Vehicle driving control methods, devices, electronic equipment and vehicles

Abstract

This invention relates to the field of intelligent driving technology, specifically to vehicle driving control methods, devices, electronic devices, and vehicles. It acquires current road information corresponding to the current road where the vehicle is located; identifies the current road information to determine whether the current road is a continuous planar road segment; if the vehicle is on a continuous planar road segment, it detects whether there is abnormal traffic flow among vehicles within a preset range ahead of the vehicle based on at least two types of sensors. Therefore, it solves the problem that traditional traffic flow monitoring systems often rely on a single sensor, which cannot comprehensively and accurately acquire traffic flow information, leading to inaccurate judgment of abnormal traffic flow situations and untimely response. This invention achieves accurate judgment of abnormal traffic flow. If abnormal traffic flow exists, it controls the vehicle based on the relationship between the abnormal traffic flow and the vehicle, ensuring the accuracy of vehicle control and thus guaranteeing driving safety.

Description

Technical Field

[0001] This invention relates to the field of intelligent driving technology, specifically to vehicle driving control methods, devices, electronic equipment, and vehicles. Background Technology

[0002] With the rapid development of Intelligent Transportation Systems (ITS), accurate acquisition and timely response to road traffic flow information have become key to improving road safety and traffic efficiency.

[0003] However, traditional traffic flow monitoring systems often fail to acquire comprehensive and accurate traffic flow information, leading to inaccurate judgments and untimely responses to traffic flow anomalies.

[0004] Therefore, how to accurately judge abnormal traffic flow and control it accordingly has become an urgent problem to be solved. Summary of the Invention

[0005] In view of this, the present invention provides a vehicle driving control method, device, electronic device and vehicle to solve the urgent problem of how to accurately judge abnormal traffic flow conditions and control them accordingly.

[0006] In a first aspect, the present invention provides a vehicle driving control method, the method comprising:

[0007] Get the current road information corresponding to the current road where this vehicle is located;

[0008] Identify the current road information to determine whether the current road is a continuous at-grade road segment;

[0009] If the vehicle is on a continuous flat road section, it will detect whether there is abnormal traffic flow among the vehicles in front of the vehicle within a preset range based on at least two types of sensors; the abnormal traffic flow consists of at least two abnormal vehicles in front of the vehicle that are in an abnormal state.

[0010] If abnormal traffic flow exists, the vehicle will be controlled according to the relationship between the abnormal traffic flow and the vehicle.

[0011] The vehicle driving control method provided in this application acquires the current road information corresponding to the current road where the vehicle is located; identifies the current road information to determine whether the current road is a continuous planar road segment, thereby ensuring the accuracy of the determination of whether the current road is a continuous planar road segment. If the vehicle is on a continuous planar road segment, it detects whether there is abnormal traffic flow among the vehicles ahead within a preset range based on at least two types of sensors, thereby ensuring the accuracy of the detection of abnormal traffic flow among the vehicles ahead. Therefore, it solves the problem that traditional traffic flow monitoring systems often rely on a single sensor, which cannot comprehensively and accurately acquire traffic flow information, leading to inaccurate judgment of abnormal traffic flow and untimely response, and achieves accurate judgment of abnormal traffic flow. If abnormal traffic flow exists, the vehicle is controlled according to the relationship between the abnormal traffic flow and the vehicle, ensuring the accuracy of vehicle control and thus ensuring the driving safety of the vehicle.

[0012] In one optional implementation, identifying the current road information and determining whether the current road is a continuous surface road segment includes:

[0013] Identify the current road information and detect whether the current road is a fork or intersection;

[0014] If the current road is neither a fork nor an intersection, then obtain the road slope corresponding to the current road.

[0015] If the road gradient is less than the preset gradient threshold, then obtain the radius of the curve corresponding to the current road.

[0016] If the curve radius is greater than the preset curve radius, the current road is determined to be a continuous flat road segment.

[0017] In one optional implementation, the detection of abnormal traffic flow among vehicles within a preset range ahead of the vehicle is based on at least two types of sensors, including:

[0018] Acquire the current position information and / or current speed information of each vehicle ahead detected by at least two types of sensors;

[0019] Track the current position and / or speed information of each vehicle ahead detected by each sensor;

[0020] For each vehicle ahead, if the current position information and / or current speed information of the vehicle ahead detected by at least two types of sensors is continuously lost for a period of time exceeding the disappearance judgment threshold, then the vehicle ahead that continuously loses its current position information and / or current speed information is determined to be an abnormal vehicle ahead.

[0021] Based on the vehicles ahead of each anomaly, determine the abnormal traffic flow.

[0022] The vehicle driving control method provided in this application acquires the current position information and / or current speed information of each vehicle ahead detected by at least two types of sensors; tracks the current position information and / or current speed information of each vehicle ahead detected by each sensor; if the current position information and / or current speed information of a vehicle ahead detected by at least two types of sensors is continuously lost for a period exceeding a disappearance judgment time threshold, then the vehicle ahead with continuously lost current position information and / or current speed information is determined to be an abnormal vehicle ahead; based on each abnormal vehicle ahead, abnormal traffic flow is determined, ensuring the accuracy of determining the abnormal vehicle ahead with continuously lost current position information and / or current speed information, thereby ensuring the accuracy of determining abnormal traffic flow. Therefore, it solves the problem that traditional traffic flow monitoring systems often rely on a single sensor, which cannot comprehensively and accurately acquire traffic flow information, leading to inaccurate judgment of abnormal traffic flow situations and untimely response.

[0023] In one optional implementation, the detection of abnormal traffic flow among vehicles within a preset range ahead of the vehicle is based on at least two types of sensors, including:

[0024] Identify the current road information and determine the current lane width;

[0025] Acquire the driving trajectories of each vehicle ahead detected by at least two types of sensors;

[0026] For each vehicle ahead, the driving trajectory detected by each sensor is compared with the lane trajectory of the vehicle ahead in the target lane before a preset time.

[0027] If at least two types of sensors detect that the lateral deviation between the trajectory of the vehicle ahead and the lane trajectory is greater than the current lane width, then the vehicle ahead is identified as an abnormal vehicle ahead; based on each abnormal vehicle ahead, an abnormal traffic flow is determined.

[0028] The vehicle driving control method provided in this application identifies current road information and determines the current lane width, ensuring the accuracy of the determined current lane width. It acquires the driving trajectories of each vehicle ahead detected by at least two types of sensors. For each vehicle ahead, the driving trajectory detected by each sensor is compared with the corresponding lane trajectory of the vehicle in the target lane a preset time ago, ensuring the accuracy of the comparison results. If the lateral deviation between the driving trajectory of a vehicle ahead detected by at least two types of sensors and the lane trajectory is greater than the current lane width, the vehicle ahead is identified as an abnormal vehicle ahead. Based on each abnormal vehicle ahead, abnormal traffic flow is determined, ensuring the accuracy of the determined abnormal traffic flow. Therefore, it solves the problem that traditional traffic flow monitoring systems often rely on a single sensor, which cannot comprehensively and accurately acquire traffic flow information, leading to inaccurate judgment of abnormal traffic flow situations and untimely response.

[0029] In one optional implementation, the detection of abnormal traffic flow among vehicles within a preset range ahead of the vehicle is based on at least two types of sensors, including:

[0030] Detect whether the future travel routes of each vehicle ahead meet the preset conditions within a preset time period;

[0031] If the future travel route of each vehicle ahead meets the preset conditions within a preset time period, then obtain the current vehicle speed of each vehicle ahead transmitted by at least two types of sensors.

[0032] For each vehicle ahead, the current speed of the vehicle ahead detected by each sensor is compared with the current speed of the adjacent vehicle corresponding to the vehicle ahead, and the speed difference between the current vehicle and the adjacent vehicle corresponding to each sensor is obtained.

[0033] For each vehicle ahead, the speed difference between the vehicle ahead and adjacent vehicles is obtained from the sensors to determine whether there is abnormal traffic flow among the vehicles ahead.

[0034] The vehicle driving control method provided in this application detects whether the future travel segments of each vehicle ahead meet preset conditions within a preset time period, ensuring the accuracy of the detection results. If the future travel segments of each vehicle ahead meet the preset conditions, the current speed of each vehicle ahead is acquired from at least two types of sensors, thus enabling multi-dimensional acquisition of the current speed of each vehicle ahead. For each vehicle ahead, the current speed of the vehicle ahead detected by each sensor is compared with the current speed of the adjacent vehicle corresponding to the vehicle ahead, obtaining the speed difference between the current vehicle and the adjacent vehicle corresponding to each sensor, ensuring the accuracy of the obtained speed difference between the current vehicle and the adjacent vehicle corresponding to each sensor. For each vehicle ahead, based on the speed difference between the vehicle ahead and the adjacent vehicle detected by each sensor, it is determined whether there is abnormal traffic flow among the vehicles ahead, ensuring the accuracy of the result of determining whether there is abnormal traffic flow among the vehicles ahead. Therefore, this solves the problem that traditional traffic flow monitoring systems often rely on a single sensor, which cannot comprehensively and accurately acquire traffic flow information, leading to inaccurate judgment of abnormal traffic flow situations and untimely response.

[0035] In one optional implementation, for each vehicle ahead, based on the speed difference between the vehicle ahead and adjacent vehicles detected by each sensor, it is determined whether there is abnormal traffic flow among the vehicles ahead, including:

[0036] For each vehicle ahead, the speed difference detected by each sensor is compared with the preset speed difference.

[0037] If at least two sensors detect a speed difference greater than a preset speed difference, and the duration of the speed difference being greater than the preset speed difference is greater than a preset duration, then the vehicle ahead is identified as an abnormal vehicle ahead.

[0038] Based on the vehicles ahead of each anomaly, determine the abnormal traffic flow.

[0039] The vehicle driving control method provided in this application compares the speed difference detected by each sensor with a preset speed difference for each vehicle ahead. If at least two sensors detect speed differences greater than the preset speed difference, and the duration of the speed difference being greater than the preset speed difference is longer than a preset duration, then the vehicle ahead is identified as an abnormal vehicle ahead. Based on each abnormal vehicle ahead, an abnormal traffic flow is determined. This ensures the accuracy of the determined abnormal traffic flow.

[0040] In one optional implementation, detecting whether the future travel segments of each vehicle ahead within a preset time period meet preset conditions includes:

[0041] Acquire the driving trajectory and current location information of each vehicle ahead detected by at least two types of sensors;

[0042] Based on the driving trajectory and current location information of each vehicle ahead, predict the future location information of each vehicle ahead within a preset time period.

[0043] For each vehicle ahead, determine its future travel route based on its future location information;

[0044] Identify future travel routes and detect whether they are continuous flat road segments;

[0045] If the future travel route is a continuous flat road segment, then obtain the road length of the future travel route;

[0046] If the road length is less than or equal to a preset road length threshold, then it is determined that the future travel segment of each vehicle in front will meet the preset conditions within a preset time period.

[0047] The vehicle driving control method provided in this application acquires the driving trajectory and current position information of each vehicle ahead detected by at least two types of sensors. Based on the driving trajectory and current position information of each vehicle ahead, it predicts the future position information of each vehicle ahead within a preset time period, ensuring the accuracy of the predicted future position information. For each vehicle ahead, the future driving segment is determined based on the future position information, ensuring the accuracy of the determined future driving segment. The future driving segment is identified, and it is detected whether the future driving segment is a continuous planar road segment, ensuring the accuracy of the detection result. If the future driving segment is a continuous planar road segment, the road length of the future driving segment is acquired. If the road length is less than or equal to a preset road length threshold, it is determined that the future driving segment of each vehicle ahead within the preset time period meets preset conditions. This ensures the accuracy of the determined future driving segment of each vehicle ahead within the preset time period meeting preset conditions.

[0048] In one optional implementation, if abnormal traffic flow exists, the vehicle is controlled based on the relationship between the abnormal traffic flow and the vehicle, including:

[0049] Obtain the current abnormal location information of each abnormal vehicle ahead in each abnormal traffic flow;

[0050] Based on the current abnormal location information, determine the target abnormal vehicle that is closest to your vehicle from among the vehicles ahead of each abnormality;

[0051] Obtain the longitudinal distance between the vehicle ahead of the target anomaly and your own vehicle;

[0052] Divide the longitudinal distance by the current speed of the vehicle to obtain the longitudinal time distance of the vehicle to the vehicle in front of the target anomaly.

[0053] The vehicle is controlled based on the longitudinal time interval.

[0054] The vehicle driving control method provided in this application obtains the current abnormal position information of each abnormal vehicle ahead in each abnormal traffic flow; based on the current abnormal position information, it determines the target abnormal vehicle ahead that is closest to the current vehicle ahead, ensuring the accuracy of the determined target abnormal vehicle ahead. It obtains the longitudinal distance between the target abnormal vehicle ahead and the current vehicle; by dividing the longitudinal distance by the current speed of the current vehicle, it obtains the longitudinal time distance between the current vehicle and the target abnormal vehicle ahead, ensuring the accuracy of the calculated longitudinal time distance. Based on the longitudinal time distance, it controls the current vehicle, ensuring the accuracy of the control and thus ensuring the driving safety of the current vehicle.

[0055] In one alternative implementation, the vehicle is controlled based on the longitudinal time distance, including:

[0056] The longitudinal time interval is compared with the preset alarm time interval; the preset alarm time interval is calculated based on the current vehicle speed, the minimum deceleration value of the vehicle to avoid danger, the shortest time required to trigger a continuous alarm to trigger a driver's reaction, and the threshold for driver takeover and response time.

[0057] If the longitudinal time interval is less than the preset alarm time interval, the system will control the vehicle to output the current status information of the vehicle ahead of the target abnormality and the current abnormality location information, and output an alarm prompt message to the driver.

[0058] The longitudinal time distance is compared with the preset emergency avoidance time distance; the preset emergency avoidance time distance is calculated based on the current vehicle speed, the minimum deceleration value of the vehicle for avoiding danger, and the tolerance threshold from the issuance of the driving control signal to the realization of control; the preset emergency avoidance time distance is less than the preset alarm time distance;

[0059] If the longitudinal time distance is less than the preset emergency avoidance time distance, the vehicle will be controlled to decelerate and stop.

[0060] The vehicle driving control method provided in this application compares the longitudinal time distance with a preset alarm time distance. If the longitudinal time distance is less than the preset alarm time distance, the method controls the vehicle to output the current status information and current abnormal location information of the vehicle ahead of the target abnormality, and outputs an alarm prompt to the driver. This ensures the accuracy of the output of the current status information and current abnormal location information of the vehicle ahead of the target abnormality, as well as the accuracy of the alarm prompt to the driver. The method also compares the longitudinal time distance with a preset emergency avoidance time distance. If the longitudinal time distance is less than the preset emergency avoidance time distance, the method controls the vehicle to decelerate and stop, ensuring the accuracy of controlling the vehicle to decelerate and stop, thereby ensuring the driving safety of the vehicle.

[0061] In a second aspect, the present invention provides a vehicle driving control device, the device comprising:

[0062] The acquisition module is used to acquire the current road information corresponding to the current road where the vehicle is located;

[0063] The determination module is used to identify the current road information and determine whether the current road is a continuous horizontal road segment;

[0064] The detection module is used to detect, based on at least two types of sensors, whether there is abnormal traffic flow among the vehicles ahead within a preset range in front of the vehicle if the vehicle is on a continuous flat road segment; the abnormal traffic flow consists of at least two abnormal vehicles ahead that are in an abnormal state.

[0065] The control module is used to control the vehicle based on the relationship between the abnormal traffic flow and the vehicle if abnormal traffic flow exists.

[0066] The vehicle driving control method provided in this application acquires the current road information corresponding to the current road where the vehicle is located; identifies the current road information to determine whether the current road is a continuous planar road segment, thereby ensuring the accuracy of the determination of whether the current road is a continuous planar road segment. If the vehicle is on a continuous planar road segment, it detects whether there is abnormal traffic flow among the vehicles ahead within a preset range based on at least two types of sensors, thereby ensuring the accuracy of the detection of abnormal traffic flow among the vehicles ahead. Therefore, it solves the problem that traditional traffic flow monitoring systems often rely on a single sensor, which cannot comprehensively and accurately acquire traffic flow information, leading to inaccurate judgment of abnormal traffic flow and untimely response, and achieves accurate judgment of abnormal traffic flow. If abnormal traffic flow exists, the vehicle is controlled according to the relationship between the abnormal traffic flow and the vehicle, ensuring the accuracy of vehicle control and thus ensuring the driving safety of the vehicle.

[0067] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the vehicle driving control method of the first aspect or any corresponding embodiment described above.

[0068] Fourthly, the present invention provides a vehicle comprising electronic equipment, a vehicle body, and at least two types of sensors, wherein the electronic equipment is used to execute the vehicle driving control method of the first aspect or any corresponding embodiment thereof.

[0069] Fifthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the vehicle driving control method of the first aspect or any corresponding embodiment thereof.

[0070] In a sixth aspect, the present invention provides a computer program product, including computer instructions for causing a computer to execute the vehicle driving control method of the first aspect or any corresponding embodiment described above. Attached Figure Description

[0071] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0072] Figure 1 This is a schematic flowchart of a vehicle driving control method according to an embodiment of the present invention;

[0073] Figure 2 This is a schematic flowchart of another vehicle driving control method according to an embodiment of the present invention;

[0074] Figure 3 This is a flowchart illustrating another vehicle driving control method according to an embodiment of the present invention;

[0075] Figure 4 This is a schematic flowchart of another vehicle driving control method according to an embodiment of the present invention;

[0076] Figure 5 This is a structural schematic diagram of the vehicle according to an embodiment of the present invention;

[0077] Figure 6 This is a structural block diagram of a vehicle driving control device according to an embodiment of the present invention;

[0078] Figure 7 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0079] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0080] With the rapid development of Intelligent Transportation Systems (ITS), accurate acquisition and timely response to road traffic flow information have become key to improving road safety and traffic efficiency.

[0081] However, traditional traffic flow monitoring systems often fail to acquire comprehensive and accurate traffic flow information, leading to inaccurate judgments and untimely responses to traffic flow anomalies.

[0082] Therefore, how to accurately judge abnormal traffic flow and control it accordingly has become an urgent problem to be solved.

[0083] It should be noted that the vehicle driving control method provided in this application can be executed by a vehicle driving control device. This device can be implemented as part or all of an electronic device through software, hardware, or a combination of both. The electronic device can be a control device within the vehicle. The vehicle can include electronic equipment, the vehicle body, and at least two types of sensors. Specifically, the vehicle may include at least two types of sensors, such as forward-looking LiDAR, forward-looking camera, forward-looking millimeter-wave radar, navigation map equipment, and a combined positioning device consisting of GNSS+RTK+IMU+wheel speed sensors.

[0084] The forward-facing LiDAR provides road information and information about vehicles ahead on the route the vehicle is traveling on. Road information includes attributes such as the position, type, shape, and surface irregularities and depth of fixed road elements relative to the vehicle, including lane lines, curbs, guardrails, traffic light poles, streetlights, traffic signs, and traffic stops. It also includes attributes such as the position, type, and shape of movable road elements relative to the vehicle, including traffic cones and temporary signs. Specifically, this includes lane line position, lane line type, lane line trajectory, lane line start and end points, curb position, curb height, curb trajectory, curb start and end points, guardrail position, guardrail type, guardrail trajectory, guardrail start and end points, traffic light pole position, streetlight number and position, traffic sign type, traffic sign position, traffic stop type, traffic stop position, surface irregularities and depth, traffic cone type, traffic cone position, temporary sign type, and temporary sign position. The information about the vehicle ahead includes the vehicle type, vehicle ID number, real-time position of the vehicle relative to the vehicle ahead, and real-time speed of the vehicle relative to the vehicle ahead.

[0085] The forward-facing camera provides road information and information about vehicles ahead, indicating the route the vehicle is traveling on. It provides the same type and attributes of information as the forward-facing lidar.

[0086] Forward-facing millimeter-wave radar provides information about the road the vehicle is traveling on and vehicles ahead. It provides the same type and attributes of information as forward-facing lidar.

[0087] Navigation map devices provide road-level road information, including the vehicle's location on the road segment, whether it is an intersection, roundabout, fork in the road, merging, curve radius, and uphill / downhill sections.

[0088] The combined positioning device, consisting of GNSS, RTK, IMU, and wheel speed sensors, integrates information from satellite positioning systems, differential positioning base stations, vehicle inertial navigation systems, and vehicle wheel speed sensors to provide road-level absolute positioning coordinates for the vehicle, including longitude, latitude, elevation, and time.

[0089] In the following method embodiments, the execution subject is an electronic device as an example for illustration.

[0090] According to an embodiment of the present invention, a vehicle driving control method embodiment is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0091] This embodiment provides a vehicle driving control method, which can be used in the aforementioned electronic device. Figure 1This is a flowchart of a vehicle driving control method according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps:

[0092] Step S101: Obtain the current road information corresponding to the current road where the vehicle is located.

[0093] Specifically, electronic devices can obtain the vehicle's current location information based on positioning devices. Then, based on navigation map devices and sensors such as LiDAR, sensing cameras, and millimeter-wave radar, they can determine the current road corresponding to the current location information, thereby determining the road segment location, whether it is an intersection, roundabout, fork in the road, merging point, curve radius, uphill / downhill slope, and other information. Furthermore, electronic devices can also acquire attribute information relative to the vehicle, including the position, type, shape, and surface unevenness and depth of fixed road elements such as lane lines, curbs, guardrails, traffic light poles, streetlights, traffic signs, and traffic stops, based on forward-looking LiDAR, forward-looking cameras, and forward-looking millimeter-wave radar. This information also includes the position, type, and shape of movable road elements such as traffic cones and temporary signs relative to the vehicle. Specifically, this includes information such as lane line position, lane line type, lane line trajectory, lane line start and end points, curb position, curb height, curb trajectory, curb start and end points, guardrail position, guardrail type, guardrail trajectory, guardrail start and end points, traffic light pole position, streetlight number and position, traffic sign type, traffic sign position, traffic stop type, traffic stop position, surface unevenness and depth, traffic cone type, traffic cone position, and temporary sign type and position. This generates current road information corresponding to the current road where the vehicle is located.

[0094] The positioning device can be a combined positioning device consisting of at least one of GNSS, RTK, IMU, and wheel speed sensor.

[0095] The current road information can include the position, type, shape, and surface irregularities and depth of fixed road elements such as lane lines, curbs, guardrails, traffic light poles, streetlights, traffic signs, and traffic stops relative to the vehicle. It also includes the position, type, and shape of movable road elements such as traffic cones and temporary signs relative to the vehicle. Specifically, this includes lane line position, lane line type, lane line trajectory, lane line start and end points, curb position, curb height, curb trajectory, curb start and end points, guardrail position, guardrail type, guardrail trajectory, guardrail start and end points, traffic light pole position, streetlight number and position, traffic sign type, traffic sign position, traffic stop type, traffic stop position, surface irregularities and depth, traffic cone type, traffic cone position, temporary sign type, and temporary sign position. It also includes the vehicle's current location on the road segment, whether it is an intersection, roundabout, fork in the road, merging point, curve radius, and uphill / downhill slope.

[0096] Step S102: Identify the current road information and determine whether the current road is a continuous horizontal road segment.

[0097] Specifically, electronic devices can identify current road information and determine whether the current road information meets the road requirements for continuous horizontal road sections.

[0098] The road requirements for continuous horizontal road segments may include at least one of the following: the current road is not a fork in the road, the current road is not an intersection, the current road slope is less than a certain threshold, and the current road curve radius is greater than a certain threshold. This embodiment does not specifically limit the road requirements for continuous horizontal road segments.

[0099] This step will be explained in detail below.

[0100] Step S103: If the vehicle is on a continuous flat road section, then based on at least two types of sensors, detect whether there is abnormal traffic flow among the vehicles in front of the vehicle within a preset range.

[0101] Abnormal traffic flow consists of at least two abnormal vehicles in each preceding vehicle that exhibits an abnormal state.

[0102] Specifically, if the vehicle is on a continuous flat road segment, the electronic equipment acquires information on each vehicle ahead within a preset range detected by at least two types of sensors. Then, it identifies the vehicle information detected by each sensor to determine whether there are any abnormal vehicles ahead, and then identifies each abnormal vehicle ahead as an abnormal traffic flow.

[0103] The information about the vehicle ahead includes the vehicle type, vehicle ID number, real-time position of the vehicle relative to the vehicle ahead, and real-time speed of the vehicle relative to the vehicle ahead.

[0104] If the detection results of at least two types of sensors are that there is abnormal traffic flow in the vehicles ahead, the electronic device determines that there is abnormal traffic flow in the vehicles ahead.

[0105] This step will be explained in detail below.

[0106] Step S104: If there is abnormal traffic flow, control the vehicle based on the relationship between the abnormal traffic flow and the vehicle.

[0107] Specifically, if there is abnormal traffic flow among the vehicles ahead, the electronic equipment determines the positional relationship between the abnormal traffic flow and the vehicle based on the location information of the abnormal traffic flow. Then, based on the positional relationship between the abnormal traffic flow and the vehicle, the electronic equipment controls the vehicle.

[0108] The vehicle driving control method provided in this application acquires the current road information corresponding to the current road where the vehicle is located; identifies the current road information to determine whether the current road is a continuous planar road segment, thereby ensuring the accuracy of the determination of whether the current road is a continuous planar road segment. If the vehicle is on a continuous planar road segment, it detects whether there is abnormal traffic flow among the vehicles ahead within a preset range based on at least two types of sensors, thereby ensuring the accuracy of the detection of abnormal traffic flow among the vehicles ahead. Therefore, it solves the problem that traditional traffic flow monitoring systems often rely on a single sensor, which cannot comprehensively and accurately acquire traffic flow information, leading to inaccurate judgment of abnormal traffic flow and untimely response, and achieves accurate judgment of abnormal traffic flow. If abnormal traffic flow exists, the vehicle is controlled according to the relationship between the abnormal traffic flow and the vehicle, ensuring the accuracy of vehicle control and thus ensuring the driving safety of the vehicle.

[0109] This embodiment provides a vehicle driving control method, which can be used in the aforementioned electronic device. Figure 2 This is a flowchart of a vehicle driving control method according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps:

[0110] Step S201: Obtain the current road information corresponding to the current road where the vehicle is located.

[0111] Please refer to the above description of step S101 for details on this step, which will not be repeated here.

[0112] Step S202: Identify the current road information and determine whether the current road is a continuous horizontal road segment.

[0113] Specifically, step S202 above may include the following steps:

[0114] Step S2021: Identify the current road information and detect whether the current road is a fork or intersection.

[0115] Optionally, the electronic device can detect whether the current road is a fork or intersection based on the road information sent by the navigation map device.

[0116] Optionally, the electronic device can also receive road images corresponding to the current road sent by the forward-facing sensing camera, identify the road images, and detect whether the current road is a fork or intersection based on the identification results.

[0117] Step S2022: If the current road is not a fork in the road and is not an intersection, then obtain the road slope corresponding to the current road.

[0118] If the current road is neither a fork in the road nor an intersection, the electronic device can optionally obtain the road slope corresponding to the current road based on the positioning device. Alternatively, the electronic device can also obtain the road slope corresponding to the current road based on the navigation map device.

[0119] Optionally, the electronic device can receive a preset slope threshold input by the user, or a preset slope threshold sent by other devices, or set a preset slope threshold based on the vehicle's attribute information, etc. This application embodiment does not specifically limit the way the electronic device obtains the preset slope threshold.

[0120] Then, the electronic device compares the current road slope with a preset slope threshold.

[0121] Step S2023: If the road slope is less than the preset slope threshold, then obtain the curve radius corresponding to the current road.

[0122] Specifically, if the road gradient is less than a preset gradient threshold, the electronic device can obtain the curve radius corresponding to the current road based on the positioning device, or it can obtain the curve radius corresponding to the current road through the navigation map device. The electronic device can also detect the surrounding environment through sensors such as forward-looking LiDAR, forward-looking camera, and forward-looking millimeter-wave radar to obtain the curve radius corresponding to the current road.

[0123] Optionally, the electronic device can receive a preset turning radius input by the user, or a preset turning radius sent by other devices, or set a preset turning radius based on the vehicle's attribute information, etc. This application embodiment does not specifically limit the way the electronic device obtains the preset turning radius.

[0124] Then, the electronic device compares the current curve radius with the preset turning radius.

[0125] Step S2024: If the curve radius is greater than the preset curve radius, then the current road is determined to be a continuous flat road segment.

[0126] Specifically, if the curve radius is greater than the preset curve radius, the electronic equipment can determine that the current road is a continuous flat road segment.

[0127] Step S203: If the vehicle is on a continuous flat road section, then based on at least two types of sensors, detect whether there is abnormal traffic flow among the vehicles in front of the vehicle within a preset range.

[0128] Specifically, step S203 above, "detecting whether there is abnormal traffic flow among vehicles in a preset range ahead of the vehicle based on at least two types of sensors," may include the following steps:

[0129] Step S2031: If the vehicle is on a continuous flat road segment, acquire the current position information and / or current speed information of each vehicle ahead detected by at least two types of sensors.

[0130] Specifically, if the vehicle is on a continuous flat road section, the electronic equipment acquires the current position information and / or current speed information of each vehicle ahead detected by at least two types of sensors.

[0131] Step S2032: Track the current position information and / or current speed information of each vehicle ahead detected by each sensor.

[0132] Specifically, electronic devices can track the current position and / or speed information of each vehicle ahead detected by various sensors.

[0133] Step S2033: For each vehicle ahead, if the current position information and / or current speed information of the vehicle ahead detected by at least two types of sensors is continuously lost for a period of time exceeding the disappearance judgment time threshold, then the vehicle ahead that continuously loses its current position information and / or current speed information is determined to be an abnormal vehicle ahead.

[0134] Specifically, for a target vehicle ahead among the vehicles ahead, if the current position information and / or current speed information of the target vehicle ahead detected by a certain sensor is lost, then the timer starts from the moment the current position information and / or current speed information of the target vehicle ahead is lost. If the continuous loss of the current position information and / or current speed information of the target vehicle ahead exceeds the disappearance judgment time threshold, then the vehicle ahead detected by the sensor is determined to be an abnormal vehicle ahead.

[0135] If at least two types of sensors produce detection results indicating that the vehicle ahead is an abnormal vehicle, the electronic device will identify the vehicle ahead as an abnormal vehicle. This ensures the accuracy of identifying abnormal vehicles ahead and avoids the limitation that a single sensor can only provide limited information, such as the number of vehicles, speed, or occupancy status, while failing to acquire multi-dimensional data, such as vehicle type and driver behavior. It also avoids the situation where a single sensor typically only covers a specific area and is not effective for monitoring large-scale traffic flow.

[0136] Among them, the disappearance judgment time threshold is greater than or equal to 2 sensor detection cycles.

[0137] Step S2034: Determine the abnormal traffic flow based on the vehicles ahead of each abnormality.

[0138] Specifically, electronic devices can determine abnormal traffic flow based on each abnormal vehicle ahead.

[0139] Step S204: If there is abnormal traffic flow, control the vehicle based on the relationship between the abnormal traffic flow and the vehicle.

[0140] Please refer to the above description of step S104 for details on this step, which will not be repeated here.

[0141] The vehicle driving control method provided in this application identifies current road information and detects whether the current road is a fork or intersection, ensuring the accuracy of the detection results. If the current road is neither a fork nor an intersection, the road slope corresponding to the current road is obtained; if the road slope is less than a preset slope threshold, the curve radius corresponding to the current road is obtained; if the curve radius is greater than a preset curve radius, the current road is determined to be a continuous planar road segment, thereby ensuring the accuracy of determining that the current road is a continuous planar road segment.

[0142] If the vehicle is on a continuous flat road segment, the system acquires the current position and / or speed information of each vehicle ahead detected by at least two types of sensors; it tracks the current position and / or speed information of each vehicle ahead detected by each sensor; if the current position and / or speed information of a vehicle ahead detected by at least two types of sensors is continuously lost for a period exceeding the disappearance judgment time threshold, the vehicle ahead with continuously lost current position and / or speed information is identified as an abnormal vehicle ahead; based on each abnormal vehicle ahead, abnormal traffic flow is determined, ensuring the accuracy of identifying vehicles ahead with continuously lost current position and / or speed information as abnormal vehicles ahead, and thus ensuring the accuracy of identifying abnormal traffic flow. Therefore, this solves the problem that traditional traffic flow monitoring systems often rely on a single sensor, which cannot comprehensively and accurately acquire traffic flow information, leading to inaccurate judgment of abnormal traffic flow situations and untimely response.

[0143] This embodiment provides a vehicle driving control method, which can be used in the aforementioned electronic device. Figure 3 This is a flowchart of a vehicle driving control method according to an embodiment of the present invention, such as... Figure 3 As shown, the process includes the following steps:

[0144] Step S301: Obtain the current road information corresponding to the current road where the vehicle is located.

[0145] Please refer to the above description of step S201 for details on this step, which will not be repeated here.

[0146] Step S302: Identify the current road information and determine whether the current road is a continuous horizontal road segment.

[0147] Please refer to the above description of step S202 for details on this step, which will not be repeated here.

[0148] Step S303: If the vehicle is on a continuous flat road section, then detect whether there is abnormal traffic flow among the vehicles in front of the vehicle within a preset range based on at least two types of sensors.

[0149] Specifically, step S303 above may include the following steps:

[0150] Step S3031: If the vehicle is on a continuous flat road segment, the current road information is identified to determine the current lane width.

[0151] The current road information may include lane line information. For example, an electronic device can acquire a road image corresponding to the current road using a forward-facing sensing camera, and then identify the lane line information in the road image.

[0152] Specifically, if the vehicle is on a continuous flat road section, the electronic equipment can identify the lane line information in the current road information, determine the distance between two adjacent lane lines, and thus determine the distance between two adjacent lane lines as the current lane width.

[0153] Step S3032: Obtain the driving trajectories of each vehicle ahead detected by at least two types of sensors.

[0154] Optionally, the electronic device can acquire the position and speed information of each vehicle in front based on the forward-looking LiDAR, and then generate the driving trajectory of each vehicle in front based on the position and speed information of each vehicle in front.

[0155] Optionally, the electronic device can acquire corresponding vehicle images of each vehicle ahead based on forward-facing sensing cameras, identify each vehicle ahead image based on image recognition algorithms, and extract the position, size, and movement trajectory of each vehicle ahead, thereby determining the driving trajectory corresponding to each vehicle ahead.

[0156] Optionally, the electronic device can acquire the position and speed information of each vehicle in front based on forward-looking millimeter-wave radar, and then generate the driving trajectory of each vehicle in front based on the position and speed information of each vehicle in front.

[0157] Step S3033: For each vehicle ahead, compare the driving trajectory detected by each sensor with the lane trajectory corresponding to the target lane where the vehicle ahead was located a preset time ago.

[0158] Specifically, for each vehicle ahead, the electronic device can also determine the target lane where each vehicle was located a preset time period based on the driving trajectory of each vehicle detected by each sensor. The preset time period can be 0.3s, 0.2s, or 0.1s, and this embodiment does not specify a particular preset time period.

[0159] Then, the electronic device determines the lane trajectory corresponding to the target lane and compares the driving trajectory detected by each sensor with the corresponding lane trajectory in the target lane.

[0160] Step S3034: If the lateral deviation between the driving trajectory of the vehicle ahead and the lane trajectory detected by at least two types of sensors is greater than the current lane width, then the vehicle ahead is identified as an abnormal vehicle ahead.

[0161] Specifically, if at least two types of sensors detect that the lateral deviation between the driving trajectory and the lane trajectory of the vehicle ahead is greater than the current lane width, the electronic device will identify the vehicle ahead as an abnormal vehicle ahead.

[0162] Step S3035: Determine the abnormal traffic flow based on the vehicles ahead of each abnormality.

[0163] Specifically, each abnormality is represented by a group of vehicles ahead, forming a defined abnormal traffic flow. The electronic equipment then determines the abnormal traffic flow based on each abnormality.

[0164] Step S304: If there is abnormal traffic flow, control the vehicle based on the relationship between the abnormal traffic flow and the vehicle.

[0165] Please refer to the above description of step S204 for details on this step, which will not be repeated here.

[0166] The vehicle driving control method provided in this application identifies current road information and determines the current lane width, ensuring the accuracy of the determined current lane width. It acquires the driving trajectories of each vehicle ahead detected by at least two types of sensors; for each vehicle ahead, the driving trajectory detected by each sensor is compared with the corresponding lane trajectory of the vehicle ahead in the target lane a preset time period prior, ensuring the accuracy of the comparison results. If the lateral deviation between the driving trajectory of the vehicle ahead detected by at least two types of sensors and the lane trajectory is greater than the current lane width, the vehicle ahead is identified as an abnormal vehicle ahead; based on each abnormal vehicle ahead, abnormal traffic flow is determined. This ensures the accuracy of the determined abnormal traffic flow. Therefore, it solves the problem that traditional traffic flow monitoring systems often rely on a single sensor, which cannot comprehensively and accurately acquire traffic flow information, leading to inaccurate judgment of abnormal traffic flow situations and untimely response.

[0167] This embodiment provides a vehicle driving control method, which can be used in the aforementioned electronic device. Figure 4 This is a flowchart of a vehicle driving control method according to an embodiment of the present invention, such as... Figure 4 As shown, the process includes the following steps:

[0168] Step S401: Obtain the current road information corresponding to the current road where the vehicle is located.

[0169] Please refer to the above description of step S301 for details on this step, which will not be repeated here.

[0170] Step S402: Identify the current road information and determine whether the current road is a continuous horizontal road segment.

[0171] Please refer to the above description of step S302 for details on this step, which will not be repeated here.

[0172] Step S403: If the vehicle is on a continuous flat road section, then detect whether there is abnormal traffic flow among the vehicles in front of the vehicle within a preset range based on at least two types of sensors.

[0173] Specifically, step S403 above may include the following steps:

[0174] Step S4031: If the vehicle is on a continuous flat road segment, check whether the future travel segments of each vehicle ahead meet the preset conditions within a preset time period.

[0175] In an optional embodiment of this application, step S4031 may include the following steps:

[0176] Step a1: Obtain the driving trajectory and current location information of each vehicle ahead detected by at least two types of sensors.

[0177] Optionally, the electronic device can acquire the position and speed information of each vehicle in front based on the forward-looking LiDAR, and then generate the driving trajectory of each vehicle in front based on the position and speed information of each vehicle in front, thereby acquiring the driving trajectory and current position information of each vehicle in front.

[0178] Optionally, the electronic device can acquire corresponding vehicle images of each vehicle ahead based on forward-facing sensing cameras, identify each vehicle ahead image based on image recognition algorithms, and extract the position, size, and movement trajectory of each vehicle ahead, thereby determining the driving trajectory and current position information of each vehicle ahead.

[0179] Optionally, the electronic device can acquire the position and speed information of each vehicle in front based on forward-looking millimeter-wave radar, and then determine the driving trajectory and current position information of each vehicle in front based on the position and speed information of each vehicle in front.

[0180] Step a2: Based on the driving trajectory and current location information of each vehicle ahead, predict the future location information of each vehicle ahead within a preset time period.

[0181] Specifically, the electronic device can input the driving trajectory and current location information of each vehicle in front into the prediction model. The prediction model extracts features from the driving trajectory and current location information of each vehicle in front and outputs the future location information of the vehicles in front within a preset time period.

[0182] The prediction model can be a linear regression model (such as ARIMA, SARIMA, etc.), a machine learning model (such as random forest, support vector machine, neural network, etc.), or a deep learning model (such as recurrent neural network RNN, long short-term memory network LSTM, gated recurrent unit GRU, etc.). This application does not specifically limit the prediction model.

[0183] It should be noted that the prediction model was trained using historical data and the model parameters were adjusted.

[0184] Step a3: For each vehicle ahead, determine the future travel route based on the future location information.

[0185] Specifically, for each vehicle ahead, the electronic equipment can determine the future travel route corresponding to each vehicle based on the future location information of each vehicle ahead.

[0186] Step a4: Identify the future travel segment and detect whether the future travel segment is a continuous flat road segment.

[0187] Specifically, electronic devices determine the location of the future driving route based on navigation map devices, including whether it is an intersection, roundabout, fork in the road, merging point, curve radius, uphill / downhill information, etc.

[0188] Specifically, the electronic device can identify information about the future travel route. If it determines that the future travel route is not a fork in the road, not an intersection, and that the road slope is less than a preset slope threshold, and the curve radius is greater than a preset curve radius, then the future travel route is determined to be a continuous flat road segment. Otherwise, the future travel route is determined not to be a continuous flat road segment.

[0189] Step a5: If the future travel segment is a continuous flat road segment, then obtain the road length of the future travel segment.

[0190] Specifically, if the future travel route is a continuous flat road segment, the electronic device obtains the road length of the future travel route based on the navigation map device.

[0191] Then, the electronic device compares the road length of the future travel segment with a preset road length threshold. The preset road length threshold can be 50m, 45m, or 55m; this embodiment does not specifically limit the preset road length threshold.

[0192] Step a6: If the road length is less than or equal to the preset road length threshold, then determine that the future travel segment of each vehicle in front meets the preset conditions within the future preset time period.

[0193] Specifically, if the road length is less than or equal to a preset road length threshold, the electronic device determines that the future travel segments of each vehicle ahead within a preset time period meet the preset conditions.

[0194] In another optional embodiment of this application, the driving trajectories and current location information of each vehicle ahead are obtained from at least two types of sensors. Based on the driving trajectories and current location information of each vehicle ahead, the future location information of each vehicle ahead within a preset time period is predicted. For each vehicle ahead, the future travel segment is determined based on the future location information. The future travel segment is identified, and it is detected whether the future travel segment is a continuous planar road segment. If the future travel segment is a continuous planar road segment, it is determined that the future travel segment of each vehicle ahead within the preset time period meets preset conditions.

[0195] Step S4032: If the future travel route of each vehicle ahead meets the preset conditions within a preset time period, then obtain the current vehicle speed of each vehicle ahead transmitted by at least two types of sensors.

[0196] Specifically, if the future travel route of each vehicle ahead meets the preset conditions within a preset time period, the electronic device can obtain the current speed of each vehicle ahead transmitted by at least two types of sensors.

[0197] For example, the electronic device may receive the current vehicle speed of each vehicle ahead from at least two of the following sensors: forward-aware lidar, forward-aware camera, and forward-aware millimeter-wave radar.

[0198] Step S4033: For each vehicle ahead, compare the current speed of the vehicle ahead detected by each sensor with the current speed of the adjacent vehicle corresponding to the vehicle ahead, and obtain the speed difference between the current vehicle and the adjacent vehicle corresponding to each sensor.

[0199] For each vehicle ahead, the electronic equipment can determine the adjacent vehicles corresponding to each vehicle based on the current location information of each vehicle ahead.

[0200] Electronic devices can compare the current speed of the vehicle ahead, detected by various sensors, with the current speed of the adjacent vehicles.

[0201] That is, the current speed of the vehicle in front in this lane is compared with the current speed of the vehicles in front in the adjacent left and right lanes, the current speed of the vehicle in front in the left lane is compared with the current speed of the vehicle in front in the left-left lane, and the current speed of the vehicle in front in the right lane is compared with the current speed of the vehicle in front in the right-right lane.

[0202] In addition, the electronic device calculates the speed difference between the current speed of the vehicle in front and the current speed of the adjacent vehicle corresponding to the vehicle in front.

[0203] Step S4034: For each vehicle ahead, based on the speed difference between the vehicle ahead and adjacent vehicles detected by each sensor, determine whether there is abnormal traffic flow among the vehicles ahead.

[0204] Specifically, step S4034 above may include the following steps:

[0205] Step b1: For each vehicle ahead, compare the speed difference detected by each sensor with the preset speed difference.

[0206] Specifically, the electronic device can receive preset speed difference values ​​input by the user, and can also receive preset speed difference values ​​sent by other devices. Furthermore, the electronic device can set preset speed difference values ​​based on the vehicle's current speed. The higher the vehicle's current speed, the larger the preset speed difference value.

[0207] For example, the preset speed difference can be calculated using the following formula:

[0208] The preset speed difference is Max[the maximum speed difference between adjacent lanes in this road segment according to the road traffic rules, and the set speed threshold]. The set speed threshold can be 30km / h or 34km / h. This application embodiment does not specifically limit the set speed threshold.

[0209] Then, the electronic device compares the speed difference between the vehicle ahead and the adjacent vehicle detected by each sensor with a preset speed difference.

[0210] Step b2: If at least two sensors detect a speed difference greater than a preset speed difference, and the duration of the speed difference being greater than the preset speed difference is greater than a preset duration, then the vehicle ahead is identified as an abnormal vehicle ahead.

[0211] Specifically, if at least two sensors detect a speed difference greater than a preset speed difference, the electronic device starts timing from the moment the speed difference exceeds the preset speed difference, obtaining the duration of the speed difference exceeding the preset speed difference. Then, it compares this duration with a preset time. If the duration of the speed difference exceeding the preset speed difference is longer than the preset time, the electronic device identifies the vehicle ahead, with a speed difference greater than the preset speed difference and a duration exceeding the preset time, as an abnormal vehicle ahead.

[0212] The preset duration can be 2 sensor detection cycles or 3 sensor detection cycles. This application embodiment does not specifically limit the preset duration.

[0213] Step b3: Determine the abnormal traffic flow based on the vehicles ahead of each abnormality.

[0214] Specifically, each abnormality is represented by a group of vehicles ahead, forming a defined abnormal traffic flow. The electronic equipment then determines the abnormal traffic flow based on each abnormality.

[0215] Step S404: If there is abnormal traffic flow, control the vehicle based on the relationship between the abnormal traffic flow and the vehicle.

[0216] Specifically, step S404 above may include the following steps:

[0217] Step S4041: Obtain the current abnormal location information corresponding to each abnormal vehicle ahead in each abnormal traffic flow.

[0218] Specifically, after identifying abnormal traffic flow, electronic devices can acquire the current abnormal location information of each abnormal vehicle ahead in each abnormal traffic flow based on at least two types of sensors.

[0219] Step S4042: Based on the current abnormal location information, determine the target abnormal vehicle that is closest to the current vehicle from among the vehicles ahead of the abnormal.

[0220] Specifically, the electronic device can compare the current abnormal position information of each abnormal vehicle ahead and determine the target abnormal vehicle that is closest to the current vehicle from among the abnormal vehicles ahead.

[0221] Step S4043: Obtain the longitudinal distance between the vehicle in front of the target abnormality and the vehicle itself.

[0222] Specifically, the electronic device can calculate the longitudinal distance between the vehicle in front of the target abnormality and the vehicle itself based on the vehicle's current location information and the current abnormal location information of the vehicle in front of the target abnormality.

[0223] Step S4044: Divide the longitudinal distance by the current speed of the vehicle to obtain the longitudinal time distance of the vehicle to the vehicle in front of the target abnormality.

[0224] Specifically, the electronic device can use the longitudinal distance divided by the current speed of the vehicle to obtain the longitudinal time distance of the vehicle relative to the vehicle in front of the target abnormality.

[0225] Step S4045: Control the vehicle according to the longitudinal time distance.

[0226] Specifically, step S3045 above may include the following steps:

[0227] Step c1: Compare the longitudinal time interval with the preset alarm time interval.

[0228] The preset alarm interval is calculated based on the vehicle's current speed, the vehicle's minimum deceleration value for avoiding danger, the shortest time required to trigger a driver's reaction and the driver's takeover response time threshold.

[0229] Specifically, the electronic device can calculate the preset alarm interval according to the following formula:

[0230] Preset alarm interval = V 本车当前车速 / a 本车避险最小减速度值 +Minimum time required to trigger a driver's response +Threshold for driver takeover response time.

[0231] The minimum time required for a sustained alarm to trigger a driver's response and the threshold for driver takeover response time can be input by the user into the electronic device based on relevant data, sent to the electronic device by other devices, or determined based on multiple experimental data. This application does not specifically limit the minimum time required for a sustained alarm to trigger a driver's response and the threshold for driver takeover response time.

[0232] Specifically, the electronic device compares the longitudinal time interval with the preset alarm time interval.

[0233] Step c2: If the longitudinal time interval is less than the preset alarm time interval, control the vehicle to output the current status information of the vehicle in front of the target abnormality and the current abnormality location information, and output alarm prompt information to the driver.

[0234] Specifically, if the longitudinal time interval is less than the preset alarm time interval, the electronic equipment can control the vehicle to output the current status information of the vehicle in front of the target abnormality and the current abnormality location information, and output alarm prompt information to the driver.

[0235] The current status information of the vehicle ahead of the target anomaly may include the vehicle's current speed, current heading angle, and current pose.

[0236] Step c3: Compare the longitudinal time interval with the preset emergency evacuation time interval.

[0237] The preset emergency avoidance time distance is calculated based on the vehicle's current speed, the vehicle's minimum deceleration value for avoiding danger, and the tolerance threshold from the issuance of the driving control signal to the realization of control; the preset emergency avoidance time distance is less than the preset alarm time distance.

[0238] For example, an electronic device can calculate the preset emergency avoidance time interval according to the following formula:

[0239] Preset emergency evacuation time interval = V 本车当前车速 / a 本车避险最小减速度值 + The tolerance threshold from the issuance of the driving control signal to the achievement of control.

[0240] The tolerance threshold from the issuance of the driving control signal to the implementation of control is used to characterize the tolerance threshold between the issuance of the driving control signal by the electronic device and the vehicle's implementation of control based on the driving control signal. This tolerance threshold can be input by the user based on relevant data, sent to the electronic device by other devices, or determined based on multiple experimental data. This application does not specifically limit the tolerance threshold from the issuance of the driving control signal to the implementation of control.

[0241] Specifically, after comparing the longitudinal time interval with the preset alarm time interval and outputting alarm prompt information, the electronic device also needs to continue comparing the longitudinal time interval with the preset emergency avoidance time interval.

[0242] Step c4: If the longitudinal time distance is less than the preset emergency avoidance time distance, then control the vehicle to decelerate and stop.

[0243] Specifically, if the longitudinal time interval is less than the preset emergency avoidance time interval, the electronic equipment can send an emergency avoidance deceleration and stop signal to the driving control module via the vehicle bus. Upon receiving the current status information and current location information of the abnormal traffic flow calculated by the electronic equipment, the alarm module issues auditory, visual, and tactile alarms to the driver.

[0244] After receiving the emergency avoidance deceleration and stopping driving control signal from the vehicle bus, the driving control module calculates the deceleration value and steering wheel angle value required for the vehicle to avoid danger according to the intelligent driving emergency collision avoidance driving control strategy, and realizes the deceleration and stopping of the vehicle through the driving actuator.

[0245] For example, such as Figure 5 The diagram shown is a structural schematic of the vehicle. The vehicle is equipped with a forward-facing lidar, a forward-facing camera, a forward-facing millimeter-wave radar, a navigation map device, and a combined positioning system consisting of GNSS, RTK, IMU, and wheel speed sensors.

[0246] (1) The ECU calculation unit determines the road where the vehicle is located. Based on the vehicle positioning information provided by the combined positioning module and the road segment location information provided by the navigation map module, the ECU determines the road segment where the vehicle is located.

[0247] (2) When the road segment where the vehicle is located is a continuous flat road segment. The ECU calculation unit determines whether the traffic flow in front of the vehicle is abnormal, that is, it detects whether there is abnormal traffic flow among the vehicles in front of the vehicle within a preset range based on at least two types of sensors.

[0248] (3) The ECU calculation unit issues an alarm message indicating abnormal traffic flow ahead. The ECU calculation unit issues a driving control signal to the vehicle in front of the abnormal traffic flow to decelerate and stop.

[0249] (4) If, after the ECU calculation unit issues an alarm message, the driving distance T between the vehicle and the nearest vehicle in the abnormal traffic flow ahead is less than the emergency avoidance condition threshold, where T = S the time distance between the vehicle and the vehicle with abnormal speed in the traffic flow / V the vehicle, and the emergency avoidance condition threshold = V the vehicle / a the minimum deceleration value of the vehicle for avoidance + the tolerance threshold for the driving control signal to be sent to the driving control module to achieve control, and an emergency avoidance deceleration and stop signal is sent to the driving control module through the vehicle bus.

[0250] (5) After receiving the abnormal traffic flow status information ahead and the location information of the nearest abnormal traffic flow vehicle from the ECU calculation unit, the alarm module sends an auditory, visual and tactile alarm to the driver.

[0251] (6) Driving control module: After receiving the emergency avoidance deceleration and stop driving control signal received by the vehicle bus, it calculates the deceleration value and steering wheel angle value required for the vehicle to avoid danger according to the intelligent driving emergency collision avoidance driving control strategy, and realizes the deceleration and stop of the vehicle through the driving actuator.

[0252] The vehicle driving control method provided in this application acquires the driving trajectory and current position information of each vehicle ahead detected by at least two types of sensors. Based on the driving trajectory and current position information of each vehicle ahead, it predicts the future position information of each vehicle ahead within a preset time period, ensuring the accuracy of the predicted future position information. For each vehicle ahead, the future driving segment is determined based on the future position information, ensuring the accuracy of the determined future driving segment. The future driving segment is identified, and it is detected whether the future driving segment is a continuous planar road segment, ensuring the accuracy of the detection result. If the future driving segment is a continuous planar road segment, the road length of the future driving segment is acquired. If the road length is less than or equal to a preset road length threshold, it is determined that the future driving segment of each vehicle ahead within the preset time period meets preset conditions. This ensures the accuracy of the determined future driving segment of each vehicle ahead within the preset time period meeting preset conditions.

[0253] If the future travel segments of vehicles ahead within a preset time period do not meet preset conditions, the current speeds of each vehicle ahead are acquired from at least two types of sensors, allowing for multi-dimensional acquisition of their current speeds. For each vehicle ahead, the current speed detected by each sensor is compared with the current speed of its adjacent vehicle to obtain the speed difference between the current vehicle and its adjacent vehicle, ensuring the accuracy of the obtained speed difference. For each vehicle ahead, the speed difference detected by each sensor is compared with a preset speed difference; if at least two sensors detect speed differences greater than the preset speed difference, and the duration of this difference exceeds a preset duration, the vehicle ahead is identified as an abnormal vehicle ahead; based on these abnormal vehicles ahead, abnormal traffic flow is determined, ensuring the accuracy of the identified abnormal traffic flow. Therefore, this solves the problem that traditional traffic flow monitoring systems often rely on a single sensor, failing to comprehensively and accurately acquire traffic flow information, leading to inaccurate judgments and untimely responses to traffic flow anomalies.

[0254] Then, the current abnormal position information of each abnormal vehicle ahead in each abnormal traffic flow is obtained. Based on the current abnormal position information, the target abnormal vehicle closest to the current vehicle ahead is determined from among the vehicles ahead, ensuring the accuracy of the determined target abnormal vehicle ahead. The longitudinal distance between the target abnormal vehicle ahead and the current vehicle is obtained. The longitudinal distance is divided by the current speed of the current vehicle ahead to obtain the longitudinal time distance between the current vehicle and the target abnormal vehicle ahead, ensuring the accuracy of the calculated longitudinal time distance. The longitudinal time distance is compared with the preset alarm time distance. If the longitudinal time distance is less than the preset alarm time distance, the current status information and current abnormal position information of the target abnormal vehicle ahead are output, and an alarm prompt is output to the driver, thus ensuring the accuracy of the output of the current status information and current abnormal position information of the target abnormal vehicle ahead, and the accuracy of the alarm prompt information output to the driver. The longitudinal time distance is compared with the preset emergency avoidance time distance. If the longitudinal time distance is less than the preset emergency avoidance time distance, the current vehicle ahead is decelerated and stopped, ensuring the accuracy of the deceleration and stopping control, thereby ensuring the driving safety of the current vehicle ahead.

[0255] This embodiment also provides a vehicle driving control device for implementing the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0256] This embodiment provides a vehicle driving control device, such as... Figure 6 As shown, it includes:

[0257] The acquisition module 501 is used to acquire the current road information corresponding to the current road where the vehicle is located;

[0258] The determination module 502 is used to identify the current road information and determine whether the current road is a continuous flat road segment; abnormal traffic flow consists of at least two abnormal vehicles in each of the vehicles ahead that are in an abnormal state;

[0259] The detection module 503 is used to detect, based on at least two types of sensors, whether there is abnormal traffic flow among the vehicles in front of the vehicle within a preset range in front of the vehicle if the vehicle is on a continuous flat road section.

[0260] The control module 504 is used to control the vehicle based on the relationship between the abnormal traffic flow and the vehicle if an abnormal traffic flow exists.

[0261] In some optional implementations, the determination module 502 is specifically used to identify the current road information and detect whether the current road is a fork or an intersection; if the current road is not a fork and not an intersection, the road slope corresponding to the current road is obtained; if the road slope is less than a preset slope threshold, the curve radius corresponding to the current road is obtained; if the curve radius is greater than a preset curve radius, the current road is determined to be a continuous planar road segment.

[0262] In some optional implementations, the detection module 503 is specifically used to acquire the current position information and / or current speed information of each vehicle ahead detected by at least two types of sensors; track the current position information and / or current speed information of each vehicle ahead detected by each sensor; for each vehicle ahead, if the current position information and / or current speed information of a vehicle ahead detected by at least two types of sensors is continuously lost for a time exceeding the disappearance judgment time threshold, then the vehicle ahead with continuously lost current position information and / or current speed information is determined to be an abnormal vehicle ahead; based on each abnormal vehicle ahead, an abnormal traffic flow is determined.

[0263] In some optional implementations, the detection module 503 is specifically used to identify the current road information and determine the current lane width; acquire the driving trajectories of each vehicle ahead detected by at least two types of sensors; for each vehicle ahead, compare the driving trajectory detected by each sensor with the lane trajectory corresponding to the vehicle ahead in the target lane a preset time ago; if the lateral deviation between the driving trajectory of the vehicle ahead detected by at least two types of sensors and the lane trajectory is greater than the current lane width, then the vehicle ahead is identified as an abnormal vehicle ahead; based on each abnormal vehicle ahead, an abnormal traffic flow is determined.

[0264] In some optional implementations, the detection module 503 is specifically used to detect whether the future travel segments of each vehicle ahead meet preset conditions within a preset time period; if the future travel segments of each vehicle ahead meet the preset conditions, then the current vehicle speed of each vehicle ahead is obtained from at least two types of sensors; for each vehicle ahead, the current vehicle speed detected by each sensor is compared with the current vehicle speed of the adjacent vehicle corresponding to the vehicle ahead to obtain the speed difference between the current vehicle and the adjacent vehicle corresponding to each sensor; for each vehicle ahead, based on the speed difference between the vehicle ahead and the adjacent vehicle detected by each sensor, it is determined whether there is abnormal traffic flow among the vehicles ahead.

[0265] In some optional implementations, the detection module 503 is specifically used to compare the speed difference detected by each sensor with a preset speed difference for each vehicle ahead; if at least two sensors detect a speed difference greater than the preset speed difference, and the duration of the speed difference being greater than the preset speed difference is greater than a preset duration, then the vehicle ahead is identified as an abnormal vehicle ahead; based on each abnormal vehicle ahead, an abnormal traffic flow is determined.

[0266] In some optional implementations, the detection module 503 is specifically used to acquire the driving trajectory and current position information of each vehicle ahead detected by at least two types of sensors; predict the future position information of each vehicle ahead within a preset time period based on the driving trajectory and current position information of each vehicle ahead; determine the future driving segment for each vehicle ahead based on the future position information; identify the future driving segment and detect whether the future driving segment is a continuous planar road segment; if the future driving segment is a continuous planar road segment, acquire the road length of the future driving segment; if the road length is less than or equal to a preset road length threshold, determine that the future driving segment of each vehicle ahead within a preset time period meets preset conditions.

[0267] In some optional implementations, the control module 504 is specifically used to acquire the current abnormal position information corresponding to each abnormal vehicle ahead in each abnormal traffic flow; based on the current abnormal position information, determine the target abnormal vehicle ahead that is closest to the vehicle from among the vehicles ahead; acquire the longitudinal distance between the target abnormal vehicle ahead and the vehicle; divide the longitudinal distance by the current speed of the vehicle to obtain the longitudinal time distance between the vehicle and the target abnormal vehicle ahead; and control the vehicle based on the longitudinal time distance.

[0268] In some optional implementations, the control module 504 is specifically used to compare the longitudinal time interval with the preset alarm time interval; the preset alarm time interval is calculated based on the current vehicle speed, the minimum deceleration value of the vehicle to avoid danger, the shortest time required to trigger a continuous alarm to cause a driver's reaction, and the driver's takeover reaction time threshold.

[0269] If the longitudinal time distance is less than the preset alarm time distance, the control system outputs the current status information and current location information of the vehicle ahead of the target abnormality, and outputs an alarm prompt to the driver; the longitudinal time distance is compared with the preset emergency avoidance time distance; the preset emergency avoidance time distance is calculated based on the current speed of the vehicle, the minimum deceleration value of the vehicle for avoiding danger, and the tolerance threshold from the issuance of the driving control signal to the realization of control; the preset emergency avoidance time distance is less than the preset alarm time distance;

[0270] If the longitudinal time distance is less than the preset emergency avoidance time distance, the vehicle will be controlled to decelerate and stop.

[0271] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0272] In this embodiment, the vehicle driving control device is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0273] This invention also provides an electronic device having the above-described features. Figure 6 The vehicle driving control device shown.

[0274] Please see Figure 7 , Figure 7 This is a schematic diagram of the structure of an electronic device provided in an optional embodiment of the present invention, such as... Figure 7 As shown, the electronic device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise as required. The processors can process instructions executed within the electronic device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple electronic devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 7 Take a processor 10 as an example.

[0275] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0276] The memory 20 stores instructions executable by at least one processor 10 to cause at least one processor 10 to perform the method shown in the above embodiments.

[0277] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the electronic device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the electronic device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0278] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0279] The electronic device also includes a communication interface 30 for communicating with other devices or communication networks.

[0280] This application also provides a vehicle, including electronic equipment, a vehicle body, and at least two types of sensors. Specifically, the vehicle may include at least two types of sensors such as forward-looking lidar, forward-looking camera, forward-looking millimeter-wave radar, navigation map equipment, and a combined positioning device consisting of GNSS+RTK+IMU+wheel speed sensors. The electronic equipment is used to execute the vehicle driving control method of any of the above embodiments.

[0281] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0282] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0283] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A vehicle driving control method, characterized in that, The method includes: Get the current road information corresponding to the current road where this vehicle is located; The current road information is identified to determine whether the current road is a continuous horizontal road segment; If the vehicle is on the continuous flat road segment, then based on at least two types of sensors, it is detected whether there is abnormal traffic flow among the vehicles in front of the vehicle within a preset range; the abnormal traffic flow consists of at least two abnormal vehicles in front of the vehicle that are in an abnormal state. If abnormal traffic flow exists, the vehicle is controlled according to the relationship between the abnormal traffic flow and the vehicle. Wherein, if abnormal traffic flow exists, the vehicle is controlled according to the relationship between the abnormal traffic flow and the vehicle, including: Obtain the current abnormal location information corresponding to each abnormal vehicle ahead in each abnormal traffic flow; Based on the current abnormal location information, determine the target abnormal vehicle that is closest to the vehicle from among the vehicles ahead of the abnormal location; Obtain the longitudinal distance between the vehicle ahead of the target anomaly and the vehicle itself; By dividing the longitudinal distance by the current speed of the vehicle, the longitudinal time distance of the vehicle relative to the vehicle in front of the target abnormality is obtained; The vehicle is controlled based on the longitudinal time interval.

2. The method according to claim 1, characterized in that, The step of identifying the current road information and determining whether the current road is a continuous surface road segment includes: The current road information is identified to detect whether the current road is a fork in the road or an intersection; If the current road is not a fork in the road and is not an intersection, then obtain the road slope corresponding to the current road. If the road gradient is less than a preset gradient threshold, then the radius of the curve corresponding to the current road is obtained; If the curve radius is greater than the preset curve radius, then the current road is determined to be the continuous planar road segment.

3. The method according to claim 1, characterized in that, The method of detecting whether there is abnormal traffic flow among vehicles within a preset range in front of the vehicle based on at least two types of sensors includes: Acquire the current position information and / or current speed information of each of the aforementioned vehicles detected by at least two types of sensors; Track the current position information and / or current speed information of each of the vehicles ahead detected by each of the sensors; For each of the aforementioned vehicles ahead, if the current position information and / or current speed information of the vehicle ahead detected by at least two types of sensors are continuously lost for a period of time exceeding the disappearance judgment time threshold, then the vehicle ahead that continuously loses its current position information and / or current speed information is determined to be the abnormal vehicle ahead. The abnormal traffic flow is determined based on the vehicles ahead of each of the aforementioned abnormalities.

4. The method according to claim 1, characterized in that, The method of detecting whether there is abnormal traffic flow among vehicles within a preset range in front of the vehicle based on at least two types of sensors includes: The current road information is identified to determine the current lane width; Acquire the driving trajectories of each of the aforementioned vehicles detected by at least two types of sensors; For each of the aforementioned vehicles ahead, the driving trajectory detected by each of the aforementioned sensors is compared with the lane trajectory corresponding to the target lane where the vehicle ahead was located a preset time ago; If the lateral deviation between the driving trajectory of the vehicle ahead and the lane trajectory detected by at least two types of sensors is greater than the current lane width, then the vehicle ahead is identified as the abnormal vehicle ahead. The abnormal traffic flow is determined based on the vehicles ahead of each of the aforementioned abnormalities.

5. The method according to claim 1, characterized in that, The method of detecting whether there is abnormal traffic flow among vehicles within a preset range in front of the vehicle based on at least two types of sensors includes: Detect whether the future travel route of each of the aforementioned vehicles within a preset time period meets the preset conditions; If the future travel route of each of the aforementioned vehicles meets the preset conditions within the preset future time period, then the current vehicle speed of each of the aforementioned vehicles transmitted by at least two types of sensors is obtained. For each of the aforementioned vehicles ahead, the current speed of the vehicle ahead detected by each of the aforementioned sensors is compared with the current speed of the adjacent vehicle corresponding to the vehicle ahead, to obtain the speed difference between the vehicle ahead and the adjacent vehicle corresponding to each of the aforementioned sensors. For each of the aforementioned vehicles ahead, based on the speed difference between the vehicle ahead and the adjacent vehicle detected by each of the aforementioned sensors, it is determined whether there is abnormal traffic flow among the vehicles ahead.

6. The method according to claim 5, characterized in that, For each of the vehicles ahead, based on the speed difference between the vehicle ahead and the adjacent vehicle detected by each of the sensors, the determination of whether there is abnormal traffic flow among the vehicles ahead includes: For each of the aforementioned vehicles ahead, the speed difference detected by each of the aforementioned sensors is compared with a preset speed difference; If at least two sensors detect a speed difference greater than a preset speed difference, and the duration of the speed difference being greater than the preset speed difference is greater than a preset duration, then the vehicle ahead is identified as the abnormal vehicle ahead. The abnormal traffic flow is determined based on the vehicles ahead of each of the aforementioned abnormalities.

7. The method according to claim 5, characterized in that, The detection of whether the future travel routes of each of the aforementioned vehicles within a preset time period meet preset conditions includes: Acquire the driving trajectory and current location information of each of the vehicles ahead, as detected by at least two types of sensors; Based on the driving trajectory and current location information of each of the vehicles ahead, predict the future location information of each of the vehicles ahead within the preset future time period; For each of the aforementioned vehicles ahead, the future travel route is determined based on the future location information; The future travel route is identified, and it is detected whether the future travel route is a continuous flat road segment; If the future travel segment is a continuous flat road segment, then obtain the road length of the future travel segment; If the road length is less than or equal to a preset road length threshold, then it is determined that the future travel segment of each of the preceding vehicles within the preset future time period meets the preset condition.

8. The method according to claim 1, characterized in that, The step of controlling the vehicle based on the longitudinal time distance includes: The longitudinal time interval is compared with the preset alarm time interval; the preset alarm time interval is calculated based on the current vehicle speed, the minimum deceleration value of the vehicle to avoid danger, the shortest time required to trigger a driver's reaction and the driver's takeover reaction time threshold. If the longitudinal time interval is less than the preset alarm time interval, the vehicle is controlled to output the current status information of the vehicle ahead of the target abnormality and the current abnormality location information, and an alarm prompt message is output to the driver. The longitudinal time interval is compared with the preset emergency avoidance time interval; the preset emergency avoidance time interval is calculated based on the current vehicle speed, the minimum deceleration value of the vehicle for avoiding danger, and the tolerance threshold from the issuance of the driving control signal to the realization of control; the preset emergency avoidance time interval is less than the preset alarm time interval; If the longitudinal time distance is less than the preset emergency avoidance time distance, then the vehicle is controlled to decelerate and stop.

9. A vehicle driving control device, characterized in that, The device includes: The acquisition module is used to acquire the current road information corresponding to the current road where the vehicle is located; The determination module is used to identify the current road information and determine whether the current road is a continuous horizontal road segment; The detection module is used to detect, based on at least two types of sensors, whether there is abnormal traffic flow among the vehicles ahead within a preset range in front of the vehicle if the vehicle is on the continuous planar road segment; the abnormal traffic flow consists of at least two abnormal vehicles ahead that are in an abnormal state. A control module is configured to control the vehicle based on the relationship between the abnormal traffic flow and the vehicle if an abnormal traffic flow exists. The control of the vehicle based on the relationship between the abnormal traffic flow and the vehicle includes: acquiring current abnormal position information corresponding to each abnormal vehicle ahead in each abnormal traffic flow; determining the target abnormal vehicle ahead that is closest to the vehicle from among the abnormal vehicles ahead based on the current abnormal position information; acquiring the longitudinal distance between the target abnormal vehicle ahead and the vehicle; obtaining the longitudinal time distance between the vehicle and the target abnormal vehicle ahead by dividing the longitudinal distance by the vehicle's current speed; and controlling the vehicle based on the longitudinal time distance.

10. An electronic device, characterized in that, include: A memory and a processor are communicatively connected, the memory stores computer instructions, and the processor executes the computer instructions to perform the vehicle driving control method according to any one of claims 1 to 8.

11. A vehicle, characterized in that, The vehicle includes electronic equipment, a vehicle body, and at least two types of sensors, wherein the electronic equipment is used to execute the vehicle driving control method according to any one of claims 1 to 8.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the vehicle driving control method according to any one of claims 1 to 8.

13. A computer program product, characterized in that, It includes computer instructions for causing a computer to perform the vehicle driving control method according to any one of claims 1 to 8.

Images