Method for actively learning driving route, electronic equipment and vehicle

Abstract

The invention provides a method for actively learning a driving route, electronic equipment and a vehicle, and relates to the technical field of intelligent driving, the method comprises the steps of firstly obtaining a driving route and a memory navigation function state of the vehicle, and when the memory navigation function state is function opening and the driving route accords with a preset active learning condition, automatically sending an active driving route learning instruction, after an instruction receiver receives the instruction, learning of the driving route is automatically started, so that the whole opportunity for entering active learning and the active learning process are automatically executed; after successful learning information is received, the driving route is stored as a learned route, storage of the successfully learned route is completed, storage of the active learning result is also automatically executed, the whole process does not need a user to actively activate the route learning function, the use frequency of the memory navigation function on the vehicle can be improved, and the user experience is improved. And the user can be actively helped to better use the auxiliary driving function of intelligent driving, and the use experience of the user is improved.

Description

Technical Field

[0001] The present application relates to the field of vehicle intelligent driving technology, and in particular to a method, electronic device, and vehicle for actively learning a driving route. Background Art

[0002] The inventors have discovered that, currently, in the route learning stage of the memory navigation function, whether to perform route learning or which route to select for learning requires the user to actively activate it. That is, route learning will only be performed when the user actively requires route learning, and there is no function to actively recommend route learning or actively enter route learning. Summary of the Invention

[0003] In view of this, the purpose of this application is to provide a method, electronic device and vehicle for actively learning a driving route.

[0004] Based on the above objectives, the first aspect of the present application provides a method for actively learning a driving route, comprising:

[0005] Obtain the vehicle's driving route and memory navigation function status;

[0006] In response to the state of the memory navigation function being enabled and the driving route meeting a preset active learning condition, sending an active learning driving route instruction;

[0007] In response to receiving the learning success information, the driving route is stored as a learned route.

[0008] Optionally, the driving route includes at least a target starting position and a target ending position;

[0009] The method further comprises:

[0010] Get the number of learned routes and learned routes that have been stored;

[0011] In response to the travel route not being included in the learned routes, determining a historical travel count of the vehicle based on the target starting position and the target ending position;

[0012] In response to the historical driving number being greater than or equal to a preset driving number and the number of routes being less than a preset number, it is determined that the driving route meets the preset active learning condition.

[0013] Optionally, determining the vehicle's historical travel times based on the target starting position and the target ending position includes:

[0014] Get the current time information;

[0015] Determining a selected time period based on the time information and a preset scrolling period;

[0016] Determining all historical driving routes of the vehicle within the selected time period as the selected driving routes;

[0017] The historical travel times of the vehicle are determined based on the target starting position, the target ending position and the selected travel route.

[0018] Optionally, determining the vehicle's historical travel times based on the target starting position, the target ending position, and the selected travel route includes:

[0019] Obtaining the historical endpoint location of each selected travel route;

[0020] Based on the target destination position, determining the selected driving routes having the same historical destination position as the target destination position as similar driving routes;

[0021] Obtaining a historical starting position of each of the similar driving routes;

[0022] In response to a distance between the historical starting position and the target starting position of a similar driving route being less than or equal to a preset distance, determining the similar driving route as a target driving route;

[0023] The number of times the target driving route is traveled within the selected time period is determined as the historical number of travels.

[0024] Optionally, the driving route includes at least a target starting position and a target ending position;

[0025] The sending of the active learning driving route instruction includes:

[0026] Sending the target starting position and target ending position to the intelligent driving domain controller;

[0027] In response to receiving the learning consent information sent by the intelligent driving domain controller, an active learning driving route instruction is sent to the intelligent driving domain controller.

[0028] Optionally, the method further includes:

[0029] Get the stored learned routes;

[0030] In response to the learned routes including the driving route, determining a recommended driving route and a recommended route number of the recommended driving routes from the learned routes based on the driving route;

[0031] determining a target recommended driving route based on the recommended driving route and the number of recommended routes;

[0032] The target recommended driving route is visually displayed or reminded.

[0033] Optionally, determining a target recommended driving route based on the recommended driving route and the number of recommended routes includes:

[0034] In response to the number of the recommended routes being greater than or equal to two, acquiring image information inside the vehicle;

[0035] In response to the image information including the target person image, obtaining road information of each of the recommended driving routes, and determining a target recommended driving route based on the road information;

[0036] Alternatively, in response to the fact that the image information does not contain the image of the target person, the driving duration of the driving route, the time information at the current moment, the starting time and / or road information of each recommended driving route are obtained, and the target recommended driving route is determined based on the driving duration, the time information, the starting time and / or the road information.

[0037] Optionally, determining the target recommended driving route based on the driving duration, the time information, the start time and / or road information includes:

[0038] In response to the driving duration being greater than or equal to a preset duration, obtaining a predicted energy consumption value for each of the recommended driving routes, and determining a target recommended driving route based on the predicted energy consumption value and the road information;

[0039] Alternatively, in response to the driving duration being less than a preset duration, the time difference between the start time of each recommended driving route and the time information is determined, and the recommended driving route corresponding to the smallest time difference among all the time differences is determined as the target recommended driving route.

[0040] Based on the same inventive concept, the second aspect of this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor, and when the processor executes the computer program, it implements any one of the methods described in the first aspect above.

[0041] Based on the same inventive concept, the third aspect of the present application provides a vehicle, comprising the electronic device described in the second aspect above.

[0042] As can be seen from the above, the method, electronic device and vehicle for actively learning driving routes provided by the present application first obtain the vehicle's driving route and memory navigation function status. When the memory navigation function status is on and the driving route meets the preset active learning conditions, it means that the user agrees to route learning and the currently acquired driving route meets the preset active learning conditions. At this time, an active learning driving route instruction is automatically sent. After the recipient of the instruction receives the instruction, it automatically starts learning the driving route. In this way, the entire timing of entering active learning and the active learning process are automatically executed; when the learning success information is received, it means that the learning of the driving route has been successfully completed at this time, and the driving route is stored as a learned route, completing the storage of the successfully learned route. In this way, the storage of the active learning results is also automatically executed. The entire process does not require the user to actively activate the route learning function, which can not only increase the frequency of use of the memory navigation function on the vehicle, but also actively help users better use the assisted driving function of intelligent driving, thereby improving the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in this application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are merely embodiments of this application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0044] Figure 1 This is a schematic diagram of a first flow chart of a method for actively learning a driving route according to an embodiment of the present application;

[0045] Figure 2 This is a second flow chart of the method for actively learning a driving route according to an embodiment of the present application;

[0046] Figure 3 A schematic diagram of an apparatus for actively learning a driving route according to an embodiment of the present application;

[0047] Figure 4 A schematic diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0048] In order to make the objectives, technical solutions and advantages of this application more clear, this application is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0049] It should be noted that, unless otherwise defined, technical terms or scientific terms used in the embodiments of the present application shall have the common meaning understood by one of ordinary skill in the art to which the embodiments of the present application belong. The terms "first", "second", and similar terms used in the embodiments of the present application do not denote any order, quantity, or importance, but are merely used to distinguish different components. The terms "include", "contain", and similar terms mean that the elements or objects before the terms encompass the elements or objects listed after the terms and their equivalents, and do not exclude other elements or objects. The terms "connect" or "connected" and similar terms do not mean physical or mechanical connection, but can include electrical connection, whether direct or indirect. The terms "upper", "lower", "left", "right", and the like are used only to represent relative positional relationships, and when the absolute positions of the described objects change, the relative positional relationships can also change accordingly.

[0050] Intelligent driving of a vehicle refers to enabling a car to partially or completely replace a human driver to realize the functions of environment perception, decision planning, and automatic control through advanced sensors, artificial intelligence (AI), big data, automatic control, and other technologies. The core goal is to improve driving safety, driving efficiency, and ride comfort, and ultimately move towards completely unmanned driving.

[0051] According to the standard of the Society of Automotive Engineers, intelligent driving is divided into six levels from L0 to L5:

[0052] L0 (no automation): completely controlled by a human driver.

[0053] L1 (assisted driving): single-function automation, such as cruise control and automatic braking.

[0054] L2 (partial automation): combined-function automation (such as adaptive cruise control + lane keeping), but the driver needs to take over at any time.

[0055] L3 (conditional automation): the system completes driving in a specific environment (such as a highway), and a human needs to take over when the system requests.

[0056] L4 (high automation): no human intervention is required in a limited scenario (such as a park or a fixed route in a city), but the operating conditions need to be preset.

[0057] L5 (complete automation): full-scenario, no steering wheel, completely unmanned driving.

[0058] The core technology components of intelligent driving include:

[0059] Environment perception: real-time detection of surrounding vehicles, pedestrians, road signs, and other information through radar, lidar, cameras, ultrasonic sensors, and other sensors;

[0060] High-precision positioning and mapping: Vehicle positioning is achieved through navigation systems, Beidou system, and high-precision maps (e.g., centimeter-level accuracy);

[0061] Decision-making and route planning: Analyze data based on AI algorithms (such as deep learning and reinforcement learning), plan routes, and predict the behavior of other traffic participants;

[0062] Vehicle control: Automatically control steering, throttle, brakes and other actuators through drive-by-wire technology.

[0063] The advantages of intelligent driving are that it reduces traffic accidents caused by human errors (according to statistics, more than 90% of accidents are caused by human factors), alleviates congestion (through the collaborative optimization of traffic flow through the Internet of Vehicles), and can also enhance the freedom of travel for special groups (such as people with disabilities).

[0064] Advanced Driver Assistance Systems (ADAS) in intelligent driving are designed to assist drivers through technological means, improving safety, comfort, and convenience. Common assisted driving features include basic safety functions (such as automatic emergency braking, lane departure warning, and blind spot monitoring), cruise and following functions (such as adaptive cruise control), lane control functions (such as lane keeping assist), parking assistance functions (such as automatic parking assist, memory parking, remote parking, and panoramic imaging), and memory navigation functions.

[0065] Memory Pilot (also known as Memory Parking, Route Learning Driving, or Home Zone Pilot) is an assisted driving feature based on environmental learning. It allows the vehicle to achieve autonomous or assisted driving on specific fixed routes (such as from a home parking lot to a residential entrance or on company campus roads) without relying on high-precision maps or complex sensors. Memory Pilot is a practical implementation of autonomous driving in limited scenarios, particularly suitable for fixed-route parking and low-speed campus commuting.

[0066] The core principle of the Memory Pilot feature is route learning. The vehicle uses sensors such as cameras and radar to record the user's frequently traveled routes (such as from home to the community entrance) and save environmental characteristics (lane lines, landmarks, obstacle locations, etc.). When the vehicle travels the same route again, the system compares real-time perception data with the memorized route to automatically control vehicle speed, steering, and obstacle avoidance. This feature is generally suitable for low-speed, closed, or semi-closed scenarios (such as parking lots, campuses, and fixed commuting routes).

[0067] However, currently, for the route learning stage of the memory navigation function, whether to conduct route learning or which route to choose to learn requires the user to actively activate it. That is, route learning will only be performed when the user actively operates to require route learning. There is no function to actively recommend route learning or actively enter route learning.

[0068] However, for most users, they rarely take the initiative to learn routes, which results in the memory navigation function on most vehicles not being used. This not only wastes the intelligent settings on the vehicle, but also fails to improve the driver's experience.

[0069] Therefore, how to control the intelligent driving domain controller to actively learn routes without relying on the user's active operation is a technical problem that needs to be solved urgently.

[0070] Based on this, see Figure 1 The present application provides a method for actively learning a driving route, which is executed by a vehicle's cockpit controller or multimedia host. The method specifically includes:

[0071] Step S100: Acquire the vehicle's driving route and memory navigation function status;

[0072] Step S200: In response to the navigation memory function being enabled and the driving route meeting a preset active learning condition, sending an active learning driving route instruction;

[0073] Step S300: In response to receiving the learning success information, the driving route is stored as a learned route.

[0074] Specifically, when the vehicle is about to start driving, the vehicle's navigation module obtains the vehicle's driving route, which includes at least a target starting location and a target ending location, and simultaneously obtains the vehicle's memory navigation function status, which can be either on or off.

[0075] When the Memory Navigation function is enabled, it indicates that the user agrees to use the Memory Navigation function. When the Memory Navigation function is disabled, it indicates that the user prohibits the use of the Memory Navigation function. If the vehicle has the Memory Navigation function, it is generally enabled by default unless the user specifically prohibits its use.

[0076] Only when the Memory Navigation function status is turned on, indicating that the user agrees to use the Memory Navigation function, can the control process of actively learning the driving route continue.

[0077] Therefore, when it is determined that the memory navigation function is enabled, it is determined whether the driving route meets the preset active learning conditions. The preset active learning conditions are preset conditions that need to be met for the driving route to be controlled by the intelligent driving domain controller to actively enter learning.

[0078] When it is determined that the driving route meets the preset active learning conditions, it means that the driving route meets the preset conditions for active learning, and an active learning driving route instruction is sent to the intelligent driving domain controller.

[0079] After receiving the active learning driving route instruction, the intelligent driving domain controller actively learns the driving route. If the learning is successful, it generates learning success information and then sends the learning success information to the cockpit controller. After receiving the learning success information, the cockpit controller stores the driving route as a learned route. At this time, the learning and storage of the driving route are completed. In this way, the entire timing of entering active learning, the active learning process, and the feedback of the active learning results are all automatically executed, without the user actively activating the route learning function.

[0080] In the present application, the vehicle's driving route and memory navigation function status are first obtained. When the memory navigation function status is on and the driving route meets the preset active learning conditions, it means that the user agrees to route learning and the currently acquired driving route meets the preset active learning conditions. At this time, an active learning driving route instruction is automatically sent. After the recipient of the instruction receives the instruction, it automatically starts learning the driving route. In this way, the entire timing of entering active learning and the active learning process are automatically executed; when the learning success information is received, it means that the learning of the driving route has been successfully completed at this time, and the driving route is stored as a learned route, completing the storage of the successfully learned route. In this way, the storage of the active learning results is also automatically executed. The entire process does not require the user to actively activate the route learning function, which can not only increase the frequency of use of the memory navigation function on the vehicle, but also actively help users better use the assisted driving function of intelligent driving, thereby improving the user experience.

[0081] In some embodiments, the method further comprises:

[0082] Get the stored learned routes and the number of learned routes;

[0083] In response to the travel route not being included in the learned routes, determining a historical travel count of the vehicle based on the target starting position and the target ending position;

[0084] In response to the historical driving number being greater than or equal to a preset driving number and the number of routes being less than a preset number, it is determined that the driving route meets the preset active learning condition.

[0085] Specifically, when determining whether the driving route meets the preset active learning conditions, it is first necessary to determine whether the driving route obtained this time is already a stored learned route. If the driving route obtained this time is already a stored learned route, then there is no need to continue active learning; if the driving route obtained this time is not a stored learned route, then it is necessary to continue to determine whether the driving route meets other conditions.

[0086] Therefore, when determining whether the driving route meets the preset active learning conditions, the stored learned routes and the number of learned routes are obtained. If it is determined that the learned routes do not include the driving route, it means that the driving route obtained is not a stored learned route. Then, it is necessary to determine whether the driving route obtained is a route that the user frequently travels.

[0087] If the driving route obtained this time is not a route that the user frequently drives, then there is no need for active learning. This is because if the driving route is not a route that the user frequently drives, the probability of the user driving along the driving route again is very small, and actively learning the driving route will hardly have any effect. Therefore, there is no need for active learning.

[0088] If the driving route obtained this time is a route that the user frequently drives, then active learning is required so that when the user drives along the driving route again in the future, assisted driving can be performed based on the successfully learned route.

[0089] Therefore, based on the target starting position and target ending position of the driving route, the historical driving times of the vehicle are determined. When the historical driving times are greater than or equal to the preset driving times, it indicates that the driving route is a route frequently used by the user.

[0090] The preset number of driving times is the minimum number of driving times of a preset frequently used driving route. For example, the preset number of driving times may be 5 times or 7 times.

[0091] When the driving route is determined to be a frequently used route by the user, the number of learned routes needs to be further determined. If the number of learned routes has reached the preset storage limit, then, even if the driving route obtained this time is not a successfully learned route and is a frequently used route by the user, active learning of the current driving route cannot be continued because there is no storage space.

[0092] Therefore, it is necessary to judge the number of learned routes. When it is determined that the number of routes is less than the preset number, it means that the number of stored learned routes has not reached the preset storage upper limit, and the routes that are about to be successfully learned can continue to be stored. At this time, it is finally determined that the driving route meets the preset active learning conditions.

[0093] In the present application, when determining whether a driving route meets the preset active learning conditions, it is necessary to determine in sequence whether the driving route is included in the learned routes, whether the number of historical driving times is greater than or equal to the preset number of driving times, and whether the number of routes is less than the preset number. Only when it is determined that the driving route is not included in the learned routes, the number of historical driving times is greater than or equal to the preset number of driving times (indicating that the driving route is a route frequently used by the user), and the number of routes is less than the preset number (indicating that there is still storage space to store the driving route that is about to be successfully learned), can it be finally determined that the driving route meets the preset active learning conditions.

[0094] In some embodiments, determining the vehicle's historical travel times based on the target starting position and the target ending position includes:

[0095] Get the current time information;

[0096] Determining a selected time period based on the time information and a preset scrolling period;

[0097] Determining all historical driving routes of the vehicle within the selected time period as the selected driving routes;

[0098] The historical travel times of the vehicle are determined based on the target starting position, the target ending position and the selected travel route.

[0099] Specifically, when determining the vehicle's historical travel times, the current time information is first obtained. Based on the current time information and a preset rolling period, a selected time period is determined. The preset rolling period is a preset time period used to determine a period that is closest to the current time information. For example, the preset rolling period can be 5 days or 10 days.

[0100] After obtaining the time information, the time period included in the preset rolling period before the time information is determined as the selected time period, and all historical driving routes of the vehicle within the selected time period are determined as selected driving routes. Then, based on the selected driving routes, it is determined whether any of these selected routes has the same starting point and end point as the driving route obtained this time.

[0101] Therefore, based on the target starting position and target end position, the same driving route or similar driving route that is consistent with the target starting position and target end position is determined from the selected driving routes, and the number of determined same driving routes or similar driving routes is determined as the historical driving times of the vehicle.

[0102] In the present application, firstly, the selected time period is determined based on the time information of the current moment and the preset scrolling cycle, and the determined selected time period is ensured to be a time period that is relatively close to the current moment, so that the time period that is relatively close can reflect the user's most recent actual driving needs. Therefore, all historical driving routes of the vehicle within the selected time period are determined as the selected driving routes, that is, the determined selected driving routes are all historical driving routes of the vehicle within the recent time period. Then, based on the target starting position and the target ending position, the same driving route or similar driving route that is consistent with the starting and ending points of the current driving route can be determined from the selected driving routes, and the number of the determined same driving routes or similar driving routes is determined as the historical driving times of the vehicle. The historical driving routes determined in this way are close in time to the driving routes obtained this time, and the starting and ending points are consistent with the driving routes obtained this time. The historical driving times determined in this way are very accurate.

[0103] In some embodiments, determining the vehicle's historical travel times based on the target starting position, the target ending position, and the selected travel route includes:

[0104] Obtaining the historical endpoint location of each selected travel route;

[0105] Based on the target destination position, determining the selected driving routes having the same historical destination position as the target destination position as similar driving routes;

[0106] Obtaining a historical starting position of each of the similar driving routes;

[0107] In response to a distance between the historical starting position and the target starting position of a similar driving route being less than or equal to a preset distance, determining the similar driving route as a target driving route;

[0108] The number of times the target driving route is traveled within the selected time period is determined as the historical number of travels.

[0109] Specifically, since the driving route and the specific starting point and end point of the selected driving route are provided by the navigation module, for the navigation module, the specific starting point and end point are both specific position coordinates or values.

[0110] Therefore, when determining whether there is a route in the selected driving route that has the same starting and ending points as the driving route obtained this time, first, based on the target end point position, the selected driving route whose historical end point position is the same as the target end point position in the selected driving route is determined as a similar driving route. At this time, it is determined that the historical end point positions of all similar driving routes are the same as the target end point position of the driving route obtained this time.

[0111] Then, the historical starting position of each of the similar driving routes is obtained, and it is further determined whether the historical starting positions of these similar driving routes are consistent with the target starting position of the driving route obtained this time.

[0112] When it is determined that the distance between the historical starting position and the target starting position of a similar driving route is less than or equal to the preset distance, it means that the distance between the historical starting position of the similar driving route and the target starting position of the driving route obtained this time is very small, and it can be determined that the two have almost the same starting position. Therefore, the similar driving route is determined as the target driving route. In this way, the target driving route finally determined is consistent with the starting and ending points of the driving route obtained this time.

[0113] The preset distance is the maximum distance difference between two preset starting positions for them to be considered the same starting position. When the distance between the two starting positions is less than or equal to the preset distance, the distance between them is very small and they can be considered the same starting position. When the distance between the two starting positions is greater than the preset distance, the distance between them is large and they cannot be considered the same starting position.

[0114] After determining the target driving route, the number of times the target driving route is traveled within the selected time period is determined as the historical driving number. The historical driving number determined in this way is the historical driving number of the route that coincides with the starting and ending points of the driving route within the selected time period. The historical driving number can be used to determine whether the starting and ending points are frequently used by the user.

[0115] In the present application, when determining the historical number of trips, it is necessary to first determine similar driving routes based on the historical end positions and the target end positions, and then determine the target driving route with the same start and end points based on the historical starting positions and the target starting positions of the similar driving routes. In this way, the target driving route with the same start and end points as the driving route can be accurately determined, and then the historical number of trips of the target driving route within the selected time period can be accurately determined. The historical number of trips can be used to determine whether the start and end points are frequently used by the user, and then it can be accurately determined whether the driving route is a frequently used driving route.

[0116] In some embodiments, the sending the active learning driving route instruction comprises:

[0117] sending the target start location and the target end location to the intelligent driving domain controller;

[0118] sending the active learning driving route instruction to the intelligent driving domain controller in response to receiving the consent learning information sent by the intelligent driving domain controller.

[0119] Specifically, when sending the active learning driving route instruction, the target start location and the target end location are first sent to the intelligent driving domain controller. After the intelligent driving domain controller accepts the target start location and the target end location, it is determined whether the route between the target start location and the target end location has been learned. If it is determined that no learning has been performed, consent learning information is generated and sent to the cockpit controller.

[0120] When the cockpit controller receives the consent learning information sent by the intelligent driving domain controller, it means that the intelligent driving domain controller agrees to learn the driving route. At this time, the active learning driving route instruction is sent to the intelligent driving domain controller.

[0121] After the intelligent driving domain controller accepts the active learning driving route instruction, it actively learns the driving route. If the learning is successful, learning success information is generated and sent to the cockpit controller. After the cockpit controller receives the learning success information, the driving route is stored as a learned route. At this time, the learning and storage of the driving route are completed. Thus, the timing of active learning, the process of active learning, and the feedback of the results of active learning are all automatically executed without the need for the user to actively activate the route learning function.

[0122] In this application, before sending the active learning driving route instruction, it is first determined whether the intelligent driving domain controller has learned the route between the target start location and the target end location. If it is determined that no learning has been performed, the active learning driving route instruction can be sent, and the intelligent driving domain controller can perform active learning based on the received active learning driving route instruction. Thus, the effectiveness of active learning can be improved.

[0123] In some embodiments, the method further comprises:

[0124] obtaining a learned route that has been stored;

[0125] in response to the learned route including the driving route, determining a recommended driving route from the learned route based on the driving route and a number of recommended routes of the recommended driving route;

[0126] determining a target recommended driving route based on the recommended driving route and the number of recommended routes;

[0127] The target recommended driving route is visually displayed or reminded.

[0128] Specifically, after executing the aforementioned steps S100 to S300 , the driving route obtained this time has been stored as a learned route.

[0129] At this time, when a new driving route is acquired again after step S400, or between step S100 and step S200, the stored learned route is acquired.

[0130] If the learned routes include the driving route, it indicates that the driving route is already a stored learned route. At this time, based on the driving route, a recommended driving route that matches the starting and ending points of the driving route and the number of recommended routes of the recommended driving route are determined from the learned routes.

[0131] When specifically determining a recommended driving route that is consistent with the starting and ending points of the driving route, the end point position of each of the learned routes can be first obtained. Based on the end point position, the learned routes whose end points are the same as the target end point position in the learned routes are determined as similar learned routes; then the starting position of each of the similar learned routes is obtained. When the distance between the starting position of a similar learned route and the target starting position is less than or equal to the preset distance, the similar learned route is determined as the recommended driving route. At this time, the recommended driving route determined is consistent with the starting and ending points of the driving route obtained this time.

[0132] In this application, after determining the recommended driving route and the number of recommended routes of the recommended driving route, the most suitable target recommended driving route is determined based on the recommended driving route and the number of recommended routes, and then the target recommended driving route is visualized or reminded to remind the user that the driving route has been route learned and assisted driving can be selected to enhance the intelligent driving experience.

[0133] In some embodiments, determining a target recommended driving route based on the recommended driving route and the number of recommended routes includes:

[0134] In response to the number of the recommended routes being greater than or equal to two, acquiring image information inside the vehicle;

[0135] In response to the image information including the target person image, obtaining road information of each of the recommended driving routes, and determining a target recommended driving route based on the road information;

[0136] Alternatively, in response to the image information not containing the target person image, the driving duration of the driving route, time information of the current time, a starting time of each of the recommended driving routes, and / or road information is obtained, and a target recommended driving route is determined based on the driving duration, the time information, the starting time, and / or the road information.

[0137] Specifically, in determining the target recommended driving route based on the recommended driving routes and the number of recommended routes, if the number of recommended routes is only one, the recommended driving route is determined as the target recommended driving route; if the number of recommended routes is greater than or equal to two, the best target recommended driving route needs to be determined based on the at least two recommended driving routes.

[0138] In determining the best target recommended driving route, image information in the vehicle can be obtained. When the image information contains a target person image, it means that there is a target person in the vehicle at this time. The target person image is an image model of a target group of people determined based on machine learning in advance, and the target group of people includes children or the elderly. Such a target group of people needs to be paid more attention when riding in a vehicle, and therefore a recommended driving route with a more gentle road is determined as the target recommended driving route to improve the riding experience of the target group of people.

[0139] The road information of each of the recommended driving routes is obtained, and the road information at least includes the road slope, the number of sharp turns, and / or the number of gas stations (or charging stations) on the recommended driving route, and then based on the road information, the recommended driving route with fewer sharp turns and smaller road slope (wherein the priority of the number of sharp turns is higher than that of the road slope) among the at least two recommended driving routes is determined as the best target recommended driving route, and the target recommended driving route determined at this time is more suitable for the target group of people to travel, improving the riding safety and comfort of the target group of people.

[0140] When the image information does not contain the target person image, it means that there is no child in the vehicle at this time, and therefore the special group of people such as children does not need to be considered, and the driving duration of the driving route, the time information of the current time, the starting time of each of the recommended driving routes, and / or the road information is obtained, and a target recommended driving route is determined based on the driving duration, the time information, the starting time, and / or the road information, so that the target recommended driving route finally determined is the best driving route most suitable for the user under the current situation.

[0141] In this application, when there are two or more recommended driving routes, it is necessary to determine the best target recommended driving route from these two or more recommended driving routes. At this time, it is possible to determine whether there is a target person in the car based on the image information in the car, and then determine different considerations based on whether there is a target person in the car, and then determine the target recommended driving route based on different considerations. In this way, the final target recommended driving route is more in line with the actual needs of the people in the car and the driving environment, thereby improving the experience of intelligent driving.

[0142] In some embodiments, determining the target recommended driving route based on the driving duration, the time information, the start time and / or the road information includes:

[0143] In response to the driving duration being greater than or equal to a preset duration, obtaining a predicted energy consumption value for each of the recommended driving routes, and determining a target recommended driving route based on the predicted energy consumption value and the road information;

[0144] Alternatively, in response to the driving duration being less than a preset duration, the time difference between the start time of each recommended driving route and the time information is determined, and the recommended driving route corresponding to the smallest time difference among all the time differences is determined as the target recommended driving route.

[0145] Specifically, when determining a target recommended driving route based on the driving duration, the time information, the start time, and / or the road information, it is first determined whether the driving duration is greater than or equal to a preset duration. The preset duration is a preset minimum driving duration for a long-distance trip.

[0146] When the driving duration is greater than or equal to the preset duration, it indicates that the driving duration corresponding to the current driving route is long, and the current driving route is a long-distance driving route.

[0147] For long-distance driving, the most important factors are energy consumption, comfort, and the number of gas or charging stations. Therefore, it is necessary to obtain a predicted energy consumption value for each recommended driving route and determine a target recommended driving route based on the predicted energy consumption value and the road information.

[0148] Specifically, a recommended driving route with lower predicted energy consumption, fewer sharp turns, a gentler road gradient, and a greater number of gas or charging stations is determined as the target recommended driving route. The predicted energy consumption is prioritized first, followed by the number of sharp turns, then the road gradient, and finally the gas or charging stations. Based on this priority order, the target recommended driving route has lower energy consumption, a smoother road for greater ride comfort, and more gas or charging stations for convenient refueling or charging. This makes the target recommended driving route ideal for long-distance trips of a preset duration or greater.

[0149] When the driving time is less than the preset time, the driving time is short and it is a short distance driving. At this time, the energy consumption value or road information has little impact on the driving. At this time, the target recommended driving route that best suits the user's driving habits can be recommended to the user based on the user's driving habits.

[0150] At this time, the time difference between the start time of each recommended driving route and the time information is determined. The recommended driving route corresponding to the smallest time difference among all the time differences is the route closest to the time information of the currently acquired driving route and has the same starting and ending points. This route is the route that the user has selected the longest within a similar time period. Therefore, the recommended driving route corresponding to the smallest time difference among all the time differences is determined as the target recommended driving route. The target recommended driving route determined at this time is more in line with the user's driving habits.

[0151] In some embodiments, see Figure 2 The active learning driving route provided by this application can accurately judge the commuting scene, guide "office workers" to learn and use the memory driving function more, and promote the silent use of active learning driving routes through some trigger conditions such as navigation data and user data analysis.

[0152] In this embodiment, the specific operation steps of actively learning driving routes described in this application are described in detail, assuming that the preset rolling period is 5 days, the time period of the preset rolling period before the current time is determined as the selected time period, the preset number of driving times is 3, the preset distance is 300m, and the number of learned routes is 10.

[0153] First, after obtaining the driving route, it is determined whether the obtained driving route can be actively learned, that is, whether it meets the preset active learning conditions.

[0154] Specifically, it is possible to first determine whether the driving route obtained this time is a route frequently used by the user, that is, determine the target driving route that is consistent with the starting and ending points of the driving route, and whether the number of times the target driving route is traveled within the selected time period is greater than or equal to the preset number of times.

[0155] For example, all the historical driving route navigation start and end point information within a 5-day rolling cycle (i.e., within the selected time period) are compared. If there are 3 navigations with the same end point within this cycle (i.e., the end point is the same), then each subsequent navigation to the end point: the starting point of this navigation is compared with the starting point of the previous navigation. If it is within a range of 300m (i.e., the distance between the historical starting position and the target starting position is less than or equal to the preset distance, i.e., the starting points are consistent), the count is +1. If the count reaches 3, it is considered that 3 navigations with the same start and end point have been initiated, i.e., the number of historical driving times is greater than or equal to the preset number of driving times. At this time, it is determined that the current driving route is a route that the user frequently drives.

[0156] Then, the driving route obtained this time is the fourth driving of the driving route with the same starting and ending points. Although it has been determined that the driving route is a route frequently used by the user, it is still necessary to determine whether the driving route is a stored learned route.

[0157] ① If not, it means that the driving route is not a learned route. In this case, it is necessary to obtain the number of stored learned routes and determine whether the stored learned routes are full of 10. If so, the driving route does not meet the preset active learning conditions and no longer needs to be learned silently. If not, it is determined that the driving route meets the preset active learning conditions and an active learning driving route instruction is sent to the intelligent driving domain controller (for example, the active learning driving route instruction can be a [Start Silent Learning] signal);

[0158] ② If yes, it means that the driving route is a learned route, then no signal is sent and the count is set to 3.

[0159] Then, after receiving the [Start Silent Learning] signal, the intelligent driving domain controller determines whether the intelligent driving domain controller has received the [No Longer Silently Learn the Start and End Point] instruction. If so, the start and end point will no longer be judged, and the active learning driving route instruction will not be executed.

[0160] If not, determine whether the stored learned routes are full of 10. If so, stop silent learning; if not, start active learning of the driving route and feed back the learning results to the cockpit controller. For example, the learning result can be a [learning success] signal or a [learning failure] signal.

[0161] If the cockpit controller receives a "Learning Success" signal from the intelligent driving domain controller, it stores the route as a learned route and no longer actively learns the start and end points of the route. If the cockpit controller receives a "Learning Failure" signal from the intelligent driving domain controller, the counter is set to 3, and the start and end points are matched again during the next navigation cycle, continuing active learning.

[0162] After the driving route is stored as a learned route, the next time a new driving route is obtained, if the new driving route is consistent with the starting and ending points of the learned route, the successfully learned route can be actively recommended, that is, the driving learning route is memorized, and interactive operations are prompted on the screen to facilitate users to use the assisted driving function.

[0163] It is worth noting that during active learning, whether successful or unsuccessful, no user notification is required; the cockpit controller silently executes the entire process. If learning fails, a notification of the failure can be sent to the cloud for synchronization and storage.

[0164] This application implements an intelligent driving assistance function in a commuting scenario, which actively learns the driving route, recommends and uses the learned route, improves the utilization rate of intelligent driving, enhances the user experience, and reduces driving risks.

[0165] 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 in a distributed scenario and performed by multiple devices working together. In such a distributed scenario, one of the multiple devices may only perform one or more steps of the method of the embodiment of the present application, and the multiple devices will interact with each other to complete the method.

[0166] It should be noted that the above describes some embodiments of the present application. In some cases, the actions or steps described in the above embodiments 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.

[0167] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides a device for actively learning driving routes.

[0168] refer to Figure 3 The device for actively learning a driving route includes:

[0169] The acquisition module 100 is configured to acquire the vehicle's driving route and the state of the memory navigation function;

[0170] The execution module 200 is configured to send an active learning driving route instruction in response to the state of the memory navigation function being enabled and the driving route meeting a preset active learning condition;

[0171] The storage module 300 is configured to store the driving route as a learned route in response to receiving the learning success information.

[0172] In some embodiments, the driving route includes at least a target starting location and a target ending location.

[0173] In some embodiments, the execution module 200 is further configured to:

[0174] Get the stored learned routes and the number of learned routes;

[0175] In response to the travel route not being included in the learned routes, determining a historical travel count of the vehicle based on the target starting position and the target ending position;

[0176] In response to the historical driving number being greater than or equal to a preset driving number and the number of routes being less than a preset number, it is determined that the driving route meets the preset active learning condition.

[0177] In some embodiments, the execution module 200 is further configured to:

[0178] Get the current time information;

[0179] Determining a selected time period based on the time information and a preset scrolling period;

[0180] Determining all historical driving routes of the vehicle within the selected time period as the selected driving routes;

[0181] The historical travel times of the vehicle are determined based on the target starting position, the target ending position and the selected travel route.

[0182] In some embodiments, the execution module 200 is further configured to:

[0183] Obtaining the historical endpoint location of each selected travel route;

[0184] Based on the target destination position, determining the selected driving routes having the same historical destination position as the target destination position as similar driving routes;

[0185] Obtaining a historical starting position of each of the similar driving routes;

[0186] In response to a distance between the historical starting position and the target starting position of a similar driving route being less than or equal to a preset distance, determining the similar driving route as a target driving route;

[0187] The number of times the target driving route is traveled within the selected time period is determined as the historical number of travels.

[0188] In some embodiments, the storage module 300 is further configured to:

[0189] The sending of the active learning driving route instruction includes:

[0190] Sending the target starting position and target ending position to the intelligent driving domain controller;

[0191] In response to receiving the learning consent information sent by the intelligent driving domain controller, an active learning driving route instruction is sent to the intelligent driving domain controller.

[0192] In some embodiments, the execution module 200 is further configured to:

[0193] Get the stored learned routes;

[0194] In response to the learned routes including the driving route, determining a recommended driving route and a recommended route number of the recommended driving routes from the learned routes based on the driving route;

[0195] determining a target recommended driving route based on the recommended driving route and the number of recommended routes;

[0196] The target recommended driving route is visually displayed or reminded.

[0197] In some embodiments, the execution module 200 is further configured to:

[0198] In response to the number of the recommended routes being greater than or equal to two, acquiring image information inside the vehicle;

[0199] In response to the image information including the target person image, obtaining road information of each of the recommended driving routes, and determining a target recommended driving route based on the road information;

[0200] Alternatively, in response to the fact that the image information does not contain the image of the target person, the driving duration of the driving route, the time information at the current moment, the starting time and / or road information of each recommended driving route are obtained, and the target recommended driving route is determined based on the driving duration, the time information, the starting time and / or the road information.

[0201] In some embodiments, the execution module 200 is further configured to:

[0202] In response to the driving duration being greater than or equal to the preset duration, a predicted energy consumption value of each of the recommended driving routes is obtained, and a target recommended driving route is determined based on the predicted energy consumption value and the road information;

[0203] Alternatively, in response to the driving duration being less than the preset duration, a time difference value between the start time of each of the recommended driving routes and the time information is determined, and the recommended driving route corresponding to the smallest time difference value among all the time difference values is determined as the target recommended driving route.

[0204] For the convenience of description, the above device is described in various modules based on functions. Of course, in the implementation of the present application, the functions of each module can be implemented in one or more software and / or hardware.

[0205] The device of the above embodiment is used to implement the method of actively learning the driving route in any of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be described here.

[0206] Based on the same inventive concept, corresponding to the method of any of the above embodiments, the present application also provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the method of actively learning the driving route according to any of the above embodiments when executing the program.

[0207] Figure 4 A more specific hardware structure of an electronic device provided by the present embodiment is shown, which can 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 for communication within the device.

[0208] The processor 1010 can be implemented in the form of a general-purpose CPU (Central Processing Unit), a microprocessor, an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits, etc., for executing related programs to implement the technical solutions provided by the present embodiment.

[0209] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage devices, dynamic storage devices, etc. The memory 1020 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0210] The input / output interface 1030 is used to connect input / output modules to implement information input and output. The input / output modules can be configured as components within the device (not shown in the figure) or can be externally connected to the device to provide corresponding functions. Input devices may include a keyboard, mouse, touch screen, microphone, various sensors, etc., and output devices may include a display, speaker, vibrator, indicator light, etc.

[0211] The communication interface 1040 is used to connect to a communication module (not shown) to enable communication between the device and other devices. The communication module can communicate via a wired method (such as USB, network cable, etc.) or a wireless method (such as mobile network, WiFi, Bluetooth, etc.).

[0212] The bus 1050 comprises a path for transmitting 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 ).

[0213] 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 a specific implementation, the device may also include other components necessary for normal operation. In addition, it will be understood by those skilled in the art that the above device may only include the components necessary to implement the embodiments of this specification, and does not necessarily include all the components shown in the figure.

[0214] The electronic device of the above embodiment is used to implement the corresponding method of actively learning the driving route in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0215] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and 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 method of actively learning driving routes as described in any of the above embodiments.

[0216] The computer-readable media of this embodiment include permanent and non-permanent, removable and non-removable media that can be used to store information by any method or technology. The information can be computer-readable instructions, data structures, program modules 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 disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, tape 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.

[0217] The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute the method of actively learning the driving route as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0218] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides a computer program product, including computer program instructions. When the computer program instructions are run on a computer, the computer executes the method of actively learning driving routes as described in any of the above embodiments, which has the beneficial effects of the corresponding method embodiments and will not be repeated here.

[0219] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, the present application also provides a vehicle, which includes the device for actively learning driving routes, electronic equipment, and computer-readable storage device described in any of the above-mentioned embodiments, which have the beneficial effects of the corresponding method embodiments and will not be repeated here.

[0220] The vehicle has the beneficial effects of any of the above embodiments, which will not be described in detail here.

[0221] It is understandable that before using the technical solutions of each embodiment of the present disclosure, the type, scope of use, usage scenarios, etc. of the personal information involved will be informed to the user in an appropriate manner, and the user's authorization will be obtained.

[0222] For example, in response to a user's active request, a prompt message is sent to the user to clearly inform the user that the requested operation will require the acquisition and use of the user's personal information. This allows the user to independently choose whether to provide personal information to the electronic device, application, server, storage medium, or other software or hardware that performs the operation of the disclosed technical solution based on the prompt message.

[0223] As an optional but non-limiting implementation, in response to a 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. Furthermore, the pop-up window may also contain a selection control for the user to select "agree" or "disagree" to provide personal information to the electronic device.

[0224] It is understandable that the above notification and user authorization process are merely illustrative and do not constitute a limitation on the implementation of the present disclosure. Other methods that comply with relevant laws and regulations may also be applied to the implementation of the present disclosure.

[0225] 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 is limited to these examples. In line with 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.

[0226] In addition, for simplicity of description and discussion, and in order not to make the embodiment of the application difficult to understand, the known power supply / ground connection with integrated circuit (IC) chip and other components may or may not be shown in the accompanying drawings provided. In addition, the device can be shown in the form of a block diagram to avoid making the embodiment of the application difficult to understand, and this also takes into account the following fact, that is, the details of the embodiment of these block diagram devices are highly dependent on the platform to be implemented in the embodiment of the application (that is, these details should be fully within the scope of understanding of those skilled in the art). When specific details (for example, circuit) are set forth to describe exemplary embodiments of the application, it will be apparent to those skilled in the art that the embodiment of the 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.

[0227] Although the present invention has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those skilled in the art based on the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may utilize the embodiments discussed.

[0228] The embodiments of the present application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the present application. 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. A method for actively learning a driving route, characterized in that: include: Obtain the vehicle's driving route and memory navigation function status; In response to the state of the memory navigation function being enabled and the driving route meeting a preset active learning condition, sending an active learning driving route instruction; In response to receiving the learning success information, the driving route is stored as a learned route.

2. The method according to claim 1, characterized in that The driving route includes at least a target starting position and a target ending position; The method further comprises: Get the number of learned routes and learned routes that have been stored; In response to the travel route not being included in the learned routes, determining a historical travel count of the vehicle based on the target starting position and the target ending position; In response to the historical driving number being greater than or equal to a preset driving number and the number of routes being less than a preset number, it is determined that the driving route meets the preset active learning condition.

3. The method according to claim 2, characterized in that The determining of the vehicle's historical travel times based on the target starting position and the target ending position includes: Get the current time information; Determining a selected time period based on the time information and a preset scrolling period; Determining all historical driving routes of the vehicle within the selected time period as the selected driving routes; The historical travel times of the vehicle are determined based on the target starting position, the target ending position and the selected travel route.

4. The method according to claim 3, characterized in that The determining of the vehicle's historical travel times based on the target starting position, the target ending position, and the selected travel route includes: Obtaining the historical endpoint location of each selected travel route; Based on the target destination position, determining the selected driving routes having the same historical destination position as the target destination position as similar driving routes; Obtaining a historical starting position of each of the similar driving routes; In response to a distance between the historical starting position and the target starting position of a similar driving route being less than or equal to a preset distance, determining the similar driving route as a target driving route; The number of times the target driving route is traveled within the selected time period is determined as the historical number of travels.

5. The method according to claim 1, wherein The driving route includes at least a target starting position and a target ending position; The sending of the active learning driving route instruction includes: Sending the target starting position and the target ending position to the intelligent driving domain controller; In response to receiving the learning consent information sent by the intelligent driving domain controller, an active learning driving route instruction is sent to the intelligent driving domain controller.

6. The method according to claim 1, characterized in that The method further comprises: Get the stored learned routes; In response to the learned routes including the driving route, determining a recommended driving route and a recommended route number of the recommended driving routes from the learned routes based on the driving route; determining a target recommended driving route based on the recommended driving route and the number of recommended routes; The target recommended driving route is visually displayed or reminded.

7. The method according to claim 6, characterized in that The determining of a target recommended driving route based on the recommended driving route and the number of recommended routes includes: In response to the number of the recommended routes being greater than or equal to two, acquiring image information inside the vehicle; In response to the image information including the target person image, obtaining road information of each of the recommended driving routes, and determining a target recommended driving route based on the road information; Alternatively, in response to the fact that the image information does not contain the image of the target person, the driving duration of the driving route, the time information at the current moment, the starting time and / or road information of each recommended driving route are obtained, and the target recommended driving route is determined based on the driving duration, the time information, the starting time and / or the road information.

8. The method according to claim 7, characterized in that The determining of the target recommended driving route based on the driving duration, the time information, the starting time and / or the road information includes: In response to the driving duration being greater than or equal to a preset duration, obtaining a predicted energy consumption value for each of the recommended driving routes, and determining a target recommended driving route based on the predicted energy consumption value and the road information; Alternatively, in response to the driving duration being less than a preset duration, the time difference between the start time of each recommended driving route and the time information is determined, and the recommended driving route corresponding to the smallest time difference among all the time differences is determined as the target recommended driving route.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 8 is implemented.

10. A vehicle, characterized in that: The electronic device comprising claim 9.

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