CN116620284A - Ramp parallel control system and method based on map signals - Google Patents

Abstract

The application relates to a ramp parallel control system based on map signals, which comprises a map signal module, a control module and a control module, wherein the map signal module is used for acquiring the real-time position of a vehicle and the ramp parallel working condition; the driver intention self-learning module is used for judging the intention of the parallel operation of the ramp of the driver and obtaining a self-learning weighting coefficient; the parallel acceleration execution coefficient calculation module activates a ramp parallel acceleration instruction; and the parallel acceleration execution module is used for executing ramp parallel acceleration instructions. The application also relates to a ramp parallel control method based on map signals, which comprises the following steps: sending a ramp parallel control instruction; calculating a ramp parallel execution coefficient; activating a ramp parallel acceleration function instruction; performing acceleration management; the application has wide application range, low cost and large customer income; the traffic jam and traffic accidents caused by the turtle speed driving are effectively avoided; the driving experience is improved, and the accident rate and traffic jam rate are further reduced; the calibration work is reduced, and the customized strategy service is aimed at different people and vehicles.

Description

Ramp merging control system and method based on map signal

technical field

The invention relates to the technical field of engine and vehicle control, in particular to a ramp merge control system and method based on map signals.

Background technique

The process of vehicles merging into the main road from the ramp is called the ramp merging process; this process needs to increase speed, but at the same time, it must take into account the vehicles coming from behind, so it is a dynamic adjustment process of vehicle speed.

Ramp merging has always been an area where it is difficult to balance speed and safety. The main reason is that each driver has different driving habits, so that the same vehicle cannot implement the same speed-up strategy on the same ramp; The road conditions in the road are also changing every moment, so it is difficult for the existing technology to provide customized strategy services for each different driver and each different car.

Typical existing technologies such as the Chinese invention patent with the application number 201811448867.8 and the patent name "A V2X-based Automatic Parallel System", which discloses an automatic parallel system, which belongs to the field of automatic driving technology. When receiving the merging command, send a merging trigger signal to the positioning module, the V2X short-range communication module, and the policy determination module; when the positioning module receives the merging trigger signal again, determine the dangerous area on the side of the vehicle The location range of the dangerous area is sent to the strategy determination module; the strategy determination module receives the speed of the vehicle collected by the sensor, and according to the speed of the vehicle obtained by the V2X short-range communication module, the location range of the dangerous area, the position, speed, accelerator pedal status of other vehicles and The state of the brake pedal determines the control strategy and sends it to the control execution module; when the control execution module receives the control strategy, it controls the vehicle accordingly according to the control strategy, so that when the line needs to be merged, the surrounding vehicles obtained by the V2X short-range communication module A variety of vehicle information to achieve safe and efficient automatic merging.

Another typical prior art is the application number 202110887375.4, and the patent name is "A method and system for expressway vehicle merging based on intelligent network connection environment. The method described in this invention includes the following steps: obtaining the outermost lane and ramp of the expressway The longitude and latitude position and speed of the vehicle to be merged into; according to the real-time latitude and longitude position and speed of the vehicle in the outermost lane of the expressway, at least one candidate merge-in gap is determined; a candidate merge-in gap is selected among all candidate merge-in gaps to determine the Whether the candidate ingress gap can be used as an ingress gap, otherwise, select another candidate ingress gap among all candidate ingress gaps to re-judge until all candidate ingress gaps are selected; The location of the merging point, the time interval between the vehicle at the current moment and the time when it is expected to arrive at the merging point, the speed of the vehicle to be merging at the time of merging, and the vehicle on the outermost side of the expressway. In this patent, it is necessary to accurately import Vehicles, the speed of the vehicle needs to reach a certain level, otherwise it will lead to import failure or accidents.

The defective of prior art is:

1. Since the existing technical solutions are only aimed at autonomous driving scenarios, using intelligent network technology, such as the V2X technology used in reference document 1, the ramp information is sent to the vehicle through the vehicle in front or the V2X equipment on both sides of the road, and then according to the highway entrance ramp Real-time acquisition of vehicle status information on the main line and ramps to determine the number of vehicles entering the main line on the ramp and optimize the order of vehicles on the ramp; however, vehicles equipped with automatic driving technology are currently expensive and have a small number of vehicles. Most of the current vehicles are ordinary vehicles , does not have automatic driving technology, so the current plan does not have a control technology that cannot be used to enter the on-ramp state for vehicles in the non-automatic driving field and in the non-intelligent network connection state;

2. Since the current patent focus of the prior art is to select the vehicle gap and perform the merge operation when the vehicle speed is reached, but the vehicle speed may not necessarily reach the set range, so that the application of the prior art has limitations and is susceptible to limit, further easily lead to accidents.

Contents of the invention

Aiming at the above problems, the present invention provides a ramp merging control system and method based on map signals, the purpose of which is to have a wide range of applications, low cost, and large customer benefits; effectively avoid lane congestion and traffic accidents caused by slow-moving driving; improve driving experience , and further reduce the accident rate and traffic jam rate; reduce the calibration work, and provide customized strategic services for different people and vehicles.

In order to solve the above problems, the technical solution provided by the invention is:

The ramp merging control system based on map signals includes the following parts:

The map signal module is used to obtain the real-time position of the vehicle and the merging condition of the ramp;

The driver's intention self-learning module is used to judge the driver's ramp merge operation intention and obtain the self-learning weighting coefficient;

The parallel acceleration execution coefficient calculation module is used to calculate the ramp parallel acceleration feasibility coefficient, and determine whether to activate the ramp parallel acceleration command according to the ramp parallel acceleration feasibility coefficient;

The line merging acceleration execution module is used to execute the ramp merging acceleration command, obtain the real-time ramp merging state in real time, and send the ramp merging real-time state back to the driver intention self-learning module.

Preferably, the self-learning weighting coefficients are obtained through self-learning based on a memory-based learning algorithm.

Preferably, the parallel acceleration execution module specifically includes the following submodules:

The torque compensation sub-module is used to provide compensation torque to the output torque of the engine in real time according to the ramp merge condition;

The filter parameter sub-module is used to smooth the output torque of the engine, suppress the occurrence of sudden torque changes, limit the rate of change of the output torque, and adjust the torque response performance of the vehicle;

The mode switching sub-module is used to switch the control mode of the vehicle; the control mode includes a ramp merge control mode and a normal control mode;

The limit value adjustment sub-module is used to adjust the vehicle limit value and the engine limit value; the vehicle limit value includes an artificially preset vehicle speed upper limit threshold; the engine limit value includes an engine speed upper limit threshold and an engine output torque upper limit threshold;

The gear control sub-module is used to control the gear up and down operation of the vehicle.

The ramp merging control method based on the map signal using the map signal-based ramp merging control system comprises the following steps:

S100. Obtain the real-time position of the vehicle; then determine whether the vehicle is in an on-ramp state according to the real-time position of the vehicle; then perform the following operations according to the determination result:

If the vehicle is in the on-ramp state, a ramp merge control command is issued;

If the vehicle is not in the on-ramp state, go back and execute S100 again;

S200. After receiving the on-ramp state, collect the accelerator pedal signal, the engine speed signal, the real-time vehicle speed signal, the steering signal, the gear signal, and the clutch signal in real time; then calculate the ramp merge execution coefficient;

S300. Determine whether the driver is in the acceleration intention of merging according to the ramp merging execution coefficient; then perform the following operations according to the determination result:

If the driver is in the said merging acceleration intention, activate the ramp merging acceleration function command;

If the driver is not in the intention of merging and accelerating, go back and execute S200 again;

S400. Set the vehicle as a ramp merging control mode; call the merging acceleration execution module to perform acceleration management; then increase the vehicle speed;

S500. Collect the real-time vehicle speed signal and brake pedal signal in real time; then determine whether to exit the ramp merging control mode according to the real-time vehicle speed signal and the brake pedal signal; then perform the following operations according to the determination result:

If it is determined not to exit the ramp merge control mode, return to and re-execute S500;

If it is determined to exit the ramp merging control mode, exit the ramp merging control mode, set the entire vehicle to the normal control mode, and then exit the current ramp merging control process.

Preferably, the ramp merging control command is a Boolean data type, wherein: when the value of the ramp merging control command is "1", it indicates that the command is valid and needs to be executed; When the value is "0", it indicates that the instruction is invalid and does not need to be executed;

The ramp merging acceleration function command is a Boolean data type, wherein: when the value of the ramp merging acceleration function command is "1", it indicates that the command is valid and needs to be executed; when the ramp merging acceleration function command is When the value is "0", it indicates that the command is invalid and does not need to be executed.

Preferably, the calculation in S200 to obtain the ramp merging execution coefficient specifically includes the following steps:

S210. Look up the table to obtain the accelerator pedal signal weight factor, the engine speed signal weight factor, the real-time vehicle speed signal weight factor, and the steering signal weight factor;

S220. Multiply the accelerator pedal signal by the accelerator pedal signal weight factor to obtain a weighted accelerator pedal signal; multiply the engine speed signal by the engine speed signal weight factor to obtain a weighted engine speed signal; use the real-time The vehicle speed signal is multiplied by the real-time vehicle speed signal weight factor to obtain a weighted real-time vehicle speed signal; the turn signal is multiplied by the turn signal weight factor to obtain a weighted turn signal;

S230. Adding the weighted accelerator pedal signal, the weighted engine speed signal, the weighted real-time vehicle speed signal, and the weighted steering signal to obtain the feasibility coefficient of ramp merging acceleration;

S240. Multiply the ramp merging acceleration feasibility coefficient by the self-learning weighting coefficient to obtain the ramp merging execution coefficient.

Preferably, in S300, it is determined whether the driver is in the acceleration intention of the merging according to the execution coefficient of the ramp merging, which specifically includes the following steps:

S310. Comparing the ramp merging execution coefficient with the manually preset merging acceleration intention threshold;

S320. Perform the following operations according to the comparison result:

If the ramp merging execution coefficient is not greater than the merging acceleration intention threshold, it is determined that the driver is not in the merging acceleration intention;

If the ramp merging execution coefficient is greater than the merging acceleration intention threshold, execute S330;

S330. According to the duration for which the ramp merging execution coefficient is greater than the merging acceleration intention threshold, perform the following operations:

If the duration of the ramp merging execution coefficient greater than the merging acceleration intention threshold is greater than the manually preset merging acceleration intention time threshold, it is determined that the driver is in the merging acceleration intention;

If the duration of the ramp merging execution coefficient being greater than the merging acceleration intention threshold is not greater than the merging acceleration intention time threshold, it is determined that the driver is not in the merging acceleration intention.

Preferably, in S500, according to the real-time vehicle speed signal and the brake pedal signal, it is determined whether to exit the ramp merge control mode, which specifically includes the following steps:

S510. Comparing the real-time vehicle speed signal with the vehicle speed upper limit threshold; and then performing the following operations according to the comparison result:

If the real-time vehicle speed signal is greater than the vehicle speed upper limit threshold, it is determined to exit the ramp merge control mode;

If the real-time vehicle speed signal is not greater than the vehicle speed upper threshold, execute S520;

S520. Perform the following operations according to the brake pedal signal value:

If the brake pedal signal value is "brake", it is determined to exit the ramp merge control mode;

If the value of the brake pedal signal is "no braking", it is determined not to exit the ramp merge control mode.

Preferably, the self-learning weighting coefficients in S240 are obtained in the following manner:

According to the brake pedal signal and the steering signal, traverse the manually preset self-learning weighting coefficient correspondence table; the self-learning weighting coefficient correspondence table is a two-dimensional table, and each 1 row is a value of the brake pedal signal, Each column is a value of the turn signal, and each cell stores the value obtained under the joint influence of the brake pedal signal represented by the row corresponding to this cell and the turn signal represented by the column. The value of the self-learning weighting coefficient; then acquire and output the value of the self-learning weighting coefficient.

Preferably, the value of the self-learning weighting coefficient in the self-learning weighting coefficient correspondence table is obtained in the following manner:

S241. Select and load a batch of initial sample data manually calibrated in advance; the initial sample data includes the brake pedal signal, the steering signal, and the self-learning weighting coefficient;

S242. Fill the brake pedal signals into each cell of the row in order of increasing value; fill the steering signal into each cell of the column in order of increasing value;

S243. Fill in the value of the self-learning weighting coefficient obtained under the joint influence of the brake pedal signal represented by the row corresponding to this cell and the steering signal represented by the column in the remaining cells in turn;

S244. During the testing process or driving process, collect the brake pedal signal, the steering signal, and the self-learning weighting coefficient in real time; then use one value of the brake pedal signal and one value of the steering signal The value of one of the self-learning weighting coefficients collected at the same time as the correspondence is packed into a self-learning corresponding group;

S245. Sort the obtained self-learning corresponding groups by time stamps to obtain the real-time state of the ramp merging; then return the real-time state of the ramp merging to the driver intention self-learning module;

S246. Demodulate the real-time state of the ramp merging to obtain the self-learning corresponding group, and update the value of the self-learning weighting coefficient in each self-learning corresponding group to the self-learning corresponding group in real time. In the cell intersected by the row corresponding to the value of the brake pedal signal and the column corresponding to the value of the steering signal in the group;

S247. Repeat S244-S246 until the rate of change of the value of the self-learning weighting coefficient in each cell is less than the manually preset threshold value of the rate of change of the self-learning weighting coefficient.

Compared with the prior art, the present invention has the following advantages:

1. Since the control method of the present invention is applicable to conventional ordinary medium and heavy commercial vehicle control systems, it has wide application range, low cost, and large customer benefits;

2. Due to the rapid speed increase and short judgment time of the control method of the present invention, lane congestion and traffic accidents caused by slow-moving driving can be effectively avoided;

3. Since the invention can greatly reduce the time for merging vehicles, the driving experience is improved, and the accident rate and traffic jam rate are further reduced;

4. Since the present invention learns the operating habits of different drivers through the self-learning function, the calibration work is reduced, and customized strategy services for different people and vehicles can also be achieved.

Description of drawings

Fig. 1 is a schematic diagram of the system structure of a specific embodiment of the present invention;

Fig. 2 is the schematic flow chart of the method of the specific embodiment of the present invention;

Fig. 3 is the test road condition schematic diagram of the specific embodiment of the present invention;

Fig. 4 is a schematic diagram of a comparison of vehicle speed before and after turning on and off the parallel acceleration system according to a specific embodiment of the present invention.

Detailed ways

Below in conjunction with specific embodiment, further illustrate the present invention, should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention, after having read the present invention, those skilled in the art will understand various equivalent forms of the present invention All modifications fall within the scope defined by the appended claims of the present application.

As shown in Figure 1, the ramp merge control system based on map signals includes the following parts:

The map signal module is used to obtain the real-time position of the vehicle and the merging condition of the ramp.

The driver's intention self-learning module is used to judge the driver's intention of ramp merge operation and obtain the self-learning weighting coefficient.

In this specific embodiment, the self-learning weighting coefficient is obtained through self-learning through a memory-based learning algorithm.

The parallel acceleration execution coefficient calculation module is used to calculate the ramp parallel acceleration feasibility coefficient, and determine whether to activate the ramp parallel acceleration command according to the ramp parallel acceleration feasibility coefficient.

The merging acceleration execution module is used to execute the ramp merging acceleration command, obtain the real-time status of the ramp merging in real time, and send the real-time status of the ramp merging back to the driver's intention self-learning module.

In this specific embodiment, the parallel acceleration execution module specifically includes the following submodules:

The torque compensation sub-module is used to provide compensation torque to the output torque of the engine in real time according to the ramp merge condition.

The filter parameter sub-module is used to smooth the output torque of the engine, suppress the occurrence of sudden torque changes, limit the rate of change of the output torque, and adjust the torque response performance of the vehicle.

The mode switching sub-module is used to switch the control mode of the vehicle; the control mode includes the ramp merge control mode and the normal control mode.

The limit value adjustment sub-module is used to adjust the vehicle limit value and the engine limit value; the vehicle limit value includes the manually preset vehicle speed upper limit threshold; the engine limit value includes the engine speed upper limit threshold and the engine output torque upper limit threshold.

The gear control sub-module is used to control the gear up and down operation of the vehicle.

As shown in Figure 2, the ramp merging control method based on the map signal using the map signal-based ramp merging control system includes the following steps:

S100. Obtain the real-time position of the vehicle; then determine whether the vehicle is in the on-ramp state according to the real-time position of the vehicle; then perform the following operations according to the determination result:

If the vehicle is in the on-ramp state, a ramp merging control command is issued.

If the vehicle is not in the on-ramp state, go back and execute S100 again.

In this specific embodiment, the ramp merging control command is a Boolean data type, wherein: when the value of the ramp merging control command is "1", the indicative command is valid and needs to be executed; when the value of the ramp merging control command is "1", When 0", it indicates that the instruction is invalid and does not need to be executed.

S200. After receiving the on-ramp state, collect the accelerator pedal signal, the engine speed signal, the real-time vehicle speed signal, the steering signal, the gear signal, and the clutch signal in real time; and then calculate the ramp merge execution coefficient.

In this specific embodiment, the execution coefficient of ramp merging calculated in S200 specifically includes the following steps:

S210. Look up the table to obtain the accelerator pedal signal weight factor, the engine speed signal weight factor, the real-time vehicle speed signal weight factor, and the steering signal weight factor;

S220. Multiply the accelerator pedal signal by the accelerator pedal signal weight factor to obtain a weighted accelerator pedal signal; multiply the engine speed signal by the engine speed signal weight factor to obtain a weighted engine speed signal; multiply the real-time vehicle speed signal by the real-time vehicle speed signal weight factor, A weighted real-time vehicle speed signal is obtained; the weighted steering signal is obtained by multiplying the steering signal by the weighting factor of the steering signal.

S230. Adding the weighted accelerator pedal signal, the weighted engine speed signal, the weighted real-time vehicle speed signal, and the weighted steering signal to obtain a ramp merge acceleration feasibility coefficient.

It should be noted that in this specific embodiment, the above-mentioned weight factors are obtained by looking up a table. In addition, there is another way to calculate the above-mentioned weight factors, that is, according to each signal weight factor based on LSTM (Long short-term memory ) The neural network algorithm calculates the feasibility coefficient of ramp merging acceleration.

It should be further explained that the LSTM neural network algorithm is a special cyclic neural network algorithm, which can be used to process data tasks with time series. In the present invention, using the LSTM neural network algorithm, according to the vehicle kinematics model, According to the time series, according to the change of accelerator pedal, engine speed, vehicle speed and steering signal, a response variable is accurately calculated, which is the feasibility coefficient of ramp merge acceleration.

S240. Multiply the ramp merging acceleration feasibility coefficient by the self-learning weighting coefficient to obtain the ramp merging execution coefficient.

In this specific embodiment, the self-learning weighting coefficient in S240 is obtained in the following manner:

According to the brake pedal signal and steering signal, traverse the artificially preset self-learning weighting coefficient corresponding table; the self-learning weighting coefficient corresponding table is a two-dimensional table, each 1 row is a value of a brake pedal signal, and each 1 column is a value of a steering signal Each cell stores the value of the self-learning weighting coefficient under the joint influence of the brake pedal signal represented by the row corresponding to this cell and the steering signal represented by the column; then obtain and output the self-learning weighting The value of the coefficient.

In this specific embodiment, the value of the self-learning weighting coefficient in the self-learning weighting coefficient correspondence table is obtained in the following manner:

S241. Select and load a batch of initial sample data manually calibrated in advance; the initial sample data includes brake pedal signals, steering signals, and self-learning weighting coefficients.

S242. Fill the brake pedal signals into each cell of the row in order of increasing value; fill the steering signal into each cell of the column in order of increasing value.

S243. Fill in the remaining cells in turn the values of the self-learning weighting coefficients obtained under the joint influence of the brake pedal signal represented by the row corresponding to this cell and the steering signal represented by the column.

S244. During the testing process or driving process, collect the brake pedal signal, the steering signal, and the self-learning weighting coefficient in real time; The values of the weighting coefficients are packed into 1 self-learning corresponding group.

S245. Sort the obtained self-learning corresponding groups by time stamps to obtain the real-time status of ramp merging; and then send back the real-time status of ramp merging to the driver intention self-learning module.

S246. Demodulate the real-time state of ramp merging to obtain the self-learning corresponding group, and update the value of the self-learning weighting coefficient in each self-learning corresponding group to the corresponding value of the brake pedal signal in this self-learning corresponding group in real time. In the cell intersected by the row, the column corresponding to the value of the turn signal.

S247. Repeat S244-S246 until the rate of change of the value of the self-learning weighting coefficient in each cell is less than the manually preset threshold value of the rate of change of the self-learning weighting coefficient.

It should be noted that the system of the present invention is to improve the recognition accuracy of the merging intention by self-learning the driver's driving habits in the ramp merging condition and self-learning the driver's merging intention. In S200, monitor and identify the accelerator pedal, engine speed, vehicle speed, and steering signal of the vehicle, and comprehensively judge that the driver is in the intention of merging. If the system judges that the driver is in the intention of accelerating and merging, the driver's ramp merging execution coefficient is given. , if the ramp merging execution coefficient is greater than a certain threshold, it is judged that the driver is in the ramp merging acceleration intention.

For example, when the driver steps on the accelerator, the size is 50%, then according to the strategy recognition of the present invention, it is considered that the driver wants to accelerate and merge, and then the strategy of the present invention executes the acceleration and merge operation, and immediately increases the speed of the vehicle; The strategy of the invention will think that the acceleration operation for the driver's 50% throttle is unreasonable. If the multiple judgment results exceed a certain probability (this probability is preset by manual, as the adjustment cursor 70%), then the present invention thinks that the system needs to modify the execution operation, Reduce the rate of vehicle speed increase, because some drivers like the rapid increase of vehicle speed, while others do not; in short, the present invention needs to be properly adjusted according to the driver's driving habits.

If the system detects in real time that the driver is about to perform braking and steering operations, according to the magnitude of the brake control signal and the magnitude of the steering angle, a self-learning weighting coefficient can be obtained by looking up the table. The self-learning weighting coefficient can be positive or negative, depending on the operation. Select, look up the table based on the magnitude of the brake signal and the turn signal, and finally feed back the self-learning weighting coefficient to the system for the final judgment and execution of the ramp merge signal.

It should be further explained that, in this specific embodiment, the self-learning weighting coefficient is self-learning through a learning algorithm based on memory: first, a batch of sample data is selected through calibration, and then in the process of testing and use, according to the approximation New data and sample data are constantly compared and updated to accurately identify a driver's ramp merging operation habits, and combined with software control functions to achieve judgment accuracy.

S300. Determine whether the driver is in the intention of merging acceleration according to the ramp merging execution coefficient; then perform the following operations according to the determination result:

If the driver is in the intention of merging and accelerating, activate the ramp merging and accelerating function command.

If the driver is not in the intention of merging and accelerating, go back and execute S200 again.

In this specific embodiment, the ramp merging acceleration function command is a Boolean data type, wherein: when the value of the ramp merging acceleration function command is "1", it indicates that the command is valid and needs to be executed; When the value is "0", it indicates that the command is invalid and does not need to be executed.

In this specific embodiment, in S300, it is determined whether the driver is in the acceleration intention of merging according to the ramp merging execution coefficient, which specifically includes the following steps:

S310. Comparing the ramp merging execution coefficient with a manually preset merging acceleration intention threshold.

S320. Perform the following operations according to the comparison result:

If the ramp merging execution coefficient is not greater than the merging acceleration intention threshold, it is determined that the driver is not in the merging acceleration intention.

If the ramp merging execution coefficient is greater than the merging acceleration intention threshold, S330 is executed.

S330. According to the duration of the ramp merging execution coefficient greater than the merging acceleration intention threshold, perform the following operations:

If the ramp merging execution coefficient is greater than the merging acceleration intention threshold and the duration is greater than the manually preset merging acceleration intention time threshold, it is determined that the driver is in the merging acceleration intention.

If the duration of the ramp merging execution coefficient greater than the merging acceleration intention threshold is not greater than the merging acceleration intention time threshold, it is determined that the driver is not in the merging acceleration intention.

It should be noted that, for safety reasons, it is absolutely not possible to determine that the driver has the intention to merge at the moment when the ramp merging execution coefficient is greater than the threshold for merging acceleration intention, but it is necessary to leave a buffer, that is, the ramp merging execution coefficient is greater than Parallel acceleration intention threshold, and the duration exceeds the specified time.

S400. Set the vehicle as a ramp merge control mode; call the parallel acceleration execution module to execute acceleration management; and then increase the vehicle speed.

It should be noted that in S400, calling the parallel acceleration execution module to execute acceleration management has the following functions:

The control system performs acceleration management, mainly from 6 angles of torque compensation, filter parameters, mode switching, limit value adjustment, gear position control, and accessory disengagement. Quickly increase the speed of the vehicle to avoid road congestion and traffic accidents caused by slow speed; specifically:

Torque Compensation: When the ramp parallel acceleration command is activated, the compensation torque is enabled, plus the external characteristic torque when the non-parallel acceleration function is turned on, so as to increase the external characteristic torque of the engine and make the engine output torque greater;

Filter parameter adjustment: When the ramp parallel acceleration command is activated, the filter parameters of the torque path signals such as throttle and torque are switched, and the filter time is reduced, so that the engine responds faster and the vehicle speed increases faster;

Mode switching: When the ramp parallel acceleration command is activated, if the engine was in the economical control mode before, it will quickly switch to the power control mode, so that the engine can output more torque faster;

Limit value adjustment: When the ramp merge acceleration command is activated, the upper limit value of the engine torque limit, the upper limit value of the acceleration limit value, and the upper limit value of the smoke limit value are released temporarily;

Gear position control: When the ramp parallel acceleration command is activated, the engine upshift switching time is delayed;

Accessory disengagement: When the ramp parallel acceleration command is activated, the fan and other accessories are temporarily disengaged, so that the torque of the engine can act more on the increase of the vehicle speed;

Since the ramp merge time is generally short, about 30s, the vehicle speed is low when the ramp is cut and merged. Performing the above operations for a short time will not have any impact on vehicle performance. Lower fuel consumption by about 0.1%.

S500. Collect real-time vehicle speed signals and brake pedal signals in real time; then determine whether to exit the ramp merging control mode according to the real-time vehicle speed signals and brake pedal signals; then perform the following operations according to the determination results:

If it is determined not to exit the ramp merge control mode, return to and re-execute S500 again.

If it is determined to exit the ramp merging control mode, then exit the ramp merging control mode, set the entire vehicle to the normal control mode, and then exit the current ramp merging control process.

In this specific embodiment, in S500, according to the real-time vehicle speed signal and the brake pedal signal, it is determined whether to exit the ramp merge control mode, which specifically includes the following steps:

S510. Comparing the real-time vehicle speed signal with the vehicle speed upper limit threshold; and then performing the following operations according to the comparison result:

If the real-time vehicle speed signal is greater than the vehicle speed upper limit threshold, it is determined to exit the ramp merge control mode.

If the real-time vehicle speed signal is not greater than the vehicle speed upper limit threshold, execute S520.

S520. Perform the following operations according to the brake pedal signal value:

If the value of the brake pedal signal is "brake", it is determined to exit the ramp merge control mode.

If the brake pedal signal value is "no braking", it is determined not to exit the ramp merge control mode.

In order to further demonstrate the technical effects of the present invention, this specific embodiment also provides the following cases:

As shown in Figure 3, it is a commercial vehicle that needs to merge on the ramp; its speed ranges from 50 to 90 km/h.

As shown in FIG. 4 , it is a comparison between the vehicle speed change curve using the ramp merge control system and method of the present invention and the vehicle speed change curve using the prior art.

The comparative test results show that the parallel acceleration time at a vehicle speed of 50-90km/h is reduced by 1-3 seconds compared to the parallel acceleration time without this function, and the total parallel time is 25-45 seconds. ~0.2% increase in fuel consumption; multiple test data show that the method of the present invention reduces merging time by 1 to 4 seconds compared to ordinary vehicles.

In the foregoing Detailed Description, various features are grouped together in a single embodiment to simplify the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments of the subject matter require more features than are expressly recited in each claim. Rather, as the following claims reflect, the invention lies in less than all features of a single disclosed embodiment. Thus the following claims are hereby expressly incorporated into the Detailed Description, with each claim standing on its own as a separate preferred embodiment of this invention.

The foregoing description of the disclosed embodiments was provided to enable any person skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments without departing from the spirit and scope of the disclosure. Thus, the present disclosure is not intended to be limited to the embodiments presented herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

The foregoing description includes illustrations of one or more embodiments. Of course, it is impossible to describe all possible combinations of components or methods to describe the above-mentioned embodiments, but those skilled in the art should recognize that various embodiments can be further combined and permuted. Accordingly, the embodiments described herein are intended to embrace all such alterations, modifications and variations that fall within the scope of the appended claims. Furthermore, to the extent that the term "comprises" is used in the specification or claims, the word is encompassed in a manner similar to the term "comprises" as interpreted when "comprises" is used as a link in the claims. Furthermore, any use of the term "or" in the specification of the claims is intended to mean a "non-exclusive or".

The specific embodiments described above have further described the purpose, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above descriptions are only specific embodiments of the present invention and are not intended to limit the scope of the present invention. Protection scope, within the spirit and principles of the present invention, any modification, equivalent replacement, improvement, etc., shall be included in the protection scope of the present invention.

Claims (10)

1. The ramp parallel control system based on map signals is characterized in that: comprises the following parts:

the map signal module is used for acquiring the real-time position of the vehicle and the ramp parallel working condition;

the driver intention self-learning module is used for judging the intention of the parallel operation of the ramp of the driver and obtaining a self-learning weighting coefficient;

the parallel acceleration execution coefficient calculation module is used for calculating a parallel acceleration feasibility coefficient of the ramp and judging whether to activate a parallel acceleration instruction of the ramp according to the parallel acceleration feasibility coefficient of the ramp;

the parallel acceleration execution module is used for executing the ramp parallel acceleration instruction, acquiring the ramp parallel real-time state in real time, and transmitting the ramp parallel real-time state back to the driver intention self-learning module.

2. The map signal-based ramp parallel control system of claim 1, wherein: the self-learning Xi Jiaquan coefficient is obtained by self-learning through a learning algorithm based on memory.

3. The map signal-based ramp parallel control system of claim 2, wherein: the parallel acceleration execution module specifically comprises the following submodules:

the torque compensation sub-module is used for providing compensation torque for the output torque of the engine in real time according to the ramp parallel working condition;

the filtering parameter submodule is used for smoothing the output torque of the engine, inhibiting the occurrence of torque abrupt change, limiting the change rate of the output torque and adjusting the torque response performance of the whole vehicle;

the mode switching sub-module is used for switching the control mode of the whole vehicle; the control mode comprises a ramp parallel control mode and a normal control mode;

the limit value adjusting sub-module is used for adjusting the limit value of the whole vehicle and the limit value of the engine; the whole vehicle limit value comprises a manually preset upper vehicle speed limit value; the engine limit value comprises an upper limit threshold value of engine rotation speed and an upper limit threshold value of engine output torque;

and the gear control submodule is used for controlling gear lifting operation of the whole vehicle.

4. A map signal-based ramp parallel control method using the map signal-based ramp parallel control system as set forth in claim 3, characterized in that: comprises the following steps:

s100, acquiring the real-time position of the vehicle; then judging whether the vehicle is in an on-ramp state according to the real-time position of the vehicle; and then according to the judgment result, the following operations are performed:

if the vehicle is in the upper ramp state, a ramp parallel control instruction is sent;

if the vehicle is not in the on-ramp state, returning to and re-executing S100 again;

s200, after receiving the state of the ramp, acquiring an accelerator pedal signal, an engine rotating speed signal, a real-time vehicle speed signal, a steering signal, a gear signal and a clutch signal in real time; then calculating to obtain a ramp parallel execution coefficient;

s300, judging whether a driver is in a parallel acceleration intention or not according to the ramp parallel execution coefficient; and then according to the judgment result, the following operations are performed:

if the driver is in the parallel acceleration intention, activating a ramp parallel acceleration function instruction;

if the driver is not in the parallel acceleration intention, returning to and re-executing S200 again;

s400, setting the whole vehicle as a ramp parallel control mode; calling the parallel acceleration execution module to execute acceleration management; then the speed of the vehicle is increased;

s500, acquiring the real-time vehicle speed signal and the brake pedal signal in real time; then judging whether to exit the ramp parallel control mode according to the real-time vehicle speed signal and the brake pedal signal; and then according to the judgment result, the following operations are performed:

if it is determined that the ramp parallel control mode is not exited, returning to and re-executing S500 again;

if the ramp parallel control mode is judged to be exited, the ramp parallel control mode is exited, the whole vehicle is set to be in the normal control mode, and then the ramp parallel control flow is exited.

5. The map signal-based ramp parallel control method as set forth in claim 4, wherein: the ramp parallel control instruction is of a boolean data type, wherein: when the value of the ramp parallel control instruction is 1, the characterization instruction is effective and needs to be executed; when the value of the ramp parallel control instruction is 0, the characterization instruction is invalid and does not need to be executed;

the ramp parallel acceleration function instruction is of a boolean data type, wherein: when the value of the ramp parallel acceleration function instruction is 1, the characterization instruction is effective and needs to be executed; and when the value of the ramp parallel acceleration function instruction is 0, the characterization instruction is invalid and does not need to be executed.

6. The map signal-based ramp parallel control method as set forth in claim 5, wherein: s200, calculating the ramp parallel execution coefficient specifically comprises the following steps:

s210, looking up a table to obtain an accelerator pedal signal weight factor, an engine speed signal weight factor, a real-time vehicle speed signal weight factor and a steering signal weight factor;

s220, multiplying the accelerator pedal signal by the accelerator pedal signal weight factor to obtain a weighted accelerator pedal signal; multiplying the engine speed signal by the engine speed signal weighting factor to obtain a weighted engine speed signal; multiplying the real-time vehicle speed signal by the real-time vehicle speed signal weight factor to obtain a weighted real-time vehicle speed signal; multiplying the steering signal by the steering signal weight factor to obtain a weighted steering signal;

s230, adding the weighted accelerator pedal signal, the weighted engine speed signal, the weighted real-time vehicle speed signal and the weighted steering signal to obtain the ramp parallel acceleration feasibility coefficient;

s240, multiplying the ramp parallel acceleration feasibility coefficient by the self-learning Xi Jiaquan coefficient to obtain the ramp parallel execution coefficient.

7. The map signal-based ramp parallel control method as set forth in claim 6, wherein: s300, judging whether a driver is in the parallel acceleration intention according to the ramp parallel execution coefficient, wherein the method specifically comprises the following steps of:

s310, comparing the ramp parallel execution coefficient with a manually preset parallel acceleration intention threshold;

s320, according to the comparison result, the following operations are performed:

if the ramp parallel execution coefficient is not greater than the parallel acceleration intention threshold, judging that a driver is not in the parallel acceleration intention;

if the ramp parallel execution coefficient is greater than the parallel acceleration intention threshold, executing S330;

s330, according to the duration that the ramp parallel execution coefficient is larger than the parallel acceleration intention threshold, the following operation is performed:

if the duration time of the ramp parallel execution coefficient which is larger than the parallel acceleration intention threshold value is larger than a manually preset parallel acceleration intention time threshold value, determining that a driver is in the parallel acceleration intention;

and if the duration that the ramp parallel execution coefficient is larger than the parallel acceleration intention threshold is not larger than the parallel acceleration intention time threshold, judging that the driver is not in the parallel acceleration intention.

8. The map signal-based ramp parallel control method as set forth in claim 7, wherein: s500, judging whether to exit the ramp parallel control mode according to the real-time vehicle speed signal and the brake pedal signal, wherein the method specifically comprises the following steps of:

s510, comparing the real-time vehicle speed signal with the vehicle speed upper limit threshold value; and then according to the comparison result, the following operations are performed:

if the real-time vehicle speed signal is larger than the vehicle speed upper limit threshold value, judging that the ramp parallel control mode is exited;

if the real-time vehicle speed signal is not greater than the upper vehicle speed threshold, performing S520;

s520, performing the following operations according to the signal value of the brake pedal:

if the brake pedal signal value is 'brake', judging that the ramp parallel control mode is exited;

and if the brake pedal signal value is 'no brake', judging that the ramp parallel control mode is not exited.

9. The map signal-based ramp parallel control method as set forth in claim 8, wherein: the self-learning Xi Jiaquan coefficient described in S240 is obtained by:

traversing a manually preset self-learning Xi Jiaquan coefficient corresponding table according to the brake pedal signal and the steering signal; the self-learning Xi Jiaquan coefficient corresponding table is a two-dimensional table, each 1 row is a value of the brake pedal signal, each 1 column is a value of the steering signal, and each 1 cell stores the value of the self-learning Xi Jiaquan coefficient, which is obtained under the common influence of the brake pedal signal represented by the row corresponding to the cell and the steering signal represented by the column; the value of the self-learning Xi Jiaquan coefficient is then obtained and output.

10. The map signal-based ramp parallel control method as set forth in claim 9, wherein: the values of the self-learning Xi Jiaquan coefficients in the self-learning Xi Jiaquan coefficient correspondence table are obtained by:

s241, selecting and loading a batch of initial sample data which is calibrated manually in advance; the initial sample data includes the brake pedal signal, the steering signal, the self-learning Xi Jiaquan coefficient;

s242, sequentially filling the brake pedal signals into the cells of the rows according to the increasing order of the values; sequentially filling the steering signals into the cells of the column according to the increasing sequence of the values;

s243, sequentially filling the rest unit cells with the values of the self-learning Xi Jiaquan coefficients obtained under the joint influence of the brake pedal signals represented by the rows corresponding to the unit cells and the steering signals represented by the columns;

s244, acquiring the brake pedal signal, the steering signal and the self-learning Xi Jiaquan coefficient in real time in a test process or a driving process; then, packing the values of 1 brake pedal signal, 1 steering signal and 1 self-learning Xi Jiaquan coefficient which are acquired correspondingly and simultaneously into 1 self-learning corresponding group;

s245, sequencing the obtained self-learning corresponding groups according to time stamps to obtain the ramp parallel real-time state; the ramp parallel real-time state is returned to the driver intention self-learning module;

s246, demodulating the ramp parallel real-time state, and updating the value of the self-learning Xi Jiaquan coefficient in each self-learning corresponding group to a cell crossed by a row corresponding to the value of the brake pedal signal and a column corresponding to the value of the steering signal in the self-learning corresponding group in real time;

s247, repeating the steps of S244-S246 until the change rate of the self-learning Xi Jiaquan coefficient value in each cell is smaller than a manually preset self-learning Xi Jiaquan coefficient change rate threshold.