Automatic driving method and device and carrier

Abstract

The invention discloses an automatic driving method and device and a carrier, and relates to the technical field of carrier driving. The method comprises the steps of obtaining speed information and type information of each obstacle carrier in a lane where the automatic driving carrier is located and lanes on the two sides of the automatic driving carrier in response to an obstacle carrier of which the driving speed is smaller than a first speed of the automatic driving carrier in front of the automatic driving carrier; according to the speed information and the type information of each obstacle carrier of the lane where the automatic driving carrier is located and the lanes on the two sides of the automatic driving carrier, the road fluency degrees of the lane where the automatic driving carrier is located and the lanes on the two sides of the automatic driving carrier are calculated respectively; and determining a target lane according to the road smoothness of the lane where the automatic driving carrier is located and the lanes on the two sides of the automatic driving carrier, and changing the lane. According to the embodiment, the technical problem that it cannot be guaranteed that the driving efficiency of the carrier can be improved can be solved.

Description

Technical Field

[0001] The present invention relates to the field of vehicle driving technology, and in particular to an automatic driving method, device and vehicle. Background Art

[0002] Autonomous driving technology is a comprehensive system that integrates perception, navigation, decision-making, planning, and artificial intelligence. Decision-making is the key to determining the intelligence of the entire system. The decision-making module is equivalent to the brain of the autonomous driving system. It collects dynamic and static environmental information around it and makes the best action selection based on the given task to ensure that the entire autonomous driving process is efficient, safe, and comfortable.

[0003] At present, when making lane-changing decisions, autonomous vehicles mainly use simple logical judgments to decide whether to change lanes. When making lane-changing decisions, they mainly consider two aspects: (1) whether a lane change is necessary; (2) which lane to change to. When judging whether to change lanes, they only consider whether there are obstacle vehicles in front of them that are traveling at a lower speed than the vehicle. When choosing a lane, they only consider whether there are obstacle vehicles in the left and right lanes. They do not conduct a deeper understanding and analysis of the surrounding environment. The resulting lane-changing decisions often cannot guarantee that the vehicle's driving efficiency can be improved, and may even cause the autonomous vehicle to fall into a less efficient driving environment. Summary of the invention

[0004] In view of this, an embodiment of the present invention provides an automatic driving method, device and vehicle to solve the technical problem that it is impossible to ensure that the driving efficiency of the vehicle can be improved.

[0005] To achieve the above objective, according to one aspect of an embodiment of the present invention, an automatic driving method is provided, including:

[0006] In response to the presence of an obstacle vehicle in front of the autonomous driving vehicle whose travel speed is less than a first speed of the autonomous driving vehicle, obtaining speed information and type information of each obstacle vehicle in a lane where the autonomous driving vehicle is located and lanes on both sides of the lane;

[0007] Calculate the road smoothness of the lane where the autonomous driving vehicle is located and the lanes on both sides of the lane according to the speed information and type information of each obstacle vehicle in the lane where the autonomous driving vehicle is located and the lanes on both sides of the lane;

[0008] The target lane is determined and lane change is performed based on the road smoothness of the lane where the autonomous driving vehicle is located and the lanes on both sides of the lane.

[0009] Optionally, determining a target lane and changing lanes according to the road smoothness of the lane where the autonomous driving vehicle is located and the lanes on both sides thereof includes:

[0010] In response to the road smoothness of the lanes on both sides of the autonomous driving vehicle being less than the sum of the road smoothness of the lane where the autonomous driving vehicle is located and the lane score difference value, continuing to drive in the current lane;

[0011] In response to the road smoothness of the lane on one side of the autonomous driving vehicle being less than the sum of the road smoothness of the lane where the autonomous driving vehicle is located and the lane score difference value, and the road smoothness of the lane on the other side of the autonomous driving vehicle being greater than or equal to the sum of the road smoothness of the lane where the autonomous driving vehicle is located and the lane score difference value, the lane on the other side is used as the target lane and lane change is performed;

[0012] In response to the road smoothness of the lanes on both sides of the autonomous driving vehicle being greater than or equal to the sum of the road smoothness of the lane where the autonomous driving vehicle is located and the lane score difference value, the target lane is determined and the lane change is performed according to the road smoothness of the lanes on both sides of the autonomous driving vehicle and the traffic rules penalty score value.

[0013] Optionally, determining a target lane and changing lanes according to the road smoothness of lanes on both sides of the autonomous driving vehicle and a traffic rule penalty score value includes:

[0014] In response to the road smoothness of the left lane of the autonomous driving vehicle being greater than or equal to the difference between the right lane of the autonomous driving vehicle and the traffic rule penalty score value, taking the left lane as the target lane and performing a lane change;

[0015] In response to the road smoothness of the left lane of the autonomous driving vehicle being less than the difference between the right lane of the autonomous driving vehicle and the traffic rule penalty score value, the right lane is used as the target lane and a lane change is performed.

[0016] Optionally, the road smoothness of the lane is determined based on speed information of the obstacle vehicles on the lane, a type correction coefficient, and the total number of obstacle vehicles on the lane.

[0017] Optionally, in response to the presence of an obstacle vehicle in front of the autonomous driving vehicle whose driving speed is less than a first speed of the autonomous driving vehicle, obtaining speed information and type information of each obstacle vehicle in a lane where the autonomous driving vehicle is located and lanes on both sides thereof includes:

[0018] In response to the presence of an obstacle vehicle in front of the autonomous driving vehicle whose travel speed is less than a first speed of the autonomous driving vehicle, starting to store image data of each obstacle vehicle in the lane where the autonomous driving vehicle is located and in lanes on both sides of the lane;

[0019] In response to the total amount of the stored image data of each obstacle vehicle reaching a valid data amount threshold, speed information and type information of each obstacle vehicle are parsed from the image data of each obstacle vehicle.

[0020] Optionally, in response to the total amount of the stored image data of each obstacle vehicle reaching a valid data amount threshold, obtaining the speed information and type information of each obstacle vehicle includes:

[0021] In response to the total amount of the stored image data of each obstacle vehicle reaching a valid data amount threshold, calculating the road smoothness of the lane where the autonomous driving vehicle is located;

[0022] In response to the road smoothness of the lane where the autonomous driving vehicle is located being less than a road smoothness threshold, speed information and type information of each obstacle vehicle is parsed from the image data of each obstacle vehicle.

[0023] Optionally, the road smoothness threshold is calculated using the following method:

[0024] Multiply the maximum speed of the autonomous driving vehicle by the pressure speed tolerance coefficient.

[0025] In addition, according to another aspect of an embodiment of the present invention, there is provided an automatic driving device, comprising:

[0026] an acquisition module, configured to acquire speed information and type information of each obstacle vehicle in a lane where the autonomous driving vehicle is located and lanes on both sides thereof in response to the presence of an obstacle vehicle in front of the autonomous driving vehicle whose driving speed is less than a first speed of the autonomous driving vehicle;

[0027] A calculation module, used to calculate the road smoothness of the lane where the autonomous driving vehicle is located and the lanes on both sides of the lane according to the speed information and type information of each obstacle vehicle in the lane where the autonomous driving vehicle is located and the lanes on both sides of the lane;

[0028] The decision module is used to determine the target lane and change lanes according to the road smoothness of the lane where the autonomous driving vehicle is located and the lanes on both sides of the lane.

[0029] Optionally, the decision module is further used to:

[0030] In response to the road smoothness of the lanes on both sides of the autonomous driving vehicle being less than the sum of the road smoothness of the lane where the autonomous driving vehicle is located and the lane score difference value, continuing to drive in the current lane;

[0031] In response to the road smoothness of the lane on one side of the autonomous driving vehicle being less than the sum of the road smoothness of the lane where the autonomous driving vehicle is located and the lane score difference value, and the road smoothness of the lane on the other side of the autonomous driving vehicle being greater than or equal to the sum of the road smoothness of the lane where the autonomous driving vehicle is located and the lane score difference value, the lane on the other side is used as the target lane and lane change is performed;

[0032] In response to the road smoothness of the lanes on both sides of the autonomous driving vehicle being greater than or equal to the sum of the road smoothness of the lane where the autonomous driving vehicle is located and the lane score difference value, the target lane is determined and the lane change is performed according to the road smoothness of the lanes on both sides of the autonomous driving vehicle and the traffic rules penalty score value.

[0033] Optionally, the decision module is further used to:

[0034] In response to the road smoothness of the left lane of the autonomous driving vehicle being greater than or equal to the difference between the right lane of the autonomous driving vehicle and the traffic rule penalty score value, taking the left lane as the target lane and performing a lane change;

[0035] In response to the road smoothness of the left lane of the autonomous driving vehicle being less than the difference between the right lane of the autonomous driving vehicle and the traffic rule penalty score value, the right lane is used as the target lane and a lane change is performed.

[0036] Optionally, the road smoothness of the lane is determined based on speed information of the obstacle vehicles on the lane, a type correction coefficient, and the total number of obstacle vehicles on the lane.

[0037] Optionally, the acquisition module is further used for:

[0038] In response to the presence of an obstacle vehicle in front of the autonomous driving vehicle whose travel speed is less than a first speed of the autonomous driving vehicle, starting to store image data of each obstacle vehicle in the lane where the autonomous driving vehicle is located and in lanes on both sides of the lane;

[0039] In response to the total amount of the stored image data of each obstacle vehicle reaching a valid data amount threshold, speed information and type information of each obstacle vehicle are parsed from the image data of each obstacle vehicle.

[0040] Optionally, the acquisition module is further used for:

[0041] In response to the total amount of the stored image data of each obstacle vehicle reaching a valid data amount threshold, calculating the road smoothness of the lane where the autonomous driving vehicle is located;

[0042] In response to the road smoothness of the lane where the autonomous driving vehicle is located being less than a road smoothness threshold, speed information and type information of each obstacle vehicle is parsed from the image data of each obstacle vehicle.

[0043] Optionally, the road smoothness threshold is calculated using the following method:

[0044] Multiply the maximum speed of the autonomous driving vehicle by the pressure speed tolerance coefficient.

[0045] According to another aspect of an embodiment of the present invention, there is further provided an electronic device, including:

[0046] one or more processors;

[0047] a storage device for storing one or more programs,

[0048] When the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any one of the above embodiments.

[0049] According to another aspect of an embodiment of the present invention, a computer-readable medium is provided, on which a computer program is stored. When the program is executed by a processor, the method described in any one of the above embodiments is implemented.

[0050] According to another aspect of an embodiment of the present invention, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the method described in any one of the above embodiments is implemented.

[0051] One embodiment of the above invention has the following advantages or beneficial effects: because the road smoothness of the lane where the autonomous driving vehicle is located and the lanes on both sides of it is calculated according to the speed information and type information of each obstacle vehicle in the lane where the autonomous driving vehicle is located and the lanes on both sides of it, and the target lane is determined and lane change is performed according to the road smoothness of the lane where the autonomous driving vehicle is located and the lanes on both sides of it, the technical problem that the driving efficiency of the vehicle cannot be guaranteed to be improved in the prior art is overcome. In the embodiment of the present invention, when the speed of the vehicle ahead is low during the normal driving of the autonomous driving vehicle, the road smoothness of the left and right lanes is calculated by sensing the driving conditions of the vehicles in the left and right lanes, and the optimal lane is selected as the target lane for lane change after considering the traffic rules, so as to ensure that the driving efficiency of the autonomous driving vehicle can be improved.

[0052] The further effects of the above-mentioned non-conventional optional manner will be described below in conjunction with the specific implementation manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0054] Figure 1 is a flowchart of an automatic driving method according to an embodiment of the present invention;

[0055] Figure 2 is a schematic diagram of an automatic driving vehicle encountering a low-speed vehicle ahead during driving according to an embodiment of the present invention;

[0056] Figure 3 is a flowchart of an automatic driving method according to a reference embodiment of the present invention;

[0057] Figure 4 is a schematic diagram of an automatic driving device according to an embodiment of the present invention;

[0058] Figure 5 is an exemplary vehicle system architecture diagram to which embodiments of the present invention may be applied;

[0059] Figure 6 It is a schematic diagram of the structure of a computer system of a terminal device or a server suitable for implementing an embodiment of the present invention. DETAILED DESCRIPTION

[0060] The following is a description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0061] It should be noted that the collection, collection, updating, analysis, processing, use, transmission, storage and other aspects of user personal information involved in the technical solution of this disclosure are in compliance with the provisions of relevant laws and regulations, are used for legitimate purposes, and do not violate public order and good morals. Necessary measures are taken for user personal information to prevent illegal access to user personal information data and maintain the security of user personal information, network security and national security.

[0062] Figure 1 is a flow chart of an automatic driving method according to an embodiment of the present invention. As an embodiment of the present invention, Figure 1 As shown, the automatic driving method may include:

[0063] Step 101, in response to the presence of an obstacle vehicle in front of the autonomous driving vehicle whose travel speed is less than a first speed of the autonomous driving vehicle, speed information and type information of each obstacle vehicle in the lane where the autonomous driving vehicle is located and in the lanes on both sides of the lane are obtained.

[0064] In an embodiment of the present invention, the first speed of the autonomous driving vehicle may be the current speed of the autonomous driving vehicle or the maximum speed of the autonomous driving vehicle. When the autonomous driving vehicle is driving, under the combined effect of various traffic rules and algorithms, there will be a final speed limit value. , indicating that the vehicle's speed cannot exceed (That is, the maximum speed of the autonomous vehicle). Usually, the minimum value is taken based on the road traffic limit and the maximum speed set by the user. For example, the traffic limit of the lane where the autonomous driving vehicle is currently located is 80km / h, and the user expects the maximum speed of the autonomous driving vehicle to be 90km / h, then the current maximum speed of the autonomous driving vehicle is 80km / h. If there are low-speed obstacle vehicles, the speed information and type information of each obstacle vehicle in the lane where the autonomous driving vehicle is located and the lanes on both sides of the lane are obtained.

[0065] Optionally, step 101 may include: in response to the presence of an obstacle vehicle in front of the autonomous driving vehicle whose driving speed is less than the first speed of the autonomous driving vehicle, starting to store image data of each obstacle vehicle in the lane where the autonomous driving vehicle is located and the lanes on both sides thereof; in response to the total amount of stored image data of each obstacle vehicle reaching a valid data amount threshold, parsing the speed information and type information of each obstacle vehicle from the image data of each obstacle vehicle. Figure 2 As shown, if there is a vehicle in front of the autonomous driving vehicle (ego) with a speed less than Low-speed obstacle vehicles (such as Figure 2 The image data of each obstacle vehicle (obs) in the lane where the autonomous driving vehicle is located and the lanes on both sides are stored, and it is determined in real time whether the total amount of the stored image data of each obstacle vehicle reaches the preset valid data amount threshold (valid data means that the sensor does not report an error during the process of collecting image data). If the total amount of the stored image data of each obstacle vehicle reaches the valid data amount threshold, the speed information and type information of each obstacle vehicle are parsed from the image data of each obstacle vehicle to ensure the accuracy of the lane change decision result.

[0066] For example, if the frequency at which the sensor collects obstacles around the autonomous driving vehicle is 10 Hz, the effective data threshold can be set to 10 images. If the sensor can collect 10 images of data around the autonomous driving vehicle per second, the speed and type information of each obstacle vehicle can be parsed from the collected image data.

[0067] It should be noted that each obstacle vehicle has speed information of the obstacle vehicle. In the embodiment of the present invention, it is assumed that each obstacle vehicle travels at a constant speed.

[0068] Optionally, in response to the number of speed information of each obstacle vehicle stored reaching a valid data volume threshold, the speed information and type information of each obstacle vehicle are obtained, including: in response to the total number of image data of each obstacle vehicle stored reaching a valid data volume threshold, calculating the road smoothness of the lane where the autonomous driving vehicle is located; in response to the road smoothness of the lane where the autonomous driving vehicle is located being less than the road smoothness threshold, parsing the speed information and type information of each obstacle vehicle from the image data of each obstacle vehicle. If the total number of image data of each obstacle vehicle stored reaches the valid data volume threshold, the road smoothness of the lane where the autonomous driving vehicle is located is calculated using the following formula:

[0069]

[0070] in, is the road smoothness of the lane where the autonomous driving vehicle is located. The speed correction factor of the obstacle vehicle. Different types of obstacle vehicles (buses, trucks, cars, etc.) will have different speed correction factors set to avoid following certain types of obstacle vehicles. is the speed of the obstacle vehicle following the autonomous driving vehicle, The total number of obstacle vehicles in the lane where the autonomous driving vehicle is located.

[0071] Then determine whether the road smoothness of the lane where the autonomous driving vehicle is located is less than a road smoothness threshold. If so, parse the speed information and type information of each obstacle vehicle from the image data of each obstacle vehicle.

[0072] Optionally, the road smoothness threshold is calculated by multiplying the maximum speed of the autonomous driving vehicle by the speed tolerance coefficient. , the speed information and type information of each obstacle vehicle are parsed from the image data of each obstacle vehicle. is the maximum speed of the autonomous vehicle, It is the pressure-speed tolerance coefficient and can be set in advance.

[0073] Step 102, based on the speed information and type information of each obstacle vehicle in the lane where the autonomous driving vehicle is located and the lanes on both sides thereof, the road smoothness of the lane where the autonomous driving vehicle is located and the lanes on both sides thereof are calculated respectively. The road smoothness of the lane is determined based on the speed information, type correction coefficient of the obstacle vehicle on the lane and the total number of obstacle vehicles in the lane.

[0074] Optionally, the method further includes: for each lane, multiplying and summing the speed information and type correction coefficient of the obstacle vehicle on the lane, and then dividing by the total number of obstacle vehicles in the lane, so as to calculate the road smoothness of the lane. For example, the road smoothness of the left lane and the right lane of the autonomous driving vehicle can be calculated using the following formula:

[0075]

[0076]

[0077] in, The road smoothness of the left lane of the autonomous driving vehicle. The road smoothness of the right lane of the autonomous driving vehicle. is the speed correction factor of the obstacle vehicle, is the speed of the obstacle vehicle in the left lane of the autonomous driving vehicle, is the speed of the obstacle vehicle in the right lane of the autonomous driving vehicle, is the total number of obstacle vehicles in the left lane of the autonomous driving vehicle, The total number of obstacle vehicles in the right lane of the autonomous driving vehicle.

[0078] It should be noted that if the road smoothness of the lane where the autonomous driving vehicle is located has been calculated in step 101, then in step 102, only the road smoothness of the left lane and the right lane of the autonomous driving vehicle needs to be calculated.

[0079] Step 103, determining the target lane and changing lanes based on the road smoothness of the lane where the autonomous driving vehicle is located and the lanes on both sides of the lane.

[0080] After calculating the road smoothness of the lane where the autonomous driving vehicle is located and the lanes on both sides thereof, determine whether to change lanes according to the road smoothness of the lane where the autonomous driving vehicle is located and the lanes on both sides thereof. Optionally, step 103 may include: in response to the road smoothness of both lanes on both sides of the autonomous driving vehicle being less than the sum of the road smoothness of the lane where the autonomous driving vehicle is located and the lane score difference value, continue driving in the current lane; in response to the road smoothness of one lane of the autonomous driving vehicle being less than the sum of the road smoothness of the lane where the autonomous driving vehicle is located and the lane score difference value, and the road smoothness of the other lane of the autonomous driving vehicle being greater than or equal to the sum of the road smoothness of the lane where the autonomous driving vehicle is located and the lane score difference value, take the other lane as the target lane and change lanes; in response to the road smoothness of both lanes of the autonomous driving vehicle being greater than or equal to the sum of the road smoothness of the lane where the autonomous driving vehicle is located and the lane score difference value, determine the target lane according to the road smoothness of both lanes of the autonomous driving vehicle and the traffic rule penalty score value and change lanes.

[0081] If the road smoothness of the lanes on both sides of the autonomous driving vehicle is less than the sum of the road smoothness of the lane where the autonomous driving vehicle is located and the lane score difference value, that is, , which means that neither the left or right lane is a good choice, so continue driving in the current lane.

[0082] If the road smoothness of the left lane of the autonomous driving vehicle is greater than or equal to the sum of the road smoothness of the lane where the autonomous driving vehicle is located and the lane score difference value, and the road smoothness of the right lane of the autonomous driving vehicle is less than the sum of the road smoothness of the lane where the autonomous driving vehicle is located and the lane score difference value, that is , indicating that the left lane is a good choice, so the left lane is selected as the target lane and lane change is performed.

[0083] If the road smoothness of the right lane of the autonomous driving vehicle is greater than or equal to the sum of the road smoothness of the lane where the autonomous driving vehicle is located and the lane score difference value, and the road smoothness of the left lane of the autonomous driving vehicle is less than the sum of the road smoothness of the lane where the autonomous driving vehicle is located and the lane score difference value, that is, , indicating that the right lane is a good choice, so the right lane is selected as the target lane and lane change is performed.

[0084] If the road smoothness of the lanes on both sides of the autonomous driving vehicle is greater than or equal to the sum of the road smoothness of the lane where the autonomous driving vehicle is located and the lane score difference value, that is, , the left and right lanes are further compared based on the road smoothness and traffic rule penalty scores of the lanes on both sides of the autonomous driving vehicle, so as to determine the target lane and change lanes.

[0085] It should be noted that the road smoothness of the left lane or the right lane must exceed the sum of the road smoothness of the lane where the autonomous driving vehicle is located and the lane score difference value before it will be used as an alternative lane. This is to prevent the target lane from having limited improvement compared to the lane where the autonomous driving vehicle is located or causing frequent lane changes.

[0086] Optionally, determining the target lane and changing lanes based on the road smoothness of the lanes on both sides of the autonomous driving vehicle and the traffic rule penalty score value includes: in response to the road smoothness of the left lane of the autonomous driving vehicle being greater than or equal to the difference between the right lane of the autonomous driving vehicle and the traffic rule penalty score value, taking the left lane as the target lane and changing lanes; in response to the road smoothness of the left lane of the autonomous driving vehicle being less than the difference between the right lane of the autonomous driving vehicle and the traffic rule penalty score value, taking the right lane as the target lane and changing lanes. If the road smoothness of the left lane of the autonomous driving vehicle is greater than or equal to the difference between the right lane of the autonomous driving vehicle and the traffic rule penalty score value, that is , the left lane is taken as the target lane and lane change is performed; if the road smoothness of the left lane of the autonomous driving vehicle is less than the difference between the right lane of the autonomous driving vehicle and the traffic rule penalty score value, that is , the right lane is used as the target lane and lane change is performed.

[0087] It should be noted that when selecting a lane change target, the left lane has a higher priority than the right lane. Therefore, the advantages and disadvantages of the left and right lanes are compared through the traffic rule penalty score, so that the left lane has a higher priority than the right lane.

[0088] According to the various embodiments described above, it can be seen that the embodiments of the present invention respectively calculate the road smoothness of the lane where the autonomous driving vehicle is located and the lanes on both sides of the lane according to the speed information and type information of each obstacle vehicle in the lane where the autonomous driving vehicle is located and the lanes on both sides of the lane, and determine the target lane and change lanes according to the road smoothness of the lane where the autonomous driving vehicle is located and the lanes on both sides of the lane, thereby solving the technical problem that the driving efficiency of the vehicle cannot be improved in the prior art. In the embodiment of the present invention, when the speed of the vehicle ahead is low during the normal driving of the autonomous driving vehicle, which makes the driving efficiency of the vehicle low, the road smoothness of the left and right lanes is calculated by sensing the driving conditions of the vehicles in the left and right lanes, and the optimal lane is selected as the target lane for lane change after considering the traffic rules, so as to ensure that the driving efficiency of the autonomous driving vehicle can be improved.

[0089] Figure 3 is a flow chart of an automatic driving method according to a reference embodiment of the present invention. As another embodiment of the present invention, Figure 3 As shown, the automatic driving method may include:

[0090] Step 301, determine whether there is an obstacle vehicle in front of the autonomous driving vehicle whose driving speed is less than the first speed of the autonomous driving vehicle; if so, execute step 302; if not, end.

[0091] Step 302, start storing image data of each obstacle vehicle in the lane where the autonomous driving vehicle is located and in the lanes on both sides of the lane.

[0092] First, determine whether there is an obstacle vehicle in front of the autonomous driving vehicle whose driving speed is lower than the first speed of the vehicle. If so, start storing image data of each obstacle vehicle in the lane where the autonomous driving vehicle is located and in the lanes on both sides of the lane.

[0093] Step 303, determine whether the autonomous driving vehicle is currently changing lanes; if not, execute step 304; if yes, end.

[0094] Step 304, determining whether the total amount of the stored image data of each obstacle vehicle reaches a valid data amount threshold; if so, executing step 305; if not, ending.

[0095] In the process of storing the image data of each obstacle vehicle in the lane where the autonomous driving vehicle is located and the lanes on both sides of the lane, it is also determined whether the autonomous driving vehicle is currently changing lanes. If so, the process ends; if not, it continues to determine whether the total amount of image data of each obstacle vehicle stored reaches the valid data volume threshold.

[0096] Step 305: parse the image data of each obstacle vehicle to obtain the speed information and type information of each obstacle vehicle.

[0097] If the total amount of stored image data of each obstacle vehicle reaches a valid data amount threshold, speed information and type information of each obstacle vehicle are parsed from the image data of each obstacle vehicle to ensure the accuracy of the lane change decision result.

[0098] Step 306, calculate the road smoothness of the lane where the autonomous driving vehicle is located, and determine whether the road smoothness of the lane where the autonomous driving vehicle is located is less than a road smoothness threshold; if so, execute step 307; if not, end.

[0099] Step 307, based on the speed information and type information of each obstacle vehicle in the lane where the autonomous driving vehicle is located and the lanes on both sides of the autonomous driving vehicle, the road smoothness of the lanes on both sides of the autonomous driving vehicle is calculated respectively.

[0100] Step 308: If the road smoothness of the right lane of the autonomous driving vehicle is less than the sum of the road smoothness of the lane where the autonomous driving vehicle is located and the lane score difference value, and the road smoothness of the left lane of the autonomous driving vehicle is greater than or equal to the sum of the road smoothness of the lane where the autonomous driving vehicle is located and the lane score difference value, then execute step 313.

[0101] In step 309, if the road smoothness of the lanes on both sides of the autonomous driving vehicle is greater than or equal to the sum of the road smoothness of the lane where the autonomous driving vehicle is located and the lane score difference value, then execute step 312.

[0102] Step 310: If the road smoothness of the left lane of the autonomous driving vehicle is less than the sum of the road smoothness of the lane where the autonomous driving vehicle is located and the lane score difference value, and the road smoothness of the right lane of the autonomous driving vehicle is greater than or equal to the sum of the road smoothness of the lane where the autonomous driving vehicle is located and the lane score difference value, then execute step 314.

[0103] Step 311, if the road smoothness of the lanes on both sides of the autonomous driving vehicle is less than the sum of the road smoothness of the lane where the autonomous driving vehicle is located and the lane score difference value, then end.

[0104] Step 312, determine whether the road smoothness of the left lane of the autonomous driving vehicle is greater than or equal to the difference between the right lane of the autonomous driving vehicle and the traffic rule penalty score value; if so, execute step 313; if not, execute step 314.

[0105] Step 313: Use the left lane as the target lane and perform lane change.

[0106] Step 314: Use the right lane as the target lane and perform lane change.

[0107] In addition, the specific implementation content of the autonomous driving method in a reference embodiment of the present invention has been described in detail in the autonomous driving method described above, so the repeated content will not be described here.

[0108] Figure 4 Schematic diagram of an automatic driving device according to an embodiment of the present invention. Figure 4As shown, the automatic driving device 400 includes an acquisition module 401, a calculation module 402 and a decision module 403; wherein the acquisition module 401 is used to obtain the speed information and type information of each obstacle vehicle in the lane where the automatic driving vehicle is located and the lanes on both sides thereof in response to the presence of an obstacle vehicle in front of the automatic driving vehicle whose driving speed is less than the first speed of the automatic driving vehicle; the calculation module 402 is used to calculate the road smoothness of the lane where the automatic driving vehicle is located and the lanes on both sides thereof according to the speed information and type information of each obstacle vehicle in the lane where the automatic driving vehicle is located and the lanes on both sides thereof;

[0109] The decision module is used to determine the target lane and change lanes according to the road smoothness of the lane where the autonomous driving vehicle is located and the lanes on both sides of the lane.

[0110] Optionally, the decision module is further used to:

[0111] In response to the road smoothness of the lanes on both sides of the autonomous driving vehicle being less than the sum of the road smoothness of the lane where the autonomous driving vehicle is located and the lane score difference value, continuing to drive in the current lane;

[0112] In response to the road smoothness of the lane on one side of the autonomous driving vehicle being less than the sum of the road smoothness of the lane where the autonomous driving vehicle is located and the lane score difference value, and the road smoothness of the lane on the other side of the autonomous driving vehicle being greater than or equal to the sum of the road smoothness of the lane where the autonomous driving vehicle is located and the lane score difference value, the lane on the other side is used as the target lane and lane change is performed;

[0113] In response to the road smoothness of the lanes on both sides of the autonomous driving vehicle being greater than or equal to the sum of the road smoothness of the lane where the autonomous driving vehicle is located and the lane score difference value, the target lane is determined and the lane change is performed according to the road smoothness of the lanes on both sides of the autonomous driving vehicle and the traffic rules penalty score value.

[0114] Optionally, the decision module is further used to:

[0115] In response to the road smoothness of the left lane of the autonomous driving vehicle being greater than or equal to the difference between the right lane of the autonomous driving vehicle and the traffic rule penalty score value, taking the left lane as the target lane and performing a lane change;

[0116] In response to the road smoothness of the left lane of the autonomous driving vehicle being less than the difference between the right lane of the autonomous driving vehicle and the traffic rule penalty score value, the right lane is used as the target lane and a lane change is performed.

[0117] Optionally, the road smoothness of the lane is determined based on speed information of the obstacle vehicles on the lane, a type correction coefficient, and the total number of obstacle vehicles on the lane.

[0118] Optionally, the acquisition module is further used for:

[0119] In response to the presence of an obstacle vehicle in front of the autonomous driving vehicle whose travel speed is less than a first speed of the autonomous driving vehicle, starting to store image data of each obstacle vehicle in the lane where the autonomous driving vehicle is located and in lanes on both sides of the lane;

[0120] In response to the total amount of the stored image data of each obstacle vehicle reaching a valid data amount threshold, speed information and type information of each obstacle vehicle are parsed from the image data of each obstacle vehicle.

[0121] Optionally, the acquisition module is further used for:

[0122] In response to the total amount of the stored image data of each obstacle vehicle reaching a valid data amount threshold, calculating the road smoothness of the lane where the autonomous driving vehicle is located;

[0123] In response to the road smoothness of the lane where the autonomous driving vehicle is located being less than a road smoothness threshold, speed information and type information of each obstacle vehicle is parsed from the image data of each obstacle vehicle.

[0124] Optionally, the road smoothness threshold is calculated using the following method:

[0125] Multiply the maximum speed of the autonomous driving vehicle by the pressure speed tolerance coefficient.

[0126] It should be noted that the specific implementation content of the automatic driving device described in the present invention has been described in detail in the automatic driving method described above, so the repeated content will not be described here.

[0127] Figure 5 An exemplary vehicle system architecture 500 is shown to which a vehicle driving method or a vehicle driving device according to an embodiment of the present invention may be applied.

[0128] like Figure 5 As shown, the vehicle system architecture 500 may include various systems, such as an autonomous driving system 501, a power system 502, a sensor system 503, a control system 504, one or more peripheral devices 505, a power supply 506, a computer system 507, and a user interface 508. Optionally, the vehicle system architecture 500 may include more or fewer systems, and each system may include multiple elements. In addition, each system and element of the vehicle system architecture 500 may be interconnected by wire or wirelessly.

[0129] Among them, the vehicle system architecture 500 includes an automatic driving system 501, and the automatic driving system 501 can be in a full or partial automatic driving mode. For example, the automatic driving system 501 can automatically control the vehicle to travel without interacting with a person; the automatic driving system 501 can also control the automatic driving of the vehicle in the automatic driving mode, and can also adjust the automatic driving behavior of the automatic driving system 501 by interacting with a person. Specifically, the automatic driving system 501 can respond to the presence of an obstacle vehicle with a driving speed less than the first speed of the automatic driving vehicle in front of the automatic driving vehicle, obtain the speed information and type information of each obstacle vehicle in the lane where the automatic driving vehicle is located and the lanes on both sides of it; calculate the road smoothness of the lane where the automatic driving vehicle is located and the lanes on both sides of it according to the speed information and type information of each obstacle vehicle in the lane where the automatic driving vehicle is located and the lanes on both sides of it; determine the target lane and change lanes according to the road smoothness of the lane where the automatic driving vehicle is located and the lanes on both sides of it.

[0130] The power system 502 may include components that provide power movement for the vehicle. For example, the power system 502 may include an engine, an energy source, a transmission, wheels, tires, etc. Among them, the engine may be an internal combustion engine, an electric motor, an air compression engine, or other types of engine combinations, such as a hybrid engine consisting of a gasoline engine and an electric motor, and a hybrid engine consisting of an internal combustion engine and an air compression engine. The engine converts the energy source into mechanical energy and provides it to the transmission. Examples of energy sources may include gasoline, diesel, other petroleum-based fuels, propane, other compressed gas-based fuels, ethanol, solar panels, batteries, and other sources of electricity. The energy source may also provide energy for other systems of the vehicle. In addition, the transmission may include a gearbox, a differential, a drive shaft, a clutch, etc.

[0131] The sensor system 503 may include sensors for sensing the surrounding environment of the vehicle. For example, a positioning system (the positioning system may be a global positioning system (GPS) system, or a Beidou system or other positioning systems), a radar, a laser rangefinder, an inertial measurement unit (IMU), and a camera. The positioning system may be used to locate the geographic location of the vehicle. The IMU is used to sense the position and orientation changes of the vehicle based on inertial acceleration. In one embodiment, the IMU may be a combination of an accelerometer and a gyroscope. The radar may utilize radio signals to sense objects in the surrounding environment of the vehicle. In some embodiments, in addition to sensing objects, the radar may also be used to sense the speed and / or direction of travel of the object, etc.

[0132] Among them, in order to detect environmental information, objects, etc. located in front, rear or side of the vehicle, radars, cameras, etc. can be configured at appropriate locations outside the vehicle. For example, in order to obtain an image in front of the vehicle, the camera can be configured in the interior of the vehicle close to the front windshield. Alternatively, the camera can be configured around the front bumper or radiator grille. For example, in order to obtain an image behind the vehicle, the camera can be configured in the interior of the vehicle close to the rear window glass. Alternatively, the camera can be configured around the rear bumper, trunk or tailgate. In order to obtain an image on the side of the vehicle, the camera can be configured in the interior of the vehicle close to at least one of the side windows. Alternatively, the camera can be configured around the side mirrors, fenders or doors, etc.

[0133] Laser rangefinders use lasers to sense objects in the vehicle's environment.

[0134] The camera can be used to capture multiple images of the vehicle's surroundings. The camera can be a still or video camera.

[0135] The control system 504 includes a software system for implementing autonomous driving, such as a system for route planning, an obstacle avoidance system, a visual system for image analysis, etc. The control system 504 may also include hardware systems such as a throttle and a steering wheel system. In addition, the control system 504 may include additional or alternative components other than those shown and described. Alternatively, some of the components shown above may be reduced.

[0136] The control system 504 interacts with external sensors, other autonomous driving devices, other computer systems, or users through the peripheral devices 505. The peripheral devices 505 may include a wireless communication system, an onboard computer, a microphone, and / or a speaker.

[0137] In some embodiments, peripheral devices 505 provide a means for a user of control system 504 to interact with user interface 508. For example, an onboard computer can provide information to a user of the vehicle. The user interface can also operate the onboard computer to receive input from the user. The onboard computer can be operated via a touch screen. In other cases, peripheral devices can provide a means for communicating with other devices located in the vehicle. For example, a microphone can receive audio (e.g., voice commands or other audio input) from a user of control system 504. Similarly, a speaker can output audio to a user of control system 504.

[0138] The wireless communication system can communicate with one or more devices wirelessly directly or via a communication network. For example, the wireless communication system can communicate with a network such as a cellular network, WiFi, and a wireless local area network (WLAN), or can communicate directly with a device using an infrared link, Bluetooth, or ZigBee. Other wireless protocols, such as various autonomous driving communication systems, etc.

[0139] The power source 506 can provide power to various components of the vehicle. The power source 506 can be a rechargeable lithium-ion or lead-acid battery.

[0140] Some or all functions of implementing autonomous driving are controlled by computer system 507. Computer system 507 may include at least one processor that executes instructions stored in a non-transitory computer-readable medium such as a memory. Computer system 507 provides the above-mentioned autonomous driving system with execution code for implementing autonomous driving.

[0141] The processor can be any conventional processor, such as a commercially available central processing unit (CPU). Alternatively, the processor can be a dedicated device such as an application specific integrated circuit (ASIC) or other hardware-based processor. Those of ordinary skill in the art will appreciate that the processor, computer, or memory can actually include multiple processors, computers, or memories that may or may not be stored in the same physical housing. For example, the memory can be a hard disk drive or other storage media that is located in a housing different from the computer. Therefore, references to processors or computers will be understood to include references to a collection of processors or computers or memories that may or may not operate in parallel. Different from using a single processor to perform the steps described herein, some components such as steering components and deceleration components can each have their own processors that only perform calculations related to the functions specific to the components.

[0142] The user interface 508 is used to provide information to or receive information from a user of the vehicle. Optionally, the user interface 508 may include one or more input / output devices within the set of peripheral devices 505, such as a wireless communication system, an onboard computer, a microphone, and a speaker.

[0143] It should be understood that the above components are only examples. In actual applications, the components in the above modules or systems may be added or deleted according to actual needs. Figure 5 It should not be understood as limiting the embodiments of the present application.

[0144] Reference below Figure 6 , which shows a schematic diagram of the structure of a computer system 600 of a terminal device suitable for implementing an embodiment of the present invention. Figure 6 The terminal device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0145] like Figure 6 As shown, the computer system 600 includes a central processing unit (CPU) 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage part 608 into a random access memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the system 600 are also stored. The CPU 601, the ROM 602, and the RAM 603 are connected to each other through a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0146] The following components are connected to the I / O interface 605: an input section 606 including a keyboard, a mouse, etc.; an output section 607 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN card, a modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the I / O interface 605 as needed. A removable medium 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 610 as needed, so that a computer program read therefrom is installed into the storage section 608 as needed.

[0147] In particular, according to the embodiments disclosed in the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present invention include a computer program, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 609, and / or installed from the removable medium 611. When the computer program is executed by the central processing unit (CPU) 601, the above-mentioned functions defined in the system of the present invention are executed.

[0148] It should be noted that the computer-readable medium shown in the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0149] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0150] The modules involved in the embodiments of the present invention may be implemented by software or hardware. The modules described may also be set in a processor, for example, it may be described as: a processor includes an acquisition module, a calculation module and a decision module, wherein the names of these modules do not constitute a limitation on the modules themselves in some cases.

[0151] As another aspect, the present invention also provides a computer-readable medium, which may be included in the device described in the above embodiment; or it may exist independently without being assembled into the device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by a device, the device implements the following method: in response to the presence of an obstacle vehicle in front of the autonomous driving vehicle whose driving speed is less than the first speed of the autonomous driving vehicle, obtain the speed information and type information of each obstacle vehicle in the lane where the autonomous driving vehicle is located and the lanes on both sides of it; calculate the road smoothness of the lane where the autonomous driving vehicle is located and the lanes on both sides of it according to the speed information and type information of each obstacle vehicle in the lane where the autonomous driving vehicle is located and the lanes on both sides of it; determine the target lane and change lanes according to the road smoothness of the lane where the autonomous driving vehicle is located and the lanes on both sides of it.

[0152] According to the technical solution of the embodiment of the present invention, the road smoothness of the lane where the autonomous driving vehicle is located and the lanes on both sides of it is calculated according to the speed information and type information of each obstacle vehicle in the lane where the autonomous driving vehicle is located and the lanes on both sides of it, and the target lane is determined and the lane change is performed according to the road smoothness of the lane where the autonomous driving vehicle is located and the lanes on both sides of it. Therefore, the technical problem that the driving efficiency of the vehicle cannot be guaranteed to be improved in the prior art is overcome. In the normal driving process of the autonomous driving vehicle, when the speed of the vehicle ahead is low and the driving efficiency of the vehicle is low, the embodiment of the present invention calculates the road smoothness of the left and right lanes by sensing the driving conditions of the vehicles in the left and right lanes, and selects the optimal lane as the target lane for lane change after considering the traffic rules, so as to ensure that the driving efficiency of the autonomous driving vehicle can be improved.

[0153] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may occur depending on design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An automatic driving method, characterized in that: include: In response to the presence of an obstacle vehicle in front of the autonomous driving vehicle whose travel speed is less than a first speed of the autonomous driving vehicle, obtaining speed information and type information of each obstacle vehicle in a lane where the autonomous driving vehicle is located and lanes on both sides of the lane; Calculate the road smoothness of the lane where the autonomous driving vehicle is located and the lanes on both sides of the lane according to the speed information and type information of each obstacle vehicle in the lane where the autonomous driving vehicle is located and the lanes on both sides of the lane; The target lane is determined and lane change is performed based on the road smoothness of the lane where the autonomous driving vehicle is located and the lanes on both sides of the lane.

2. The method according to claim 1, characterized in that Determining a target lane and changing lanes according to the road smoothness of the lane where the autonomous driving vehicle is located and the lanes on both sides thereof, including: In response to the road smoothness of the lanes on both sides of the autonomous driving vehicle being less than the sum of the road smoothness of the lane where the autonomous driving vehicle is located and the lane score difference value, continuing to drive in the current lane; In response to the road smoothness of the lane on one side of the autonomous driving vehicle being less than the sum of the road smoothness of the lane where the autonomous driving vehicle is located and the lane score difference value, and the road smoothness of the lane on the other side of the autonomous driving vehicle being greater than or equal to the sum of the road smoothness of the lane where the autonomous driving vehicle is located and the lane score difference value, the lane on the other side is used as the target lane and lane change is performed; In response to the road smoothness of the lanes on both sides of the autonomous driving vehicle being greater than or equal to the sum of the road smoothness of the lane where the autonomous driving vehicle is located and the lane score difference value, the target lane is determined and the lane change is performed according to the road smoothness of the lanes on both sides of the autonomous driving vehicle and the traffic rules penalty score value.

3. The method according to claim 2, characterized in that Determine the target lane and change lanes according to the road smoothness and traffic rule penalty score of the lanes on both sides of the autonomous driving vehicle, including: In response to the road smoothness of the left lane of the autonomous driving vehicle being greater than or equal to the difference between the right lane of the autonomous driving vehicle and the traffic rule penalty score value, taking the left lane as the target lane and performing a lane change; In response to the road smoothness of the left lane of the autonomous driving vehicle being less than the difference between the right lane of the autonomous driving vehicle and the traffic rule penalty score value, the right lane is used as the target lane and a lane change is performed.

4. The method according to claim 1, characterized in that: The road smoothness of the lane is determined according to speed information of the obstacle vehicles on the lane, a type correction coefficient, and the total number of obstacle vehicles on the lane.

5. The method according to claim 1, characterized in that In response to the presence of an obstacle vehicle in front of the autonomous driving vehicle whose driving speed is less than a first speed of the autonomous driving vehicle, obtaining speed information and type information of each obstacle vehicle in a lane where the autonomous driving vehicle is located and lanes on both sides thereof, including: In response to the presence of an obstacle vehicle in front of the autonomous driving vehicle whose travel speed is less than a first speed of the autonomous driving vehicle, starting to store image data of each obstacle vehicle in the lane where the autonomous driving vehicle is located and in lanes on both sides of the lane; In response to the total amount of the stored image data of each obstacle vehicle reaching a valid data amount threshold, speed information and type information of each obstacle vehicle are parsed from the image data of each obstacle vehicle.

6. The method according to claim 5, characterized in that In response to the total amount of the stored image data of each obstacle vehicle reaching a valid data amount threshold, obtaining speed information and type information of each obstacle vehicle includes: In response to the total amount of the stored image data of each obstacle vehicle reaching a valid data amount threshold, calculating the road smoothness of the lane where the autonomous driving vehicle is located; In response to the road smoothness of the lane where the autonomous driving vehicle is located being less than a road smoothness threshold, speed information and type information of each obstacle vehicle is parsed from the image data of each obstacle vehicle.

7. The method according to claim 6, characterized in that The road smoothness threshold is calculated using the following method: Multiply the maximum speed of the autonomous driving vehicle by the pressure speed tolerance coefficient.

8. An automatic driving device, characterized in that: include: an acquisition module, configured to acquire speed information and type information of each obstacle vehicle in a lane where the autonomous driving vehicle is located and lanes on both sides thereof in response to the presence of an obstacle vehicle in front of the autonomous driving vehicle whose driving speed is less than a first speed of the autonomous driving vehicle; A calculation module, used to calculate the road smoothness of the lane where the autonomous driving vehicle is located and the lanes on both sides of the lane according to the speed information and type information of each obstacle vehicle in the lane where the autonomous driving vehicle is located and the lanes on both sides of the lane; The decision module is used to determine the target lane and change lanes according to the road smoothness of the lane where the autonomous driving vehicle is located and the lanes on both sides of the lane.

9. An electronic device, characterized in that: include: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 7.

10. A computer readable medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

11. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

Images