Ramp merging control system and method based on map signal

Abstract

The present invention relates to a ramp merging control system based on map signals, comprising a map signal module for obtaining a real-time vehicle position and ramp merging conditions; a driver intention self-learning module for judging the driver's ramp merging operation intention and obtaining a self-learning weighted coefficient; a merging acceleration execution coefficient calculation module for activating a ramp merging acceleration instruction; and a merging acceleration execution module for executing a ramp merging acceleration instruction. The present invention also relates to a ramp merging control method based on map signals, comprising the steps of: issuing a ramp merging control instruction; calculating a ramp merging execution coefficient; activating a ramp merging acceleration function instruction; and executing acceleration management. The present invention has a wide range of applications, low cost, and great customer benefits; effectively avoids lane congestion and traffic accidents caused by slow driving; improves driving experience, and further reduces accident rates and traffic jam rates; reduces calibration work, and provides customized strategy services for different people and vehicles.

Description

Technical Field

[0001] The present invention relates to the technical field of engine and vehicle control, and in particular to a ramp merging control system and method based on map signals. Background Art

[0002] The process of vehicles merging from the ramp into the main road is called the ramp merging process; this process requires speeding up, but at the same time taking into account the vehicles coming from behind, so it is a dynamic adjustment process of the vehicle speed.

[0003] Merging on ramps 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, which results in the same car being unable to use the same speed-up strategy on the same ramp. In addition, the road conditions on the main road are also changing all the time, so it is difficult for existing technology to provide customized strategy services for each different driver and each different car.

[0004] Typical prior art includes a Chinese invention patent with application number 201811448867.8 and patent name “An automatic lane-changing system based on V2X”, which discloses an automatic lane-changing system belonging to the field of autonomous driving technology. When the lane-changing trigger module receives the lane-changing instruction, it sends a lane-changing trigger signal to the positioning module, the V2X short-range communication module and the strategy determination module; when the positioning module receives the lane-changing trigger signal, it determines the position range of the dangerous area on the side of the vehicle and sends the position range of the dangerous area to the strategy determination module; the strategy determination module receives the speed of the vehicle acquired by the sensor, and determines the control strategy based on the speed of the vehicle, the position range of the dangerous area, the position, speed, accelerator pedal status and brake pedal status of other vehicles acquired by the V2X short-range communication module, 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 lane merging is required, a variety of vehicle information of surrounding vehicles acquired by the V2X short-range communication module is used to achieve safe and efficient automatic lane merging.

[0005] Another typical prior art is the application number 202110887375.4, and the patent name is "A method and system for merging vehicles on a highway based on an intelligent network environment. The method described in the invention includes the following steps: obtaining the latitude and longitude positions and speeds of the vehicles to be merged in the outermost lane of the highway and the ramp; determining at least one candidate merging gap according to the real-time latitude and longitude positions and speeds of the vehicles in the outermost lane of the highway; selecting a candidate merging gap from all candidate merging gaps to determine whether the candidate merging gap can be used as a merging gap, otherwise selecting another candidate merging gap from all candidate merging gaps for re-judgment until all candidate merging gaps are selected; the vehicle to be merged merges into the outermost vehicle of the highway based on the merging gap and the corresponding merging point position, the time interval between the vehicle at the current time and the expected arrival time at the merging point, and the speed of the vehicle to be merged when merging. In this patent, in order to accurately merge into the vehicle, the vehicle speed needs to reach a certain level, otherwise it will lead to merging failure or accidents.

[0006] The defects of the prior art are:

[0007] 1. Since the existing technical solutions are only for the autonomous driving scenario, using intelligent network technology, such as the V2X technology used in Comparative Document 1, the ramp information is sent to the vehicle through the V2X equipment in front of the vehicle or on both sides of the road, and then the number of vehicles merging into the main line from the ramp is determined based on the real-time main line and ramp vehicle status information at the entrance ramp of the highway, and the order of ramp vehicle merging is optimized; however, the current vehicles equipped with autonomous driving technology are relatively expensive and the number of vehicles in use is small. At present, most vehicles are ordinary vehicles and do not have autonomous driving technology. Therefore, the current solution does not have control technology for vehicles in non-autonomous driving fields and non-intelligent network states that cannot use the merging ramp state;

[0008] 2. Since the current patent focus of the prior art is to select the vehicle gap and perform the merging operation when the vehicle speed is reached, but the vehicle speed may not reach the set range, the application of the prior art has limitations and is easily restricted, which further easily leads to accidents. Summary of the invention

[0009] In response to the above-mentioned problems, the present invention provides a ramp merging control system and method based on map signals, which aims to have a wide range of applications, low cost, and great customer benefits; effectively avoid lane congestion and traffic accidents caused by slow driving; enhance driving experience and further reduce accident rates and traffic jam rates; reduce calibration work, and provide customized strategy services for different people and vehicles.

[0010] To solve the above problems, the technical solution provided by the present invention is:

[0011] The ramp merging control system based on map signals includes the following parts:

[0012] Map signal module, used to obtain the real-time vehicle location and ramp merging conditions;

[0013] The driver intention self-learning module is used to determine the driver's intention to merge on the ramp and obtain the self-learning weighting coefficient;

[0014] A ramp merging acceleration execution coefficient calculation module is used to calculate the ramp merging acceleration feasibility coefficient and determine whether to activate the ramp merging acceleration instruction according to the ramp merging acceleration feasibility coefficient;

[0015] The lane-merging acceleration execution module is used to execute the ramp lane-merging acceleration instruction, obtain the ramp lane-merging real-time status in real time, and transmit the ramp lane-merging real-time status back to the driver intention self-learning module.

[0016] Preferably, the self-learning weighting coefficient is obtained by self-learning through a memory-based learning algorithm.

[0017] Preferably, the parallel acceleration execution module specifically includes the following submodules:

[0018] A torque compensation submodule, used for providing a compensation torque to the output torque of the engine in real time according to the ramp merging condition;

[0019] A filtering parameter submodule, used to smooth the output torque of the engine, suppress the occurrence of torque mutations, limit the rate of change of the output torque, and adjust the torque response performance of the vehicle;

[0020] A mode switching submodule is used to switch the control mode of the whole vehicle; the control mode includes a ramp merging control mode and a normal control mode;

[0021] The limit adjustment submodule is used to adjust the vehicle limit and the engine limit; the vehicle limit includes an artificially preset vehicle speed upper limit threshold; the engine limit includes an engine speed upper limit threshold and an engine output torque upper limit threshold;

[0022] The gear control submodule is used to control the gear raising and lowering operations of the entire vehicle.

[0023] A ramp merging control method based on a map signal using a ramp merging control system based on a map signal comprises the following steps:

[0024] S100. Obtain the real-time position of the vehicle; then determine whether the vehicle is on the ramp according to the real-time position of the vehicle; then perform the following operations according to the determination result:

[0025] If the vehicle is in the on-ramp state, a ramp merging control instruction is issued;

[0026] If the vehicle is not in the on-ramp state, return to and re-execute S100;

[0027] S200. After receiving the on-ramp status, real-time acquisition of accelerator pedal signal, engine speed signal, real-time vehicle speed signal, steering signal, gear signal, clutch signal; and then calculating the ramp merge execution coefficient;

[0028] S300. Determine whether the driver intends to accelerate by merging lanes according to the ramp merging execution coefficient; and then perform the following operations according to the determination result:

[0029] If the driver intends to accelerate while merging, then the ramp merging acceleration function instruction is activated;

[0030] If the driver is not in the lane-changing acceleration intention, then return to and re-execute S200;

[0031] S400. Set the vehicle to ramp merging control mode; call the merging acceleration execution module to perform acceleration management; and then increase the vehicle speed;

[0032] 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:

[0033] If it is determined not to exit the ramp merging control mode, the process returns to and re-executes S500;

[0034] If it is determined to exit the ramp merging control mode, then the ramp merging control mode is exited, the entire vehicle is set to the normal control mode, and then the ramp merging control process is exited.

[0035] Preferably, the ramp merging control instruction is a Boolean data type, wherein: when the value of the ramp merging control instruction is "1", the characterization instruction is valid and needs to be executed; when the value of the ramp merging control instruction is "0", the characterization instruction is invalid and does not need to be executed;

[0036] The ramp lane merging acceleration function instruction is a Boolean data type, wherein: when the value of the ramp lane merging acceleration function instruction is "1", the characterization instruction is valid and needs to be executed; when the value of the ramp lane merging acceleration function instruction is "0", the characterization instruction is invalid and does not need to be executed.

[0037] Preferably, calculating the ramp merging execution coefficient in S200 specifically includes the following steps:

[0038] S210. Look up the table to obtain the accelerator pedal signal weight factor, engine speed signal weight factor, real-time vehicle speed signal weight factor, and turn signal weight factor;

[0039] 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 to obtain a weighted real-time vehicle speed signal; multiply the turn signal by the turn signal weight factor to obtain a weighted turn signal;

[0040] S230. Add 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 merging acceleration feasibility coefficient;

[0041] S240. Multiply the ramp merging acceleration feasibility coefficient by the self-learning weighting coefficient to obtain the ramp merging execution coefficient.

[0042] Preferably, determining whether the driver is in the lane-merging acceleration intention according to the ramp-merging execution coefficient in S300 specifically includes the following steps:

[0043] S310. Compare the ramp merging execution coefficient with a manually preset merging acceleration intention threshold;

[0044] S320. Perform the following operations according to the comparison result:

[0045] If the ramp lane merging execution coefficient is not greater than the lane merging acceleration intention threshold, it is determined that the driver is not in the lane merging acceleration intention;

[0046] If the ramp lane merging execution coefficient is greater than the lane merging acceleration intention threshold, executing S330;

[0047] S330. Perform the following operations according to the duration that the ramp lane merging execution coefficient is greater than the lane merging acceleration intention threshold:

[0048] If the duration during which the ramp lane merging execution coefficient is greater than the lane merging acceleration intention threshold is greater than a manually preset lane merging acceleration intention time threshold, it is determined that the driver is in the lane merging acceleration intention;

[0049] If the duration that the ramp lane merging execution coefficient is greater than the lane merging acceleration intention threshold is not greater than the lane merging acceleration intention time threshold, it is determined that the driver is not in the lane merging acceleration intention.

[0050] Preferably, in S500, determining whether to exit the ramp merging control mode according to the real-time vehicle speed signal and the brake pedal signal specifically includes the following steps:

[0051] S510. Compare the real-time vehicle speed signal with the vehicle speed upper limit threshold; and then perform the following operations according to the comparison result:

[0052] If the real-time vehicle speed signal is greater than the vehicle speed upper limit threshold, it is determined to exit the ramp merging control mode;

[0053] If the real-time vehicle speed signal is not greater than the vehicle speed upper limit threshold, execute S520;

[0054] S520. Perform the following operations according to the brake pedal signal value:

[0055] If the brake pedal signal value is "brake", it is determined to exit the ramp merging control mode;

[0056] If the brake pedal signal value is "no braking", it is determined not to exit the ramp merging control mode.

[0057] Preferably, the self-learning weighting coefficient in S240 is obtained by:

[0058] According to the brake pedal signal and the steering signal, a manually preset self-learning weighted coefficient correspondence table is traversed; the self-learning weighted coefficient correspondence table is a two-dimensional table, each row is a value of the brake pedal signal, each column is a value of the steering signal, and each cell stores the value of the self-learning weighted coefficient obtained under the combined influence of the brake pedal signal represented by the row corresponding to the cell and the steering signal represented by the column; then the value of the self-learning weighted coefficient is obtained and output.

[0059] Preferably, the value of the self-learning weighting coefficient in the self-learning weighting coefficient corresponding table is obtained by:

[0060] S241. Select and load a batch of initial sample data pre-calibrated manually; the initial sample data includes the brake pedal signal, the steering signal, and the self-learning weighting coefficient;

[0061] S242. Fill the brake pedal signal into each cell of the row in increasing order of value; fill the turn signal into each cell of the column in increasing order of value;

[0062] S243. Fill in the remaining cells in turn with the value of the self-learning weighted coefficient obtained under the combined influence of the brake pedal signal represented by the row corresponding to the cell and the steering signal represented by the column;

[0063] S244. During the test process or driving process, the brake pedal signal, the steering signal, and the self-learning weighting coefficient are collected in real time; then the value of the brake pedal signal, the value of the steering signal, and the corresponding value of the self-learning weighting coefficient collected at the same time are packaged into a self-learning corresponding group;

[0064] S245. Sort the obtained self-learning corresponding groups by timestamp to obtain the real-time status of the ramp merging; and then transmit the real-time status of the ramp merging back to the driver intention self-learning module;

[0065] S246. demodulate the ramp merging real-time state to obtain the self-learning corresponding groups, and update the value of the self-learning weighting coefficient in each of the self-learning corresponding groups in real time to the cell where the row corresponding to the value of the brake pedal signal and the column corresponding to the value of the turn signal in the self-learning corresponding group intersect;

[0066] S247. Repeat S244 to S246 until the rate of change of the value of the self-learning weighting coefficient in each cell is less than a manually preset threshold value of the rate of change of the self-learning weighting coefficient.

[0067] Compared with the prior art, the present invention has the following advantages:

[0068] 1. Since the control method of the present invention is applicable to conventional medium and heavy-duty commercial vehicle control systems, it has a wide range of applications, low cost, and great customer benefits;

[0069] 2. Since the control method of the present invention accelerates quickly and has a short judgment time, it can effectively avoid lane congestion and traffic accidents caused by slow driving;

[0070] 3. Since the present invention can significantly reduce the time for vehicles to merge, it improves the driving experience and further reduces the accident rate and traffic jam rate;

[0071] 4. Since the present invention learns the operating habits of different drivers through the self-learning function, the calibration work is reduced and customized strategic services can be provided for different people and vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 A schematic diagram of the system structure of a specific embodiment of the present invention;

[0073] Figure 2 A schematic diagram of a method flow chart of a specific embodiment of the present invention;

[0074] Figure 3 A schematic diagram of a test road condition in a specific embodiment of the present invention;

[0075] Figure 4 It is a schematic diagram comparing the vehicle speed changes before and after turning on and off the lane-changing acceleration system according to a specific embodiment of the present invention. DETAILED DESCRIPTION

[0076] The present invention is further explained below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, various equivalent forms of modifications to the present invention by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0077] like Figure 1 As shown in the figure, the ramp merging control system based on map signals includes the following parts:

[0078] Map signal module, used to obtain the real-time vehicle location and ramp merging conditions.

[0079] The driver intention self-learning module is used to determine the driver's intention to merge on the ramp and obtain the self-learning weighting coefficient.

[0080] In this specific embodiment, the self-learning weighting coefficient is obtained by self-learning through a memory-based learning algorithm.

[0081] The ramp merging acceleration execution coefficient calculation module is used to calculate the ramp merging acceleration feasibility coefficient and determine whether to activate the ramp merging acceleration instruction according to the ramp merging acceleration feasibility coefficient.

[0082] The lane-merging acceleration execution module is used to execute the ramp lane-merging acceleration command, obtain the real-time status of the ramp lane-merging in real time, and transmit the real-time status of the ramp lane-merging back to the driver intention self-learning module.

[0083] In this specific embodiment, the parallel acceleration execution module specifically includes the following submodules:

[0084] The torque compensation submodule is used to provide compensation torque to the engine output torque in real time according to the ramp merging condition.

[0085] The filter parameter submodule is used to smooth the output torque of the engine and suppress the occurrence of torque mutations, while limiting the rate of change of the output torque and adjusting the torque response performance of the vehicle.

[0086] The mode switching submodule is used to switch the control mode of the whole vehicle; the control mode includes ramp merging control mode and normal control mode.

[0087] The limit adjustment submodule is used to adjust the vehicle limit and the engine limit; the vehicle limit includes the manually preset upper speed threshold; the engine limit includes the upper engine speed threshold and the upper engine output torque threshold.

[0088] The gear control submodule is used to control the gear raising and lowering operations of the entire vehicle.

[0089] like Figure 2 As shown, a ramp merging control method based on a map signal using a ramp merging control system based on a map signal comprises the following steps:

[0090] S100. Obtain the real-time position of the vehicle; then determine whether the vehicle is on the ramp according to the real-time position of the vehicle; then perform the following operations according to the determination result:

[0091] If the vehicle is on the ramp, a ramp merging control command is issued.

[0092] If the vehicle is not in the on-ramp state, the process returns to and re-executes S100 .

[0093] In this specific embodiment, the ramp merging control instruction is a Boolean data type, wherein: when the value of the ramp merging control instruction is "1", the characterization instruction is valid and needs to be executed; when the value of the ramp merging control instruction is "0", the characterization instruction is invalid and does not need to be executed.

[0094] S200. After receiving the on-ramp status, collect the accelerator pedal signal, engine speed signal, real-time vehicle speed signal, steering signal, gear signal, and clutch signal in real time; and then calculate the ramp merging execution coefficient.

[0095] In this specific embodiment, calculating the ramp merging execution coefficient in S200 specifically includes the following steps:

[0096] S210. Look up the table to obtain the accelerator pedal signal weight factor, engine speed signal weight factor, real-time vehicle speed signal weight factor, and turn signal weight factor;

[0097] 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 to obtain a weighted real-time vehicle speed signal; multiply the steering signal by the steering signal weight factor to obtain a weighted steering signal.

[0098] S230. Add 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 merging acceleration feasibility coefficient.

[0099] It should be noted that in this specific embodiment, the above-mentioned weight factors are obtained by table lookup. In addition, there is another way to calculate the above-mentioned weight factors, that is, to calculate the ramp merging acceleration feasibility coefficient based on the LSTM (Long short-term memory) neural network algorithm according to each signal weight factor.

[0100] It should be further explained that the LSTM neural network algorithm is a special recurrent neural network algorithm, which can be used to process data tasks with time series. In the present invention, the LSTM neural network algorithm is used to accurately calculate a response variable, which is the ramp merging acceleration feasibility coefficient, according to the vehicle kinematic model, according to the time series, and according to the changes in the accelerator pedal, engine speed, vehicle speed and turn signal.

[0101] S240. Multiply the ramp merging acceleration feasibility coefficient by the self-learning weighting coefficient to obtain the ramp merging execution coefficient.

[0102] In this specific embodiment, the self-learning weighting coefficient in S240 is obtained by:

[0103] According to the brake pedal signal and the steering signal, the manually preset self-learning weighted coefficient correspondence table is traversed; the self-learning weighted coefficient correspondence table is a two-dimensional table, each row is a brake pedal signal value, each column is a steering signal value, and each cell stores the value of the self-learning weighted coefficient obtained under the combined influence of the brake pedal signal represented by the row corresponding to the cell and the steering signal represented by the column; then the value of the self-learning weighted coefficient is obtained and output.

[0104] In this specific embodiment, the value of the self-learning weighting coefficient in the self-learning weighting coefficient corresponding table is obtained by the following method:

[0105] S241. Select and load a batch of initial sample data that has been manually calibrated in advance; the initial sample data includes brake pedal signals, steering signals, and self-learning weighting coefficients.

[0106] S242. Fill the brake pedal signal into each cell of the row in increasing order of value; fill the turn signal into each cell of the column in increasing order of value.

[0107] S243. Fill in the remaining cells in turn with the value of the self-learning weighted coefficient obtained under the combined influence of the brake pedal signal represented by the row corresponding to this cell and the steering signal represented by the column.

[0108] S244. During the test process or driving process, the brake pedal signal, steering signal, and self-learning weighting coefficient are collected in real time; then the value of one brake pedal signal, the value of one steering signal, and the value of one self-learning weighting coefficient collected at the same time are packaged into one self-learning corresponding group.

[0109] S245. Sort the obtained self-learning corresponding groups by timestamp to obtain the real-time status of the ramp merging; and then transmit the real-time status of the ramp merging back to the driver intention self-learning module.

[0110] S246. Demodulate the real-time status of the ramp merging and obtain the self-learning corresponding groups, and update the value of the self-learning weighting coefficient in each self-learning corresponding group in real time to the cells where the rows corresponding to the values ​​of the brake pedal signals and the columns corresponding to the values ​​of the steering signals in this self-learning corresponding group intersect.

[0111] S247. Repeat S244 to S246 until the rate of change of the value of the self-learning weighting coefficient in each cell is less than a manually preset threshold value of the rate of change of the self-learning weighting coefficient.

[0112] It should be noted that the system of the present invention improves the recognition accuracy of the intention to merge by self-learning the driver's driving habits under ramp merging conditions and the driver's intention to merge. In S200, the vehicle's accelerator pedal, engine speed, vehicle speed, and turn signal are monitored and identified, and the driver's intention to merge is comprehensively judged. If the system judges that the driver intends to accelerate the lane 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 intends to accelerate the ramp merging.

[0113] For example, when the driver steps on the accelerator to 50%, the strategy of the present invention recognizes that the driver is going to accelerate and change lanes, and then the strategy of the present invention executes the acceleration and lane change operation to immediately increase the vehicle speed; but if the driver immediately steps on the brakes, the strategy of the present invention will believe that the acceleration operation of the driver's 50% throttle is unreasonable. If the results of multiple judgments exceed a certain probability (this probability is preset manually and used as an adjustment cursor of 70%), the present invention believes that the system needs to modify the execution operation to reduce the rate of increase in vehicle speed, because some drivers like to increase the vehicle speed quickly, while others do not; in short, the present invention needs to be appropriately adjusted according to the driver's driving habits.

[0114] If the system detects in real time that the driver is about to perform braking and steering operations, it will look up a table to obtain a self-learning weighting coefficient based on the size of the braking control signal and the size of the steering angle. The self-learning weighting coefficient can be a positive or negative number, which is selected according to different operations. The table is looked up based on the size of the braking signal and the steering signal, and finally the self-learning weighting coefficient is fed back to the system for the final judgment and execution of the ramp merging signal.

[0115] It should be further explained that, in this specific embodiment, the self-learning weighting coefficient is self-learned through a memory-based learning algorithm: first, a batch of sample data is selected through calibration, and then during testing and use, the new data and the sample data are continuously compared and updated based on the similarity to accurately identify a driver's ramp merging operation habits, and combined with the software control function to achieve judgment accuracy.

[0116] S300. Determine whether the driver intends to accelerate by merging lanes according to the ramp merging execution coefficient; then perform the following operations according to the determination result:

[0117] If the driver intends to accelerate while merging into another lane, the ramp merging acceleration function command is activated.

[0118] If the driver does not intend to accelerate while merging into another lane, the process returns to and re-executes S200 .

[0119] In this specific embodiment, the ramp lane merging acceleration function instruction is a Boolean data type, wherein: when the value of the ramp lane merging acceleration function instruction is "1", the characterization instruction is valid and needs to be executed; when the value of the ramp lane merging acceleration function instruction is "0", the characterization instruction is invalid and does not need to be executed.

[0120] In this specific embodiment, determining whether the driver intends to accelerate by merging lanes according to the ramp merging execution coefficient in S300 specifically includes the following steps:

[0121] S310. Compare the ramp lane merging execution coefficient with a manually preset lane merging acceleration intention threshold.

[0122] S320. Perform the following operations according to the comparison result:

[0123] If the ramp lane merging execution coefficient is not greater than the lane merging acceleration intention threshold, it is determined that the driver is not in the lane merging acceleration intention.

[0124] If the ramp merging execution coefficient is greater than the merging acceleration intention threshold, S330 is executed.

[0125] S330. Perform the following operations according to the duration that the ramp lane merge execution coefficient is greater than the lane merge acceleration intention threshold:

[0126] If the duration that the ramp lane merging execution coefficient is greater than the lane merging acceleration intention threshold is greater than the manually preset lane merging acceleration intention time threshold, it is determined that the driver is in the lane merging acceleration intention.

[0127] If the duration that the ramp lane merging execution coefficient is greater than the lane merging acceleration intention threshold is not greater than the lane merging acceleration intention time threshold, it is determined that the driver is not in the lane merging acceleration intention.

[0128] It should be noted that, for safety reasons, it is absolutely not possible to determine that the driver has the intention to accelerate into lane as soon as the ramp lane change execution coefficient is greater than the lane change acceleration intention threshold. Instead, a buffer needs to be left, that is, the ramp lane change execution coefficient is greater than the lane change acceleration intention threshold and the duration exceeds the specified time.

[0129] S400. Set the vehicle to ramp lane change control mode; call the lane change acceleration execution module to perform acceleration management; and then increase the vehicle speed.

[0130] It should be noted that in S400, the parallel acceleration execution module is called to execute acceleration management, which has the following functions:

[0131] The control system manages acceleration, mainly from six angles, including torque compensation, filter parameters, mode switching, limit adjustment, gear control, and accessory disengagement, to temporarily increase vehicle acceleration, allowing the vehicle to quickly merge onto the ramp, and quickly increase the vehicle speed to avoid road congestion and traffic accidents caused by slow speed. Specifically:

[0132] Torque compensation: When the ramp lane-merging acceleration command is activated, the compensation torque is enabled, and the external characteristic torque obtained when the non-lane-merging acceleration function is turned on is added to increase the engine's external characteristic torque, making the engine output torque greater;

[0133] Filter parameter adjustment: When the ramp acceleration command is activated, the filter parameters of the torque path signals such as throttle and torque are switched to reduce the filter time, so that the engine responds faster and the vehicle speed increases faster;

[0134] Mode switching: When the ramp-merging acceleration command is activated, if the engine was previously in the economic control mode, it will quickly switch to the power control mode, allowing the engine to output greater torque faster;

[0135] Limit adjustment: When the ramp merging acceleration command is activated, the upper limit of the engine torque limit is released, the upper limit of the acceleration limit is released, and the upper limit of the smoke limit is temporarily released;

[0136] Gear control: When the ramp acceleration command is activated, the engine upshift switching time is delayed;

[0137] Accessory disengagement: When the ramp-merging acceleration command is activated, accessories such as the fan are temporarily disengaged, so that the engine torque can be used more to increase the vehicle speed;

[0138] Since the ramp merging time is generally short, about 30 seconds, and the vehicle speed is low when merging on the ramp, performing the above operations for a short period of time will not have any effect on vehicle performance, but will only cause a slight increase in engine fuel consumption, which is about 0.1% more than when this function is not turned on.

[0139] S500. Real-time acquisition of real-time vehicle speed signal and brake pedal signal; then, according to the real-time vehicle speed signal and brake pedal signal, determining whether to exit the ramp merging control mode; then, performing the following operations according to the determination result:

[0140] If it is determined not to exit the ramp merging control mode, the process returns to and re-executes S500 .

[0141] If it is determined to exit the ramp lane merging control mode, then exit the ramp lane merging control mode, set the entire vehicle to the normal control mode, and then exit the current ramp lane merging control process.

[0142] In this specific embodiment, S500 determines whether to exit the ramp merging control mode according to the real-time vehicle speed signal and the brake pedal signal, which specifically includes the following steps:

[0143] S510. Compare the real-time vehicle speed signal with the vehicle speed upper limit threshold; then perform the following operations based on the comparison result:

[0144] If the real-time vehicle speed signal is greater than the upper speed threshold, it is determined to exit the ramp merging control mode.

[0145] If the real-time vehicle speed signal is not greater than the vehicle speed upper limit threshold, execute S520.

[0146] S520. Perform the following operations according to the brake pedal signal value:

[0147] If the brake pedal signal value is "brake", it is determined to exit the ramp merging control mode.

[0148] If the brake pedal signal value is "no braking", it is determined that the ramp merging control mode will not be exited.

[0149] In order to further demonstrate the technical effect of the present invention, this specific embodiment also provides the following case:

[0150] like Figure 3 As shown, a commercial vehicle is about to merge into a ramp; its speed range is 50 to 90 km / h.

[0151] like Figure 4 FIG. 1 is a comparison of a vehicle speed change curve using the ramp merging control system and method of the present invention and a vehicle speed change curve using the prior art.

[0152] Comparative test results show that the lane-changing acceleration time at a speed of 50 to 90 km / h is reduced by 1 to 3 seconds compared to the acceleration time without this function, and the total lane-changing time is 25 to 45 seconds. In addition, the use of existing technology will bring an increase in fuel consumption of 0.03 to 0.2%. Multiple test data show that the method of the present invention reduces the lane-changing time by 1 to 4 seconds compared to ordinary vehicles.

[0153] In the above detailed description, various features are grouped together in a single embodiment to simplify the disclosure. This method of disclosure should not be interpreted as reflecting an intention that the embodiments of the claimed subject matter require more features than are clearly stated in each claim. On the contrary, as reflected in the appended claims, the invention is in a state of having less than all the features of the disclosed individual embodiments. Therefore, the appended claims are hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate preferred embodiment of the invention.

[0154] The disclosed embodiments are described above to enable any person skilled in the art to implement or use the present invention. Various modifications of these embodiments are obvious to those skilled in the art, and the general principles defined herein may also be applied to other embodiments without departing from the spirit and scope of the present disclosure. Therefore, the present disclosure is not limited to the embodiments given herein, but is consistent with the broadest scope of the principles and novel features disclosed in this application.

[0155] The above description includes examples of one or more embodiments. Of course, it is not possible to describe all possible combinations of components or methods for the purpose of describing the above embodiments, but it should be recognized by those skilled in the art that the various embodiments may be further combined and arranged. Therefore, the embodiments described herein are intended to cover all such changes, modifications and variations that fall within the scope of protection of the appended claims. In addition, with respect to the term "comprising" used in the specification or claims, the word is covered in a manner similar to the term "including", just as "including," is explained as a transitional word in the claims. In addition, any term "or" used in the specification of the claims is intended to mean "non-exclusive or".

[0156] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A ramp merging control method based on map signals, characterized in that: A ramp merging control system based on a map signal is used; the ramp merging control system based on a map signal comprises the following parts: a map signal module for obtaining the real-time position of the vehicle and the ramp merging condition; The driver intention self-learning module is used to determine the driver's intention to merge on the ramp and obtain the self-learning weighting coefficient; A ramp merging acceleration execution coefficient calculation module is used to calculate the ramp merging acceleration feasibility coefficient and determine whether to activate the ramp merging acceleration instruction according to the ramp merging acceleration feasibility coefficient; a lane-merging acceleration execution module, used for executing the ramp lane-merging acceleration instruction, obtaining the ramp lane-merging real-time status in real time, and transmitting the ramp lane-merging real-time status back to the driver intention self-learning module; The self-learning weighting coefficient is obtained by self-learning through a memory-based learning algorithm; The parallel acceleration execution module specifically includes the following submodules: A torque compensation submodule, used for providing a compensation torque to the output torque of the engine in real time according to the ramp merging condition; A filtering parameter submodule, used to smooth the output torque of the engine, suppress the occurrence of torque mutations, limit the rate of change of the output torque, and adjust the torque response performance of the vehicle; A mode switching submodule is used to switch the control mode of the whole vehicle; the control mode includes a ramp merging control mode and a normal control mode; The limit adjustment submodule is used to adjust the vehicle limit and the engine limit; the vehicle limit includes an artificially preset vehicle speed upper limit threshold; the engine limit includes an engine speed upper limit threshold and an engine output torque upper limit threshold; The gear control submodule is used to control the gear raising and lowering operations of the vehicle; The ramp merging control method based on map signals comprises the following steps: S100. Obtain the real-time position of the vehicle; then determine whether the vehicle is on the ramp 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 instruction is issued; If the vehicle is not in the on-ramp state, return to and re-execute S100; S200. After receiving the on-ramp status, real-time acquisition of accelerator pedal signal, engine speed signal, real-time vehicle speed signal, steering signal, gear signal, clutch signal; and then calculating the ramp merge execution coefficient; S300. Determine whether the driver intends to accelerate by merging lanes according to the ramp merging execution coefficient; and then perform the following operations according to the determination result: If the driver intends to accelerate while merging, then the ramp merging acceleration function instruction is activated; If the driver is not in the lane-changing acceleration intention, then return to and re-execute S200; S400. Set the vehicle to ramp merging control mode; call the merging acceleration execution module to perform acceleration management; and 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 merging control mode, the process returns to and re-executes S500; If it is determined to exit the ramp merging control mode, then the ramp merging control mode is exited, the entire vehicle is set to the normal control mode, and then the ramp merging control process is exited.

2. The ramp merging control method based on map signals according to claim 1, characterized in that: The ramp merging control instruction is a Boolean data type, wherein: when the value of the ramp merging control instruction is "1", the instruction is valid and needs to be executed; when the value of the ramp merging control instruction is "0", the instruction is invalid and does not need to be executed; The ramp lane merging acceleration function instruction is a Boolean data type, wherein: when the value of the ramp lane merging acceleration function instruction is "1", the characterization instruction is valid and needs to be executed; when the value of the ramp lane merging acceleration function instruction is "0", the characterization instruction is invalid and does not need to be executed.

3. The ramp merging control method based on map signals according to claim 2, characterized in that: Calculating the ramp merging execution coefficient in S200 specifically includes the following steps: S210. Look up the table to obtain the accelerator pedal signal weight factor, engine speed signal weight factor, real-time vehicle speed signal weight factor, and turn 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 to obtain a weighted real-time vehicle speed signal; multiply the turn signal by the turn signal weight factor to obtain a weighted turn signal; S230. Add 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 merging acceleration feasibility coefficient; S240. Multiply the ramp merging acceleration feasibility coefficient by the self-learning weighting coefficient to obtain the ramp merging execution coefficient.

4. The ramp merging control method based on map signals according to claim 3 is characterized in that: In S300, judging whether the driver is in the lane-merging acceleration intention according to the ramp-merging execution coefficient specifically includes the following steps: S310. Compare 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 lane merging execution coefficient is not greater than the lane merging acceleration intention threshold, it is determined that the driver is not in the lane merging acceleration intention; If the ramp lane merging execution coefficient is greater than the lane merging acceleration intention threshold, executing S330; S330. Perform the following operations according to the duration that the ramp lane merging execution coefficient is greater than the lane merging acceleration intention threshold: If the duration during which the ramp lane merging execution coefficient is greater than the lane merging acceleration intention threshold is greater than a manually preset lane merging acceleration intention time threshold, it is determined that the driver is in the lane merging acceleration intention; If the duration that the ramp lane merging execution coefficient is greater than the lane merging acceleration intention threshold is not greater than the lane merging acceleration intention time threshold, it is determined that the driver is not in the lane merging acceleration intention.

5. The ramp merging control method based on map signals according to claim 4 is characterized in that: S500 determines whether to exit the ramp merging control mode according to the real-time vehicle speed signal and the brake pedal signal, which specifically includes the following steps: S510. Compare the real-time vehicle speed signal with the vehicle speed upper limit threshold; and then perform 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 merging 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 brake pedal signal value is "brake", it is determined to exit the ramp merging control mode; If the brake pedal signal value is "no braking", it is determined not to exit the ramp merging control mode.

6. The ramp merging control method based on map signals according to claim 5, characterized in that: The self-learning weighting coefficient in S240 is obtained by: According to the brake pedal signal and the steering signal, a manually preset self-learning weighted coefficient correspondence table is traversed; the self-learning weighted coefficient correspondence table is a two-dimensional table, each row is a value of the brake pedal signal, each column is a value of the steering signal, and each cell stores the value of the self-learning weighted coefficient obtained under the combined influence of the brake pedal signal represented by the row corresponding to the cell and the steering signal represented by the column; then the value of the self-learning weighted coefficient is obtained and output.

7. The ramp merging control method based on map signals according to claim 6, characterized in that: The value of the self-learning weighting coefficient in the self-learning weighting coefficient corresponding table is obtained in the following manner: S241. Select and load a batch of initial sample data pre-calibrated manually; the initial sample data includes the brake pedal signal, the steering signal, and the self-learning weighting coefficient; S242. Fill the brake pedal signal into each cell of the row in increasing order of value; fill the turn signal into each cell of the column in increasing order of value; S243. Fill in the remaining cells in turn with the value of the self-learning weighted coefficient obtained under the combined influence of the brake pedal signal represented by the row corresponding to the cell and the steering signal represented by the column; S244. During the test process or driving process, the brake pedal signal, the steering signal, and the self-learning weighting coefficient are collected in real time; then the value of the brake pedal signal, the value of the steering signal, and the corresponding value of the self-learning weighting coefficient collected at the same time are packaged into a self-learning corresponding group; S245. Sort the obtained self-learning corresponding groups by timestamp to obtain the real-time status of the ramp merging; and then transmit the real-time status of the ramp merging back to the driver intention self-learning module; S246. demodulate the ramp merging real-time state to obtain the self-learning corresponding groups, and update the value of the self-learning weighting coefficient in each of the self-learning corresponding groups in real time to the cell where the row corresponding to the value of the brake pedal signal and the column corresponding to the value of the turn signal in the self-learning corresponding group intersect; S247. Repeat S244 to S246 until the rate of change of the value of the self-learning weighting coefficient in each cell is less than a manually preset threshold value of the rate of change of the self-learning weighting coefficient.

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