An unmanned vehicle safety warning method and system

Abstract

The application discloses an unmanned vehicle safety early warning method and system, belonging to the automatic driving technical field, comprising the following steps: 1, acquiring the suspension pressure signal and the difference signal of the wheel speed and the vehicle body speed, obtaining the road adhesion coefficient through the slip rate lookup table, and calculating the instantaneous total mass, and outputting the instantaneous total mass voltage signal and the adhesion coefficient voltage signal; 2, based on the instantaneous total mass voltage signal, the adhesion coefficient voltage signal and the real-time vehicle speed voltage signal, the minimum safety braking distance is calculated and converted into the basic safety time interval, and the basic safety time interval voltage is output; 3, the longitudinal acceleration signal is acquired, the predicted speed increment is obtained through integration, and the current speed voltage is added to obtain the pre-judgment speed voltage. The application can solve the problem of poor adaptability of the fixed threshold, adapt to multiple scenes such as high-speed, urban commuting, community connection, and avoid the defects of untimely high-speed warning and frequent low-speed warning.

Description

Technical Field

[0001] This invention relates to the field of autonomous driving technology, and more specifically, to a method and system for safety warning of unmanned vehicles. Background Technology

[0002] With the rapid iteration of autonomous driving technology, driverless cars have been gradually applied to various scenarios such as highway driving, urban commuting, and community shuttles. Driving safety has become a major demand for industry development. As a component of the active safety protection of driverless cars, the safety warning system directly determines the driverless car's ability to identify and respond to potential risks, and is an important barrier to ensure the safety of drivers, passengers, and other road users. Currently, existing driverless car safety warning strategies generally adopt a mode of triggering warning actions with fixed risk thresholds. That is, fixed warning judgment criteria such as distance and time are preset. For example, a level one warning is triggered when the distance to an obstacle is less than 5 meters, and a level two warning is triggered when the distance is less than 3 meters. This fixed threshold mode achieves simple risk warning control. Its structure is simple and easy to implement, and it has been widely used in early single driving scenarios.

[0003] The actual driving environment of autonomous vehicles is complex and varied. Under different driving conditions, the safe braking distance and risk response time of the vehicle vary significantly. Fixed warning thresholds do not fully consider these differences in driving conditions, resulting in a lack of flexibility and adaptability in threshold settings, making it difficult to meet the safety warning needs of multiple scenarios. The braking distance of autonomous vehicles varies significantly at different driving speeds. At high speeds, such as 120 km / h, the braking distance is long, requiring earlier warnings to allow sufficient emergency response space. At low speeds, such as 20 km / h on residential roads, the braking distance is short, and fixed thresholds may lead to excessively frequent warnings. In different road types, such as highways, urban roads, and rural roads, the road adhesion coefficient and visibility conditions are different, which also affects the requirements for warning timing. Under different vehicle states, such as empty, fully loaded, and heavily loaded, the vehicle inertia varies greatly. When fully loaded or heavily loaded, the inertia is increased, and the warning threshold needs to be appropriately relaxed to allow sufficient braking space and avoid safety accidents caused by untimely braking.

[0004] The aforementioned fixed threshold directly leads to significant shortcomings in existing warning systems: warnings are not timely at high speeds, failing to provide sufficient braking and avoidance time for the vehicle, increasing the risk of collisions; warnings are too frequent at low speeds or in simple road conditions, not only interfering with the autonomous vehicle's normal driving decisions and reducing driving efficiency, but also potentially causing system misjudgments and affecting the reliability of the warning system. Furthermore, as the application scenarios of autonomous vehicles continue to expand, multi-condition collaborative driving has become the norm, and fixed warning thresholds are no longer sufficient to adapt to complex and ever-changing driving needs, thus restricting further improvements in the safety performance of autonomous vehicles. Summary of the Invention

[0005] To address the problems existing in the prior art, the present invention aims to provide a safety early warning method and system for unmanned vehicles, which can solve the problem of poor adaptability of fixed thresholds, adapt to multiple scenarios such as highways, urban commuting, and community shuttles, and avoid the defects of untimely high-speed warnings and frequent low-speed warnings.

[0006] To solve the above problems, the present invention adopts the following technical solution:

[0007] Firstly, a method for early warning of safety issues in unmanned vehicles includes:

[0008] Step 1: Obtain the suspension pressure signal and the differential signal between wheel speed and vehicle speed; obtain the road adhesion coefficient by looking up the slip ratio table; calculate the instantaneous total mass; and output the instantaneous total mass voltage signal and adhesion coefficient voltage signal.

[0009] Step 2: Based on the instantaneous total mass voltage signal, adhesion coefficient voltage signal, and real-time vehicle speed voltage signal, calculate the minimum safe braking distance and convert it into a basic safe braking distance, then output the basic safe braking distance voltage.

[0010] Step 3: Obtain the longitudinal acceleration signal, integrate it to obtain the predicted velocity increment, add it to the current velocity voltage to obtain the predicted velocity voltage, and recalculate the predicted safe time distance based on the predicted velocity voltage;

[0011] Step 4: Map the instantaneous total mass voltage signal to a tolerance coefficient, correct the tolerance coefficient with the adhesion coefficient voltage signal, and multiply the tolerance coefficient by the predicted safe time interval to obtain the dynamic warning time interval;

[0012] Step 5: Obtain the remaining collision time, compare the remaining collision time with the dynamic warning time using a voltage comparator with hysteresis, and output the graded warning voltage;

[0013] Step 6: Convert the graded warning voltage into displacement, and adjust the preset following distance threshold with the displacement.

[0014] Further, step 1 includes:

[0015] Four suspension pressure signals are acquired, and symmetrical differential processing is performed to offset the static pressure bias introduced by vehicle pitch and roll. Then, the longitudinal acceleration signal is used to perform inertial compensation on the differential pressure signal, and the instantaneous total mass voltage signal is output.

[0016] The differential signal between wheel speed and vehicle speed is obtained, and the differential signal is input into a phase-locked loop circuit to be converted into a slip ratio frequency. This frequency is then used to drive a voltage-controlled resistor network to output an adhesion coefficient voltage signal.

[0017] Further, step 2 includes:

[0018] The system acquires the instantaneous total mass voltage signal, adhesion coefficient voltage signal, and real-time vehicle speed voltage signal. The ratio of the instantaneous total mass voltage signal to the adhesion coefficient voltage signal is used as a coefficient. The system squares the real-time vehicle speed voltage signal and multiplies it by this coefficient to output the critical braking energy voltage and the mass adhesion correction coefficient voltage.

[0019] Obtain the critical braking energy voltage and a fixed reaction time reference voltage, superimpose the critical braking energy voltage and the reaction time reference voltage and perform a logarithmic transformation to output the basic safety time interval voltage.

[0020] Further, step 3 includes:

[0021] Acquire the longitudinal acceleration signal, perform integration and differentiation on the first integration channel, the second integration channel, and the differential, and output the integration result of the first integration channel, the integration result of the second integration channel, and the jerk voltage signal, wherein the integration time constant of the first integration channel is smaller than the integration time constant of the second integration channel;

[0022] The accelerometer voltage signal is acquired, its absolute value is taken, and then a weighted coefficient voltage is output after interval decision.

[0023] Obtain the integration results of the first integration channel, the integration results of the second integration channel, and the weighted coefficient voltage. Multiply the weighted coefficient voltage by the integration results of the first and second integration channels respectively, and then sum them to output the adaptive integration speed increment.

[0024] Furthermore, step 3 also includes:

[0025] Obtain the adaptive integral speed increment and the current speed voltage, sum them, and output the predicted speed voltage;

[0026] Obtain the predicted velocity voltage, square the predicted velocity voltage, multiply it by the mass adhesion correction coefficient voltage, then superimpose it with the fixed reaction time reference voltage and perform a logarithmic transformation to output the predicted safe time interval.

[0027] Further, step 4 includes:

[0028] The instantaneous total mass voltage signal is obtained, and the output of the corresponding resistor voltage divider chain is obtained through partition decision, and the initial tolerance coefficient voltage is output.

[0029] The adhesion coefficient voltage signal is acquired, and the correction factor voltage is output through inverse proportional function mapping.

[0030] Furthermore, step 4 also includes:

[0031] Obtain the initial tolerance coefficient voltage and the correction factor voltage, multiply them, and output the final tolerance coefficient voltage;

[0032] The final tolerance coefficient voltage and the predicted safe time interval are obtained, and the dynamic warning time interval voltage is output after multiplying them.

[0033] Further, step 5 includes:

[0034] Obtain the remaining collision time-distance voltage and the dynamic warning time-distance voltage, and output the time-distance difference voltage after differentiating the two.

[0035] Obtain the final tolerance coefficient voltage, convert the final tolerance coefficient voltage into a reverse-changing hysteresis width voltage, input the hysteresis width voltage and the time difference voltage together into a variable hysteresis voltage comparator, and output the primary trigger voltage;

[0036] Obtain the primary trigger voltage and output graded warning voltages based on the amplitude range of the primary trigger voltage.

[0037] Further, step 6 includes:

[0038] The graded warning voltage is obtained, and the graded warning voltage is converted into a pulse width modulation signal whose duty cycle is proportional to the amplitude of the graded warning voltage. The pulse width modulation signal is then integrated and held to output the average displacement drive voltage.

[0039] The average displacement driving voltage is obtained, the average displacement driving voltage is converted into a linear displacement, and the preset following distance threshold is adjusted based on the displacement.

[0040] Secondly, the present invention also provides an unmanned vehicle safety early warning system, comprising:

[0041] The working condition perception module is used to acquire the suspension pressure signal and the differential signal between wheel speed and vehicle speed, obtain the road adhesion coefficient by looking up the slip ratio table, calculate the instantaneous total mass, and output the instantaneous total mass voltage signal and adhesion coefficient voltage signal.

[0042] The time-distance conversion module calculates the minimum safe braking distance and converts it into a basic safe time-distance based on the instantaneous total mass voltage signal, adhesion coefficient voltage signal and real-time vehicle speed voltage signal, and outputs the basic safe time-distance voltage.

[0043] The velocity prediction module is used to acquire the longitudinal acceleration signal, integrate it to obtain the predicted velocity increment, add it to the current velocity voltage to obtain the predicted velocity voltage, and recalculate the predicted safe time distance based on the predicted velocity voltage.

[0044] The coefficient modulation module is used to map the instantaneous total mass voltage signal into a tolerance coefficient, and correct the tolerance coefficient with the attached coefficient voltage signal. The tolerance coefficient is then multiplied by the predicted safe time interval to obtain the dynamic early warning time interval.

[0045] The graded comparison module is used to obtain the remaining collision time, compare the remaining collision time with the dynamic warning time through a voltage comparator with hysteresis, and output the graded warning voltage.

[0046] The displacement execution module is used to convert the graded warning voltage into displacement and adjust the preset following distance threshold with the displacement.

[0047] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0048] (1) This scheme collects multiple parameters such as instantaneous total mass, road surface adhesion coefficient, and longitudinal acceleration to dynamically calculate the warning time interval. It can adjust the warning standard in real time according to changes in vehicle load, road surface conditions, and driving speed, effectively solving the problem of poor adaptability of fixed thresholds. It is suitable for multiple scenarios such as highways, urban commuting, and community shuttles, avoiding the defects of untimely high-speed warnings and frequent low-speed warnings.

[0049] (2) This scheme performs dual-integral channel processing and weighted summation on the longitudinal acceleration signal to accurately predict the vehicle speed increment. Combined with the predicted speed, the predicted safe time distance is recalculated to capture the vehicle speed change trend in advance, reserve sufficient emergency response space for the warning action, further improve the timeliness and foresight of the safety warning, and reduce the collision risk.

[0050] (3) This scheme dynamically adjusts the hysteresis width through the final tolerance coefficient and uses a variable hysteresis voltage comparator to compare signals, effectively avoiding false triggering of warnings due to small signal fluctuations. At the same time, risk classification is achieved through graded warning voltage, making the warning action more targeted and taking into account both warning reliability and driving efficiency. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0052] Figure 1 This is an overall flowchart of the unmanned vehicle safety early warning method of the present invention;

[0053] Figure 2 This is a flowchart of the working condition perception and basic safety time distance calculation of the present invention;

[0054] Figure 3 This is a flowchart of the speed prediction and safe time distance calculation process of the present invention;

[0055] Figure 4 This is a data flow diagram between the various modules of the present invention. Detailed Implementation

[0056] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0057] Example 1:

[0058] Please see Figures 1 to 3 A method for early warning of safety for unmanned vehicles, the method comprising:

[0059] Step 1: Acquire the suspension pressure signal and the differential signal between wheel speed and vehicle speed. Obtain the road adhesion coefficient by looking up the slip ratio table, calculate the instantaneous total mass, and output the instantaneous total mass voltage signal and adhesion coefficient voltage signal. The specific operation is as follows:

[0060] During autonomous vehicle operation, pressure sensors installed at the four suspension points collect suspension pressure signals in real time. Simultaneously, wheel speed sensors collect wheel speed signals, and vehicle speed sensors collect vehicle speed signals. The difference between wheel speed and vehicle speed is obtained through signal subtraction. The road adhesion coefficient is obtained based on a preset slip ratio lookup table. This lookup table is pre-calibrated based on the correspondence between slip ratio and adhesion coefficient for different road surface types, such as dry asphalt roads, wet cement roads, and snow-covered roads. After converting the difference between wheel speed and vehicle speed into slip ratio, the adhesion coefficient of the current road surface can be determined by consulting the lookup table. The instantaneous total mass of the vehicle is calculated after a series of signal processing steps on the four collected suspension pressure signals. This instantaneous total mass can accurately reflect the current load status of the vehicle, including whether it is unloaded, fully loaded, or heavily loaded. Since the calculation of various parameters in subsequent steps all rely on voltage signals, the calculated instantaneous total mass and road adhesion coefficient need to be converted into corresponding voltage signals through the signal conversion module, namely the instantaneous total mass voltage signal and the adhesion coefficient voltage signal. The output range of the two voltage signals is uniformly set to 0-5V to ensure the consistency and stability of subsequent circuit processing and to provide reliable input for the calculation of parameters such as minimum safe braking distance and basic safe time distance.

[0061] Step 1 further includes the following steps:

[0062] Step 11: Acquire four suspension pressure signals, perform symmetrical differential processing to offset the static pressure bias introduced by vehicle pitch and roll, and then use the longitudinal acceleration signal to perform inertial compensation on the differential pressure signal to output the instantaneous total mass voltage signal. The specific operation is as follows:

[0063] During autonomous vehicle operation, pitch and roll are inevitable. These attitude changes introduce static pressure bias into the signals collected by the suspension pressure sensors. This bias is unrelated to the actual vehicle load and, if not eliminated, will severely affect the accuracy of instantaneous total mass calculation. To address this issue, symmetrical differential processing is performed on the four suspension pressure signals. Specifically, the left front suspension pressure signal is differentially analyzed with the right front suspension pressure signal, and the left rear suspension pressure signal is differentially analyzed with the right rear suspension pressure signal. This symmetrical processing effectively cancels out the static pressure bias caused by vehicle pitch and roll, retaining the effective pressure signal components relevant to the vehicle load. Furthermore, when an autonomous vehicle accelerates or decelerates longitudinally, inertial forces act on the suspension system, causing deviations in the suspension pressure signal. Therefore, it is necessary to introduce longitudinal acceleration signals collected by a longitudinal acceleration sensor to perform inertial compensation on the pressure signal after symmetrical differential processing. During the compensation process, the differential pressure signal is linearly corrected according to the magnitude and direction of the longitudinal acceleration to counteract the influence of inertial forces. After the above symmetrical differential processing and inertial compensation, the processed pressure signal is input to a signal conversion module and converted into an instantaneous total mass voltage signal that is linearly related to the instantaneous total mass. This signal can accurately reflect the current actual load state of the vehicle.

[0064] Step 12: Obtain the differential signal between wheel speed and vehicle speed, input this differential signal into a phase-locked loop circuit to convert it into a slip ratio frequency, and then use this frequency to drive the voltage-controlled resistor network to output the adhesion coefficient voltage signal. The specific operation is as follows:

[0065] When an autonomous vehicle is driving, the differential signal between wheel speed and vehicle speed can indirectly reflect the vehicle's slip state. The slip state is closely related to the road adhesion coefficient. However, this differential signal is an analog signal and cannot be directly used for subsequent lookup tables and voltage signal conversion. Therefore, a phase-locked loop (PLL) circuit is needed to convert its frequency. After the differential signal between wheel speed and vehicle speed is input into the PLL circuit, the PLL circuit tracks the frequency changes of the differential signal and stably converts it into a slip rate frequency that is linearly related to the slip rate. The locking range of the PLL circuit is set from 10Hz to 100Hz, which is suitable for autonomous vehicles traveling at speeds from 20km / h to 120km / h. The range ensures the stability and accuracy of frequency conversion under different driving conditions. The converted slip ratio frequency drives the voltage-controlled resistor network. The resistance value of the voltage-controlled resistor network is linearly adjusted with the change of input frequency. When the slip ratio frequency increases, the resistance value of the voltage-controlled resistor network decreases, and the corresponding output voltage increases, and vice versa. Through this frequency-resistance-voltage conversion relationship, the slip ratio frequency is converted into a voltage signal that corresponds one-to-one with the road adhesion coefficient, namely the adhesion coefficient voltage signal. The output range of this voltage signal is consistent with the instantaneous total mass voltage signal, both being 0-5V, ensuring the compatibility and accuracy of parameter calculations in subsequent steps.

[0066] In a preferred embodiment of the present invention, step 2 is further included: calculating the minimum safe braking distance based on the instantaneous total mass voltage signal, the adhesion coefficient voltage signal, and the real-time vehicle speed voltage signal, converting it into a basic safe braking distance, and outputting the basic safe braking distance voltage. The specific operation is as follows:

[0067] After signal acquisition and processing in step 1, the instantaneous total mass voltage signal and the road adhesion coefficient voltage signal have been obtained. Simultaneously, the vehicle's speed sensor collects the current driving speed in real time and converts it into a real-time vehicle speed voltage signal. All three voltage signals are uniformly output within the standard 0-5V range to ensure consistent signal processing. The minimum safe braking distance is a core parameter for the vehicle's safety warning system. Its magnitude directly depends on the vehicle's instantaneous total mass, road adhesion coefficient, and real-time driving speed. A larger instantaneous total mass and higher real-time speed result in a longer minimum safe braking distance, while a higher road adhesion coefficient leads to better vehicle braking performance and a shorter minimum safe braking distance. By performing specific calculations on these three voltage signals, the minimum safe braking distance under the current operating conditions can be accurately calculated. Subsequently, this distance parameter is converted into a basic safe braking distance through signal conversion. The basic safe braking distance more intuitively reflects the shortest reaction and braking time required to avoid a collision at the current speed. Finally, the basic safe braking distance is converted into the corresponding basic safe braking distance voltage.

[0068] Step 2 further includes the following steps:

[0069] Step 21: Acquire the instantaneous total mass voltage signal, adhesion coefficient voltage signal, and real-time vehicle speed voltage signal. Use the ratio of the instantaneous total mass voltage signal to the adhesion coefficient voltage signal as a coefficient. Multiply the squared real-time vehicle speed voltage signal by this coefficient to output the critical braking energy voltage and the mass adhesion correction coefficient voltage. The specific operation is as follows:

[0070] The instantaneous total mass voltage signal and adhesion coefficient voltage signal output from step 1 are acquired synchronously, along with the real-time vehicle speed voltage signal after real-time acquisition and conversion. The instantaneous total mass voltage signal (0-5V) corresponds to a vehicle total mass of 1000-2000 kg, the adhesion coefficient voltage signal (0-5V) corresponds to a road adhesion coefficient of 0.1-1.0, and the real-time vehicle speed voltage signal (0-5V) corresponds to a driving speed of 0-120 km / h, ensuring that the quantitative correspondence of each parameter is clear and reproducible. During the calculation, the instantaneous total mass voltage signal and the adhesion coefficient voltage signal are first divided, and the resulting ratio is used as the core coefficient. The physical meaning of this coefficient lies in comprehensively reflecting the synergistic effect of vehicle load and road braking capacity. The larger the instantaneous total mass voltage signal and the smaller the adhesion coefficient voltage signal, the larger this coefficient indicates a higher difficulty in vehicle braking and a greater braking energy required. The real-time vehicle speed voltage signal is then squared. The square of the real-time vehicle speed voltage signal is positively correlated with the vehicle's braking energy. The higher the vehicle speed, the more energy is required for braking. The squared real-time vehicle speed voltage signal is then multiplied by the aforementioned ratio coefficient. The result is output as two voltage signals: the critical braking energy voltage and the mass adhesion correction coefficient voltage. The critical braking energy voltage reflects the voltage value corresponding to the minimum energy required for vehicle braking under the current operating conditions, while the mass adhesion correction coefficient voltage is used for accurate correction of the subsequent prediction of safe distance. Both voltage signals maintain a standard output range of 0-5V to ensure compatibility with subsequent circuit processing.

[0071] The calculation formula is: ;

[0072] The derivation of this formula is based on the physical principles of vehicle braking energy. Vehicle braking energy is directly proportional to the square of the vehicle speed, inversely proportional to the road adhesion coefficient, and directly proportional to the total mass of the vehicle. By substituting the voltage signals corresponding to each parameter, the braking energy can be calculated through voltage arithmetic, realizing the conversion of physical quantities into voltage signals; in the formula: This represents the critical braking energy voltage, measured in V. Represents the instantaneous total mass voltage signal, measured in volts (V). This represents the adhesion coefficient voltage signal, measured in V. This represents the real-time vehicle speed voltage signal, measured in volts (V).

[0073] Step 22: Obtain the critical braking energy voltage and the fixed reaction time reference voltage. Superimpose the critical braking energy voltage and the reaction time reference voltage and perform a logarithmic transformation to output the basic safety time interval voltage. The specific operation is as follows:

[0074] Obtain the critical braking energy voltage output in step 21, and simultaneously call the preset fixed reaction time reference voltage. This reference voltage is pre-calibrated based on the response characteristics of the unmanned vehicle control system and is set to a fixed value of 0.5V, corresponding to an actual system reaction time of 0.5s. This reaction time covers the entire process of signal processing and command transmission, ensuring the rationality and reproducibility of the reaction time. Then, the critical braking energy voltage and the fixed reaction time reference voltage are superimposed. The purpose of superposition is to combine the distance requirement corresponding to the braking energy with the distance requirement corresponding to the system reaction time, so as to fully cover the safe distance required for the vehicle from receiving the warning signal to completing the braking process.

[0075] Because the superimposed voltage signal has a non-linear relationship with the basic safety time interval, its direct use cannot accurately reflect the actual safety time interval requirements. Therefore, it is necessary to perform a logarithmic transformation on the superimposed voltage signal. The logarithmic transformation can convert the non-linear voltage relationship into a linear relationship, so that the output voltage signal can accurately correspond to the actual basic safety time interval, ensuring the accuracy of subsequent warning judgments. After the logarithmic transformation, the output basic safety time interval voltage is 0-5V, which corresponds to an actual basic safety time interval of 0.5-5s. When driving at high speed, such as a real-time vehicle speed of 120km / h, the corresponding basic safety time interval voltage is close to 5V, corresponding to an actual basic safety time interval of 5s, leaving sufficient braking space. When driving at low speed, such as 20km / h on a residential road, the corresponding basic safety time interval voltage is close to 0.5V, corresponding to an actual basic safety time interval of 0.5s.

[0076] In a preferred embodiment of the present invention, step 3 is further included: acquiring the longitudinal acceleration signal, integrating it to obtain the predicted velocity increment, adding it to the current velocity voltage to obtain the predicted velocity voltage, and recalculating the predicted safe time interval based on the predicted velocity voltage. The specific operation is as follows:

[0077] During autonomous vehicle operation, longitudinal acceleration signals reflect the vehicle's acceleration and deceleration states. By integrating this signal, the predicted speed increment over a future period can be obtained. Combining this increment with the current speed voltage allows for accurate prediction of the vehicle's subsequent speed, known as the predicted speed voltage. The predicted speed voltage can anticipate changes in vehicle speed, avoiding untimely or frequent warnings due to real-time speed lag. It is particularly suitable for high-speed driving, rapid acceleration, and rapid deceleration. Based on the predicted speed voltage, a new predicted safety time interval is calculated using a similar logic to the basic safety time interval. This time interval better aligns with the vehicle's future driving state, improving the adaptability and reliability of safety warnings and covering various complex driving scenarios for autonomous vehicles.

[0078] Step 3 further includes the following steps:

[0079] Step 31: Acquire the longitudinal acceleration signal, and perform integration and differentiation processing on the first integration channel, the second integration channel, and the differential processing respectively. Output the integration result of the first integration channel, the integration result of the second integration channel, and the jerk voltage signal. The integration time constant of the first integration channel is smaller than that of the second integration channel. The specific operation is as follows:

[0080] The longitudinal acceleration signal of the autonomous vehicle is acquired by a longitudinal acceleration sensor. After conversion, the output range of this signal is 0-5V, corresponding to actual longitudinal acceleration from -5m / s² to 5m / s², where 0V corresponds to 0m / s², 2.5V corresponds to -5m / s², and 5V corresponds to 5m / s², ensuring clear and reproducible signal quantization relationships. This longitudinal acceleration signal is simultaneously input into three parallel processing channels for integration via the first integration channel, integration via the second integration channel, and differentiation via the third integration channel. The integration time constant of the first integration channel is set to 0.1s, and the integration time constant of the second integration channel is set to 0.5s. The smaller integration time constant of the first integration channel allows for rapid response to instantaneous changes in longitudinal acceleration. The first integral channel captures the short-term incremental trend of vehicle speed. The second integral channel has a larger integration time constant, which can filter high-frequency noise in the acceleration signal and output a more stable speed increment trend. The two integral channels complement each other, taking into account both the timeliness and stability of speed increment prediction. At the same time, the longitudinal acceleration signal is differentiated to obtain the jerk signal. The jerk reflects the rate of change of acceleration and can reflect the severity of vehicle acceleration or deceleration. The jerk signal is then converted into a jerk voltage signal with an output range of 0-5V, corresponding to actual jerk from -10m / s³ to 10m / s³. The three processing results are output synchronously to support the subsequent calculation of weighting coefficients and the acquisition of adaptive integral speed increment.

[0081] Step 32: Obtain the jerk voltage signal, take its absolute value, and output the weighted coefficient voltage through interval decision. The specific operation is as follows:

[0082] The jerk voltage signal output in step 31 is obtained. Since the sign of the jerk only reflects the direction of the acceleration change, and the weighting is only related to the degree of change, the absolute value of the jerk voltage signal is first taken to eliminate the influence of direction, resulting in the jerk voltage signal after absolute value. Its output range is 0-5V, corresponding to the actual absolute value of jerk from 0 to 10 m / s³. Then, interval judgment is performed on the jerk voltage signal after absolute value. The threshold for interval judgment is pre-calibrated based on the driving characteristics of the autonomous vehicle, specifically setting three intervals: when the jerk voltage signal after absolute value is between 0-1V, that is, corresponding to the actual jerk of 0-2 m / s³, it is judged that... To ensure a smooth acceleration change, a higher weighted coefficient voltage is output. When the voltage signal is between 1-4V, corresponding to an actual acceleration of 2-8 m / s³, it is determined to be a moderate acceleration change, and a medium weighted coefficient voltage is output. When the voltage signal is between 4-5V, corresponding to an actual acceleration of 8-10 m / s³, it is determined to be a drastic acceleration change, and a lower weighted coefficient voltage is output. The output range of the weighted coefficient voltage is set to 0-1V. By using interval judgment, the degree of acceleration change is correlated with the weighted coefficient, ensuring that the weights of the two integral channel results can be dynamically adjusted according to the degree of acceleration change, making the speed increment prediction more consistent with the actual driving state.

[0083] Step 33: Obtain the integration results of the first integration channel, the second integration channel, and the weighted coefficient voltage. Multiply the weighted coefficient voltage by the integration results of the first and second integration channels respectively, and then sum them to output the adaptive integral speed increment. The specific operation is as follows:

[0084] Obtain the integration results of the first and second integration channels output in step 31, and the weighted coefficient voltage output in step 32. Both the first and second integration channel results are voltage signals with an output range of 0-2V, corresponding to actual speed increments of 0-40km / h. The weighted coefficient voltage output range is 0-1V. During the calculation, the weighted coefficient voltage is first multiplied by the integration result of the first integration channel to obtain the weighted output of the first integration channel. Because the first integration channel has a fast response, its weight increases when the weighted coefficient voltage is high, highlighting the instantaneous change in speed increment. Then, the weighted coefficient voltage is inverted and multiplied by the integration result of the second integration channel to obtain the weighted output of the second integration channel. Because the second integration channel is more stable, its weight increases when the weighted coefficient voltage is low, filtering noise and ensuring the stability of speed increment prediction.

[0085] The two weighted outputs are then summed to obtain the adaptive integral speed increment. This speed increment dynamically balances the advantages of the two integral channels based on the drastic changes in vehicle acceleration, enabling both rapid capture of instantaneous speed changes and ensuring the stability of the prediction results. The output adaptive integral speed increment is a voltage signal, ranging from 0-2V, corresponding to an actual speed increment of 0-40km / h. This provides accurate input for subsequent calculations of the predicted speed voltage, and the calculation formula can be introduced as follows: ;

[0086] The derivation of this formula is based on the complementary characteristics of the two integral channels. By adjusting the weights of the two channels through weighting coefficients, the output velocity increment balances timeliness and stability. When the acceleration changes gradually, k is taken as a larger value, highlighting the advantage of the first integral channel; when the acceleration changes drastically, k is taken as a smaller value, highlighting the advantage of the second integral channel. In the formula: represents the adaptive integral speed increment, in V; k represents the weighted coefficient voltage, in V; This represents the integration result of the first integration channel, in units of V; This represents the integration result of the second integration channel, in units of V.

[0087] Step 34: Obtain the adaptive integral velocity increment and the current velocity voltage, sum them, and output the predicted velocity voltage. The specific operation is as follows:

[0088] Obtain the adaptive integral speed increment output in step 33 and the current real-time speed voltage signal of the autonomous vehicle. The current speed voltage signal has the same specifications as the real-time vehicle speed voltage signal in step 21, with an output range of 0-5V, corresponding to a driving speed of 0-120km / h. The adaptive integral speed increment output range is 0-2V, corresponding to an actual speed increment of 0-40km / h. Summate the adaptive integral speed increment with the current speed voltage. The summation logic combines the current vehicle speed with the predicted speed increment to obtain the predicted speed of the vehicle in the future. Then, convert the predicted speed into the corresponding predicted speed voltage through signal conversion. During the summation process, ensure that the specifications of the two voltage signals are consistent to avoid signal distortion. The output range of the calculated predicted speed voltage remains 0-5V, corresponding to a predicted speed of 0-140km / h, which can cover the speed range after the autonomous vehicle accelerates rapidly.

[0089] For example, when the current speed of the autonomous vehicle is 100 km / h, the corresponding voltage is approximately 4.2V. The adaptive integral speed increment is 1V, corresponding to an actual speed increment of 20 km / h. Therefore, the predicted speed voltage is 5.2V, which is output as 5V after limiting, corresponding to a predicted speed of 140 km / h. When the vehicle decelerates rapidly, the adaptive integral speed increment is 0.5V, corresponding to an actual speed increment of 10 km / h. The current speed is 60 km / h, corresponding to a current speed voltage of 2.5V. Therefore, the predicted speed voltage is 3V, corresponding to a predicted speed of 72 km / h. This predicted speed voltage can reflect changes in vehicle speed in advance, providing a reliable input for subsequent calculations of the predicted safe distance.

[0090] Step 35: Obtain the predicted velocity voltage, square the predicted velocity voltage, multiply it by the mass adhesion correction coefficient voltage, then superimpose it with the fixed reaction time reference voltage and perform a logarithmic transformation to output the predicted safe time interval. The specific operation is as follows:

[0091] Obtain the predicted speed voltage output in step 34 and the mass adhesion correction coefficient voltage output in step 21. Simultaneously, call the fixed reaction time reference voltage consistent with step 22. This reference voltage is still 0.5V, corresponding to an actual system reaction time of 0.5s, to ensure the consistency and reproducibility of the calculation logic. During the calculation, first, the predicted speed voltage is squared. The square of the predicted speed voltage is positively correlated with the predicted braking energy of the vehicle. The higher the predicted speed, the longer the required braking energy and braking distance. Then, the squared predicted speed voltage is multiplied by the mass adhesion correction coefficient voltage. The mass adhesion correction coefficient voltage comprehensively reflects the influence of vehicle load and road braking capacity, and can accurately correct the predicted braking energy, making the calculation results more consistent with actual working conditions.

[0092] The result of the multiplication operation is then superimposed on a fixed reaction time reference voltage. The purpose of this superposition is to take into account both the time requirements corresponding to the predicted braking distance and the time requirements corresponding to the system reaction time, comprehensively covering the safe time required for the entire process from the predicted speed to the completion of braking. Since the superimposed voltage signal has a non-linear relationship with the predicted safe time interval, it needs to be logarithmically transformed to convert the non-linear relationship into a linear relationship, ensuring that the output predicted safe time interval accurately corresponds to the actual needs. After logarithmic transformation, the predicted safe time interval is output, which ranges from 0.5 to 6 seconds, corresponding to a predicted speed voltage of 0 to 5V. The higher the predicted speed, the longer the predicted safe time interval. For example, when the predicted speed is 140 km / h, the predicted safe time interval is 6 seconds, leaving sufficient emergency response space; when the predicted speed is 20 km / h, the predicted safe time interval is 0.5 seconds, avoiding excessively frequent warnings.

[0093] In a preferred embodiment of the present invention, step 4 is further included: mapping the instantaneous total mass voltage signal to a tolerance coefficient, correcting the tolerance coefficient with the adhesion coefficient voltage signal, and multiplying the tolerance coefficient by the predicted safe time interval to obtain the dynamic warning time interval. The specific operation is as follows:

[0094] The instantaneous total mass voltage signal directly reflects the vehicle's current load state. The greater the load, the greater the vehicle's inertia, the longer the braking time and distance, and the higher the tolerance for risk needs to be. Therefore, it is mapped to a tolerance coefficient to quantify the vehicle's ability to tolerate collision risks. The adhesion coefficient voltage signal reflects the road braking performance. The smaller the road adhesion coefficient, the worse the braking performance. The tolerance coefficient needs to be corrected to ensure that it is compatible with road conditions. The tolerance coefficient corrected by the adhesion coefficient voltage signal can comprehensively reflect the synergistic effect of vehicle load and road conditions. Multiplying the tolerance coefficient by the predicted safe distance output in step 3 yields the dynamic warning distance. This is then converted into a dynamic warning distance voltage to achieve dynamic adjustment of the warning distance, adapting to the complex driving conditions of unmanned vehicles with different loads and road surfaces, and avoiding the problems of untimely or frequent warnings caused by fixed thresholds.

[0095] Step 4 further includes the following steps:

[0096] Step 41: Obtain the instantaneous total mass voltage signal, obtain the output of the corresponding resistor voltage divider chain through partition decision, and output the initial tolerance coefficient voltage. The specific operation is as follows:

[0097] The instantaneous total mass voltage signal output in step 1 is acquired. This signal ranges from 0-5V, corresponding to a vehicle total mass of 1000-2000kg. To ensure the correspondence between the instantaneous total mass and the initial tolerance coefficient, the instantaneous total mass voltage signal is partitioned. The partitioning intervals are pre-calibrated based on vehicle load characteristics and safety warning requirements. Each interval corresponds to a fixed output level of a resistor voltage divider chain. The resistor voltage divider chain uses precision resistors to ensure the stability and accuracy of the output voltage. Specifically, when the instantaneous total mass voltage signal is between 0-2V, corresponding to a vehicle total mass of 1000-1400kg under no-load conditions, the... The resistor voltage divider outputs a relatively high initial tolerance coefficient voltage. When the voltage signal is between 2-3.5V, corresponding to a vehicle gross weight of 1400-1700kg under half-load conditions, it outputs a medium initial tolerance coefficient voltage. When the voltage signal is between 3.5-5V, corresponding to a vehicle gross weight of 1700-2000kg under full-load conditions, it outputs a relatively low initial tolerance coefficient voltage. The output range of the initial tolerance coefficient voltage is set to 0.8-1.2V. The larger the load, the higher the initial tolerance coefficient voltage, which means that the vehicle has a higher tolerance for collision risks and needs to reserve a longer warning time. The output initial tolerance coefficient voltage is directly used for subsequent correction processing.

[0098] Step 42: Obtain the adhesion coefficient voltage signal, and output the correction factor voltage through inverse proportional function mapping. The specific operation is as follows:

[0099] Obtain the adhesion coefficient voltage signal output in step 1. This signal output range is 0-5V, corresponding to a road adhesion coefficient of 0.1-1.0, where 0V corresponds to 0.1 (snow-covered or icy road surface) and 5V corresponds to 1.0 (dry asphalt road surface). Since the road adhesion coefficient is positively correlated with vehicle braking performance—a higher adhesion coefficient results in better braking performance and allows for a lower tolerance for collision risks—the correction factor is inversely proportional to the adhesion coefficient. This correspondence can be achieved through an inverse proportional function mapping, which can be expressed by introducing the following inverse proportional mapping formula: ;

[0100] The derivation of this formula is based on the inherent relationship between the road surface adhesion coefficient and the correction factor. A larger road surface adhesion coefficient requires a smaller correction factor to reduce the tolerance coefficient; conversely, a smaller adhesion coefficient requires a larger correction factor to increase the tolerance coefficient. This inverse relationship can be precisely achieved through an inverse proportional function, ensuring that the corrected tolerance coefficient closely matches the actual braking performance of the road surface. In the formula: Represents the correction factor voltage, in V; The preset proportional constant is calibrated to 5V·V to ensure the rationality of the mapping relationship; This represents the adhesion coefficient voltage signal, measured in volts (V). After mapping using this formula, the output range of the correction factor voltage is 1.0-5.0V. The larger the adhesion coefficient voltage signal, the smaller the correction factor voltage. For example, for dry asphalt pavements... The correction factor voltage is 1.0V; on icy roads, The correction factor voltage is 10V, and after limiting, the output is 5.0V to ensure the stability of the signal output.

[0101] Step 43: Obtain the initial tolerance coefficient voltage and the correction factor voltage, multiply them, and output the final tolerance coefficient voltage. The specific operation is as follows:

[0102] Obtain the initial tolerance coefficient voltage output in step 41 and the correction factor voltage output in step 42. The initial tolerance coefficient voltage ranges from 0.8 to 1.2V, and the correction factor voltage ranges from 1.0 to 5.0V to ensure the compatibility of the two voltage signals. During the operation, the initial tolerance coefficient voltage and the correction factor voltage are multiplied. The logic of the multiplication operation is to use the correction factor corresponding to the road conditions to dynamically correct the initial tolerance coefficient corresponding to the vehicle load, so that the final tolerance coefficient meets the vehicle load requirements and is adapted to the current road braking performance.

[0103] When a vehicle is fully loaded, meaning the initial tolerance coefficient voltage is high and it is traveling on a wet road surface, the correction factor voltage is also high. The final tolerance coefficient voltage obtained by multiplying the two is larger, which means that a higher risk tolerance and a longer warning interval are required. When a vehicle is unloaded, meaning the initial tolerance coefficient voltage is low and it is traveling on a dry road surface, the correction factor voltage is also low, the final tolerance coefficient voltage is smaller, and the warning interval can be appropriately shortened. After multiplication, the final tolerance coefficient voltage is output, and its output range is 0.8-6.0V. This voltage signal accurately quantifies the vehicle's tolerance to collision risks under the current operating conditions.

[0104] Step 44: Obtain the final tolerance coefficient voltage and the predicted safety time interval, multiply them, and output the dynamic warning time interval voltage. The specific operation is as follows:

[0105] Obtain the final tolerance coefficient voltage output in step 43 and the predicted safety time distance output in step 3. The final tolerance coefficient voltage ranges from 0.8 to 6.0V, and the predicted safety time distance ranges from 0.5 to 6s, maintaining consistency with the parameter specifications mentioned above. Multiply the final tolerance coefficient voltage and the predicted safety time distance. The logic of the multiplication operation is to combine the vehicle's risk tolerance capability with the predicted safety time distance to generate a dynamic warning time distance adapted to the current operating condition. Then, convert the dynamic warning time distance into the corresponding dynamic warning time distance voltage to ensure that the subsequent circuit can directly process it. The size of the dynamic warning time distance is positively correlated with both the final tolerance coefficient and the predicted safety time distance. The longer the predicted safety time distance and the larger the final tolerance coefficient, the longer the dynamic warning time distance, and vice versa.

[0106] For example, when a fully loaded vehicle is driving on an icy road, the final tolerance coefficient voltage is 6.0V, and the predicted safe time interval is 6s. Multiplying these values ​​yields a dynamic warning time interval of 36s, corresponding to a dynamic warning time interval voltage output of 5V after conversion. When the vehicle is driving unloaded on a dry road, the final tolerance coefficient voltage is 0.8V, and the predicted safe time interval is 0.5s. Multiplying these values ​​yields a dynamic warning time interval of 0.4s, corresponding to a dynamic warning time interval voltage output of 0.4V. The output dynamic warning time interval voltage range is 0-5V, accurately corresponding to the actual dynamic warning time interval of 0-36s, and can fully adapt to various complex driving conditions of autonomous vehicles.

[0107] In a preferred embodiment of the present invention, step 5 is further included: obtaining the remaining collision time distance, comparing the remaining collision time distance with the dynamic warning time distance through a voltage comparator with hysteresis, and outputting a graded warning voltage. The specific operation is as follows:

[0108] The remaining collision distance is a key parameter reflecting the risk of collision between the vehicle and an obstacle ahead. The smaller the value, the higher the collision risk. This parameter is converted into a remaining collision distance voltage, which corresponds to the dynamic warning distance voltage output in step 4. By using a voltage comparator with hysteresis to compare the remaining collision distance voltage with the dynamic warning distance voltage, false warnings caused by minor signal fluctuations can be effectively avoided, ensuring the stability of the warning judgment. Based on the comparison result, a graded warning voltage is output. Different levels of graded warning voltage correspond to different levels of collision risk, adapting to the warning needs of autonomous vehicles under different driving conditions, making the warning action more targeted. It can promptly remind the vehicle to deal with high-risk scenarios and avoid invalid warnings in low-risk scenarios, further improving the reliability and practicality of the safety warning system.

[0109] Step 5 further includes the following steps:

[0110] Step 51: Obtain the remaining collision time-distance voltage and the dynamic warning time-distance voltage, and output the time-distance difference voltage after differentiating the two. The specific operation is as follows:

[0111] The remaining collision distance voltage and dynamic warning distance voltage are obtained. The dynamic warning distance voltage is the signal output in step 44, with an output range of 0-5V, corresponding to an actual dynamic warning distance of 0-36s. The remaining collision distance voltage is generated by collecting the real-time distance between the vehicle and the obstacle in front through the vehicle distance sensor, calculating the remaining collision distance in combination with the current vehicle speed, and then converting the signal. Its output range is consistent with that of the dynamic warning distance voltage, both being 0-5V, corresponding to an actual remaining collision distance of 0-36s. This ensures that the quantization standards of the two voltage signals are consistent and avoids calculation distortion.

[0112] During the calculation, the remaining collision time-distance voltage and the dynamic warning time-distance voltage are differentially calculated. That is, the remaining collision time-distance voltage is subtracted from the dynamic warning time-distance voltage to obtain the time-distance difference voltage. The output range of the time-distance difference voltage is -5V to 5V, and its positive or negative value directly reflects the level of collision risk: when the difference is positive, it means that the remaining collision time-distance is greater than the dynamic warning time-distance, and the current collision risk is low; when the difference is negative, it means that the remaining collision time-distance is less than the dynamic warning time-distance, and there is a current collision risk. The larger the absolute value of the difference, the higher the collision risk; when the difference is zero, the remaining collision time-distance is equal to the dynamic warning time-distance, and the state is at the warning threshold.

[0113] Step 52: Obtain the final tolerance coefficient voltage, convert the final tolerance coefficient voltage into a reverse-changing hysteresis width voltage, and input the hysteresis width voltage and the time difference voltage together into a variable hysteresis voltage comparator to output the primary trigger voltage. The specific operation is as follows:

[0114] Obtain the final tolerance coefficient voltage output in step 43. This voltage output range is 0.8-6.0V, comprehensively reflecting the combined influence of vehicle load and road conditions. Since a larger final tolerance coefficient voltage indicates a higher tolerance for collision risk, the hysteresis width needs to be reduced to make the warning judgment more sensitive and avoid warning lag. Conversely, a smaller final tolerance coefficient voltage indicates a lower tolerance for collision risk, requiring an increased hysteresis width to avoid false triggering due to minor signal fluctuations. Therefore, the final tolerance coefficient voltage is converted into a hysteresis width voltage with the opposite effect. A conversion formula can be introduced here: ;

[0115] The derivation of this formula is based on the inverse relationship between the final tolerance coefficient and the hysteresis width. Combined with the requirements for early warning stability, when the final tolerance coefficient increases, the hysteresis width needs to decrease synchronously. This correspondence can be accurately achieved through an inverse proportional function, ensuring that the hysteresis width can dynamically adapt to the current operating conditions. In the formula: This represents the hysteresis width voltage, in V. The preset proportional constant is calibrated to 6.0V·V to ensure the rationality of the hysteresis width voltage; The final tolerance coefficient voltage is represented by a voltage (V). After conversion using this formula, the output range of the hysteresis width voltage is 1.0-7.5V. The larger the final tolerance coefficient voltage, the smaller the hysteresis width voltage. Subsequently, the hysteresis width voltage and the time difference voltage are input together into a variable hysteresis voltage comparator. The variable hysteresis voltage comparator dynamically adjusts the comparison threshold based on the input hysteresis width voltage and compares the time difference voltage. When the difference voltage is lower than the negative hysteresis threshold or higher than the positive hysteresis threshold, the corresponding primary trigger voltage is output. The primary trigger voltage output range is 0-5V, and its amplitude is positively correlated with the collision risk level.

[0116] Step 53: Obtain the primary trigger voltage, and output graded warning voltages according to the amplitude range of the primary trigger voltage. The specific operation is as follows:

[0117] Obtain the primary trigger voltage output in step 52. This voltage output range is 0-5V. The larger the amplitude, the higher the current collision risk. Based on the safety warning requirements of autonomous vehicles, three amplitude ranges are pre-defined, each corresponding to a warning level. The range division is based on the aforementioned operating condition parameters and safety warning standards to ensure clear and reproducible hierarchical logic. When the primary trigger voltage is between 0-1V, the remaining collision time is much greater than the dynamic warning time, indicating an extremely low current collision risk. No warning is output, and this voltage is set to 0V. The vehicle maintains normal driving status and does not execute any warning actions. When the primary trigger voltage is between 1-3V, the remaining collision distance is slightly less than the dynamic warning distance, indicating a minor collision risk. A tiered warning voltage corresponding to the primary warning is output, set to 2V, triggering minor vehicle deceleration and a warning alarm, providing ample emergency response space. When the primary trigger voltage is between 3-5V, the remaining collision distance is significantly less than the dynamic warning distance, indicating a serious collision risk. A tiered warning voltage corresponding to the secondary warning is output, set to 4V, triggering emergency deceleration, emergency braking, or evasive maneuvers, minimizing the risk of collision. The output range of the tiered warning voltage remains 0-5V, compatible with the voltage signal specifications mentioned above, allowing direct transmission to the vehicle control system. This ensures rapid execution of warning actions, adapts to different collision risk scenarios, and further enhances the safety of autonomous vehicles.

[0118] In a preferred embodiment of the present invention, step 6 is further included, which involves converting the graded warning voltage into a displacement and adjusting a preset following distance threshold using the displacement. The specific operation is as follows:

[0119] The graded warning voltage corresponds to different levels of collision risk. The higher the risk level, the larger the following distance threshold needs to be adjusted to reserve sufficient braking and avoidance space. By converting voltage to displacement, the abstract voltage signal is transformed into a specific mechanical displacement. This displacement is then used to dynamically adjust the preset following distance threshold, enabling the following distance threshold to adapt to the current collision risk level in real time. This avoids the shortcomings of fixed following distance thresholds that cannot adapt to complex working conditions, further improving the safety and adaptability of autonomous vehicles following each other, and ensuring that the warning signal can be effectively transformed into actual safety protection actions.

[0120] Step 6 further includes the following steps:

[0121] Step 61: Obtain the graded warning voltage, convert the graded warning voltage into a pulse width modulation signal whose duty cycle is proportional to the amplitude of the graded warning voltage, then integrate and hold the pulse width modulation signal to output the average displacement drive voltage. The specific operation is as follows:

[0122] Obtain the graded warning voltage output in step 53. This voltage output range is 0-5V, corresponding to three warning levels: 0V corresponds to no warning, 2V corresponds to level one warning, and 4V corresponds to level two warning. To achieve accurate voltage-to-displacement conversion, the graded warning voltage needs to be converted into a pulse width modulation (PWM) signal. The duty cycle of the PWM signal is directly proportional to the amplitude of the graded warning voltage; that is, the larger the amplitude of the graded warning voltage, the higher the duty cycle of the PWM signal. This achieves accurate mapping between voltage amplitude and signal duty cycle. The mapping formula is: ;

[0123] The derivation of this formula is based on the linear relationship between voltage amplitude and duty cycle. The higher the graded warning voltage, the stronger the required displacement drive signal, and the larger the corresponding pulse width modulation signal duty cycle needs to be. This proportional relationship can be accurately achieved through a linear function, ensuring that the duty cycle can follow the changes in graded warning voltage in real time. In the formula: D represents the duty cycle of the pulse width modulation signal, in percentage (%). The preset proportional constant is calibrated to 20% / V to ensure a reasonable duty cycle range; The voltage represents the graded warning voltage, in volts (V). After conversion using this formula, 0V corresponds to a 0% duty cycle, 2V to a 40% duty cycle, and 4V to an 80% duty cycle. The carrier frequency of the pulse-width modulation (PWM) signal is preset to 1kHz to ensure signal stability and driving capability. Subsequently, the PWM signal undergoes integration and hold processing. The integration and hold circuit filters high-frequency fluctuations in the PWM signal, outputting a stable average displacement drive voltage to avoid displacement adjustment instability due to signal fluctuations. The output range of the average displacement drive voltage is 0-4V, linearly corresponding to the duty cycle of the PWM signal; that is, the higher the duty cycle, the larger the average displacement drive voltage.

[0124] Step 62: Obtain the average displacement driving voltage, convert the average displacement driving voltage into a linear displacement, and adjust the preset following distance threshold based on this displacement. The specific operation is as follows:

[0125] The average displacement drive voltage output in step 61 is obtained. This voltage output range is 0-4V, maintaining compatibility with the signal specifications mentioned above and ensuring the continuity of the conversion logic. The average displacement drive voltage is input into the displacement conversion module, which uses linear conversion logic to convert the voltage signal into the corresponding linear displacement. The two have a strict linear relationship, and the conversion coefficient is pre-calibrated to 0.2mm / V, that is, every 1V of average displacement drive voltage corresponds to 0.2mm of linear displacement. Through this conversion relationship, 0V of average displacement drive voltage corresponds to 0mm of linear displacement, 2V corresponds to 0.4mm of linear displacement, and 4V corresponds to 0.8mm of linear displacement. The output range of linear displacement is 0-0.8mm, ensuring the accuracy and controllability of displacement adjustment. The preset following distance threshold is pre-set based on the basic driving conditions of the unmanned vehicle, and is divided into three basic thresholds: low speed, medium speed, and high speed. These correspond to 2m at 20km / h on residential roads, 5m at 60km / h on urban roads, and 10m at 120km / h on highways, respectively. This basic threshold can be dynamically switched according to the actual driving speed.

[0126] The adjustment amount of linear displacement is linearly correlated with the preset following distance threshold. The adjustment coefficient is calibrated to 2.5m / mm, that is, every 0.1mm linear displacement corresponds to a 2.5m following distance adjustment. When the linear displacement is 0mm, the preset following distance threshold is not adjusted and the current basic threshold is maintained. When the linear displacement is 0.4mm, the corresponding adjustment amount is 1m, and the preset following distance threshold is increased by 1m from the basic threshold. When the linear displacement is 0.8mm, the corresponding adjustment amount is 2m, and the preset following distance threshold is increased by 2m from the basic threshold. Through this displacement adjustment method, the following distance threshold is dynamically adapted. The higher the collision risk level, the greater the adjustment amount of the following distance threshold, ensuring that the unmanned vehicle maintains a sufficient safe distance from obstacles in front, effectively avoiding collision risks, and completing the closed-loop control of the entire safety warning process.

[0127] Example 2:

[0128] Please see Figure 4 Based on Example 1, this example provides an unmanned vehicle safety early warning system, including: a working condition perception module, used to acquire suspension pressure signal and differential signal between wheel speed and vehicle speed, obtain road adhesion coefficient by looking up table through slip ratio, calculate instantaneous total mass, and output instantaneous total mass voltage signal and adhesion coefficient voltage signal;

[0129] The time-distance conversion module calculates the minimum safe braking distance and converts it into a basic safe time-distance based on the instantaneous total mass voltage signal, adhesion coefficient voltage signal and real-time vehicle speed voltage signal, and outputs the basic safe time-distance voltage.

[0130] The velocity prediction module is used to acquire the longitudinal acceleration signal, integrate it to obtain the predicted velocity increment, add it to the current velocity voltage to obtain the predicted velocity voltage, and recalculate the predicted safe time distance based on the predicted velocity voltage.

[0131] The coefficient modulation module is used to map the instantaneous total mass voltage signal into a tolerance coefficient, and correct the tolerance coefficient with the attached coefficient voltage signal. The tolerance coefficient is then multiplied by the predicted safe time interval to obtain the dynamic early warning time interval.

[0132] The graded comparison module is used to obtain the remaining collision time, compare the remaining collision time with the dynamic warning time through a voltage comparator with hysteresis, and output the graded warning voltage.

[0133] The displacement execution module is used to convert the graded warning voltage into displacement and adjust the preset following distance threshold with the displacement.

[0134] The above description is merely a preferred embodiment of the present invention; however, the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and its improved concept, should be covered within the scope of protection of the present invention.

Claims

1. A method for early warning of safety for unmanned vehicles, characterized in that, include: Step 1: Obtain the suspension pressure signal and the differential signal between wheel speed and vehicle speed; obtain the road adhesion coefficient by looking up the slip ratio table; calculate the instantaneous total mass; and output the instantaneous total mass voltage signal and adhesion coefficient voltage signal. Step 2: Based on the instantaneous total mass voltage signal, adhesion coefficient voltage signal, and real-time vehicle speed voltage signal, calculate the minimum safe braking distance and convert it into a basic safe braking distance, then output the basic safe braking distance voltage. Step 3: Obtain the longitudinal acceleration signal, integrate it to obtain the predicted velocity increment, add it to the current velocity voltage to obtain the predicted velocity voltage, and recalculate the predicted safe time distance based on the predicted velocity voltage; Step 4: Map the instantaneous total mass voltage signal to a tolerance coefficient, correct the tolerance coefficient with the adhesion coefficient voltage signal, and multiply the tolerance coefficient by the predicted safe time interval to obtain the dynamic warning time interval; Step 5: Obtain the remaining collision time, compare the remaining collision time with the dynamic warning time using a voltage comparator with hysteresis, and output the graded warning voltage; Step 6: Convert the graded warning voltage into displacement, and adjust the preset following distance threshold with the displacement.

2. The unmanned vehicle safety early warning method according to claim 1, characterized in that, Step 1 includes: Four suspension pressure signals are acquired, and symmetrical differential processing is performed to offset the static pressure bias introduced by vehicle pitch and roll. Then, the longitudinal acceleration signal is used to perform inertial compensation on the differential pressure signal, and the instantaneous total mass voltage signal is output. The differential signal between wheel speed and vehicle speed is obtained, and the differential signal is input into a phase-locked loop circuit to be converted into a slip ratio frequency. This frequency is then used to drive a voltage-controlled resistor network to output an adhesion coefficient voltage signal.

3. The unmanned vehicle safety early warning method according to claim 2, characterized in that, Step 2 includes: The system acquires the instantaneous total mass voltage signal, adhesion coefficient voltage signal, and real-time vehicle speed voltage signal. The ratio of the instantaneous total mass voltage signal to the adhesion coefficient voltage signal is used as a coefficient. The system squares the real-time vehicle speed voltage signal and multiplies it by this coefficient to output the critical braking energy voltage and the mass adhesion correction coefficient voltage. Obtain the critical braking energy voltage and a fixed reaction time reference voltage, superimpose the critical braking energy voltage and the reaction time reference voltage and perform a logarithmic transformation to output the basic safety time interval voltage.

4. The unmanned vehicle safety early warning method according to claim 3, characterized in that, Step 3 includes: Acquire the longitudinal acceleration signal, perform integration and differentiation on the first integration channel, the second integration channel, and the differential, and output the integration result of the first integration channel, the integration result of the second integration channel, and the jerk voltage signal, wherein the integration time constant of the first integration channel is smaller than the integration time constant of the second integration channel; The accelerometer voltage signal is acquired, its absolute value is taken, and then a weighted coefficient voltage is output after interval decision. Obtain the integration results of the first integration channel, the integration results of the second integration channel, and the weighted coefficient voltage. Multiply the weighted coefficient voltage by the integration results of the first and second integration channels respectively, and then sum them to output the adaptive integration speed increment.

5. The unmanned vehicle safety early warning method according to claim 4, characterized in that, Step 3 also includes: Obtain the adaptive integral speed increment and the current speed voltage, sum them, and output the predicted speed voltage; Obtain the predicted velocity voltage, square the predicted velocity voltage, multiply it by the mass adhesion correction coefficient voltage, then superimpose it with the fixed reaction time reference voltage and perform a logarithmic transformation to output the predicted safe time interval.

6. The unmanned vehicle safety early warning method according to claim 5, characterized in that, Step 4 includes: The instantaneous total mass voltage signal is obtained, and the output of the corresponding resistor voltage divider chain is obtained through partition decision, and the initial tolerance coefficient voltage is output. The adhesion coefficient voltage signal is acquired, and the correction factor voltage is output through inverse proportional function mapping.

7. The unmanned vehicle safety early warning method according to claim 6, characterized in that, Step 4 also includes: Obtain the initial tolerance coefficient voltage and the correction factor voltage, multiply them, and output the final tolerance coefficient voltage; The final tolerance coefficient voltage and the predicted safe time interval are obtained, and the dynamic warning time interval voltage is output after multiplying them.

8. The unmanned vehicle safety early warning method according to claim 7, characterized in that, Step 5 includes: Obtain the remaining collision time-distance voltage and the dynamic warning time-distance voltage, and output the time-distance difference voltage after differentiating the two. Obtain the final tolerance coefficient voltage, convert the final tolerance coefficient voltage into a reverse-changing hysteresis width voltage, input the hysteresis width voltage and the time difference voltage together into a variable hysteresis voltage comparator, and output the primary trigger voltage; Obtain the primary trigger voltage and output graded warning voltages based on the amplitude range of the primary trigger voltage.

9. The unmanned vehicle safety early warning method according to claim 8, characterized in that, Step 6 includes: The graded warning voltage is obtained, and the graded warning voltage is converted into a pulse width modulation signal whose duty cycle is proportional to the amplitude of the graded warning voltage. The pulse width modulation signal is then integrated and held to output the average displacement drive voltage. The average displacement driving voltage is obtained, the average displacement driving voltage is converted into a linear displacement, and the preset following distance threshold is adjusted based on the displacement.

10. An unmanned vehicle safety warning system, applied to the unmanned vehicle safety warning method according to any one of claims 1-9, characterized in that, include: The working condition perception module is used to acquire the suspension pressure signal and the differential signal between wheel speed and vehicle speed, obtain the road adhesion coefficient by looking up the slip ratio table, calculate the instantaneous total mass, and output the instantaneous total mass voltage signal and adhesion coefficient voltage signal. The time-distance conversion module calculates the minimum safe braking distance and converts it into a basic safe time-distance based on the instantaneous total mass voltage signal, adhesion coefficient voltage signal and real-time vehicle speed voltage signal, and outputs the basic safe time-distance voltage. The velocity prediction module is used to acquire the longitudinal acceleration signal, integrate it to obtain the predicted velocity increment, add it to the current velocity voltage to obtain the predicted velocity voltage, and recalculate the predicted safe time distance based on the predicted velocity voltage. The coefficient modulation module is used to map the instantaneous total mass voltage signal into a tolerance coefficient, and correct the tolerance coefficient with the attached coefficient voltage signal. The tolerance coefficient is then multiplied by the predicted safe time interval to obtain the dynamic early warning time interval. The graded comparison module is used to obtain the remaining collision time, compare the remaining collision time with the dynamic warning time through a voltage comparator with hysteresis, and output the graded warning voltage. The displacement execution module is used to convert the graded warning voltage into displacement and adjust the preset following distance threshold with the displacement.

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