on attributes associated with the road infrastructure, the driver's emotions, and / or information reported by the vehicle 400 Computing device

[0083] 402 Bus

[0084] 404 Handler

[0085] 406 Input Device

[0086] 408 Output Device

[0087] 410 Storage Devices

[0088] 412 Communication equipment

[0089] 414 Working memory

[0090] 416 Operating System

[0091] 418 Applications

Claims

1. A navigation method for an autonomous vehicle, comprising: Get navigation destination; as well as A driving route is planned from a current location to the navigation destination based at least in part on attributes associated with road infrastructure, a driver's emotion, and / or information reported by the vehicle. 2 . The method of claim 1 , wherein the attributes associated with the road infrastructure include attributes associated with one or more of: road classification, road facilities, and / or road users.

3. The method of claim 2, wherein the road classification comprises: Dedicated roads for autonomous vehicles, highways, urban roads, and / or residential areas.

4. The method of claim 2, wherein the attributes associated with the road facility include: The number of intersections with traffic lights, the number of intersections without traffic lights, the number of irregular road sections, the number of non-motorized lanes, and / or the number of sidewalks.

5. The method of claim 2, wherein the attributes associated with the road user include: The number of vulnerable road users, and / or the number of two-wheelers. The method of claim 1 , wherein the driver's emotions include positive emotions, negative emotions, high arousal, and / or low arousal.

7. The method according to claim 1, wherein the information reported by the vehicle includes: The number of unidentified objects on the corresponding road, and / or the number of times a user took over the autonomous vehicle during previous travel along the corresponding road.

8. The method of claim 1, wherein the information reported by the vehicle is obtained from a public cloud server.

9. The method of claim 1, wherein different priority weights are assigned to the attributes associated with the road infrastructure, the driver's emotions, and / or the information reported by the vehicle when planning the driving route.

10. The method of claim 9, wherein the attributes associated with the road infrastructure are assigned the highest priority weight.

11. A navigation system for an autonomous vehicle, comprising: Memory; a processor coupled to the memory, the processor being configured to: Get navigation destination; as well as A driving route is planned from a current location to the navigation destination based at least in part on attributes associated with road infrastructure, a driver's emotion, and / or information reported by the vehicle.

12. The navigation system of claim 11, wherein the processor is further configured to assign different priority weights to the attributes associated with the road infrastructure, the driver's emotions, and / or the vehicle-reported information.

13. The navigation system of claim 12, wherein the attributes associated with the road infrastructure are assigned the highest priority weight.

14. A vehicle comprising the navigation system according to any one of claims 11-13.

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