Intelligent driving path learning method, automatic cruising method, related equipment and vehicles

Abstract

The present application provides an intelligent driving path learning method, an automatic cruise method, related equipment and a vehicle, wherein the method includes: obtaining a preset driving path; according to the driving path and the navigation map, using the first path information on the driving path collected by the vehicle body sensor, and the second path information on the driving path obtained based on the navigation map to complete the path learning, wherein the first path information and the second path information include different path information, and the first path information and the second path information jointly determine all the path information required for the path learning. The path learning method provided by the present application uses the vehicle sensor to collect path information, and assists in collecting path information based on the navigation map, which reduces the amount of data collected by the vehicle sensor, improves the efficiency of path learning, and can also supplement some information that cannot be collected by the vehicle sensor, thereby improving the accuracy of path learning.

Description

Technical Field

[0001] The present application relates to the field of intelligent driving technology, and in particular to an intelligent driving path learning method, an automatic cruising method, related equipment and vehicles. Background Art

[0002] In intelligent driving, the cruise-type end-to-end memory driving function can control the vehicle to cruise automatically along a pre-learned path. Before using this function, users need to use the mapping function to learn the path and build a route map of the cruise path in advance.

[0003] When the user activates the mapping function, the vehicle needs to drive from the starting point to the destination. The vehicle system collects data through the vehicle sensors, builds the entire driving path and stores it. However, under actual driving conditions, the amount of external path information that needs to be obtained is very large. Even so, the data that can be collected by the vehicle sensors cannot fully cover the path learning needs. Summary of the invention

[0004] In view of this, the purpose of this application is to propose an intelligent driving path learning method, an automatic cruising method, related equipment and vehicles to solve the problem that the amount of data collected for path learning is large and the data cannot fully cover the path learning needs.

[0005] Based on the above purpose, the present application provides an intelligent driving path learning method, including: obtaining a preset driving path; according to the driving path and the navigation map, using the first path information on the driving path collected by the vehicle's body sensor, and the second path information on the driving path obtained based on the navigation map to complete the path learning, wherein the first path information is different from the path information included in the second path information, and the first path information and the second path information jointly determine all the path information required for the path learning.

[0006] Optionally, the path learning is completed using the first path information on the driving path collected by the vehicle's body sensor and the second path information on the driving path obtained based on a navigation map, including: obtaining the second path information on the driving path based on the navigation map; based on the second path information, using the first path information on the driving path collected by the vehicle body sensor; and completing the path learning using the first path information and the second path information.

[0007] Optionally, using the first path information and the second path information to complete the path learning includes: fusing the first path information and the second path information into fused path information; and storing the fused path information to complete the path learning.

[0008] Optionally, the second path information includes road network information, background information and point of interest index information; the road network information includes road name, road travel direction, road grade, highway grade and / or road width, and the background information includes railway information, river information and / or regional division information.

[0009] Optionally, the vehicle body sensors include a long-range sensor, a medium-range sensor and a short-range sensor; according to the driving path and the navigation map, the first path information on the driving path is collected using the vehicle body sensors, including: obtaining the real-time position of the vehicle; in response to the real-time position being outside the coverage of the navigation map, collecting the first path information using the long-range sensor, the medium-range sensor and the short-range sensor; in response to the real-time position being within the coverage of the navigation map, collecting the first path information using the medium-range sensor and the short-range sensor.

[0010] Based on the same inventive concept, the present application also provides an intelligent driving automatic cruising method, comprising: obtaining path information, wherein the path information is learned using the intelligent driving path learning method; and controlling the vehicle's automatic cruising according to the path information.

[0011] Based on the same inventive concept, the present application also provides an intelligent driving path learning device, including: a path acquisition module, configured to acquire a preset driving path; a path learning module, configured to complete the path learning based on the driving path and the navigation map, using the first path information on the driving path collected by the vehicle's body sensor, and the second path information on the driving path acquired based on the navigation map, wherein the first path information is different from the path information included in the second path information, and the first path information and the second path information jointly determine all the path information required for the path learning.

[0012] Based on the same inventive concept, the present application also provides an intelligent driving automatic cruise device, including: an information acquisition module, configured to obtain path information, wherein the path information is learned using the intelligent driving path learning device; an automatic cruise module, configured to control the vehicle's automatic cruise according to the path information.

[0013] Based on the same inventive concept, the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the method is implemented when the processor executes the program.

[0014] Based on the same inventive concept, the present application also provides a vehicle, which includes the device or the electronic device.

[0015] From the above, it can be seen that the intelligent driving path learning method, automatic cruise method, related equipment and vehicle provided by the present application, wherein the method includes: obtaining a preset driving path; according to the driving path and the navigation map, using the first path information on the driving path collected by the vehicle body sensor, and the second path information on the driving path obtained based on the navigation map to complete the path learning, wherein the first path information is different from the path information included in the second path information, and the first path information and the second path information jointly determine all the path information required for the path learning. The path learning method provided by the present application uses the vehicle sensor to collect path information, and assists in collecting path information based on the navigation map, which reduces the amount of data collected by the vehicle sensor, improves the efficiency of path learning, and can also supplement some information that cannot be collected by the vehicle sensor, thereby improving the accuracy of path learning. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the present application or related technologies, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 A flowchart of an intelligent driving path learning method according to an embodiment of the present application;

[0018] Figure 2 A schematic diagram of a process of performing path learning using path information according to an embodiment of the present application;

[0019] Figure 3 This is a schematic diagram of the structure of an intelligent driving path learning device and an automatic cruise device according to an embodiment of the present application;

[0020] Figure 4 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0021] In order to make the objectives, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0022] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present application should be the usual meanings understood by people with ordinary skills in the field to which the present application belongs. The "first", "second" and similar words used in the embodiments of the present application do not represent any order, quantity or importance, but are only used to distinguish different components. "Including" or "comprising" and similar words mean that the elements or objects appearing in front of the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0023] An embodiment of the present application proposes a method for learning an intelligent driving path. The execution subject of the embodiment of the present application may be a vehicle ECU (Electronic Control Unit) and / or an intelligent driving controller. The vehicle ECU is connected to the vehicle body sensor and the navigation controller in communication, and can obtain the information required in the embodiment of the present application, and execute the method provided in the embodiment of the present application accordingly; the intelligent driving controller can use the information obtained by the vehicle body sensor to control the vehicle to achieve intelligent driving. Figure 1 As shown, the method includes:

[0024] Step S101: Obtain a preset driving route.

[0025] In specific implementation, after activating the self-learning driving mapping function, the user uses the HMI (Human Machine Interface) to specify the starting point, destination and driving path information of the learning path, and the ECU and navigation controller of the intelligent driving vehicle receive them synchronously.

[0026] Step S102, according to the driving path and the navigation map, the path learning is completed using the first path information on the driving path collected by the vehicle's body sensor and the second path information on the driving path obtained based on the navigation map, wherein the first path information is different from the path information included in the second path information, and the first path information and the second path information jointly determine all the path information required for the path learning. Compared with the related art that requires the use of body sensors to collect all the path information required for path learning, the method provided in the present application reduces the amount of data collected by the body sensors. It should be noted that the difference in the path information included in the above-mentioned first path information and the second path information may be partial or completely different.

[0027] The path learning method provided in the present application uses vehicle sensors to collect path information while assisting in path information collection based on navigation maps, thereby reducing the amount of data collected by vehicle sensors, improving the efficiency of path learning, and being able to supplement some information that cannot be collected by vehicle sensors, thereby improving the accuracy of path learning.

[0028] The vehicle body sensor includes multiple radar sensors and multiple image sensors. The image sensor includes a camera and an image processing unit. The camera can be a 360° panoramic camera, or multiple vehicle-mounted cameras can be used to capture images at different angles to achieve full coverage monitoring of the vehicle's surroundings. The image processing unit includes a feature extraction neural network. During implementation, the image captured by the camera will be sent to the image processing unit. The image processing unit obtains path information in the image by extracting features on the image, and adds the first path information.

[0029] Specifically, the vehicle-mounted camera may include a monocular camera and a binocular camera, and they are installed in various parts of the vehicle such as the front view, surround view, rear view, side view, and internal view. The vehicle-mounted camera specifically includes a lens module, an image sensor, and a digital image signal processing module. The lens module of the vehicle-mounted sensor transmits the photoelectric signal to the image sensor and the digital image signal processing module for image signal processing and conversion into a digital signal. Image sensor technology is an integration of analog circuits and digital circuits, wherein the pixel array of the analog circuit is configured to convert the optical signal into an electrical signal, and the timing control circuit of the digital circuit completes the processing of the electrical signal and converts the electrical signal into the required digital signal for final output.

[0030] The radar sensor includes a transmitter that generates electromagnetic waves, a transmitting antenna, a receiving antenna (usually the same antenna is used for transmitting and receiving), and a receiver. The radar sensor sends radio waves to the outside world through the transmitter and receives signals reflected from the surface of objects. The distance is calculated based on the return time of the radio waves, thereby detecting objects around the vehicle and obtaining the location and movement path of the objects.

[0031] Specifically, the radar sensor includes an ultrasonic radar and a millimeter-wave radar. The operating frequencies of the probe of the automotive ultrasonic radar are 40kHz, 48kHz and 58kHz. The higher the frequency, the higher the sensitivity, but the smaller the detection angle. The millimeter-wave radar uses millimeter waves with a frequency of 30GHz-300GHz to illuminate the target and receive echoes, and obtains information such as the distance, direction, and speed between the target and the emission point through signal processing. In specific implementation, radar sensors of different frequencies are generally used to detect objects at different distances around the vehicle.

[0032] By collecting the first path information through the above-mentioned body sensor, the surface morphology, physical properties and other characteristics of the objects in the surrounding environment of the vehicle on the driving route can be obtained, and finally an object model can be established. In the specific implementation of the embodiment of the present application, the first path information that can be detected by the above-mentioned body sensor includes but is not limited to: road width, road markings (center single solid line, center virtual and solid line, center double solid line, lane dividing line, guide lane line, lane edge line, stop line, yield line, pedestrian crossing line, guide line, etc.), traffic signs, traffic facilities, lane slope, etc. In specific implementation, the lane slope can be obtained in the following way: based on the radar sensor to obtain a three-dimensional model of the vehicle's surrounding environment, according to the radial distance of each data point with respect to the sensor at the same pitch angle, the vehicle's drivable route is determined, the azimuth of the road section ahead in the vehicle's drivable route is determined, and the slope of the road section ahead is identified according to the pitch angle of each data point at the azimuth and its radial distance with respect to the sensor.

[0033] When the vehicle body sensor collects the first path information, it is also necessary to combine the real-time position of the vehicle to jointly draw the entire learning path map. In a specific embodiment, the vehicle obtains the real-time position through real-time kinematic, RTK, global navigation satellite system (GNSS) and inertial measurement unit (IMU).

[0034] The RTK carrier phase differential technology is a differential method for real-time processing of carrier phase observations of two measuring stations. The carrier phase collected by the base station is sent to the user receiver to calculate the coordinates by difference. Specifically, the satellite data observed by the GPS receiver at the base station is sent out in real time through the data communication link (radio station), and the GPS receiver of the nearby mobile station receives the radio signal from the base station while observing the satellite. The received signal is processed in real time to give the three-dimensional coordinates of the mobile station. This satellite positioning measurement method can obtain centimeter-level positioning accuracy measurements in real time in the field.

[0035] The Global Navigation Satellite System is an air-based radio navigation and positioning system that can provide users with all-weather three-dimensional coordinates, speed and time information at any location on the Earth's surface or in near-Earth space.

[0036] An inertial measurement unit is a device that measures the three-axis attitude angle (or angular rate) and acceleration of an object. Gyroscopes and accelerometers are the main components of an inertial measurement unit, and their accuracy directly affects the accuracy of the inertial system. Typically, an inertial measurement unit includes three single-axis accelerometers and three single-axis gyroscopes. The accelerometer detects the acceleration signal of the object in the independent three axes of the carrier coordinate system, while the gyroscope detects the angular velocity signal of the carrier relative to the navigation coordinate system, measures the angular velocity and acceleration of the object in three-dimensional space, and uses this to calculate the attitude and absolute position of the object.

[0037] In some embodiments, the second path information includes road network information, background information, and point of interest index information.

[0038] A POI (Point of Interest) index can be an index mark for a house, a store, a bus stop, a lake or a road on a navigation map. Searching for a POI on a navigation map is equivalent to searching for data on a web page. On the navigation map client, when a user selects a POI, a floating balloon will point to the POI. The POI index can provide users with rich information and support decision-making, and can also supplement the POI information that cannot be obtained by the vehicle body sensors.

[0039] In some embodiments, the road network information includes road name, road travel direction, road grade, highway grade, and road width.

[0040] When only body sensors are used for path learning, the road name can only be obtained when the body sensors collect information on road signs. However, when using a navigation map, after obtaining the path, the names of all the roads passed on the path can be obtained, and then more in-depth path learning can be performed based on the road names, further improving the accuracy of path learning.

[0041] Similarly, the direction of road traffic also requires the vehicle body sensors to collect a large amount of data to obtain. For example, when passing a section of road, if the vehicle body sensors do not sense oncoming vehicles, the road will be judged as a one-way street. Even so, the above method cannot guarantee accurate judgment of the direction of road traffic. However, using the navigation map, after obtaining the path, the direction of traffic information of all roads passed on the path can be obtained, which further reduces the amount of data collected by the vehicle sensors and improves the efficiency of path learning.

[0042] There are different angles to classify highway grades: 1) Functional grades are mainly divided according to traffic volume, and are usually divided into expressways, first-class highways, second-class highways, third-class highways, and fourth-class highways according to the use mission, function and flow of the highway. Among them, expressways and first-class highways are high-grade highways, second-class highways are in the middle, and third and fourth-class highways are low-grade highways; 2) Administrative level grades are divided into national highways, provincial highways, and county roads; 3) The classification of speed is divided into three grades: expressways, express highways, and ordinary roads.

[0043] Road grades are divided into four levels: expressways, trunk roads, secondary trunk roads, and branch roads. There are no mandatory requirements for the red line width of each grade, but only requirements for road width, cross-sectional composition, and the minimum width of each functional zone: 1) Expressways: Urban roads with a central dividing strip, with more than four motor vehicle lanes, all or part of which use grade separations and controlled access, for cars to travel at higher speeds. They are also called car lanes. The design speed of expressways is 60-100km / h; 2) Trunk roads: Trunk roads connecting various urban districts, with traffic functions as the main function, and the design speed of trunk roads is 40-60km / h; 3) Secondary trunk roads: They serve as traffic distribution between trunk roads and various districts, and also have service functions. The design speed of secondary trunk roads is 30-50km / h; 4) Branch roads: The connecting lines between secondary trunk roads and neighborhood roads (community roads), with service functions as the main function, and the design speed of branch roads is 20-40km / h.

[0044] After knowing the highway grade and road grade of each road on the path to be learned through the navigation map, the traffic volume and cruising speed range of each road can be directly obtained. In the related technology, the cruising speed needs to be obtained by detecting the driving speed of other vehicles on the same road and the traffic volume on the road through the body sensor. The method provided in the above embodiment of the present application further reduces the amount of data collected by the vehicle sensor and improves the efficiency of path learning.

[0045] In some embodiments, the background information includes railway information, river information, and regional division information.

[0046] When only vehicle body sensors are used for path learning, it is difficult for vehicle body sensors to obtain information about surrounding railways and rivers because railways and rivers are often on the same plane as roads. However, by using a navigation map, after obtaining the path, information about all railways and rivers passing through the path can be obtained, and then more in-depth path learning can be performed based on the railway and river information, further improving the accuracy of path learning.

[0047] Although vehicle cruise control technology has gradually matured in related technologies, electronic systems may also fail. For this reason, most vehicle cruise control technologies have the function of switching back to driver control. In view of this, the above-mentioned in-depth path learning based on railway and river information in this application is mainly to keep the vehicle away from railways and rivers during automatic cruising, that is, to drive in lanes away from railways and rivers as much as possible. Once the automatic cruise system fails, it can reduce the probability of the vehicle rushing into dangerous areas such as railways and rivers, and ensure the safety of people in the vehicle.

[0048] In a specific embodiment, the area division information includes school area division information, residential area division information, etc. When only the vehicle body sensor is used for path learning, the area division information is also difficult to obtain through the vehicle body sensor. However, by using the navigation map, after obtaining the path, the school area division information and residential area division information of all the schools passed on the path can be obtained, so that the vehicle speed can be reduced when automatically cruising through school areas and residential areas to ensure pedestrian safety.

[0049] In a specific embodiment, the second path information may also include traffic congestion information. In the related art, when only the vehicle body sensor is used for path learning, if a congested road section is passed during the path learning process, although the vehicle body sensor can determine that the current road section is in a congested state based on the vehicle speed and the number of surrounding vehicles, the congestion information is not universal, that is, it is impossible to determine that the road section is also congested when the vehicle performs automatic cruising at another time based on the judgment that the road section is congested only once. However, based on the navigation map, the congestion status of a certain road section at multiple times can be obtained, so that it can be concluded that a certain road section is likely to be congested at a certain time. When the vehicle uses the second path information including the above information for automatic cruising, it can adjust the driving state according to the road congestion condition, further improving the accuracy of path learning.

[0050] In some embodiments, Figure 2 As shown, the step S102 includes:

[0051] Step S201: acquiring the second path information on the driving path based on the navigation map.

[0052] Step S202: Based on the second path information, use the first path information on the driving path collected by the vehicle body sensor.

[0053] Step S203: Complete the path learning by using the first path information and the second path information.

[0054] The first path information collected by the vehicle's body sensor and the second path information obtained based on the navigation map may have repeated path information, such as lane width information, which can be collected by the body sensor and is also marked in the navigation map. In this case, there is no need to repeatedly obtain the path information. The above embodiment preferentially obtains the second path information shown through the navigation map information, and based on this amount of information, the first path information is collected through the body sensor to complete the path learning, which reduces the workload of the body sensor at the data collection and processing level, optimizes the duration of path learning, and realizes efficient utilization and allocation of vehicle resources.

[0055] In some embodiments, the step S102 or the step S203 includes:

[0056] The first path information and the second path information are fused into fused path information, the fused path information is stored, and the path learning is completed.

[0057] The fusion of the first path information and the second path information enables the vehicle to directly call the fused path information during the subsequent automatic cruising process, thereby simplifying the resource scheduling process.

[0058] In some embodiments, the body sensor includes a long-range sensor, a medium-range sensor, and a short-range sensor; in step S102, according to the driving path and the navigation map, using the body sensor of the vehicle to collect the first path information on the driving path includes:

[0059] Obtaining the real-time position of the vehicle;

[0060] In response to the real-time position being outside the coverage of the navigation map, the first path information is collected using the long-range sensor, the medium-range sensor and the short-range sensor; in response to the real-time position being within the coverage of the navigation map, the first path information is collected using the medium-range sensor and the short-range sensor.

[0061] The long-range sensor can collect path information at a position far from the vehicle, and the medium-range and short-range sensors can collect path information at a position close to the vehicle. The path information collected by the vehicle using the long-range sensor is usually road width information, building information, intersection setting information, etc., and this information can be covered by the path information obtained from the navigation map. Therefore, for the range covered by the navigation map, the vehicle does not enable the long-range sensor, which reduces the amount of data collected by the body sensor and also saves power and computing resources.

[0062] In a more specific embodiment, the above-mentioned vehicle body sensors can be divided into long-range sensors, medium-range sensors and short-range sensors. Specifically, the long-range sensors include long-range radar sensors and long-range image sensors; the medium-range sensors include medium-range radar sensors and medium-range image sensors; and the short-range sensors include short-range radar sensors and short-range image sensors.

[0063] The detection distance of the radar sensor is related to the radar performance. The greater the radar's transmission power, the higher the antenna gain and the receiver sensitivity, the longer the detection distance. For the embodiment of the present application, the detection distance of the long-range radar sensor is 200 meters and above, the detection distance of the medium-range radar sensor is 60 meters to 200 meters, and the detection distance of the short-range radar sensor is 60 meters and below.

[0064] The detection distance of the image sensor is related to the viewing angle. For the embodiment of the present application, the long-range image sensor includes a narrow-angle camera (viewing angle of 20° to 40°), the medium-range image sensor includes a wide-angle camera (viewing angle of 60° to 90°), and the short-range image sensor includes an ultra-wide-angle camera (viewing angle of 100° to 130°).

[0065] Based on the same inventive concept, an embodiment of the present application further provides an intelligent driving automatic cruising method, comprising:

[0066] Step S301: Acquire path information, wherein the path information is learned using the intelligent driving path learning method;

[0067] Step S302: Control the vehicle to cruise automatically according to the path information.

[0068] The automatic cruising method provided in the present application performs automatic cruising based on high-precision path information, thereby ensuring the safety and reliability of automatic cruising and improving the riding experience of users in the vehicle.

[0069] In specific implementation, the above-mentioned control of vehicle automatic cruising also includes:

[0070] The vehicle body sensor is used to continuously scan obstacle information around the vehicle body, and the obstacles include other vehicles and road facilities such as roadblocks. At the same time, the vehicle is controlled to travel at a predetermined speed, and the engine throttle opening is automatically adjusted according to the change of driving resistance to keep the driving speed constant. The automatic adjustment of the engine throttle opening to keep the driving speed constant further includes: when the cruise speed command signal and the actual speed feedback signal are input into the vehicle speed control unit of the vehicle controller, the comparator in the vehicle speed control unit can obtain the difference between the two signals, that is, the error signal, through comparison operation. After the error signal is subjected to proportional operation and integral operation, it is amplified to obtain a control signal for controlling the throttle opening size. The speed control unit sends the control signal to the execution unit of the vehicle controller, and the execution unit can drive the throttle cable to adjust the size of the engine throttle opening, and quickly adjust the actual vehicle speed to the speed value set by the driver, thereby realizing constant speed control, that is, cruise control.

[0071] When the distance to the vehicle in front is too small, the vehicle speed control unit coordinates with the anti-lock braking system and the engine control system to brake the wheels appropriately, reduce the throttle opening, and lower the engine output power to keep the vehicle in front at all times.

[0072] When acceleration is required, the vehicle speed control unit coordinates with the engine control system to increase the throttle opening and increase the engine's output power.

[0073] The automatic cruise in the above-mentioned embodiment is used to implement the corresponding intelligent driving path learning method in any of the above-mentioned embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0074] It should be noted that the automatic cruise method provided in the above embodiments of the present application is performed under the supervision of the driver in the vehicle. Once the automatic cruise system fails, the control of the vehicle will be handed over to the driver in the vehicle to avoid accidents.

[0075] It is understandable that before using the technical solutions of each embodiment of the present application, the type, scope of use, usage scenarios, etc. of the personal information involved (such as the driving route in the above embodiments of the present application, etc.) will be informed to the user in an appropriate manner, and the user's authorization will be obtained.

[0076] For example, in response to receiving an active request from a user, a prompt message is sent to the user to clearly remind the user that the operation requested to be performed will require obtaining and using the user's personal information. Thus, the user can independently choose whether to provide personal information to software or hardware such as an electronic device, application, server, or storage medium that performs the operation of the technical solution of the present disclosure according to the prompt message.

[0077] As an optional but non-limiting implementation, in response to receiving the user's active request, the prompt information may be sent to the user in the form of a pop-up window, in which the prompt information may be presented in text form. In addition, the pop-up window may also carry a selection control for the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0078] It is understandable that the above notification and the process of obtaining user authorization are merely illustrative and do not constitute a limitation on the implementation of the present disclosure. Other methods that meet relevant laws and regulations may also be applied to the implementation of the present disclosure.

[0079] It should be noted that the method of the embodiment of the present application can be performed by a single device, such as a computer or server. The method of this embodiment can also be applied to a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present application, and the multiple devices will interact with each other to complete the described method.

[0080] It should be noted that the above describes some embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0081] Based on the same inventive concept, corresponding to any of the above embodiments and methods, Figure 3 As shown, the present application also provides an intelligent driving path learning device 100, comprising:

[0082] The path acquisition module 110 is configured to acquire a preset driving path;

[0083] The path learning module 120 is configured to complete the path learning based on the driving path and the navigation map, using the first path information on the driving path collected by the vehicle body sensor, and the second path information on the driving path obtained based on the navigation map, wherein the first path information is different from the path information included in the second path information, and the first path information and the second path information jointly determine all the path information required for the path learning.

[0084] The path learning device provided in the present application uses vehicle sensors to collect path information while assisting in path information collection based on navigation maps, thereby reducing the amount of data collected by vehicle sensors, improving the efficiency of path learning, and being able to supplement some information that cannot be collected by vehicle sensors, thereby improving the accuracy of path learning.

[0085] In some embodiments, Figure 3 As shown, the path learning module 120 includes:

[0086] The acquisition unit 121 is configured to acquire the second path information on the driving path based on the navigation map.

[0087] The collecting unit 122 is configured to collect the first path information on the driving path by using the vehicle body sensor based on the second path information.

[0088] The path learning unit 123 is configured to complete the path learning by using the first path information and the second path information.

[0089] In some embodiments, the path learning module 120 or the path learning unit 123 is further configured to:

[0090] The first path information and the second path information are fused into fused path information, the fused path information is stored, and the path learning is completed.

[0091] In some embodiments, the second path information includes road network information, background information and interest point index information. The road network information includes road name, road direction, road grade, highway grade, and road width. The background information includes railway information, river information, and regional division information.

[0092] In some embodiments, the vehicle body sensor includes a long-range sensor, a medium-range sensor, and a short-range sensor; the path learning module 120 is further configured to:

[0093] Obtaining the real-time position of the vehicle;

[0094] In response to the real-time position being outside the coverage of the navigation map, the first path information is collected using the long-range sensor, the medium-range sensor and the short-range sensor; in response to the real-time position being within the coverage of the navigation map, the first path information is collected using the medium-range sensor and the short-range sensor.

[0095] Based on the same inventive concept, an embodiment of the present application further provides an intelligent driving automatic cruise device 200, such as Figure 3 As shown, including:

[0096] The information acquisition module 210 is configured to acquire path information, wherein the path information is learned by using the intelligent driving path learning device;

[0097] The automatic cruise module 220 is configured to control the automatic cruise of the vehicle according to the path information.

[0098] The automatic cruise device provided in the present application performs automatic cruise based on high-precision path information, thereby ensuring the safety and reliability of automatic cruise and improving the riding experience of users in the vehicle.

[0099] For the convenience of description, the above device is described in terms of functions divided into various modules. Of course, when implementing the present application, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0100] The device of the above embodiment is used to implement the corresponding intelligent driving path learning method or automatic cruising method in any of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0101] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the intelligent driving path learning method or automatic cruising method described in any of the above embodiments is implemented.

[0102] Figure 4 A more specific schematic diagram of the hardware structure of an electronic device provided in this embodiment is shown, and the device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are connected to each other through the bus 1050 in the device.

[0103] The processor 1010 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0104] The memory 1020 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 may store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program codes are stored in the memory 1020 and are called and executed by the processor 1010.

[0105] The input / output interface 1030 is used to connect the input / output module to realize information input and output. The input / output module can be configured in the device as a component (not shown in the figure), or it can be externally connected to the device to provide corresponding functions. The input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

[0106] The communication interface 1040 is used to connect a communication module (not shown) to realize communication interaction between the device and other devices. The communication module can realize communication through a wired mode (such as USB, network cable, etc.) or a wireless mode (such as mobile network, WIFI, Bluetooth, etc.).

[0107] The bus 1050 includes a path that transmits information between the various components of the device (eg, the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040).

[0108] It should be noted that, although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040 and the bus 1050, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, it can be understood by those skilled in the art that the above device may also only include the components necessary for implementing the embodiments of the present specification, and does not necessarily include all the components shown in the figure.

[0109] The electronic device of the above-mentioned embodiment is used to implement the corresponding intelligent driving path learning method or automatic cruising method in any of the above-mentioned embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0110] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the intelligent driving path learning method or automatic cruising method as described in any of the above embodiments.

[0111] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0112] The computer instructions stored in the storage medium of the above embodiments are used to enable the computer to execute the intelligent driving path learning method or automatic cruising method described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0113] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides a vehicle, which includes the above-mentioned electronic device or the above-mentioned intelligent driving path learning device or automatic cruise device, and has the beneficial effects of the corresponding intelligent driving path learning method or automatic cruise method embodiment, which will not be repeated here.

[0114] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present application (including the claims) is limited to these examples. In line with the concept of the present application, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present application as described above, which are not provided in detail for the sake of simplicity.

[0115] In addition, to simplify the description and discussion, and in order not to make the embodiments of the present application difficult to understand, the known power supply / ground connection with the integrated circuit (IC) chip and other components may or may not be shown in the provided drawings. In addition, the device can be shown in the form of a block diagram to avoid making the embodiments of the present application difficult to understand, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform to be implemented in the embodiments of the present application (that is, these details should be fully within the scope of understanding of those skilled in the art). In the case of elaborating specific details (e.g., circuits) to describe exemplary embodiments of the present application, it is obvious to those skilled in the art that the embodiments of the present application can be implemented without these specific details or when these specific details are changed. Therefore, these descriptions should be considered to be illustrative rather than restrictive.

[0116] Although the present application has been described in conjunction with specific embodiments of the present application, many replacements, modifications and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.

[0117] The embodiments of the present application are intended to cover all such substitutions, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present application should be included in the scope of protection of the present application.

Claims

1. An intelligent driving path learning method, characterized in that: include: After activating the self-learning driving mapping function, obtaining a preset driving route includes: receiving the starting point, destination and driving route information determined by the user through the human-computer interaction interface; According to the driving path and the navigation map, the path learning is completed using the first path information on the driving path collected by the vehicle body sensor and the second path information on the driving path obtained based on the navigation map, including: obtaining the second path information on the driving path based on the navigation map; based on the second path information, collecting the first path information on the driving path using the vehicle body sensor; completing the path learning using the first path information and the second path information, including: fusing the first path information and the second path information into fused path information; storing the fused path information to complete the path learning; The first path information and the second path information include different path information, and the first path information and the second path information jointly determine all path information required for the path learning; The vehicle body sensor includes a long-range sensor, a medium-range sensor and a short-range sensor; According to the driving path and the navigation map, using a body sensor of the vehicle to collect the first path information on the driving path includes: Obtaining the real-time position of the vehicle; In response to the real-time position being outside the coverage of the navigation map, the first path information is collected using the long-range sensor, the medium-range sensor and the short-range sensor; in response to the real-time position being within the coverage of the navigation map, the first path information is collected using the medium-range sensor and the short-range sensor.

2. The method according to claim 1, characterized in that The second path information includes road network information, background information and interest point index information; The road network information includes road names, road travel directions, road grades, highway grades and / or road widths, and the background information includes railway information, river information and / or regional division information.

3. An intelligent driving automatic cruising method, characterized in that: include: Acquire path information, wherein the path information is learned using the method according to claim 1; The vehicle is controlled to cruise automatically according to the path information.

4. An intelligent driving path learning device, characterized in that: include: The path acquisition module is configured to acquire a preset driving path after activating the self-learning driving mapping function, including: receiving a starting point, a destination, and driving path information determined by a user through a human-computer interaction interface; The path learning module is configured to complete the path learning according to the driving path and the navigation map, using the first path information on the driving path collected by the vehicle body sensor, and the second path information on the driving path obtained based on the navigation map, including: obtaining the second path information on the driving path based on the navigation map; based on the second path information, using the vehicle body sensor to collect the first path information on the driving path; completing the path learning using the first path information and the second path information, including: fusing the first path information and the second path information into fused path information; storing the fused path information to complete the path learning; The first path information and the second path information include different path information, and the first path information and the second path information jointly determine all path information required for the path learning; The body sensor includes a long-range sensor, a medium-range sensor, and a short-range sensor; according to the driving path and the navigation map, using the body sensor of the vehicle to collect the first path information on the driving path, including: Obtaining the real-time position of the vehicle; In response to the real-time position being outside the coverage of the navigation map, the first path information is collected using the long-range sensor, the medium-range sensor and the short-range sensor; in response to the real-time position being within the coverage of the navigation map, the first path information is collected using the medium-range sensor and the short-range sensor.

5. An intelligent driving automatic cruise device, characterized in that: include: an information acquisition module, configured to acquire path information, wherein the path information is learned using the apparatus according to claim 4; The automatic cruise module is configured to control the automatic cruise of the vehicle according to the path information.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method according to any one of claims 1 to 3 is implemented.

7. A vehicle, characterized in that: The vehicle comprises the device according to claim 4 or 5 or the electronic device according to claim 6.

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