Vehicle collision safety control method, device, controller and vehicle

Abstract

The application relates to the technical field of vehicle control, and discloses a vehicle collision safety control method, a vehicle collision safety control device, a controller and a vehicle, wherein the relative speed and the axial distance of a vehicle and each surrounding obstacle are acquired, the relative speed and the axial distance of the vehicle and each surrounding obstacle are input into a preset attenuation model, and the collision probability of the vehicle and each obstacle is calculated; when the collision probability is greater than a preset probability threshold, the collision avoidance distance of each intervention mode is calculated according to the relative speed corresponding to the maximum collision probability, the speed adjustment value and the reaction time of each preset intervention mode; a target intervention mode is determined, the corresponding protection measures of the target intervention mode are used, and the vehicle is controlled to execute the corresponding protection measures. The beneficial effect is that the driving safety of a user is improved, and the user experience is improved.

Description

Technical Field

[0001] This application relates to the field of vehicle control technology, and in particular to a vehicle collision safety control method, device and controller. Background Technology

[0002] In the field of automotive safety, seats are key components that come into direct contact with occupants and vehicles, and their protective performance is crucial to the life safety of occupants in collision accidents.

[0003] Currently, passive safety technologies primarily activate corresponding control strategies to protect occupants after a collision. Active safety technologies, on the other hand, primarily provide early warnings before a collision occurs.

[0004] However, both of these methods have the problem of not being able to handle different collision risks. Summary of the Invention

[0005] This application provides a vehicle collision safety control method, which solves the technical problem of the overly one-size-fits-all approach in the prior art when there is a risk of collision with a vehicle. It achieves the technical effect of different treatments for different collision risks, improving user driving safety and user experience.

[0006] To achieve the above objectives, the main technical solutions adopted in this application include:

[0007] In a first aspect, embodiments of this application provide a vehicle collision safety control method, the method comprising:

[0008] The relative speed and axial distance between the vehicle and each surrounding obstacle are obtained, and the relative speed and axial distance between the vehicle and each surrounding obstacle are input into a preset attenuation model to calculate the collision probability between the vehicle and each obstacle.

[0009] When the collision probability is greater than a preset probability threshold, the collision avoidance distance of each intervention method is calculated based on the relative speed corresponding to the maximum collision probability, as well as the preset speed adjustment value and reaction time of each intervention method; and the target intervention method is determined based on the axial distance corresponding to the maximum collision probability and the collision avoidance distance of each intervention method.

[0010] Using the protective measures corresponding to the target intervention method, the vehicle is controlled to execute the corresponding protective measures.

[0011] In this embodiment, the relative speed and axial distance between the vehicle and each obstacle are obtained, and the collision probability is calculated using an attenuation model based on the relative speed and axial distance. When the collision probability exceeds the collision threshold, the target intervention method is determined based on the relative speed and axial distance, the speed adjustment value of each intervention method, and the reaction time. The corresponding protective measures are then controlled according to the target intervention method, thereby achieving early identification of collision risks and taking different intervention measures according to different collision risks, thus improving the vehicle's driving safety and increasing the survival rate of occupants.

[0012] Secondly, embodiments of this application provide a vehicle collision safety control device, the device comprising:

[0013] The acquisition module is used to acquire the relative speed and axial distance between the vehicle and each surrounding obstacle, and input the relative speed and axial distance between the vehicle and each surrounding obstacle into a preset attenuation model to calculate the collision probability between the vehicle and each obstacle.

[0014] The selection module is used to calculate the collision avoidance distance of each intervention method based on the relative speed corresponding to the maximum collision probability, as well as the preset speed adjustment value and reaction time of each intervention method when the collision probability is greater than a preset probability threshold; and to determine the target intervention method based on the axial distance corresponding to the maximum collision probability and the collision avoidance distance of each intervention method.

[0015] The control module is used to control the vehicle to perform the corresponding protection measures using the protection measures corresponding to the target intervention method.

[0016] Thirdly, embodiments of this application provide a controller, including: 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 method described in any of the above embodiments.

[0017] Fourthly, embodiments of this application provide a vehicle, including: a controller as shown in the third aspect.

[0018] Fifthly, embodiments of this application provide a computer-readable storage medium storing computer instructions, which are used to cause a computer to perform the method described in any one of the above embodiments.

[0019] Sixthly, embodiments of this application provide a computer program product, including computer instructions, which are used to cause a computer to perform the method described in any of the above embodiments. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the specific embodiments of this application or 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 this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0021] Figure 1 A flowchart of a vehicle collision safety control provided in this application embodiment;

[0022] Figure 2 A flowchart of a vehicle collision safety control provided in this application embodiment;

[0023] Figure 3 A block diagram of a vehicle collision safety control device provided in this application embodiment;

[0024] Figure 4 This is a schematic diagram of the structure of a controller provided in an embodiment of this application. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, 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 some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0026] With the continuous increase in car ownership and the increasing complexity of road traffic environments, vehicle safety has become a growing focus of public attention. In the field of automotive safety, the seat, as a key component in direct contact between occupants and the vehicle, directly impacts the safety of occupants in collisions. However, current traditional car seat protection technologies face numerous challenges.

[0027] Current passive safety automotive technologies primarily activate protection mechanisms after a collision, which presents two significant problems. Firstly, there's the issue of insufficient survival space. At the moment of impact, occupants are propelled forward by inertia, making contact with hard objects like the steering wheel and dashboard, greatly increasing the risk of injuries to critical areas such as the chest and head. Secondly, there's the problem of delayed collision response. For example, the time from airbag activation to full deployment is approximately 50-100ms. This long reaction time prevents the system from optimizing occupant positioning in advance.

[0028] While active safety technologies can mitigate vehicle collisions to some extent, they pay insufficient attention to actively adjusting occupant positions. Seat adjustment functions are mostly passive and lack automatic response capabilities in emergencies. Even seat memory functions in high-end models require manual operation and lack the ability to automatically respond in emergencies, failing to provide timely and effective protection for occupants in critical moments.

[0029] Furthermore, for new energy vehicles, the energy absorption zone is shortened due to battery layout and increased demand for passenger cabin entertainment. In this situation, the need for seats to compensate for occupant survival space through displacement during a collision, in order to ensure occupant safety, is even more urgent.

[0030] Currently, most active safety technologies in the market focus on preventing collisions before they occur, but they cannot optimize occupant posture in advance, making it difficult to address the problem of insufficient escape space after a collision. Some vehicles are equipped with methods to increase escape space by shifting or rotating the seats after a collision. However, insufficient reaction time and structural conflicts make it difficult to effectively ensure occupant safety. Therefore, the development of a new type of automotive seat protection technology that can detect collisions in advance and actively optimize occupant posture to effectively compensate for survival space is urgently needed.

[0031] To address the shortcomings of existing technologies, this application proposes a novel vehicle collision safety control method. This method acquires the axial distance and velocity of an obstacle, as well as the vehicle's own speed. Then, based on the calculated acceleration of the vehicle, it determines whether the acceleration exceeds an ignition threshold. Furthermore, based on the collision probability calculated from the axial distance, obstacle velocity, and vehicle speed, it determines whether the collision probability exceeds a probability threshold. When the acceleration exceeds the ignition threshold and the collision probability exceeds the probability threshold, this method can select the appropriate intervention method for both the vehicle and the obstacle based on the speed adjustment value and reaction time of each intervention method. The computer equipment can then execute the corresponding control strategy based on this intervention method, pre-arranging protective measures for the vehicle occupants, thereby improving occupant safety.

[0032] The intervention methods can include human intervention, primary intervention, intermediate intervention, and ultimate intervention. This method can determine the appropriate intervention method by sequentially judging whether a particular intervention method has sufficient time in the current scenario, based on a pre-defined set of criteria.

[0033] This application can combine collision recognition to control the vehicle and seats to perform different safety measures based on different scenarios, thereby ensuring that occupants have sufficient warning time or can retain sufficient survival space after a collision, thereby increasing the probability of occupant survival and improving vehicle safety.

[0034] According to an embodiment of this application, a vehicle collision safety control method is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed on a controller via 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. The controller can be a terminal device installed in the vehicle, an external terminal device communicating with the vehicle, etc.

[0035] Figure 1 A flowchart of a vehicle collision safety control provided in this application embodiment is shown below. Figure 1 As shown, taking the controller on the vehicle as the execution subject, the process includes the following steps:

[0036] S101. Obtain the relative speed and axial distance between the vehicle and each surrounding obstacle, and input the relative speed and axial distance between the vehicle and each surrounding obstacle into a preset attenuation model to calculate the collision probability between the vehicle and each obstacle.

[0037] For example, the controller first determines the obstacles present around the vehicle. Then, the controller can obtain the relative speed between the vehicle and each obstacle. Also, the controller can obtain the axial distance between the vehicle and each obstacle.

[0038] In one implementation, the obstacles around the vehicle can be other vehicles located around the vehicle.

[0039] Optionally, for the collision scenario of this application, the obstacle can be further defined as an obstacle in the same lane as the vehicle and located in front of or behind the vehicle.

[0040] In one implementation, the relative speed refers to the difference between the speed of the vehicle and the speed of the obstacle.

[0041] In one implementation, the axial distance is the distance between the vehicle and the obstacle along the vehicle's axis. This axial distance is used to measure the spatial proximity of the vehicle and the obstacle.

[0042] Optionally, when the vehicle and the obstacle are in the same lane, the axial distance can be the distance between the vehicle and the obstacle in the direction of travel in that lane.

[0043] In one implementation, the controller can collect the relative speed and axial distance between the vehicle and various obstacles in real time using various sensors equipped on the vehicle.

[0044] Optionally, the sensor may include a vehicle speed sensor. The controller can obtain the vehicle's speed through the vehicle speed sensor.

[0045] Optionally, the sensor may include devices such as a camera, infrared sensor, and radar. The controller can use information captured by the camera, infrared sensor, radar, etc., of the area in front of and behind the vehicle to determine vehicles in front of and behind the vehicle, thereby identifying the obstacle target. Furthermore, the controller can also determine the longitudinal distance between the vehicle and the obstacle based on the information captured by the camera, infrared sensor, radar, etc., of the area in front of and behind the vehicle. Additionally, the controller can calculate the speed of the obstacle based on the longitudinal distances to the same obstacle acquired multiple times consecutively, combined with the vehicle's speed. The controller can determine the relative speed by the difference between the vehicle's speed and the obstacle's speed.

[0046] Optionally, the controller can identify the first vehicle in front of the vehicle that is in the same lane and at a distance less than a preset distance as an obstacle. The controller can also identify the first vehicle behind the vehicle that is in the same lane and at a distance less than a preset distance as an obstacle.

[0047] Optionally, this preset distance can be determined based on experience. Generally, it is considered that the risk of a vehicle collision is higher within this preset range. For example, the preset distance can be 5m, 10m, 20m, etc.

[0048] Optionally, the controller can also determine the preset distance based on the scenario in which the vehicle is located. For example, this distance could be 10m on city roads, or 100m on highways.

[0049] For example, after acquiring the relative speed and axial distance between the vehicle and each obstacle, the controller can input this relative speed and axial distance data into a pre-set attenuation model. After the attenuation model calculates and processes the data, the controller can obtain the probability value of the vehicle colliding with each obstacle.

[0050] In one implementation, the decay model is a mathematical model built on a large amount of experimental data and theoretical analysis, which can comprehensively consider the influence of multiple factors on the probability of collision.

[0051] In one implementation, when multiple obstacles exist, the controller can calculate the collision probabilities of the multiple obstacles and directly select the highest collision probability as the vehicle's collision probability. Furthermore, the controller can also determine that the obstacle corresponding to the highest collision probability is the obstacle that may collide with the vehicle.

[0052] S102. When the probability of a collision is greater than the preset probability threshold, the collision avoidance distance of each intervention method is calculated based on the relative speed corresponding to the maximum collision probability, as well as the preset speed adjustment value and reaction time of each intervention method; and the target intervention method is determined based on the axial distance corresponding to the maximum collision probability and the collision avoidance distance of each intervention method.

[0053] For example, after the controller calculates the collision probability between the vehicle and each obstacle through the above steps, the controller can compare these collision probabilities with a preset probability threshold. If the collision probability of an obstacle is greater than the preset probability threshold, the controller will initiate the subsequent intervention method determination process.

[0054] In one implementation, the probability threshold is a pre-defined numerical standard used to determine the level of collision risk. This probability threshold can be an empirical value.

[0055] Optionally, when the collision probability is a value between 0 and 1, 0 indicates that no collision will occur, and 1 indicates that a collision will occur. The threshold value for this probability is also between 0 and 1, and is relatively close to 1. For example, the probability threshold value could be 0.8.

[0056] In one implementation, when the collision probability exceeds this value, a high collision risk is considered to exist, requiring intervention. Otherwise, if the collision risk is less than this probability threshold, the vehicle is highly unlikely to collide with the obstacle. The vehicle can continue driving under the current circumstances.

[0057] For example, the controller first finds the maximum value among all collision probabilities greater than the probability threshold, and then uses the relative velocity and axial distance of the obstacle corresponding to the maximum collision probability for subsequent calculations.

[0058] In one implementation, the obstacle with the highest probability of collision can be directly selected as the obstacle to which the vehicle has the highest probability of colliding. Based on this obstacle, predictions can be made under the most pessimistic scenario, thereby achieving a safer vehicle control effect. Furthermore, using this obstacle for subsequent calculations avoids the need to calculate each obstacle individually, reducing computational load and improving vehicle control efficiency.

[0059] For example, the controller can calculate the collision avoidance distance required to avoid a collision under each intervention method by combining the pre-set parameters such as speed adjustment values ​​and reaction times corresponding to different intervention methods with the aforementioned determined relative speed, using the corresponding physical formulas. Next, the controller finds the axial distance of the obstacle corresponding to the maximum collision probability, compares this axial distance with the previously calculated collision avoidance distances for each intervention method, and thus determines the most suitable target intervention method for the current situation.

[0060] In one implementation, the speed adjustment value refers to the amount by which the vehicle's speed increases or decreases during braking. Different intervention methods will have different speed adjustment values. For example, the speed adjustment value for a primary intervention will be smaller than that for a secondary intervention.

[0061] In one implementation, reaction time refers to the time elapsed from when the controller issues an intervention command to when the vehicle actually begins to perform the corresponding braking action. Typically, the reaction time for human intervention is longer than the reaction time for direct intervention by equipment, such as primary intervention.

[0062] In one implementation, the collision avoidance distance is the estimated travel time required for the vehicle to achieve the desired collision avoidance effect after intervention using the corresponding intervention method.

[0063] In one implementation, if the collision avoidance distance of a certain intervention method is greater than or equal to the axial distance, it indicates that when the vehicle uses this intervention method for braking, a collision with the obstacle will occur before the braking operation is completed. Therefore, the controller needs to select another intervention method. Otherwise, if the collision avoidance distance of a certain intervention method is less than the axial distance, it indicates that when the vehicle uses this intervention method for braking, a collision will not occur between the vehicle and the obstacle when the vehicle completes the braking operation and reaches the collision avoidance state. Therefore, the controller can select this intervention method for intervention.

[0064] In one implementation, the target intervention method is for the controller to select one intervention method from a set of preset intervention methods.

[0065] S103. Use the protective measures corresponding to the target intervention method to control the vehicle to perform the corresponding protective measures.

[0066] For example, once the controller determines the target intervention method, it immediately controls the vehicle to execute the protective measures corresponding to that intervention method. By controlling the vehicle to execute these protective measures, the controller can minimize the possibility of a collision between the vehicle and an obstacle, thus ensuring the safety of the occupants. Furthermore, by controlling the vehicle to execute these protective measures, the controller can also ensure that the vehicle has already adjusted its seating position and provided greater survival space for the user in the event of a collision, thereby improving the occupant survival rate.

[0067] In one implementation method, the target intervention method is the most suitable intervention measure for the current collision risk situation, determined after comprehensive analysis and judgment in the preceding steps.

[0068] In one implementation, protective measures are a series of operations that match the target intervention method. Different intervention methods can correspond to different protective measures.

[0069] For example, in cases requiring human intervention, alarms or other means can be used to alert users to take action.

[0070] For example, in the case of initial intervention, the protection of occupants can be further improved by reducing the vehicle speed, adjusting the seat belts, folding the foot pedals, and switching the seat's redundant battery, etc.

[0071] For example, in the case of intermediate intervention, adjusting the seat back angle or seat position can ensure that more survival space is provided for the user after a collision.

[0072] For example, in the event of ultimate intervention, the front travel of the seat can be switched to a collapsible damping energy absorption mode to ensure that the collapsible damping energy absorption mode has been activated when the impact occurs, thereby reducing injury to the occupants and improving their survival rate during the collision.

[0073] In this embodiment, the relative speed and axial distance between the vehicle and each obstacle are obtained, and the collision probability is calculated using an attenuation model based on the relative speed and axial distance. When the collision probability exceeds the collision threshold, the target intervention method is determined based on the relative speed and axial distance, the speed adjustment value of each intervention method, and the reaction time. The vehicle is then controlled to perform corresponding protective measures according to the target intervention method. This achieves the effect of early identification of collision risks and taking different intervention measures according to different collision risks, thereby improving the driving safety of the vehicle and increasing the survival rate of the occupants.

[0074] In one example, in step S101 above, the relative speed and axial distance are input into a preset attenuation model to calculate the collision probability between the vehicle and each obstacle. The controller can calculate the collision probability between the vehicle and each obstacle one by one. This example uses one obstacle as an example, and the calculation process may include:

[0075] S1011. Obtain the relative speed and axial distance between the vehicle and an obstacle.

[0076] For example, after initiating the process of monitoring the vehicle and its surrounding environment, the controller can obtain the relative speed and axial distance between the vehicle and an obstacle.

[0077] In one implementation, "self-driving vehicle" refers to the currently controlled vehicle.

[0078] In one implementation, the obstacle is another vehicle in the vehicle's driving environment that may pose a collision threat to the vehicle. The obstacle selected in this step can be any obstacle that could collide with the vehicle.

[0079] Optionally, the obstacle can be a vehicle in the same lane as the vehicle and at a distance less than a preset distance from the vehicle.

[0080] Optionally, the obstacle can be the first vehicle located in front of the vehicle. Alternatively, the obstacle can be the first vehicle located behind the vehicle.

[0081] In one implementation, the controller can obtain the vehicle's speed through sensors installed on the vehicle.

[0082] In one implementation, the controller can collect information from the front or rear of the vehicle. Based on this information, the controller can identify obstacles and monitor the axial distance between the obstacle and the vehicle, as well as the speed of the obstacle.

[0083] Alternatively, the controller can acquire information from the front or rear of the vehicle using sensors such as millimeter-wave radar, lidar, or cameras.

[0084] In one implementation, the controller can determine the relative speed based on the difference between the vehicle's speed and the obstacle's speed.

[0085] In one implementation, the controller can obtain the axial distance between the vehicle and the obstacle through infrared detection, laser detection, or other methods.

[0086] Optionally, the axis can be the distance along the vehicle's axis. Alternatively, the axis can also be the direction of travel in the lane in which the vehicle is located.

[0087] S1012. Input the relative velocity into the activation function and calculate the activation value.

[0088] For example, after obtaining the relative speed between the vehicle and an obstacle, the controller can input this relative speed into a pre-set activation function for processing. The controller can then calculate the activation value using this activation function.

[0089] In one implementation, the activation function is a mathematical function. This activation function performs a non-linear transformation on the relative velocity of the input, mapping the relative velocity to a new numerical range, thereby obtaining an activation value. Optionally, the activation function can be a Sigmoid function, ReLU function, Tanh function, etc.

[0090] In one implementation, the activation value is the numerical value obtained after processing the relative velocity through an activation function. The activation value can better reflect the characteristics and influence of relative velocity in subsequent collision probability calculations, providing a more discriminative input for subsequent model calculations.

[0091] In one implementation, the relative speed is the difference between the vehicle's speed and the obstacle's speed. Based on this relative speed, the specific process of calculating the activation value using an activation function may include the following steps:

[0092] Step 121: If the vehicle is in front of the obstacle, the activation value is set to 0 when the relative speed is greater than or equal to 0, and the activation value is set to 1 when the relative speed is less than 0.

[0093] For example, the controller first determines the relative position of the vehicle to the obstacle. Once it is determined that the vehicle is in front of the obstacle, the controller further compares the relative speed with 0.

[0094] If the relative speed is greater than or equal to 0, it means that the vehicle's speed is greater than or equal to the obstacle's speed. In this case, since the vehicle is in front, the distance between the vehicle and the obstacle will either increase or remain constant. Therefore, while maintaining the speed of both the vehicle and the obstacle, the vehicle will not collide with the obstacle. Thus, the controller determines the activation value to be 0.

[0095] Conversely, if the relative speed is less than 0, it indicates that the vehicle's speed is less than the obstacle's speed. In this case, since the vehicle is in front, the distance between the vehicle and the obstacle will continuously decrease. Therefore, while maintaining the vehicle's speed and the obstacle's speed, the vehicle may be overtaken by the obstacle, leading to a collision. Therefore, the controller determines the activation value to be 1.

[0096] Step 122: If the vehicle is behind the obstacle, the activation value is set to 1 when the relative speed is greater than 0, and the activation value is set to 0 when the relative speed is less than or equal to 0.

[0097] For example, when the vehicle is determined to be behind an obstacle, the controller will further compare the relative speed with 0.

[0098] If the relative speed is greater than 0, it means that the vehicle's speed is greater than or equal to the obstacle's speed. In this case, since the vehicle is behind, it will gradually catch up with the obstacle, which may lead to a collision. Therefore, the controller determines the activation value to be 1.

[0099] Conversely, if the relative speed is less than or equal to 0, it means that the vehicle's speed is less than the obstacle's speed. In this case, since the vehicle is behind, it cannot catch up with the obstacle and will gradually increase the distance between them. Therefore, the controller determines the activation value to be 0.

[0100] In this implementation, the activation value is determined based on the relative position of the vehicle and the obstacle, combined with the relative speed of the vehicle and the obstacle. This provides accurate activation parameters for subsequent collision probability calculations. Setting these activation parameters can directly set the collision probability to zero in cases where a collision will not occur, thereby improving the accuracy and efficiency of collision probability calculations.

[0101] In one implementation, the activation function can be formulated as follows:

[0102]

[0103] in, It can be an activation function. The speed difference can be obtained by subtracting the speed of the vehicle in front from the speed of the vehicle behind. That is, when one vehicle is in front, the speed difference is... It can be a negative value for relative speed. When the vehicle is behind, this... This refers to relative velocity. 0 and 1 represent the possible activation values ​​output by this activation function. A value greater than 0 indicates that the following vehicle's speed is greater than the speed of the vehicle in front. In this case, a collision may occur; therefore, the activation value is 1. Otherwise, in A value less than or equal to 0 indicates that the speed of the following vehicle is less than or equal to the speed of the vehicle in front. In this case, the following vehicle cannot catch up with the vehicle in front, therefore, the activation value is 0.

[0104] S1013. Determine the exponent value of the attenuation model based on the ratio of the square of the relative velocity to the axial distance.

[0105] For example, after obtaining the relative speed and axial distance between the vehicle and the obstacle, the controller determines the exponential value in the attenuation model by calculating the ratio of the square of the relative speed to the axial distance.

[0106] In one implementation, the ratio of the square of the relative velocity to the axial distance is a numerical value used to measure characteristics related to collision risk. In this step, this value is referred to as the exponential value of the attenuation model.

[0107] In one implementation, the ratio of the square of the relative velocity to the axial distance can reflect the combined influence of relative velocity and axial distance on the collision probability. Different exponent values ​​will lead to significant differences in the calculation results of the collision probability by the decay model.

[0108] In one implementation, the ratio of the square of the relative velocity to the axial distance can be denoted as: .in, That is, the square of the relative velocity. The speed difference is the difference between the speed of the following vehicle and the speed of the vehicle in front. That is, when the following vehicle is in front, the speed difference is... It can be a negative value for relative speed. When the vehicle is behind, this... That is, relative velocity. This represents the axial distance.

[0109] S1014. Input the exponent value and activation value into the decay model to calculate the collision probability between the vehicle and the obstacle.

[0110] For example, after determining the exponent and activation values ​​of the attenuation model, the controller can simultaneously input the exponent and activation values ​​into the preset attenuation model. Through the calculation of the attenuation model, the controller can obtain the final output collision probability between the vehicle and the obstacle.

[0111] In one implementation, the decay model is a mathematical model constructed based on a large amount of experimental data and theoretical analysis. This exponential model comprehensively considers the influence of factors such as relative velocity and axial distance on the collision probability.

[0112] In one implementation, the collision probability is a value between 0 and 1. This value directly reflects the likelihood of the vehicle colliding with an obstacle. The closer the collision probability is to 0, the lower the probability of a collision. The closer the collision probability is to 1, the higher the probability of a collision.

[0113] In one implementation, the attenuation model can be constructed based on probabilistic statistics and regression analysis. The controller can collect a large amount of data such as relative velocity and axial distance in actual collision and non-collision scenarios, use regression algorithms to fit the model parameters, and then calculate the collision probability based on the input exponent value and activation value.

[0114] In another implementation, the decay model can be built based on a neural network. The controller can use the exponent and activation values ​​as input layer data for the neural network. Through the forward propagation process of the network, the data is calculated and processed through multiple hidden layers, and finally the collision probability is obtained at the output layer.

[0115] In another implementation, the formula for the attenuation model can be:

[0116]

[0117] in, This represents the collision probability. This is the exponential value. This is the activation value. This is the attenuation coefficient.

[0118] Optionally, the controller can optimize the attenuation coefficient in reverse based on the calculated collision probability and the final collision result.

[0119] In this example, by calculating activation and exponent values ​​based on the relative speed and axial distance between the vehicle and the obstacle, and then inputting these activation and exponent values ​​into the decay model to calculate the final collision probability, the method achieves the effect of accurately quantifying the possibility of a collision between the vehicle and the obstacle based on the relative speed and axial distance between the vehicle and the obstacle, and improving the accuracy of the collision probability calculation.

[0120] In one example, in step S102 above, based on the relative speed corresponding to the highest collision probability, and the preset speed adjustment values ​​and reaction times for each intervention method, the collision avoidance distance for each intervention method is calculated, including:

[0121] S1021. Calculate the ratio of the relative velocity corresponding to the maximum collision probability to the velocity adjustment value of each intervention method to obtain the zeroing time.

[0122] For example, the controller can first filter out the highest collision probability from the multiple acquired collision probabilities. Then, the controller can obtain the obstacle corresponding to the highest collision probability, as well as the relative speed and axial distance between the vehicle and the obstacle.

[0123] The controller can pre-store the speed adjustment values ​​corresponding to each intervention method. The controller can use the ratio of this relative speed to the speed adjustment value corresponding to each intervention method to determine the time required for the vehicle's speed to adjust to match the obstacle's speed when using each intervention method. This time is the zeroing time.

[0124] In one implementation, the maximum collision probability is obtained by comparing the collision probabilities of various obstacles. The obstacle with the highest collision probability is the one most likely to collide with the vehicle. The controller can use this obstacle to calculate the most pessimistic collision scenario for the vehicle and, based on this most pessimistic scenario, implement safety control for the vehicle.

[0125] In one implementation, relative speed is the difference in speed between the vehicle and the obstacle in the direction of motion.

[0126] In one implementation, the intervention method is a control strategy adopted by the controller to avoid a collision or improve occupant safety after a collision. For example, the intervention method may include human intervention, primary intervention, intermediate intervention, and ultimate intervention.

[0127] In one implementation, the speed adjustment value is the amount by which the vehicle increases or decreases its speed per unit time during collision avoidance. Different intervention methods correspond to different speed adjustment values. For example, the speed adjustment values ​​for human intervention, primary intervention, and intermediate intervention can be shown in Table 1.

[0128] Table 1

[0129]

[0130] In one implementation, when the vehicle is in front, the speed adjustment amount is used to increase the vehicle speed, thereby making the vehicle's speed the same as the obstacle's, thus preventing the vehicle from being overtaken by the obstacle and colliding with it. When the vehicle is behind, the speed adjustment amount is used to decrease the vehicle speed, thereby reducing the vehicle's speed to the same level as the obstacle, thus preventing the vehicle from rear-ending the vehicle in front.

[0131] In one implementation, the zeroing time reflects the time required for the vehicle to adjust from its current relative speed to zero under a specific intervention method.

[0132] S1022. Calculate the zeroing distance based on the vehicle's speed, relative speed, and zeroing time.

[0133] For example, after the vehicle calculates the zeroing time, the controller will control the vehicle to linearly adjust its speed according to the speed adjustment amount within the zeroing time, so that the vehicle speed gradually matches the speed of the obstacle. In this process, the controller can use relevant kinematic formulas to calculate the zeroing distance based on the vehicle speed, relative speed, and zeroing time.

[0134] In one implementation, the zeroing distance refers to the distance traveled by the vehicle from its current state to its relative speed after a specific intervention is implemented, adjusting its speed until the relative speed reaches zero.

[0135] In one implementation, when the vehicle is ahead of the vehicle, the controller first calculates half of the relative speed to obtain a first parameter. The controller then calculates the sum of the vehicle's speed and the first parameter to obtain a second parameter. Finally, the controller calculates the product of the second parameter and the zeroing time to obtain the zeroing distance.

[0136] In one implementation, when the vehicle is following another vehicle, the controller first calculates half of the relative speed to obtain a first parameter. The controller then calculates the difference between the vehicle's speed and the first parameter to obtain a third parameter. Finally, the controller calculates the product of the third parameter and the zeroing time to obtain the zeroing distance.

[0137] S1023. Calculate the product of reaction time and vehicle speed to obtain the reaction distance.

[0138] For example, after calculating the zeroing distance, the controller begins to calculate the reaction distance. The controller can directly calculate the product of the reaction time and the vehicle's speed to obtain the reaction distance.

[0139] In one implementation, reaction time refers to the time required for the controller to detect an obstacle and begin executing the operation corresponding to the intervention method. The operation corresponding to the intervention method includes vehicle speed adjustment.

[0140] In one implementation, the reaction distance refers to the distance traveled by the vehicle during the time it takes for the controller to detect an obstacle and then begin adjusting the vehicle speed corresponding to the intervention method.

[0141] In one implementation method, the reaction times for different intervention modes can be shown in Table 1. The reaction time for human intervention can be measured experimentally.

[0142] S1024. The sum of the zeroing time and the reaction time is used as the collision avoidance distance.

[0143] For example, after calculating the zeroing distance and the reaction distance respectively, the controller adds the zeroing time to the reaction time to obtain the collision avoidance distance.

[0144] In one implementation, the collision avoidance distance indicator indicates the distance the vehicle needs to travel to avoid a collision when executing an intervention mode, based on that intervention mode.

[0145] In this example, by sequentially calculating the zeroing distance and reaction distance, and then calculating the collision avoidance distance, we can accurately assess the driving distance required for the vehicle to avoid collisions with obstacles under different intervention methods, thus providing a data basis for the selection of subsequent intervention modes.

[0146] In one example, the intervention methods include human intervention, primary intervention, intermediate intervention, and final intervention. In step S102 above, the controller can determine whether to use an intervention method based on the order of human intervention, primary intervention, and intermediate intervention, the axial distance corresponding to the highest collision probability, and the collision avoidance distance of each intervention method, thereby determining the target intervention method. This process may include:

[0147] S1025. Based on the speed of the obstacle corresponding to the highest collision probability, as well as the zeroing time and reaction time, the travel distance is calculated. The pursuit distance is then determined based on the travel distance and the axial distance.

[0148] For example, the controller can obtain the speed of the obstacle corresponding to the highest collision probability. The controller can also obtain the zeroing time determined in step S1021 above and the reaction time from Table 1. Furthermore, the controller can calculate the sum of the zeroing time and the reaction time to obtain the total time. The controller can calculate the product of the total time and the speed to obtain the travel distance. This travel distance is the distance traveled by the obstacle. The controller can calculate the pursuit distance based on this travel distance and the axial distance.

[0149] In one implementation, when the vehicle is in front of an obstacle, the controller can calculate the difference between the traveled distance and the axial distance to obtain the pursuit distance. When the vehicle is behind an obstacle, the controller can calculate the sum of the traveled distance and the axial distance to obtain the pursuit distance.

[0150] In one implementation, the travel distance is the distance the obstacle travels during the process of the vehicle performing safety measures, calculated based on the obstacle's speed, zeroing time, and reaction time.

[0151] In one implementation, the pursuit distance is an intermediate distance calculated after comprehensively considering the positional relationship between the vehicle and the obstacle. This intermediate distance is used for comparison with the collision avoidance distance.

[0152] S1026. If the collision avoidance distance and the pursuit distance of human intervention match, then the target intervention method is determined to be human intervention.

[0153] For example, the controller first determines whether to perform human intervention. The controller can match the collision avoidance distance of the human intervention with the pursuit distance. If the collision avoidance distance of the human intervention matches the pursuit distance, the controller can determine that the human intervention is the target intervention method. Otherwise, if the collision avoidance distance of the human intervention does not match the pursuit distance, the controller needs to continue matching other intervention methods.

[0154] In one implementation, if the vehicle is in front, a match is determined when the collision avoidance distance is greater than or equal to the catch-up distance. This indicates that after the vehicle intervened, the obstacle could not rear-end it, thus ensuring the vehicle's safety. Otherwise, if the vehicle is in front, a mismatch is determined when the collision avoidance distance is less than the catch-up distance.

[0155] In one implementation, if the vehicle is behind, a match is determined when the collision avoidance distance is less than or equal to the approach distance. This indicates that after human intervention, the vehicle is unable to rear-end the obstacle, thus ensuring its safety. Otherwise, if the vehicle is behind, a mismatch is determined when the collision avoidance distance is greater than the approach distance.

[0156] In one implementation, if a mismatch is determined, the controller can conclude that a collision cannot be avoided by human intervention. In this case, the controller needs to perform subsequent steps to determine other intervention methods.

[0157] In one implementation, human intervention is used for a low-emergency collision risk, typically employed when the driver can manually avoid the collision. In this human intervention mode, the vehicle will only alert the driver.

[0158] S1027. Otherwise, if the collision avoidance distance of the primary intervention matches the pursuit distance, then the target intervention method is determined to be primary intervention.

[0159] For example, when the controller determines that the collision avoidance distance and the pursuit distance of the human intervention do not match, it will further check whether the collision avoidance distance and the pursuit distance of the primary intervention match. If the collision avoidance distance and the pursuit distance of the primary intervention match, the controller can determine that the primary intervention is the target intervention method. Otherwise, if the collision avoidance distance and the pursuit distance of the primary intervention do not match, the controller needs to continue matching other intervention methods.

[0160] In one implementation, if the vehicle is in front, a match is determined when the collision avoidance distance is greater than or equal to the catch-up distance. This indicates that after the vehicle performs initial intervention, the obstacle cannot rear-end the vehicle, thus ensuring its safety. Otherwise, if the vehicle is in front, a mismatch is determined when the collision avoidance distance is less than the catch-up distance.

[0161] In one implementation, if the vehicle is behind, a match is determined when the collision avoidance distance is less than or equal to the approach distance. This indicates that after the vehicle performs initial intervention, it cannot rear-end the obstacle, thus ensuring its safety. Otherwise, if the vehicle is behind, a mismatch is determined when the collision avoidance distance is greater than the approach distance.

[0162] In one implementation, if a mismatch is determined, the controller can conclude that a collision cannot be avoided using primary intervention. At this point, the controller needs to perform subsequent steps to determine other intervention methods.

[0163] One approach involves initial intervention in a less urgent situation. While manual control alone is considered potentially insufficient, necessitating controller intervention, the vehicle is assumed to have ample time to avoid a collision. Therefore, even if a collision occurs, the risk to occupants is minimized with controller intervention in place.

[0164] S1028. Otherwise, if the collision avoidance distance and pursuit distance of the intermediate intervention match, then the target intervention method is determined to be intermediate intervention.

[0165] For example, when the controller determines that the collision avoidance distance and the pursuit distance of the primary intervention do not match, it will further check whether the collision avoidance distance and the pursuit distance of the intermediate intervention match. If the collision avoidance distance and the pursuit distance of the intermediate intervention match, the controller can determine that the intermediate intervention is the target intervention method. Otherwise, if the collision avoidance distance and the pursuit distance of the intermediate intervention do not match, the controller needs to execute step S1029.

[0166] In one implementation, if the vehicle is in front, a match is determined when the collision avoidance distance is greater than or equal to the catch-up distance. This indicates that after the vehicle performs intermediate-level intervention, the obstacle cannot rear-end the vehicle, thus ensuring its safety. Otherwise, if the vehicle is in front, a mismatch is determined when the collision avoidance distance is less than the catch-up distance.

[0167] In one implementation, if the vehicle is behind, a match is determined when the collision avoidance distance is less than or equal to the approach distance. This indicates that after the vehicle performs intermediate intervention, it cannot rear-end the obstacle, thus ensuring its safety. Otherwise, if the vehicle is behind, a mismatch is determined when the collision avoidance distance is greater than the approach distance.

[0168] In one implementation, if a mismatch is determined, the controller can determine that a collision cannot be avoided using intermediate intervention. In this case, the controller needs to execute step S1029.

[0169] One implementation approach involves intermediate intervention, which represents a relatively urgent situation. At this point, it can be assumed that a collision is highly unavoidable. Therefore, in intermediate intervention mode, the controller adjusts the seat to ensure the occupant has more survival space after a collision, thereby increasing the occupant's survival rate and improving rescue efficiency.

[0170] S1029. Otherwise, determine the target intervention method as the final intervention.

[0171] For example, when the controller, based on the preceding judgments, finds that the collision avoidance distances for human intervention, primary intervention, and intermediate intervention are all mismatched with the pursuit distance, it indicates that the current situation is extremely urgent and requires more drastic measures to avoid a collision. At this point, the controller will determine the target intervention method as the ultimate intervention.

[0172] In one implementation, ultimate intervention is typically the most powerful safety measure taken by the vehicle's automatic control system. In this ultimate intervention mode, the controller switches the seat's forward travel to a collapsible damping energy absorption mode, ensuring that this collapsible damping energy absorption mode has been activated and is functioning when a collision occurs, thereby improving the vehicle's safety, reducing injury to occupants, and increasing the probability of occupant survival.

[0173] In this example, by calculating the pursuit distance and sequentially matching the collision avoidance distance of different intervention methods with the pursuit distance, the means of the target intervention method are determined, so as to accurately select the appropriate intervention method according to the actual collision risk and ensure driving safety.

[0174] In one example, the intervention methods include human intervention, primary intervention, intermediate intervention, and ultimate intervention. In step S103 above, the protective measures corresponding to the target intervention method are used to control the vehicle to execute the corresponding protective measures, including:

[0175] S1031. If the target intervention method is human intervention, then control the vehicle to execute an audible and visual warning.

[0176] For example, when the controller determines that the target intervention method is human intervention, it will immediately initiate the corresponding control process to control the vehicle to perform an audible and visual warning.

[0177] In one implementation, human intervention means relying primarily on the driver's actions to avoid collisions. The controller first sends control commands to the vehicle's audible and visual warning system, activating the sound and light warning modules within the system, and using both sound and light to deliver a warning signal to the driver. The driver can then immediately adopt control strategies to avoid a collision based on the sound or light signals.

[0178] In one implementation, the sound warning module can alert the driver to potential collision risks by emitting alarm sounds of different frequencies and volumes.

[0179] In one implementation, the controller can use a buzzer to emit a sharp alarm sound via the sound warning module. Alternatively, the controller can use a voice prompt system via the sound warning module to play a pre-recorded warning message to inform the driver of the danger ahead.

[0180] In one implementation, the light warning module uses flashing warning lights inside or outside the vehicle to attract the driver's visual attention, thereby prompting the driver to take timely actions such as braking or steering to avoid a collision.

[0181] In one implementation, the controller can set up a special warning light on the vehicle's dashboard and alert the driver by flashing the warning light.

[0182] S1032. If the target intervention method is primary intervention, then control the vehicle to perform seat belt motor warning, folding foot pedal, and switching to backup redundant power supply for the seat.

[0183] For example, if the controller determines that the target intervention method is a primary intervention, it will control the vehicle to perform operations such as seat belt motor warning, folding foot pedals, and switching the seat to backup redundant power, thereby improving the vehicle's safety in the event of a collision.

[0184] In one implementation, under the primary intervention method, the controller will also adjust the vehicle speed according to a preset speed adjustment value to avoid collisions.

[0185] In one implementation, primary intervention is a relatively mild collision avoidance measure taken by the vehicle's automatic control system.

[0186] In one implementation, the seatbelt motor warning system alerts the driver by moving the seatbelt motor. The controller sends a command to the seatbelt motor, causing the seatbelt to tighten or vibrate to a certain extent, thereby alerting the driver to the potential collision risk.

[0187] Alternatively, in addition to tightening or vibrating, the seatbelt motor warning can also drive the seatbelt to move slowly around the driver's body, creating a dynamic reminder effect.

[0188] In one implementation, the method of folding the pedal depends on the pedal's design structure. Common methods include electric folding and mechanical folding. The controller can select the appropriate folding method based on the vehicle's configuration. The controller controls the pedal's folding mechanism to fold the pedal, preventing secondary injury to the driver in the event of a collision.

[0189] In one implementation, the seat's switching to a backup redundant power supply is primarily achieved through a power management module. This module monitors the status of both the main and backup power supplies and automatically switches between them when necessary, ensuring a stable power supply to the seat. The controller manages the seat's power system, switching the seat's power supply from the main power supply to the backup redundant power supply. This ensures that the seat's functions continue to operate normally even if the main power supply fails, protecting the driver's safety. This setup ensures that even after a collision, if the main power supply malfunctions and becomes unusable, the seat remains controllable. Furthermore, this allows the user to create more survival space by moving the seat after a collision.

[0190] S1033. If the target intervention method is intermediate intervention, then control the vehicle to perform seat adjustment.

[0191] For example, when the controller determines that the target intervention method is a medium-level intervention, it will control the vehicle to perform a seat adjustment operation.

[0192] In one implementation, the controller sends precise control commands to the seat adjustment motor based on factors such as the vehicle's driving status, the degree of collision risk, and the occupants' body shape and posture, so that the seat can be adjusted in the forward, backward, up, down, and tilt directions.

[0193] For example, if the vehicle is about to be involved in a frontal collision, the controller may move the seat backward a certain distance to provide the driver with more buffer space.

[0194] For example, if the vehicle is at risk of overturning, the controller will adjust the seat tilt angle to keep the driver's body relatively stable and reduce the possibility of injury.

[0195] In one implementation, intermediate intervention is a collision avoidance measure of moderate intensity taken by the vehicle's automatic control system.

[0196] In one implementation, seat adjustment is achieved by changing the seat position and angle using an adjustment motor on the seat.

[0197] In one implementation, to improve the response efficiency of seat adjustment, the controller can use electric adjustment, driving the various adjustment mechanisms of the seat with a motor to achieve rapid seat adjustment.

[0198] S1034. If the target intervention method is ultimate intervention, then control the vehicle to switch the front travel of the seat to collapsible damping energy absorption mode.

[0199] For example, if the target intervention method is again determined by the controller to be the ultimate intervention, the controller will control the vehicle to switch the front travel of the seat to a collapsible damping energy absorption mode.

[0200] In one implementation, the controller sends a control signal to the damping device in the collapsible structure at the front of the seat, adjusts the parameters of the damping device, so that the front of the seat can collapse in a preset manner when subjected to a collision force, and absorbs and disperses the collision energy through the damping device, thereby reducing the impact force transmitted to the driver and reducing the risk of driver injury.

[0201] In one implementation, ultimate intervention is the appropriate measure taken by the vehicle's automatic control system to avoid a collision.

[0202] In one implementation, the collapsible damping energy absorption mode at the front of the seat is a seat safety design aimed at absorbing collision energy through a deformable structure and damping device at the front of the seat. The collapsible structure can employ special materials and structural designs to allow it to undergo plastic deformation under a certain force; common materials include high-strength aluminum alloys and engineering plastics. The damping device can be a hydraulic damper, a spring damper, etc., and the collapse speed and energy absorption effect of the front of the seat are controlled by adjusting the damping coefficient.

[0203] In this example, the controller executes different safety control measures based on different target intervention methods, thereby achieving the effect of accurately taking corresponding measures to ensure driving safety and reduce personal injury based on the specific circumstances of the possible collision.

[0204] In one example, step S1033 above, controlling the vehicle to perform seat adjustment, includes:

[0205] Step 331: Obtain occupant body shape. Determine occupant type based on occupant body shape.

[0206] For example, the controller can obtain the occupant's body shape when it determines that an intermediate intervention is needed. After determining the occupant's body shape, the controller will classify the occupant into different types according to pre-set criteria, such as children, adult women, and adult men.

[0207] In one implementation, the controller accesses various sensors installed in the vehicle to collect relevant data about the occupant's body. For example, the controller can access sensors such as seat pressure sensors, infrared sensors, and cameras. It can collect information such as the pressure distribution of different body parts on the seat and body contour dimensions. Then, based on the collected data, the controller analyzes and processes it using preset algorithms and models to accurately determine the occupant's body shape characteristics.

[0208] In one implementation, occupant body type refers to the external dimensions and morphological characteristics of the occupant's body. Optionally, the occupant body type may specifically include information such as height, weight, and body proportions.

[0209] In one implementation, the controller can classify passengers into different types based on factors such as their body shape and age.

[0210] In one implementation, the controller can infer body shape by detecting the pressure exerted by the occupant's body on different parts of the seat.

[0211] In another implementation, the controller can use infrared sensors to sense the heat distribution of the occupant's body to help determine body shape.

[0212] In another implementation, the controller can directly capture images of the occupants' bodies through a camera and analyze their body shape using image recognition technology.

[0213] In one implementation, the controller can compare the acquired body size data with standard data for different types of occupants based on preset classification rules, thereby determining the type of occupant.

[0214] In another implementation, the controller can input the acquired body size data into the algorithm model of the classification algorithm based on a preset classification algorithm to obtain the type of the occupant.

[0215] Step 332: Determine the seat position adjustment parameters corresponding to the occupant type.

[0216] For example, after determining the occupant type, the controller obtains the seat position adjustment parameters from the calibration data pre-stored in the controller.

[0217] In one implementation, the controller stores a table mapping different occupant types to seat position adjustment parameters. Once an occupant type is determined, the controller retrieves the matching seat position adjustment parameters from this table.

[0218] In one implementation, the controller can calculate the specific seat position adjustment parameters based on the specific body shape data within the relationship table by using the difference method.

[0219] In one implementation, the seat position adjustment parameters may include the seat rearward position and the seat recline position. These seat position adjustment parameters are derived from extensive experimental and simulation analyses and can provide the most suitable safety assurance for different types of occupants.

[0220] For example, for child occupants, the seat recline will be relatively smaller to avoid excessive impact during emergency braking due to the seat being too far forward. The seat recline will also be appropriately reduced based on the child's body shape to ensure safety in the event of a collision.

[0221] For example, for adult male occupants, the seat will be moved back a relatively large distance and the seat will recline more, thus providing more space to ensure that the occupant still has enough room to move after a collision.

[0222] In one implementation, the seat rearward position refers to the distance the seat moves backward in the vehicle's longitudinal direction. The distance the seat moves backward affects the occupant's legroom, which in turn affects the buffer distance during a collision.

[0223] In one implementation, the seat recline position refers to the angle at which the seat back is tilted backward. A suitable recline angle can better protect the occupant's neck and back during a collision.

[0224] Step 333: Adjust the parameters according to the seat position and adjust the seat.

[0225] For example, after determining the seat position adjustment parameters, the controller immediately controls the seat to adjust. The controller sends precise control commands to the seat's drive motor, and the drive motor starts operating according to the parameters in the commands.

[0226] In one implementation, for the seat to move backward, the drive motor drives the seat to move backward on the vehicle's slide rail through a transmission device, such as a gear or lead screw, until it reaches the preset backward position.

[0227] In one implementation, when the seat is reclined, the drive motor controls the adjustment mechanism of the seat back to tilt the backrest backward at a set angle until the preset recline position is reached.

[0228] In one implementation, during the adjustment process, the controller monitors the position and angle information of the seat in real time and ensures that the seat can be accurately adjusted to the target position through a feedback mechanism.

[0229] In one implementation, if a driver's braking signal is detected during seat adjustment, the controller can stop adjusting the seat.

[0230] In this example, by obtaining the occupant's body shape and determining the occupant type accordingly, then determining the seat position adjustment parameters based on the occupant type, and adjusting the seat according to these seat position adjustment parameters, the method achieves the effect of providing suitable seat position adjustment parameters for different types of occupants, thereby improving the safety of different occupants in the event of a collision.

[0231] In one example, the controller may also determine whether the vehicle's acceleration is higher than the ignition threshold before performing the above step S101.

[0232] If the vehicle's acceleration exceeds the ignition threshold, it indicates that the vehicle's acceleration is too high, which would pose a significant risk in the event of a collision. In this case, the controller can continue to execute step S101 and subsequent steps.

[0233] Otherwise, if the vehicle's acceleration is less than or equal to the ignition threshold, it indicates that the vehicle's acceleration is very small, and a collision is highly unlikely. Even if a collision occurs, it will likely result in scratches on the vehicle's surface and will not affect the safety of the occupants. Therefore, the controller can directly terminate this safety control operation.

[0234] The ignition threshold is preset based on experience. When the vehicle speed is greater than the ignition threshold, it means the vehicle has started and is in motion. When the vehicle speed is less than the ignition threshold, it means the vehicle has not started and is stationary.

[0235] Figure 2 A flowchart of a vehicle collision safety control method provided in this application embodiment is shown. Figure 1 Based on the illustrated embodiments, as Figure 2 As shown, taking the controller as the execution subject, when there is only one obstacle around the vehicle, including the front of the vehicle, one embodiment of the controller performing safety control may include the following steps:

[0236] S201, Obtain the axial distance L1 and the speed V1 of the obstacle in front of the vehicle.

[0237] S202, Obtain the vehicle speed V2.

[0238] S203. Determine if the collision acceleration is higher than the ignition threshold. If the collision acceleration is higher than the ignition threshold, proceed to step S204. Otherwise, if the collision acceleration is less than or equal to the ignition threshold, return to step S201.

[0239] S204. Calculate the collision probability based on L1, V1, and V2.

[0240] The collision probability can be calculated in the same way as in step S101. This collision probability can be calculated using an exponential decay model. This collision probability indicates the probability of a collision between the vehicle and the obstacle.

[0241] S205. Determine if the collision probability is higher than 0.8. If the collision probability is greater than 0.8, proceed to step S206. Otherwise, if the collision probability is less than 0.8, return to step S201.

[0242] Here, 0.8 is the probability threshold.

[0243] S206. Obtain the collision time (TTC) between the vehicle and the obstacle. Then, compare the collision time (TTC) with the braking time corresponding to various intervention methods to determine the target intervention method to be used in this case.

[0244] The controller can calculate the speed difference based on the obstacle's speed V1 and the vehicle's speed V2. The controller can then determine the collision time TTC based on the ratio of the axial distance L1 to this speed difference.

[0245] Intervention methods can include human intervention, primary intervention, intermediate intervention, and ultimate intervention.

[0246] S207. Compare the manual braking time and TTC (Total Time Tolerance) during human intervention. If the TTC is greater than the manual braking time, it indicates that human intervention can achieve safe control of the vehicle before a collision occurs, preventing a collision. In this case, the controller can execute an audible and visual warning.

[0247] S208. If, according to step S207, the TTC is less than or equal to the manual braking time, the controller needs to obtain the initial braking time under the initial intervention. If the TTC is less than the initial braking time, it means that although manual intervention cannot avoid the collision, the initial intervention can prevent the vehicle from colliding with the obstacle. In this case, the controller can execute the operation corresponding to the initial intervention.

[0248] The operations corresponding to this initial intervention include, but are not limited to, seat belt motor warning, folding foot pedal, and switching to a backup redundant power supply for the seat.

[0249] S209. If, according to step S208, the TTC is less than or equal to the primary braking time, the controller needs to obtain the intermediate braking time under intermediate intervention. If the TTC is less than the intermediate braking time, it means that although the primary intervention cannot avoid the collision, the intermediate intervention can avoid the collision between the vehicle and the obstacle. At this time, the controller can continue to execute step S210.

[0250] S210. Determine if a driver's manual braking signal exists. If it exists, return to step S206. If it does not exist, continue to step S211.

[0251] S211. Obtain the occupant's body shape. If the occupant's body shape determines that they are an adult, move the seat rail back 100mm and recline the backrest by 5°. If the occupant's body shape determines that they are a child, move the seat rail back 50mm and do not adjust the backrest.

[0252] S212. If, according to step S209, the TTC is less than or equal to the intermediate braking time, the controller can execute ultimate protection. The controller can switch the seat front travel to a collapsible damping energy absorption mode. By using a mechanical collapsible structure combined with a hydraulic damping tube, stepless energy absorption adjustment can be achieved during a collision.

[0253] In this embodiment, the collision risk is calculated, and when the collision risk is high, the collision time is calculated. Then, based on the collision time, an appropriate intervention method is selected to achieve safety control of the vehicle under the collision method and improve the safety of the vehicle.

[0254] Figure 3 A structural diagram of a vehicle collision safety control device provided in this application embodiment is shown below. Figure 3 As shown, the vehicle collision safety control device 300 includes:

[0255] The acquisition module 301 is used to acquire the relative speed and axial distance between the vehicle and the surrounding obstacles, and input the relative speed and axial distance between the vehicle and the surrounding obstacles into a preset attenuation model to calculate the collision probability between the vehicle and each obstacle.

[0256] The selection module 302 is used to calculate the collision avoidance distance for each intervention method when the collision probability is greater than a preset probability threshold, based on the relative velocity corresponding to the highest collision probability, as well as the preset speed adjustment values ​​and reaction times for each intervention method. Then, based on the axial distance corresponding to the highest collision probability and the collision avoidance distances for each intervention method, the target intervention method is determined.

[0257] The control module 303 is used to control the vehicle to execute the corresponding protection measures using the protection measures corresponding to the target intervention method.

[0258] In one example, module 302 is selected for:

[0259] The zeroing time is obtained by calculating the ratio of the relative speed corresponding to the highest collision probability to the speed adjustment value of each intervention method. The zeroing distance is calculated based on the vehicle's speed, relative speed, and zeroing time. The reaction distance is obtained by multiplying the reaction time and vehicle speed. The collision avoidance distance is the sum of the zeroing time and the reaction time.

[0260] In one example, the intervention methods include human intervention, primary intervention, intermediate intervention, and final intervention. Module 302 is selected for:

[0261] The travel distance is calculated based on the speed of the obstacle corresponding to the highest collision probability, the zeroing time, and the reaction time. The pursuit distance is then determined based on the travel distance and the axial distance. If the collision avoidance distance of the human intervention matches the pursuit distance, the target intervention method is determined to be human intervention. Otherwise, if the collision avoidance distance of the primary intervention matches the pursuit distance, the target intervention method is determined to be primary intervention. Otherwise, if the collision avoidance distance of the intermediate intervention matches the pursuit distance, the target intervention method is determined to be intermediate intervention. Otherwise, the target intervention method is determined to be final intervention.

[0262] In one example, module 301 is used for:

[0263] Obtain the relative velocity and axial distance between the vehicle and an obstacle. Input the relative velocity into an activation function to calculate the activation value. Determine the exponent value of the attenuation model based on the ratio of the square of the relative velocity to the axial distance. Input the exponent value and the activation value into the attenuation model to calculate the collision probability between the vehicle and the obstacle.

[0264] In one example, module 301 is used for:

[0265] If the vehicle is in front of the obstacle, the activation value is 0 when the relative speed is greater than or equal to 0, and 1 when the relative speed is less than 0. If the vehicle is behind the obstacle, the activation value is 1 when the relative speed is greater than 0, and 0 when the relative speed is less than or equal to 0. The relative speed is the difference between the vehicle's speed and the obstacle's speed.

[0266] In one example, the intervention methods include human intervention, primary intervention, intermediate intervention, and ultimate intervention. Control module 303 is used for:

[0267] If the target intervention method is human intervention, the vehicle will be controlled to execute an audible and visual warning. If the target intervention method is a primary intervention, the vehicle will be controlled to execute a seatbelt motor warning, fold the foot pedals, and switch the seat to a backup redundant power supply. If the target intervention method is an intermediate intervention, the vehicle will be controlled to adjust the seat. If the target intervention method is a final intervention, the vehicle will be controlled to switch the front travel of the seat to a collapsible damping energy absorption mode.

[0268] In one example, control module 303 is used for:

[0269] Obtain occupant body measurements. Determine occupant type based on body measurements. Determine corresponding seat position adjustment parameters based on occupant type. Adjust the seat based on seat recline and backrest positions.

[0270] 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.

[0271] In this embodiment, the vehicle collision safety control device is presented in the form of a functional unit. Here, a unit refers to an application-specific integrated circuit (ASIC), a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0272] Figure 4 A structural diagram of a controller provided in an embodiment of this application is shown below. Figure 4 As shown, the controller 400 includes one or more processors 401, a memory 402, 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 controller, 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 interface). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple controllers 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 4 Take a processor 401 as an example.

[0273] Processor 401 may be a central processing unit, a network processor, or a combination thereof. Processor 401 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.

[0274] The memory 402 stores instructions executable by at least one processor 401 to cause at least one processor 401 to perform the method shown in the above embodiments.

[0275] Memory 402 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 controller. Furthermore, memory 402 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, memory 402 may optionally include memory remotely located relative to processor 401, which can be connected to the controller 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.

[0276] Memory 402 may include volatile memory, such as random access memory. Memory may also include non-volatile memory, such as flash memory, hard disk, or solid-state drive. Memory 402 may also include combinations of the above types of memory.

[0277] The controller also includes a communication interface 403 for communicating with other devices or communication networks.

[0278] This application also provides a vehicle. The vehicle may be equipped with the aforementioned controller. The controller can execute the aforementioned vehicle collision safety control method.

[0279] This application also provides a computer-readable storage medium. The methods described in this application can be implemented in hardware or firmware, or implemented as recordable on a storage medium, or implemented as computer code downloaded over a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and subsequently 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 may 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.

[0280] This application provides a computer program product including computer instructions stored in a computer-readable storage medium. A controller's processor reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the controller to perform the method of any embodiment of this application.

[0281] Although embodiments of this application 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 this application, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A vehicle collision safety control method, characterized in that, The method includes: The relative speed and axial distance between the vehicle and each surrounding obstacle are obtained, and the relative speed and axial distance between the vehicle and each surrounding obstacle are input into a preset attenuation model to calculate the collision probability between the vehicle and each obstacle. When the collision probability is greater than a preset probability threshold, the collision avoidance distance of each intervention method is calculated based on the relative speed corresponding to the maximum collision probability, as well as the preset speed adjustment value and reaction time of each intervention method; and the target intervention method is determined based on the axial distance corresponding to the maximum collision probability and the collision avoidance distance of each intervention method. Using the protective measures corresponding to the target intervention method, control the vehicle to execute the corresponding protective measures; The relative velocity and axial distance are input into a preset attenuation model to calculate the collision probability between the vehicle and each obstacle, including: Obtain the relative speed and axial distance between the vehicle and an obstacle; The relative velocity is input into the activation function to calculate the activation value; The exponential value of the attenuation model is determined based on the ratio of the square of the relative velocity to the axial distance. The index value and the activation value are input into the attenuation model to calculate the collision probability between the vehicle and the obstacle.

2. The method according to claim 1, characterized in that, Based on the relative velocity corresponding to the highest collision probability, and the preset velocity adjustment values ​​and reaction times for each intervention method, the collision avoidance distance for each intervention method is calculated, including: The zeroing time is obtained by calculating the ratio of the relative velocity corresponding to the highest collision probability to the velocity adjustment value of each intervention method; The zeroing distance is calculated based on the vehicle's speed, the relative speed, and the zeroing time. The product of the reaction time and the vehicle speed is calculated to obtain the reaction distance; The sum of the zeroing time and the reaction time is taken as the collision avoidance distance.

3. The method according to claim 2, characterized in that, The intervention methods include human intervention, primary intervention, intermediate intervention, and ultimate intervention; based on the axial distance corresponding to the highest collision probability and the collision avoidance distance of each intervention method, the target intervention method is determined, including: The travel distance is calculated based on the speed of the obstacle corresponding to the highest collision probability, the zeroing time, and the reaction time; and the pursuit distance is determined based on the travel distance and the axial distance. If the collision avoidance distance of the human intervention matches the pursuit distance, then the target intervention method is determined to be human intervention; Otherwise, if the collision avoidance distance of the primary intervention matches the pursuit distance, then the target intervention method is determined to be a primary intervention; Otherwise, if the collision avoidance distance of the intermediate intervention matches the pursuit distance, then the target intervention method is determined to be intermediate intervention; Otherwise, the target intervention method is determined to be the final intervention.

4. The method according to claim 3, characterized in that, The relative velocity is input into the activation function to calculate the activation value, including: If the vehicle is in front of the obstacle, the activation value is determined to be 0 when the relative speed is greater than or equal to 0, and the activation value is determined to be 1 when the relative speed is less than 0. If the vehicle is behind the obstacle, the activation value is determined to be 1 when the relative speed is greater than 0, and the activation value is determined to be 0 when the relative speed is less than or equal to 0. The relative speed is the difference between the vehicle's speed and the obstacle's speed.

5. The method according to any one of claims 1-4, characterized in that, The intervention methods include human intervention, primary intervention, intermediate intervention, and ultimate intervention; using the protective measures corresponding to the target intervention method, the vehicle is controlled to execute the corresponding protective measures, including: If the target intervention method is human intervention, then control the vehicle to perform an audible and visual warning; If the target intervention method is a primary intervention, then control the vehicle to perform seat belt motor warning, folding foot pedals, and switching the seat to backup redundant power supply; If the target intervention method is intermediate level, then control the vehicle to perform seat adjustment; If the target intervention method is the ultimate intervention, then the vehicle will be controlled to switch the front travel of the seat to a collapsible damping energy absorption mode.

6. The method according to claim 5, characterized in that, Controlling the vehicle to perform seat adjustments includes: Obtain the occupant's body shape; and determine the occupant type based on the occupant's body shape; Based on the occupant type, determine the seat position adjustment parameters corresponding to the occupant type; Adjust the seat according to the seat position adjustment parameters.

7. A vehicle collision safety control device, characterized in that, The device includes: The acquisition module is used to acquire the relative speed and axial distance between the vehicle and each surrounding obstacle, and input the relative speed and axial distance between the vehicle and each surrounding obstacle into a preset attenuation model to calculate the collision probability between the vehicle and each obstacle. The selection module is used to calculate the collision avoidance distance of each intervention method based on the relative speed corresponding to the maximum collision probability, as well as the preset speed adjustment value and reaction time of each intervention method when the collision probability is greater than a preset probability threshold; and to determine the target intervention method based on the axial distance corresponding to the maximum collision probability and the collision avoidance distance of each intervention method. The control module is used to control the vehicle to execute the corresponding protection measures using the protection measures corresponding to the target intervention method; The relative velocity and axial distance are input into a preset attenuation model to calculate the collision probability between the vehicle and each obstacle, including: Obtain the relative speed and axial distance between the vehicle and an obstacle; The relative velocity is input into the activation function to calculate the activation value; The exponential value of the attenuation model is determined based on the ratio of the square of the relative velocity to the axial distance. The index value and the activation value are input into the attenuation model to calculate the collision probability between the vehicle and the obstacle.

8. A controller, characterized in that, include: A memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, the processor executing the computer instructions to perform the method of any one of claims 1 to 6.

9. A vehicle, characterized in that, include: The vehicle is equipped with a controller as described in claim 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing a computer to perform the method of any one of claims 1 to 6.

11. A computer program product, characterized in that, Includes computer instructions for causing a computer to perform the method of any one of claims 1 to 6.

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