Vehicle escape control method, electronic equipment and vehicle

Abstract

The invention provides a vehicle escape control method, electronic equipment and a vehicle. According to the scheme, the vehicle is controlled to escape at least according to the current escape state of the vehicle and the type of a road scene where the vehicle is located. The method and the device are used for solving the problem that different trapping scenes are difficult to deal with through a single trapping-out means and effective trapping-out cannot be realized in the prior art.

Description

Technical Field

[0001] This invention relates to the field of intelligent vehicle technology, and in particular to a vehicle traction control method, electronic equipment, and vehicle. Background Technology

[0002] When a vehicle is driving in complex terrain or in bad weather conditions in the wild, it may get stuck in mud, snow or other soft ground, causing the rear wheels or all four wheels to slip and become unable to move forward. In this case, not only will driving safety be affected, but rescue costs and time will also be increased.

[0003] In related technologies, vehicle escaping methods mainly include single escaping means such as using the suspension to generate dynamic load or the rear wheels to rotate. However, vehicle entrapment scenarios are quite diverse, and the specific scenarios of vehicle entrapment states also vary. Relying on a single escaping method cannot adapt to various different entrapment scenarios and therefore cannot achieve effective escaping. Summary of the Invention

[0004] This invention aims to at least solve one of the technical problems existing in the prior art. To this end, this invention proposes a vehicle extrication control method, electronic device, and vehicle. This solution controls the vehicle's extrication based at least on the vehicle's current state of being stuck and the type of road surface in which the vehicle is located. It can fully consider the vehicle's current state of being stuck and the road surface in which the vehicle is located to control the vehicle's extrication, and can cope with different stuck scenarios, thereby achieving effective extrication.

[0005] To achieve the above objectives, this application adopts the following technical solution:

[0006] In a first aspect, this application provides a vehicle extrication control method, the method comprising: controlling the vehicle to extricate itself from the predicament based at least on the vehicle’s current state of being stuck and the type of road surface in which the vehicle is located.

[0007] In some embodiments of this application, the method further includes: controlling the vehicle to get out of trouble based at least on the vehicle's current stuck state and the type of road surface scene in which the vehicle is located under the stuck state, including: determining a target getting-out strategy based at least on the vehicle's current stuck state and the type of road surface scene in which the vehicle is located; and executing the target getting-out strategy to control the vehicle to get out of trouble.

[0008] Secondly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, causes the computer to perform the method described in the first aspect.

[0009] Thirdly, this application provides a computer program product that stores instructions which, when executed by a computer, cause the computer to perform the method described in the first aspect.

[0010] Fourthly, this application provides an electronic device, comprising: a memory having a computer program stored thereon; and a processor for executing the computer program in the memory to implement the method as described in the first aspect.

[0011] Fifthly, this application provides a vehicle comprising: an electronic device as described in the fourth aspect; or, a processor configured to perform the method as described in the first aspect.

[0012] The advantages and control methods of the vehicle and electronic equipment compared to the prior art are the same, and will not be elaborated here.

[0013] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] To gain a more complete understanding of this application and its beneficial effects, the following description will be provided in conjunction with the accompanying drawings, wherein the same reference numerals in the following description denote the same parts.

[0016] Figure 1 This is a schematic diagram of the system composition provided according to an embodiment of the present invention;

[0017] Figure 2 This is a schematic flowchart of a vehicle traction control method according to an embodiment of the present invention;

[0018] Figure 3 This is a flowchart illustrating the adaptive escape method provided according to an embodiment of the present invention;

[0019] Figure 4 This is a schematic diagram of adaptive escape strategy selection according to an embodiment of the present invention;

[0020] Figure 5 This is a schematic diagram of the vehicle rollover risk factor judgment process provided in an embodiment of the present invention;

[0021] Figure 6 This is a schematic diagram of the vehicle entrapment sign judgment process provided in an embodiment of the present invention;

[0022] Figure 7 This is a schematic diagram of the vehicle chassis bottoming mark determination process provided in an embodiment of the present invention;

[0023] Figure 8 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present invention. Detailed Implementation

[0024] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the protection scope of this application.

[0025] In related technologies, vehicle escaping methods mainly include single escaping means such as using the suspension to generate dynamic load or the rear wheels to rotate. However, vehicle entrapment scenarios are quite diverse, and the specific scenarios of vehicle entrapment states also vary. Relying on a single escaping method cannot adapt to various different entrapment scenarios and therefore cannot achieve effective escaping.

[0026] To address this, the present invention proposes a vehicle extrication control method, electronic device, and vehicle. This solution controls the vehicle's extrication based at least on the vehicle's current state of being stuck and the type of road surface in which the vehicle is located. It can fully consider the vehicle's current state of being stuck and the road surface in which the vehicle is located to control the vehicle's extrication, and can cope with different stuck scenarios, thereby achieving effective extrication.

[0027] The present invention will now be described in further detail with reference to the embodiments.

[0028] like Figure 1 The diagram shown is a schematic representation of the system composition provided in an embodiment of the present invention. The system mainly consists of onboard sensors, a VCU system, and a user interface (UI) system.

[0029] The vehicle traction control method provided in this invention is built into the VCU controller and is applicable to vehicles equipped with active suspension and rear-wheel steering. The VCU controller receives information from inertial measurement unit sensors, wheel speed sensors, vehicle speed sensors, ultrasonic sensors, etc., and identifies and calculates the vehicle's entrapment state. This includes calculating the vehicle's attitude angles (roll angle, pitch angle) based on the acceleration and angular velocity signals of the XYZ axes from the inertial measurement unit, calculating the slip ratio of each wheel using wheel speed sensors and acceleration sensors, and calculating the vehicle's ground clearance using ultrasonic sensors, thereby obtaining the vehicle's entrapment depth. Different entrapment states require different traction strategies. For example, if the vehicle's roll angle is too large when entrapped, the risk of rollover during traction must be considered; if the vehicle's chassis bottoms out and the ground clearance is too low, the corresponding wheels need to be controlled to raise the active suspension. Furthermore, when the vehicle's chassis bottoms out, it is not advisable to control the active suspension to move at a certain frequency to increase dynamic load. The VCU controller identifies the road conditions when the vehicle is stuck using ARI technology and calculates the vehicle's stuck state based on onboard sensors. It then selects an appropriate vehicle escaping method according to adaptive escaping logic and actively performs the escaping operation, enabling one-click intelligent escaping and improving the efficiency and success rate of vehicle escaping.

[0030] Vehicle-mounted sensors include suspension height sensors, vision sensors, IMU sensors, radar sensors, wheel speed sensors, infrared sensors, accelerator pedal opening sensors, and brake pedal opening sensors. Among these, ARI technology uses vision camera sensors, radar sensors, and infrared sensors to identify the vehicle's location within a given environment. This includes identifying various road conditions such as soft dry snow, compacted snow, mixed snow, mud, dry mud, mixed mud, soft dry sand, and soft wet sand.

[0031] Meanwhile, onboard sensors such as the accelerator pedal opening sensor and brake pedal opening sensor monitor the user's current needs, while wheel speed sensors monitor the vehicle's speed and wheel slip ratio. The vehicle's IMU sensor (inertial measurement unit) can monitor the vehicle's attitude angle, and the vehicle's ultrasonic sensor can identify the height of the left and right rearview mirrors from the ground to monitor the chassis bottoming status when the vehicle is stuck.

[0032] The VCU control system is responsible for receiving various information from onboard sensors, processing and calculating it to obtain the road scene and vehicle status when the vehicle is stuck. It then performs adaptive algorithm calculations to determine the optimal escape control method for the current vehicle stuck state and interacts with the user through the UI.

[0033] This invention applies to vehicles with actively controllable four-wheel suspension and rear-wheel steering. Different road conditions and vehicle entrapment situations require different evacuation strategies, enabling intelligent and efficient vehicle extrication. Choosing an inappropriate evacuation method when stuck can not only fail to extricate the vehicle effectively but may even cause it to sink deeper. Therefore, selecting the appropriate evacuation method based on different scenarios and vehicle conditions, rather than using a single method for all situations, can significantly increase the vehicle's intelligence, reliability, and safety, and enhance its off-road capabilities.

[0034] like Figure 2 The diagram shown is a flowchart illustrating a vehicle traction control method according to an embodiment of the present invention. The method includes: 101. Controlling the vehicle to escape traction based at least on the vehicle's current entrapment state and the type of road surface in which the vehicle is located.

[0035] In the above embodiments, by constructing a multi-dimensional perception and decision-making closed loop, the system no longer relies on preset fixed escape logic. Instead, it dynamically matches the optimal combination of escape actions based on real-time perception of road surface type and vehicle dynamic status, significantly improving the success rate and safety of escape. This enables it to cope with different stuck scenarios and achieve effective escape.

[0036] For example, by combining the results of road surface scene type recognition (such as ARI visual recognition of soft dry snow) with the state of being stuck (such as pitch angle >8° and four-wheel slip rate >40% measured by IMU), it can be accurately determined to be a "shallow sinking + low adhesion" composite working condition, thereby activating the "low torque pulse + differential lock intervention + moderate suspension lifting" combined strategy to avoid energy waste and secondary getting stuck, and significantly improve the success rate and safety of getting out of trouble.

[0037] In some embodiments, step 101 above includes the following: 101a, determining a target extrication strategy based at least on the current stuck state of the vehicle and the type of road surface where the vehicle is located; 101b, executing the target extrication strategy to control the vehicle to get out of trouble.

[0038] In the above embodiments, the system achieves precise matching of strategies by mapping the trapped state, road surface type and vehicle state parameters in a three-dimensional decision space.

[0039] For example, when the system identifies a situation as "soft dry sand + overall stuck + chassis bottoming out indicator," it prioritizes a combined strategy of "raising the suspension to its highest position + disabling rear-wheel steering + activating differential torque distribution via wheel-side motors + reducing throttle opening to the set value" to prevent further stuckness due to chassis scraping. When the situation is "mixed mud + partial stuck + rollover risk factor > set value," it disables high-throttle acceleration and instead activates a strategy of "single-wheel low-speed reverse rotation + suspension roll compensation + rear-wheel steering reverse deflection" to effectively reduce lateral slip moment and prevent rollover. During execution, the system sends coordinated commands to the active air suspension controller, rear-wheel steering system controller, and wheel-side motor controller via the CAN bus. Each actuator responds within 100ms, forming a closed-loop control to ensure a smooth and shock-free escape maneuver.

[0040] In some embodiments, step 101a above specifically includes the following: A1. Determine the target extrication strategy based on the vehicle's current stuck state, the type of road scene where the vehicle is located, and the vehicle's state parameters.

[0041] The vehicle status parameters mentioned above include: vehicle chassis bottoming-out markings and / or vehicle rollover risk factors. These vehicle status parameters can serve as safety constraints, enhancing the boundary control capabilities of policy execution.

[0042] For example, in a "muddy road surface" scenario, if the system detects that the vehicle chassis has bottomed out, it will prohibit the "full suspension lift" strategy to avoid excessive suspension extension that could lead to driveshaft breakage or oil pipe tearing. Instead, the system will select a "low-speed continuous thrust + rear differential lock + front wheel steering fine-tuning" strategy to maintain structural integrity by minimizing ground clearance. Similarly, in a "soft, wet sandy road surface" scenario, if the vehicle rollover risk factor exceeds a threshold, the system will disable the "single-wheel high torque output" strategy to prevent rollover caused by a sudden increase in lateral torque. Instead, it will activate a "four-wheel synchronous low torque pulse + steering angle zeroing" strategy to ensure center of gravity stability. This multi-parameter fusion mechanism upgrades the traction strategy from "single-function triggering" to "safety constraint driving," effectively mitigating the risk of mechanical damage under extreme conditions.

[0043] For example, A1 above may include the following:

[0044] A11. Determine the target control strategy based on the vehicle's current stuck state, the type of road surface the vehicle is on, and the vehicle's undercarriage bottoming marks.

[0045] Alternatively, A12, determine the target extrication strategy based on the vehicle's current stuck state, the type of road surface the vehicle is on, and the vehicle rollover risk factors.

[0046] Alternatively, A13, determine the extrication strategy based on the vehicle's current stuck state, the type of road surface the vehicle is on, the vehicle's undercarriage bottoming marks, and the vehicle's rollover risk factors.

[0047] In the above embodiments, the system of the present invention adopts a triple priority decision tree: the first priority is the chassis bottoming-out indicator; if it is "vehicle chassis bottoming out," then all strategies involving suspension lifting or drastic adjustment of vehicle body posture are blocked; the second priority is the rollover risk factor; if it is greater than the threshold, then any single-wheel drive or differential lock-up action is prohibited; the third priority is the chassis bottoming-out indicator and the rollover risk factor; if both of these conditions are met, then related actions are simultaneously prohibited or blocked. For example, in a "mixed muddy road surface," if the vehicle is "completely stuck," and the chassis bottoming-out indicator is "vehicle chassis bottoming out," and the rollover risk factor is greater than the threshold, then the system can allow the execution of the "four-wheel low torque continuous output + steering angle to maintain straight driving + braking assistance stability" strategy, and prohibit differential lock intervention. This strategy limits the torque of each wheel to the rated value through the wheel speed controller, continuously outputs for a set time, and cooperates with the ESP system to apply slight braking to the inner wheel to form a stable thrust torque. This combination successfully navigated multiple complex scenarios involving bottoming out and high rollover risk in real-vehicle testing without any structural damage or rollover. During strategy execution, the system continuously monitors chassis sensor data. If the bottoming out indicator disappears and the rollover factor drops below the set value, it automatically switches to the "high torque pulse" strategy, achieving dynamic strategy evolution.

[0048] As an optional implementation, the main implementation process of A13 described above can be referred to Figure 3 The flowchart shown illustrates the adaptive escape method. Figure 3 As shown, the method includes:

[0049] S301. Determine if the vehicle is stuck. If it is stuck, proceed to S302. Otherwise, end the process.

[0050] S302. Determine whether the vehicle is partially or completely stuck.

[0051] S303. Determine the type of road surface where the vehicle is located—sand, mud, or snow—through ARI vehicle scene road recognition.

[0052] S304. Determine whether the chassis bottoming indicator is for bottoming or not.

[0053] S305. Determine whether the rollover risk factor is less than the threshold.

[0054] S306, Output of adaptive escape control method.

[0055] It should be noted that the execution order of steps S302-S305 is not limited to the above order. It can also be executed in the order of S305-S304-S303-S302. This is just an example and is not a limitation.

[0056] The adaptive escape strategy selection output by S306 mentioned above can be referenced. Figure 4 The contents of the logic diagram shown.

[0057] For example, Figure 4 The specific strategies involved are as follows:

[0058] (1) Suspension motor coupling control method 1 and method 2: The suspension height mode under this strategy is high, and the target speed of the front and rear motors is obtained by looking up the table according to the rollover risk factor. The difference between method 1 and method 2 is that the speed corresponding to method 1 is lower than the speed corresponding to method 2.

[0059] (2) Suspension motor coupling control method 3: Under this strategy, the target suspension height is distinguished according to the roll angle. When the vehicle tilts to the right (roll angle is greater than 0), the target height of the right suspension and the target speed of the front and rear motors (initial set value) are obtained by looking up the table according to the roll angle. When the vehicle tilts to the left (roll angle is less than 0), the target height of the left suspension and the target speed of the front and rear motors (initial set value) are obtained by looking up the table according to the roll angle.

[0060] (3) Suspension motor coupling control method 4: Under this strategy, the initial target speed of the front and rear motors is obtained by looking up the table based on the roll angle. At the same time, the suspension height moves up and down according to a certain rule, and the target speed offset is increased according to the actual change of the suspension height.

[0061] (4) Rotation fusion control algorithm 1: Under this strategy, when the system function is entered, the control state is divided into left rotation control, left rotation return to center, right rotation control, and right rotation return to center. The target steering wheel angle, target braking torque of the left rear wheel and target braking torque of the right rear wheel, and target speed of the front and rear motors are obtained by looking up the table according to the different control states.

[0062] (5) Inertial characteristic fusion control method: Under this strategy, when the vehicle is stuck in the overall state, the chassis is stuck in the bottom state, the ARI road surface recognition result is a sand pit or a soil pit, and the rollover factor is less than a certain threshold, the inertial characteristic fusion control is entered, and the front and rear motors respond to the speed control. When the target gear is D and the actual gear is D, the target vehicle speed is obtained by looking up the table according to the ARI scene recognition, and the vehicle is driven forward. When the pitch angle of the whole vehicle is greater than a certain threshold, the pitch angle velocity is less than a certain threshold, the vehicle speed is less than a certain threshold and the acceleration is less than a certain threshold, the target gear is switched to R and the same logic is performed until the number of DR gear switching meets a certain threshold or the vehicle is stuck in the state of not being stuck.

[0063] It should be noted that the above adaptive escape strategy is only for illustrative purposes. In practical applications, other strategies can be set, which are not limited here.

[0064] In some embodiments, the method further includes: 102. Calculating the vehicle rollover risk factor based on the vehicle static stability coefficient and the vehicle load transfer rate.

[0065] For example, step 102 above includes the following: a vehicle rollover risk factor can be obtained by weighting the static stability coefficient and the vehicle load transfer rate.

[0066] For example, the rollover risk factor is calculated using the formula R_f = (SSF×a) + (LTR×b), where SSF is the static stability coefficient. Where B is the wheel track width. Body roll angle, Where is the vehicle's center of gravity height, and f is a correction factor. LTR is the load transfer rate. .in, The vertical load is on the left tire. This refers to the vertical load on the left tire. See reference for details. Figure 5 The diagram shows the process for determining vehicle rollover risk factors. The specific implementation details are shown in the figure and will not be elaborated upon here.

[0067] In some embodiments, the method described above further includes: 100. Determining the current stuck state of the vehicle.

[0068] For example, step 100 above can also be achieved by the following: A2. Determine the current stuck state of the vehicle based on the vehicle gear, pedal opening, whether the vehicle is in the pit, and the actual vehicle speed.

[0069] In the above embodiment, the system integrates four types of signals—gear position, pedal opening, pothole detection, and vehicle speed—to construct a multi-condition entrapment judgment logic, avoiding false triggering by a single sensor. This judgment is initiated when the driver actively operates the system, ensuring that the system response aligns with the driver's intentions. Figure 1 This will improve the safety of human-machine collaboration.

[0070] Further optionally, step A2 above specifically includes the following: A21, when the vehicle gear and pedal opening meet the set threshold, if the vehicle is in the pit and the actual vehicle speed is greater than the first threshold and less than the second threshold, the vehicle is determined to be in a first trapped state; or, A22, when the vehicle gear and pedal opening meet the set threshold, if the vehicle is not in the pit and the actual vehicle speed is less than the third threshold, the vehicle is determined to be in a second trapped state.

[0071] The first trapped state mentioned above includes overall trapped state, and the second trapped state mentioned above includes partial trapped state. In practical applications, trapped states can also be customized according to actual needs; this is only an example and not a limitation.

[0072] Further optionally, the above method also includes: A01, if the identification result shows both a front slope and a rear slope, then the vehicle is in a pit; and / or, A02, if the identification result shows both a left slope and a right slope, then the vehicle is in a pit. In the above embodiments, the identification logic has been verified to be effective in real vehicle testing: in three typical scenarios—desert, snow, and mud—the system's accuracy in identifying pits reached 96.3% (n=127 tests). When the vehicle is in a "front slope + rear slope" state, the system calculates the pit depth (by fusing lidar and IMU). If the pit depth > a threshold, the system automatically activates the "four-wheel independent suspension lift + rear wheel steering symmetrical deflection" mode, causing the vehicle to adopt a "climbing posture." When it is a "left slope + right slope," the system determines it to be a lateral pit and activates the "differential lock + roll compensation + lateral torque distribution" strategy, causing the vehicle to "drift" laterally out of the pit, avoiding the false triggering caused by the traditional system relying solely on wheel speed difference for judgment.

[0073] Optionally, the aforementioned pedal opening includes the accelerator pedal opening and / or the brake pedal opening. In steps A21 and A22, the vehicle gear and pedal opening satisfying the set thresholds include: the vehicle gear is a drive gear and the accelerator pedal opening is greater than a first calibration value; or, the vehicle gear is a drive gear and the brake pedal opening is less than a second calibration value; or, the vehicle gear is a drive gear and the accelerator pedal opening is greater than the first calibration value and the brake pedal opening is less than the second calibration value.

[0074] In the above embodiments, the triple-condition design covers three typical operating modes of the driver when stuck: 1) pure accelerator pedal application (such as flooring the accelerator on snow); 2) light braking + accelerator (such as intermittent braking to get out of trouble in mud); 3) coordinated accelerator and brake application (such as alternating operation on sand). The first calibration value can be determined based on the average accelerator opening of the driver when stuck in a large number of real vehicle tests; the second calibration value can be a low braking threshold, used to identify the intention of "light braking". For example, in a mixed muddy terrain scenario, the driver attempts to get out of trouble by "lightly pressing the accelerator + intermittent braking". The system detects that the accelerator is greater than threshold 1 and the brake is less than threshold 2, which activates the get-out-of-trouble preparation process, pre-lifts the suspension and pre-locks the differential, so that the get-out action starts after the pedal is applied, realizing "human-machine collaborative" get-out.

[0075] In the above embodiment, the system continuously monitors the vehicle's gear position (D / R / N), accelerator / brake pedal opening, wheel speed, vehicle speed, and the state inside the ditch. When the vehicle is in drive gear (D or R), and the accelerator opening continuously exceeds the first calibration value (35%) and / or the brake opening is lower than the second calibration value (5%), the system enters the ditch judgment mode. At this time, if the system detects that the vehicle is inside the ditch and the vehicle speed is between the first threshold (5km / h) and the second threshold (15km / h), it is judged as "overall ditch"—that is, all four wheels are stuck and there is no effective displacement; if the system does not detect the vehicle inside the ditch and the vehicle speed is lower than the third threshold (2km / h), it is judged as "partial ditch"—that is, some wheels are slipping but the vehicle body is not completely stuck. The state machine logic is set with a certain delay filter to effectively filter out instantaneous bump interference.

[0076] For example, the determination of the stuck state in step 100 above can rely on a dynamic evaluation model based on multi-sensor fusion. The system collects data in real time from IMU (three-axis acceleration and angular velocity), wheel speed sensors (independent rotational speed of four wheels), vehicle speed sensors (GPS + wheel speed fusion), and ultrasonic sensors (ground clearance at four corners). This data is then fused using a Kalman filter to output key state quantities such as vehicle pitch angle, roll angle, wheel slip ratio, and average ground clearance. For instance, if the IMU detects a pitch angle >6° for 2 consecutive seconds, and all four wheel slip ratios >25%, while the ultrasonic sensors measure a front wheel ground clearance <180mm (calibrated value is 220mm), the system determines it as "overall stuck." If only the front wheel slip ratio is >40% and the rear wheels are normal, it is determined as "partial stuck." This model updates the state evaluation every 100ms, ensuring a response delay <150ms, meeting the real-time requirements of the escape action. This mechanism overcomes the one-sidedness of traditional methods that rely solely on vehicle speed or throttle opening, and achieves three-dimensional quantification of the depth, distribution range, and dynamic trend of the entrapment.

[0077] like Figure 6 The diagram shown illustrates the vehicle entrapment sign determination process provided in an embodiment of the present invention. The specific steps are as follows:

[0078] S601: Determine if the vehicle is actually in a drive gear, i.e., D / R. If so, proceed to the next step; otherwise, the process ends.

[0079] S602: If the vehicle is actually in drive mode, the next step is to determine if the accelerator pedal opening is greater than the threshold. If so, it means that the user has a certain driving need; otherwise, the process ends.

[0080] S603: Determine if the vehicle's brake pedal opening is less than the threshold. If so, proceed to the next step; otherwise, the process ends.

[0081] S604: Determine if the vehicle is in a ditch before proceeding to the next step.

[0082] S605: Determine the actual vehicle speed. If the actual vehicle speed is less than threshold 1, or if threshold 2 is less than the actual vehicle speed and threshold 3 is less than the actual vehicle speed.

[0083] S606: If the vehicle's actual speed is less than threshold 1, the vehicle is determined to be in a stuck state, and it is a partial stuck state; if the vehicle's actual speed meets the condition that threshold 2 < vehicle's actual speed < threshold 3, the vehicle is determined to be in a stuck state, and it is a completely stuck state. Otherwise, the process ends.

[0084] S607: Outputs a vehicle stuck sign.

[0085] In some embodiments, the method further includes: 100a, determining the chassis bottoming status of the vehicle based on the height of the vehicle reference point above the ground and a third calibration value; 100b, outputting a chassis bottoming indicator based on the chassis bottoming status.

[0086] For example, the system deploys ultrasonic sensors at the four corners of the vehicle to measure the ground clearance of four reference points: front left, front right, rear left, and rear right. The calibration value is based on measurements taken on a flat, hard surface when the vehicle is unloaded; for example, it can be set to 220mm. When any sensor measures a height <120mm (i.e., below 45% of the calibration value), the system determines that the point has "bottomed out" and sets the "chassis bottoming-out indicator" to 1. If all four points are >180mm, the indicator is set to 0. This indicator is a binary output and participates in all escape strategy decisions. In a "soft, dry sand" scenario, if the front point bottoms out but the rear point does not, the system only restricts the front suspension from lifting, while the rear suspension continues to operate normally, achieving localized protection. This mechanism avoids the coarse-grained judgment of traditional "full vehicle bottoming out," improving the precision of the strategy.

[0087] like Figure 7 The diagram shown illustrates the process for determining a vehicle chassis bottoming-out mark according to an embodiment of the present invention. It mainly includes the following steps:

[0088] S701: Ultrasonic sensors are installed at the bottom of the left and right rearview mirrors of the vehicle. The vertical distance of the left and right rearview mirrors from the ground is obtained by measuring the distance of the left and right rearview mirrors from the ground based on the ultrasonic sensors.

[0089] S702: Based on the acceleration and angular velocity signals in the XYZ directions from the IMU sensor, and using the Kalman filtering algorithm, the roll angle and pitch angle of the vehicle body are calculated.

[0090] The Kalman filter algorithm for the vehicle body attitude angle mentioned above is as follows:

[0091] (1) Attitude angle estimation based on acceleration

[0092] ① Roll angle_acceleration_rad=arctan((Y-axis acceleration_mps2-yaw rate_radps*actual vehicle speed_mps) / Z-axis acceleration_mps2), and the output is roll angle_acceleration_filtered_rad after first-order low-pass filtering.

[0093] ② Pitch angle_acceleration_rad=-arctan((X-axis acceleration_mps2-actual vehicle acceleration_mps2) / sqrt(Y-axis acceleration_mps2^2+Z-axis acceleration_mps2^2)), and after passing through a first-order low-pass filter, the output is pitch angle_acceleration_filtered_rad.

[0094] (2) Attitude angle estimation based on acceleration and angular velocity

[0095] The rate of change of roll angle _radps = roll angular velocity _radps + pitch angular velocity _radps * tan(pitch angle _ angular velocity _ rad (k-1)) * sin(roll angle _ angular velocity _ rad (k-1)) + yaw angular velocity _radps * tan(pitch angle _ angular velocity _ rad (k-1)) * cos(roll angle _ angular velocity _ rad (k-1)).

[0096] Pitch angle change rate _radps = pitch angular velocity _radps * cos(pick angle _ angular velocity _ rad (k-1)) - yaw angular velocity _radps * sin(pick angle _ angular velocity _ rad (k-1)).

[0097] Perform Kalman estimation as follows, and output the roll angle_angular velocity_rad and pitch angle_angular velocity_rad:

[0098] ① Prior estimation: .in, , Let x be the prior estimate of the state variable x at time k. , The result is the Kalman estimation of the state variable x at time k-1. dT is the program execution cycle. u(k) is the control quantity u at time k.

[0099] ② Noise covariance estimation: .in, Let be the noise covariance matrix of the prior estimation error. Let be the covariance matrix of the posterior estimation error, and Q be the covariance matrix of the process noise.

[0100] ③ Kalman gain: .in, R is the covariance matrix of the observation noise.

[0101] ④ Posterior estimation: .in, The Kalman estimation result of the state variable x at time k. .

[0102] ⑤ Update covariance: .

[0103] ⑥ Initial values ​​and standardization values: ; (P0 is the calibration value), (Q is the calibration value) (R is the calibration value).

[0104] S703: The distance from the vehicle reference point position to the ultrasonic sensors of the left and right rearview mirrors in the X, Y, and Z directions, obtained according to the calibration.

[0105] S704: Based on the vehicle attitude angles and calibration distances obtained from S302 and S303, the ground clearance of each reference point of the vehicle is calculated.

[0106] For example, in this embodiment of the invention, the height of the reference point above the ground is calculated according to geometric relationships based on the vehicle body attitude angle and the height values ​​of the left and right rearview mirror height sensors. The specific calculation formula is as follows: , , .

[0107] in, That is, the height of the reference point above the ground. The reference point is the height of the projection of the reference point onto the plane where the left height sensor is located. The reference point is the height of its projection onto the plane where the right height sensor is located, above the ground. The height of the left rearview mirror from the ground. The height of the right rearview mirror from the ground. For intelligent escape pitch angle, For intelligent off-road tilt angle, The distance in the Z direction from the reference point to the mounting point of the left height sensor. The distance in the X direction from the reference point to the mounting point of the left height sensor. The distance in the Z direction from the reference point to the mounting point of the right height sensor. The distance in the X direction from the reference point to the mounting point of the right height sensor. The distance in the Y direction from the reference point to the mounting point of the left height sensor. The distance in the Y direction from the reference point to the mounting point of the right height sensor.

[0108] S705: Calibrate the ground clearance of the vehicle reference point when the vehicle is not stuck, and compare it with the value calculated in step S304 to obtain the ground clearance status of each part of the vehicle.

[0109] S706: Based on the calculated distance from the ground of each reference point of the vehicle, the chassis bottoming status of the vehicle is obtained, which can be divided into front chassis bottoming, rear chassis bottoming, left side chassis bottoming, right side chassis bottoming, and complete chassis bottoming.

[0110] S707: Outputs vehicle chassis bottoming indicator; process ends.

[0111] For example, the reference points mentioned above include, but are not limited to, at least one of the following: the center point of the vehicle chassis, the center point of the front axle, and the center point of the rear axle.

[0112] In some embodiments, the method further includes: identifying the type of road surface scene where the vehicle is located using multiple sensors. When the vehicle is stuck, ARI (Augmented Reality Imaging) technology, using cameras, radar sensors, etc., is employed to identify the specific scene in which the vehicle is stuck. ARI can identify stuck road surface scenes including soft dry snow, compacted snow, mixed snow, silt, dry mud, mixed mud, soft dry sand, and soft wet sand. Different road surfaces have different coefficients of adhesion, and different extrication strategies are required when vehicles are stuck on different road surfaces. The ARI technology sends the identified stuck road surface information to the VCU controller.

[0113] For example, the system uses an ARI vision system (equipped with a forward-looking binocular camera and infrared illumination) and a radar sensor (77GHz millimeter-wave radar) to collaboratively identify road surface types. The vision system extracts road surface texture features (such as snow particle size, mud crack structure, and sand ripple direction) and classifies them into 12 road surface types using a CNN network. The millimeter-wave radar simultaneously measures the surface dielectric constant and roughness to verify the visual results. For instance, when the visual identification is "soft dry snow" and the radar reflection intensity is <15dB and the Doppler frequency shift is <2Hz, the system confidence level is >92%. If the visual identification is "compacted snow" but the radar shows a dielectric constant >4.2 (typical silt characteristics), the system activates a "radar priority correction" mechanism and ultimately outputs the "silt" type. This multimodal fusion mechanism improves the scene recognition accuracy by 37% compared to a single vision system and maintains a recognition rate of >85% even in harsh environments such as nighttime, strong light, snow, and fog.

[0114] In summary, the adaptive vehicle traction control method proposed in this invention actively identifies the scenario when a vehicle is stuck using ARI technology. Through various vehicle sensing modules, it accurately calculates key information such as the vehicle's attitude, speed, chassis bottoming status, and rollover risk. By integrating the road surface scenario and the vehicle's stuck state, it efficiently determines the optimal traction strategy, enabling one-click intelligent traction recovery, increasing vehicle driving safety and reliability, and enhancing off-road performance.

[0115] like Figure 8 The above is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. The electronic device 800 includes a processor 801 with one or more processing cores, a memory 802 with one or more computer-readable storage media, and a computer program stored in the memory 802 and executable on the processor. The processor 801 and the memory 802 are electrically connected.

[0116] The processor 801 is the control center of the electronic device 800. It connects various parts of the electronic device 800 via various interfaces and lines. By running or loading software programs and / or units stored in the memory 802, and by calling data stored in the memory 802, it executes various functions and processes data of the electronic device 800, thereby providing overall monitoring of the electronic device 800. The processor 801 can be a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a Network Processor (NP), etc., and can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application.

[0117] In this embodiment of the application, the processor 801 in the electronic device 800 loads the computer program corresponding to the process of one or more application programs into the memory 802 according to the method or steps of the above embodiment, and the processor 801 runs the application program stored in the memory 802 to execute the above method.

[0118] According to the embodiments of the present invention, the electronic device controls the vehicle to get out of trouble by executing the above-described method at least according to the current state of the vehicle being stuck and the type of road surface where the vehicle is located. It can fully consider the current state of the vehicle being stuck and the road surface where the vehicle is located to control the vehicle to get out of trouble, and can cope with different getting-out-of-trouble scenarios, thereby achieving effective getting out of trouble.

[0119] Embodiments of the present invention also provide a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, enables the computer to implement the vehicle control method described above. For example, the computer-readable storage medium may be the aforementioned memory including program instructions, which may be executed by a processor of an electronic device to implement or execute the methods, steps, and logic diagrams disclosed in the embodiments of this application.

[0120] This invention also provides a computer program product storing instructions that, when executed by a computer, cause the computer to implement the vehicle control method described above. For example, when executed by a computer, the instructions implement or execute the methods, steps, and logic diagrams disclosed in the embodiments of this application.

[0121] Embodiments of the present invention also provide a vehicle comprising the electronic equipment described above, or a processor, the processor being used to execute the methods described above. The vehicle may be a gasoline-powered vehicle, a plug-in hybrid electric vehicle, or a new energy vehicle, etc., and this specification does not specifically limit it.

[0122] According to the vehicle of the present invention, the above method is executed by an electronic device or a control system or controller to control the vehicle to get out of trouble by at least considering the current state of the vehicle in trouble and the type of road surface scene in which the vehicle is located. This method can fully consider the current state of the vehicle in trouble and the road surface scene in order to control the vehicle to get out of trouble, and can cope with different trouble scenarios, thereby achieving effective escape from trouble.

[0123] The above-described embodiments are only used to illustrate the technical solutions of applying the above methods to vehicles, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the method can also be used in motor vehicles, trains, and ships, etc., without causing the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0124] In one embodiment, the vehicle can be configured for fully or partially autonomous driving. For example, the vehicle can control itself while in autonomous driving mode, and can determine the current state of the vehicle and its surrounding environment through human intervention, determine the possible behaviors of at least one other vehicle in the surrounding environment, and determine the confidence level corresponding to the probability of that other vehicle performing a possible behavior, and control the vehicle based on the determined information. When the vehicle is in autonomous driving mode, it can be configured to operate without human interaction.

[0125] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0126] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," "optional example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0127] The embodiments, implementation methods, and related technical features of this application can be combined and substituted for each other without conflict.

[0128] The above are merely preferred embodiments of this application and are not intended to limit this application in any way. Although the descriptions of each embodiment in this application have different focuses, and the parts not described in detail in a certain embodiment can be referred to the relevant embodiments of other embodiments, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of this application without departing from the content of the technical solution of this application shall still fall within the scope of the technical solution of this application.

Claims

1. A vehicle traction control method, characterized in that, The method includes: The vehicle's ability to escape from the predicament is controlled based at least on its current state of being stuck and the type of road surface in which it is located.

2. The method according to claim 1, characterized in that, The control of the vehicle's extrication from the predicament based at least on the vehicle's current state of being stuck and the type of road surface in which the vehicle is located during the state of being stuck includes: The target extrication strategy should be determined based at least on the vehicle's current stuck state and the type of road surface in which the vehicle is located; The target escape strategy is executed to control the vehicle to escape from the predicament.

3. The method according to claim 2, characterized in that, The determination of the target extrication strategy based at least on the vehicle's current stuck state and the type of road surface in which the vehicle is located includes: The target extrication strategy is determined based on the vehicle's current stuck state, the type of road surface in which the vehicle is located, and the vehicle's state parameters.

4. The method according to claim 3, characterized in that, The vehicle status parameters include: vehicle chassis bottoming-out markings and / or vehicle rollover risk factors. Determining the target extrication strategy based on the vehicle's current stuck state, the type of road surface the vehicle is on, and the vehicle status parameters includes: The target control strategy is determined based on the vehicle's current stuck state, the type of road surface the vehicle is on, and the bottoming marks on the vehicle's chassis. Alternatively, a target extrication strategy may be determined based on the vehicle's current state of being stuck, the type of road surface in which the vehicle is located, and the risk factors for the vehicle to roll over. Alternatively, an escape strategy can be determined based on the vehicle's current stuck state, the type of road surface the vehicle is on, the vehicle's undercarriage bottoming marks, and the vehicle's rollover risk factors.

5. The method according to claim 1, characterized in that, The method further includes: determining the current stuck state of the vehicle.

6. The method according to claim 5, characterized in that, Determining the current stuck state of the vehicle includes: The current state of the vehicle being stuck is determined based on the vehicle's gear position, pedal opening, whether the vehicle is in a ditch, and the vehicle's actual speed.

7. The method according to claim 6, characterized in that, The process of determining the vehicle's current stuck state based on the vehicle's gear position, pedal opening, whether the vehicle is in a ditch, and the vehicle's actual speed includes: When the vehicle gear and pedal opening meet the set threshold, if the vehicle is in a ditch and the actual vehicle speed is greater than the first threshold and less than the second threshold, the vehicle is determined to be in a first trapped state. Alternatively, when the vehicle gear and pedal opening meet the set thresholds, if it is determined that the vehicle is not in the pit and the actual vehicle speed is less than the third threshold, the vehicle is determined to be in the second trapped state.

8. The method according to claim 7, characterized in that, The method further includes: If the identification results show both a front slope and a rear slope, then the vehicle is in the pit; And / or, if the identification results show a left slope and a right slope, then the vehicle is in the pit.

9. The method according to claim 7, characterized in that, The pedal opening includes the accelerator pedal opening and / or the brake pedal opening, and the vehicle gear and pedal opening satisfy a set threshold, including: The vehicle gear is a drive gear and the accelerator pedal opening is greater than a first calibration value; Alternatively, the vehicle gear is a drive gear and the brake pedal opening is less than the second calibration value; Alternatively, the vehicle gear is a drive gear and the accelerator pedal opening is greater than a first calibration value and the brake pedal opening is less than a second calibration value.

10. The method according to claim 4, characterized in that, The method further includes: The vehicle rollover risk factor is calculated based on the vehicle's static stability coefficient and load transfer rate.

11. The method according to claim 4, characterized in that, The method further includes: The vehicle's chassis bottoming status is determined based on the height of the vehicle's reference point above the ground and the third calibration value. The vehicle chassis bottoming indicator is output based on the chassis bottoming status.

12. The method according to claim 1, characterized in that, The method also includes: identifying the type of road surface scene where the vehicle is located using multiple sensors.

13. The method according to any one of claims 1-12, characterized in that, The road surface type where the vehicle is located includes any one of the following: soft dry snow road surface, compacted snow road surface, mixed snow road surface, silt road surface, dry mud road surface, mixed mud road surface, soft dry sand road surface, and soft wet sand road surface.

14. An electronic device, characterized in that, include: A memory on which computer programs are stored; A processor for executing the computer program in the memory to implement the method of any one of claims 1 to 13.

15. A vehicle, characterized in that, include: The electronic device according to claim 14; Alternatively, a processor, said processor being configured to perform the method of any one of claims 1-13.

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