Cruise control method and device, electronic equipment and vehicle

Abstract

The invention provides a cruise control method and device, electronic equipment and a vehicle, and the method comprises the steps: obtaining a corresponding vehicle map signal in real time based on an advanced driver assistance system (ADAS) map in a vehicle driving process; performing map reconstruction on the vehicle map signal to obtain a reconstructed map corresponding to the vehicle; processing according to pre-acquired driving data and the reconstructed map to obtain dimension reduction data; therefore, the speed after different constraints and dimension reduction data combined by corresponding gears are obtained, so that the dimension of an optimization problem is reduced, and the calculation efficiency is improved; processing based on the driving data of the current vehicle, the reconstructed map corresponding to the vehicle and the dimension reduction data, and determining a target gear curve and a target vehicle speed curve; and controlling the vehicle to perform cruise control based on the target gear curve and the target vehicle speed curve. Therefore, the effect of predictive cruise control can be improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent driving processing technology, and in particular to a cruise control method, device, electronic equipment and vehicle. Background Art

[0002] Heavy-duty commercial vehicles are the mainstay of road logistics, used to transport large and heavy items. They boast high cargo capacity, long mileage, and high fuel consumption. When navigating mountain roads or roads with ups and downs, improper acceleration, deceleration, or gear shifting can lead to significant fuel consumption. Predictive cruise control utilizes data on the road's slope to optimize the vehicle's speed and gear, thereby improving vehicle efficiency.

[0003] Currently, existing predictive cruise control methods are often implemented by establishing rules, classifying and discussing different operating conditions and calculating the target vehicle speed, gear, whether to enter coasting mode, and coasting length, etc. However, the rules established by this type of method are difficult to cover complex road conditions, resulting in poor predictive cruise control effects. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide a cruise control method, device, electronic device, and vehicle to solve the problem of poor predictive cruise control in the prior art.

[0005] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions:

[0006] A first aspect of an embodiment of the present invention provides a cruise control method, the method comprising:

[0007] During vehicle driving, the corresponding vehicle map signal is obtained in real time based on the Advanced Driver Assistance System (ADAS) map;

[0008] Reconstructing the vehicle map signal to obtain a reconstructed map corresponding to the vehicle;

[0009] Processing the pre-acquired driving data and the reconstructed map to obtain dimension-reduced data;

[0010] Determining a target gear curve and a target vehicle speed curve based on the current vehicle driving data, the reconstructed map corresponding to the vehicle, and the dimensionality reduction data;

[0011] The vehicle is controlled to perform cruise control based on the target gear curve and the target vehicle speed curve.

[0012] Optionally, before reconstructing the vehicle map signal, the method further includes:

[0013] The vehicle map signal is processed to obtain a processed vehicle map signal.

[0014] Optionally, processing the vehicle map signal to obtain a processed vehicle map signal includes:

[0015] If it is determined that abnormal data exists in the vehicle map information, deleting the abnormal data in the vehicle map signal;

[0016] If the vehicle location information message in the vehicle map signal contains abnormal data, a new offset function is calculated based on the vehicle location information message and the vehicle's real-time speed integral;

[0017] Filling the vehicle location information message with data based on the new cancellation function to obtain a processed vehicle map signal;

[0018] If abnormal data exists in the short road characteristic information message in the vehicle map signal, determining a previous normal function;

[0019] The short road characteristic information message is filled with data based on the value corresponding to the previous normal function to obtain a processed vehicle map signal.

[0020] Optionally, performing map reconstruction on the vehicle map signal to obtain a reconstructed map corresponding to the vehicle includes:

[0021] If the reconstruction state of the current map of the vehicle is reconstruction start, latching the short road feature information message in the vehicle map signal;

[0022] Based on the real-time vehicle map signal and the latched short road characteristic information message, a map within a preset range of the vehicle is reconstructed to obtain a reconstructed map corresponding to the vehicle.

[0023] Optionally, processing the pre-acquired driving data and the reconstructed map to obtain dimensionality-reduced data includes:

[0024] Collect driving data of different vehicles within a preset historical time period;

[0025] constructing state space data within a preset range of the vehicle based on the driving data;

[0026] Processing the reconstructed map corresponding to the vehicle and the driving data to obtain constraint data;

[0027] optimizing the velocity in the state-space data based on the constraint data to update the state-space data;

[0028] The state space data is used as dimension-reduced data.

[0029] Optionally, the constraint data is obtained by processing the reconstructed map corresponding to the vehicle and the driving data, including:

[0030] Determining the road type of each road section divided by the preset range of the vehicle according to the slope value of the multi-dimensional numerical value in the reconstructed map corresponding to the vehicle;

[0031] The road type is constrained according to the driving data to obtain constraint data, where the constraint data is the driving behavior of the current road section.

[0032] Optionally, determining a target gear curve and a target vehicle speed curve based on the driving data of the current vehicle, the reconstructed map corresponding to the vehicle, and the dimensionality reduction data includes:

[0033] Determining current fuel consumption based on basic vehicle information in the current vehicle's driving data and a current road slope determined in a reconstructed map corresponding to the vehicle;

[0034] Determining an initial vehicle speed and an initial gear position at a current waypoint, as well as an initial vehicle speed and an initial gear position at a previous waypoint, based on the dimensionality reduction data and the reconstructed map corresponding to the vehicle and the driving data of the current vehicle;

[0035] Constructing a state transition cost based on the current fuel consumption, the initial vehicle speed and initial gear at the current waypoint, the initial vehicle speed and initial gear at the previous waypoint, and the current vehicle's driving data;

[0036] Predicting the cost of each waypoint starting from the vehicle's current position based on the state transition cost;

[0037] A target vehicle speed curve is constructed according to the vehicle speed index in the predicted cost of each waypoint, and a target gear curve is constructed according to the gear index in the cost of each waypoint.

[0038] A second aspect of an embodiment of the present invention provides a cruise control device, comprising:

[0039] An acquisition unit, configured to acquire a corresponding vehicle map signal in real time based on an advanced driver assistance system (ADAS) map while the vehicle is traveling;

[0040] a map reconstruction unit, configured to perform map reconstruction on the vehicle map signal to obtain a reconstructed map corresponding to the vehicle;

[0041] a dimensionality reduction processing unit, configured to process the pre-acquired driving data and the reconstructed map to obtain dimensionality-reduced data;

[0042] a vehicle speed and gear planning unit, configured to determine a target gear curve and a target vehicle speed curve based on the current vehicle's driving data, the reconstructed map corresponding to the vehicle, and the dimension reduction data;

[0043] A control unit is used to control the vehicle to perform cruise control based on the target gear curve and the target vehicle speed curve.

[0044] A third aspect of an embodiment of the present invention shows an electronic device, which includes a processor and a memory, wherein the memory is used to store program code and data generated by data, and the processor is used to call the program instructions in the memory to execute the cruise control method shown in the first aspect of the embodiment of the present invention.

[0045] A fourth aspect of an embodiment of the present invention shows a vehicle, which includes a stored program, wherein when the program is running, the device where the storage medium is located is controlled to execute any of the cruise control methods shown in the first aspect of the embodiment of the present invention.

[0046] Based on the above-mentioned embodiments of the present invention, a cruise control method, device, electronic device and vehicle are provided. The method includes: while the vehicle is traveling, obtaining a corresponding vehicle map signal in real time based on an ADAS map; performing map reconstruction on the vehicle map signal to obtain a reconstructed map corresponding to the vehicle; processing pre-acquired driving data and the reconstructed map to obtain reduced-dimensionality data; determining a target gear curve and a target vehicle speed curve based on the current vehicle's driving data, the reconstructed map corresponding to the vehicle, and the reduced-dimensionality data; and controlling the vehicle to perform cruise control based on the target gear curve and the target vehicle speed curve. In an embodiment of the present invention, a map is first reconstructed to obtain a reconstructed map constructed from multiple slope values. Next, roads are classified by slope value, and corresponding driving behaviors are determined based on the types of two consecutive road segments, incorporating human driver experience. Driving behaviors are then used to constrain vehicle speeds in a state space, effectively reducing the state space. This results in reduced-dimensional data based on the speeds and corresponding gears after different constraints, thereby reducing the dimensionality of the optimization problem and improving computational efficiency. A target gear curve and target speed curve for cruise control are predicted using the current vehicle's driving data, the reconstructed map corresponding to the vehicle, and the reduced-dimensional data. Predictive cruise control is then performed based on the target gear curve and target speed curve. This improves the effectiveness of predictive cruise control. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0048] Figure 1 A schematic diagram of a vehicle cruise control system according to an embodiment of the present invention;

[0049] Figure 2 A schematic flow chart of a cruise control method according to an embodiment of the present invention;

[0050] Figure 3 A schematic flow chart of another cruise control method according to an embodiment of the present invention;

[0051] Figure 4 The figure is a schematic structural diagram of a cruise control device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0052] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0053] The terms "first," "second," "third," "fourth," and the like (if any) in the specification and claims of this application and in the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or sequential sequence. It should be understood that the terms used in this manner are interchangeable where appropriate so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" and "having," and any variations thereof, are intended to cover non-exclusive inclusions, e.g., a process, method, system, product, or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0054] It should be noted that the descriptions of "first", "second", etc. in the present invention are for descriptive purposes only and should not be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" or "second" may explicitly or implicitly include at least one of such features. In addition, the technical solutions between the various embodiments can be combined with each other, but this must be based on the fact that they can be implemented by ordinary technicians in this field. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0055] In this application, the terms "comprises," "comprising," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not preclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0056] As we learned from the background information, existing methods have also optimized the calculation of speed and gear limits by adding uphill assessment to the road network reconstruction. This method categorizes uphill control states into five categories, determines the required speed and gear position before starting an uphill climb based on the uphill control state, and then calculates the speed and gear limits. This method can reduce computational complexity and improve real-time performance when deployed in an embedded controller. However, it only considers uphill conditions, making it difficult to handle more complex roads.

[0057] Based on this, an embodiment of the present invention provides a dimensionality reduction optimization method for predictive cruise control of commercial vehicles. Combining the experience of human drivers, it considers all types of working conditions such as flat roads, uphill and downhill to reduce the state space of the optimization problem, which can effectively reduce the optimization dimension and improve computational efficiency.

[0058] See also Figure 1 , is a schematic diagram of a vehicle cruise control system shown in an embodiment of the present invention.

[0059] The system includes an advanced driving assistance system ADAS map 10 and a vehicle control unit VCU 20.

[0060] It should be noted that the ADAS map 10 is connected to the VCU 20 via the controller area network CAN bus;

[0061] Among them, the CAN bus is responsible for transmitting vehicle status information, ADAS map data and VCU20 calculation results;

[0062] The ADAS map 10 is provided with a vehicle position information message POSITION and a short road feature information message PROFILE SHORT; the VCU20 is deployed with a predictive cruise control module PCC21. The predictive cruise control module PCC21 is the core of the VCU20 and mainly includes sub-modules such as map signal processing, map reconstruction, dimensionality reduction optimization, and vehicle speed gear planning. It can be used to implement functions such as map signal processing, map reconstruction, dimensionality reduction optimization, map reconstruction, and vehicle speed gear planning.

[0063] Specifically, the ADAS map 10 is used to provide road slope information in front of the vehicle, and transmits the vehicle position information message POSITION and the short road feature information message PROFILE SHORT to the VCU20 via the CAN bus based on the communication protocol ADASIS v2 protocol; the predictive cruise control module is deployed in the VCU20 to calculate the PCC target vehicle speed and gear, and output control instructions.

[0064] It should be noted that the vehicle position information message POSITION contains at least information such as the path number, offset, speed, relative direction to the road, and the reliability of the position information;

[0065] The short road feature information message PROFILE SHORT contains at least information such as the path number, offset, road feature type, and road feature value;

[0066] The road feature type is used to distinguish road features represented by the short road feature information message PROFILE SHORT, such as slope, curvature, etc.

[0067] Based on the vehicle cruise control system shown above, the process of implementing the vehicle cruise control method is as follows: Figure 2 As shown, the method includes:

[0068] Step S201: While the vehicle is traveling, a corresponding vehicle map signal is acquired in real time based on the ADAS map;

[0069] In the specific implementation of step S201, when the vehicle starts adaptive cruise control, the ADAS map records the vehicle's driving information in real time, and generates a corresponding vehicle position information POSITION message based on the vehicle position information in the vehicle's driving information, and generates a corresponding short road feature information PROFILE SHORT message based on the short road feature information in the vehicle's driving information based on the communication protocol ADASIS v2;

[0070] Then, the ADAS map sends the vehicle map signal composed of the real-time POSITION message containing vehicle position information and the PROFILE SHORT message containing short road feature information to the VCU through the CAN bus.

[0071] It should be noted that the ADAS map sends the corresponding vehicle map signal to the VCU via the CAN bus according to a preset period.

[0072] The preset period is set in advance based on multiple experiments, for example, it can be set to 1s.

[0073] It should be noted that the vehicle location information also includes the latitude and longitude of the vehicle, the intersection information, and time, etc.; the short road feature information also includes slope, road conditions, lane width, curve radius, etc.

[0074] Road conditions are used to indicate highways, national roads, rural roads, etc.

[0075] Step S202: reconstructing the vehicle map signal to obtain a reconstructed map corresponding to the vehicle;

[0076] It should be noted that the specific implementation of step S203 includes the following steps:

[0077] Step S11: If the reconstruction state of the current vehicle map is reconstruction start, latching the short road feature information PROFILE SHORT message in the vehicle map signal;

[0078] In the specific implementation of step S11, the predictive cruise control module PCC in the VCU enters the reconstruction start state by default after the map reconstruction starts, latches the Offset and location index data PathIdx in the PROFILE SHORT message, and jumps to the reconstruction process state.

[0079] It should be noted that the predictive cruise control module PCC in the VCU starts map reconstruction when it receives the vehicle map signal.

[0080] Step S12: reconstructing a map within a preset range of the vehicle based on the real-time vehicle map signal and the latched short road characteristic information message to obtain a reconstructed map corresponding to the vehicle.

[0081] It should be noted that the preset range is set in advance by technicians based on actual conditions. For example, it can be used to reconstruct the road 2km ahead of the vehicle. The map within the preset range refers to a 10-dimensional slope array constructed by reconstructing the road 2km ahead of the vehicle, where each value in the array represents the average slope within 200m.

[0082] The initial 10-dimensional slope in the map within the preset range is manually collected based on the landform.

[0083] In the specific implementation of step S22, the predictive cruise control module PCC in the VCU calculates the average value of the function Value0 in the short road feature information PROFILE SHORT message obtained in real time during the reconstruction process to obtain the average slope value. At this time, it is necessary to determine whether the difference between the Offset of the currently received vehicle position information POSITION message and the Offset latched at the start of reconstruction is greater than a preset value. Until the difference between the current Offset and the Offset latched at the start of reconstruction is greater than the preset value, the state jumps to the reconstruction start; at this time, when the reconstruction state jumps from the reconstruction process to the reconstruction start, the map reconstruction module needs to store the average slope value in the slope array, and the array is first-in-first-out, so as to update the initial map and obtain the reconstructed map corresponding to the vehicle.

[0084] Optionally, when calculating the average slope, it is also determined whether the Path Index in the currently received short road feature information PROFILESHORT message has changed. If the Path Index has changed, the state jumps to the reconstruction start, and the array is cleared at this time.

[0085] Step S203: Process the pre-acquired driving data to obtain dimension-reduced data.

[0086] It should be noted that the specific implementation of step S204 includes the following steps:

[0087] Step S21: collecting driving data of different vehicles within a preset historical time period.

[0088] In the specific implementation of step S21 , the predictive cruise control module PCC in the VCU collects driving data of the heavy vehicle within a preset historical time period during the adaptive cruise driving process.

[0089] It should be noted that the driving data includes vehicle speed, gear position, road conditions, etc.

[0090] Step S22: constructing a state space within a preset range of the vehicle according to the driving data.

[0091] In the process of implementing step S22, the state space is initialized. Specifically, the vehicle speed is uniformly discretized into M states according to the PCC upper and lower speed limits set by the driver in the driving data; the gear is uniformly discretized into N states according to the PCC gear upper and lower limits set by the driver in the driving data; the M states of uniformly discretized vehicle speed and the N states of uniformly discretized gear are set at preset waypoints within the preset range of the vehicle, and M*N state combinations, i.e., the state space, can be obtained.

[0092] It should be noted that the preset map range is centered on the vehicle and is set in advance based on actual conditions. For example, it can be set to 2 km on the road ahead of the vehicle.

[0093] The preset waypoints are waypoints on a preset map range, which are set in advance based on actual conditions or experience. For example, every 200 meters of the road 2 km ahead of the vehicle is a waypoint.

[0094] Step S23: Constraining roads within a preset range of the vehicle according to the reconstructed map corresponding to the vehicle and the driving data to obtain constraint data;

[0095] It should be noted that the specific implementation of step S23 includes the following steps.

[0096] Step S31: determining the road type of each road section divided into a preset range of the vehicle according to the slope value of the multi-dimensional numerical value in the reconstructed map corresponding to the vehicle.

[0097] It should be noted that each waypoint corresponds to a road section.

[0098] In the specific implementation of step S31, the predictive cruise control module PCC in the VCU determines the road type of the road segment as uphill based on the slope value of the multidimensional numerical value in the reconstructed map corresponding to the vehicle. If the slope value is greater than a first threshold, the road type of the road segment is determined to be uphill; if the slope value is less than a second threshold, the road type of the road segment is determined to be downhill; if the slope value is greater than or equal to the second threshold and less than or equal to the first threshold, the road type of the road segment is determined to be flat. In this way, the road type of each 200-meter road segment, i.e., waypoint, is determined, and adjacent roads of the same type are merged.

[0099] The road types include three types: flat road, uphill road and downhill road.

[0100] Step S32: constraining the road type according to the driving data to obtain constraint data, where the constraint data is the driving behavior of the current road section.

[0101] In the process of implementing step S32, the predictive cruise control module PCC in the VCU obtains specific constraints on the driving behavior of the next road point, that is, the next road section, based on the driving data. The process of obtaining the constraint data includes: if the road type of the current section, that is, the first section is a flat road, and the road type of the previous section is also a flat road, training and learning are performed based on the driving data to determine that the driving behavior of the current road can be uniform speed, that is, the corresponding constraint data at this time is uniform speed; if the road type of the current section is a flat road, and the road type of the previous section is also uphill, training and learning are performed based on the driving data to determine that the driving behavior of the current road can be uphill. Based on this, according to the road types of two consecutive sections, combined with the driving data, training and learning can indeed determine the driving behavior of the current section, as shown in Table (1).

[0102] Table (1):

[0103]

[0104] The first section in Table (1) refers to the previous section of the road that the vehicle traveled on, and the second section refers to the current section of the road that the vehicle travels on.

[0105] Step S24: Optimizing the speed in the state-space data based on the constraint data to update the state-space data.

[0106] In the process of implementing step S24, the predictive cruise control module PCC in the VCU reduces the state space according to different driving behaviors in the constraint data. Specifically, when the driving behavior is uniform speed, the vehicle speed in the state space data can be optimized to [cruise target speed - vehicle speed discrete step, cruise target speed + discrete speed step]; when the driving behavior is acceleration, the vehicle speed is: [cruise target speed - vehicle speed discrete step, PCC speed upper limit]; when the driving behavior is deceleration, the vehicle speed is: [PCC speed lower limit, cruise target speed + discrete speed step]; to write it into the state space data, at this time the state space data is combined due to the speed after different constraints and the corresponding gear.

[0107] Each vehicle speed in the state space data can be called a vehicle speed index, and each gear can also be called a gear index.

[0108] It should be noted that the vehicle speed discrete step size refers to the value corresponding to the uniform discretization of the vehicle speed.

[0109] Step S25: using the state space data as dimension-reduced data.

[0110] In the specific implementation of step S25, the updated state space data refers to the state combinations obtained by uniformly discretizing the gear positions in step S24.

[0111] Step S204: determining a target gear curve and a target vehicle speed curve based on the current vehicle driving data, the reconstructed map corresponding to the vehicle, and the dimension reduction data;

[0112] It should be noted that the specific implementation of step S204 includes the following steps:

[0113] Step S41: determining the current fuel consumption based on the basic vehicle information in the current vehicle's driving data and the current road slope determined in the reconstructed map corresponding to the vehicle.

[0114] The current fuel consumption refers to the fuel consumption of the vehicle from the previous waypoint to the current waypoint.

[0115] In the specific implementation of step S41, first, the predictive cruise control module PCC in the VCU substitutes the main reduction ratio, engine transmission ratio, vehicle speed and tire radius in the basic vehicle information into formula (1) to calculate and obtain the current engine speed.

[0116] Formula (1):

[0117]

[0118] Where, is the main reduction ratio, is the engine transmission ratio, is the vehicle speed, is the tire radius, and N is the current engine speed.

[0119] Next, the vehicle gravity, rolling resistance coefficient, air resistance coefficient, frontal area, rotational mass conversion coefficient, vehicle mass and transmission efficiency in the basic vehicle information, as well as the road slope of the current vehicle driving section determined in the reconstructed map corresponding to the vehicle, are substituted into formula (2) for calculation to obtain the torque.

[0120] Formula (2):

[0121]

[0122] Where, is the vehicle gravity, is the rolling resistance coefficient, is the road slope, is the air resistance coefficient, is the windward area, is the rotation mass conversion factor, is the vehicle mass, is the transmission efficiency, is the torque.

[0123] Finally, based on the calculated engine speed and torque, the pre-calibrated engine MAP two-dimensional interpolation table is queried to determine the fuel consumption corresponding to the current engine speed and torque. .

[0124] It should be noted that the engine MAP two-dimensional interpolation table is calibrated in advance based on multiple experiments. The horizontal and vertical axes of the engine MAP are the speed and torque, respectively. By looking up the current engine speed and torque, the current fuel consumption of the engine can be obtained, that is, the fuel consumption of the vehicle from the previous waypoint to the current waypoint.

[0125] Step S42: Determine the initial vehicle speed and initial gear position at the current waypoint, as well as the initial vehicle speed and initial gear position at the previous waypoint based on the dimensionality reduction data and the reconstructed map corresponding to the vehicle and the driving data of the current vehicle.

[0126] In the specific implementation of step S42, the predictive cruise control module PCC in the VCU determines the road type corresponding to the current waypoint and the road type corresponding to the previous waypoint based on the reconstructed map corresponding to the vehicle, and determines the driving behavior of the vehicle at this time based on the road type query table (1) connecting the two waypoints; then, based on the vehicle speed and driving behavior in the current vehicle's driving data, the dimensionality reduction data is queried to determine the optional range of the initial vehicle speed and the gear position in the corresponding combination state with the initial vehicle speed, that is, based on the vehicle speed and driving behavior in the current vehicle's driving data, it is determined that the vehicle is currently in the first gear. That is, the speed index of the last road point is , the gear index is , drive to That is, at the current waypoint, the vehicle speed index when arriving is , the gear index is .

[0127] It should be noted that there are multiple data for the initial vehicle speed and the initial gear position. The initial speed refers to multiple data within a certain range, such as: [cruise target speed - discrete speed steps, cruise target speed + discrete speed steps], [cruise target speed - discrete speed steps, PCC speed upper limit], or [PCC speed lower limit, cruise target speed + discrete speed steps], which are multiple corresponding combinations of gear positions and vehicle speeds.

[0128] Step S43: constructing a state transition cost based on the current fuel consumption, the initial vehicle speed and initial gear at the current waypoint, the initial vehicle speed and initial gear at the previous waypoint, and the driving data of the current vehicle.

[0129] In the specific implementation of step S43, the current fuel consumption, the initial vehicle speed and initial gear position at the current waypoint, the initial vehicle speed and initial gear position at the previous waypoint, and the first vehicle speed in the current vehicle's driving data are calculated. The speed of the waypoint, the cruising target speed, and the Substitute the gear position of each waypoint into formula (3) to calculate and determine the corresponding state transfer cost .

[0130] Formula (3):

[0131]

[0132] in, Indicates the The speed of the road point, Indicates the cruising target speed. Indicates that the vehicle Drive to the waypoint Fuel consumption at each road point, Indicates that the vehicle Drive to the waypoint The time of the waypoint, Indicates the The gear position of each road point, Indicates the weight coefficient for maintaining the cruising target speed, Indicates the weight coefficient for reducing fuel consumption, Indicates the weight coefficient for reducing travel time, Indicates reducing the weight coefficient of vehicle speed change, Indicates reducing the gear change weight coefficient.

[0133] It should be further explained that 、 、 、 、 These are all coefficients set in advance based on multiple experiments.

[0134] Step S44: predicting the cost of each waypoint starting from the current position of the vehicle according to the state transition cost.

[0135] In the specific implementation of step S44, for each waypoint, the state transition cost and the cost of the previous waypoint are substituted into formula (4) to calculate the cost of the road segment.

[0136] It should be noted that when the i-th waypoint is the first waypoint, the cost of the road section is calculated by substituting the state transition cost into formula (4).

[0137] Formula (4):

[0138]

[0139] in, Indicates that the vehicle starts from the current position and travels to the Waypoints, the speed index is , the gear index is The minimum cost; Indicates that the vehicle starts from the previous position and travels to the -1 waypoint, vehicle speed index is , the gear index is The minimum cost.

[0140] Step S45: constructing a target vehicle speed curve according to the vehicle speed index in the predicted cost of each waypoint, and constructing a target gear curve according to the gear index in the cost of each waypoint.

[0141] In the specific implementation of step S45, according to the recursive formula of formula (4), the minimum cost of all states of the vehicle starting from the current position and traveling to the first waypoint is first calculated. , store the minimum cost and the path index corresponding to the minimum cost. Then calculate ,…, , until all waypoints within the preset range of the vehicle are predicted and the cost and path index are stored; then a target speed curve is constructed based on the speed index of each waypoint in the stored cost, and a target gear curve is constructed based on the gear index of each waypoint in the stored cost. In other words, the optimal speed curve and gear curve are obtained by reverse traversal based on the stored minimum cost and minimum cost path index.

[0142] Step S205: Controlling the vehicle to perform cruise control based on the target gear curve and the target vehicle speed curve.

[0143] In the specific implementation of step S205 , corresponding control instructions are generated according to the next target vehicle speed and target gear in the target gear curve and the target vehicle speed curve, so as to perform adaptive cruise control of the vehicle based on the control instructions.

[0144] In an embodiment of the present invention, a map is first reconstructed to obtain a reconstructed map constructed from multiple slope values. Next, roads are classified by slope value, and corresponding driving behaviors are determined based on the types of two consecutive road segments, incorporating human driver experience. Driving behaviors are then used to constrain vehicle speeds in a state space, effectively reducing the state space. This results in reduced-dimensional data based on the speeds and corresponding gears after different constraints, thereby reducing the dimensionality of the optimization problem and improving computational efficiency. The target gear curve and target speed curve for cruise control are predicted using the current vehicle's driving data, the corresponding reconstructed map, and the reduced-dimensional data. Predictive cruise control is then performed based on the target gear curve and target speed curve. This improves the effectiveness of predictive cruise control.

[0145] Based on the method shown in the above embodiment of the present invention, the embodiment of the present invention also shows a flow chart of another vehicle cruise control method, such as Figure 3 As shown, the method includes:

[0146] Step S301: While the vehicle is traveling, a corresponding vehicle map signal is acquired in real time based on the ADAS map;

[0147] It should be noted that the specific implementation process of step S301 is the same as the specific implementation process of step S201 described above, and they can refer to each other.

[0148] Step S302: Process the vehicle map signal to obtain a processed vehicle map signal.

[0149] The specific implementation of step S202 includes the following steps.

[0150] Step S51: Determine whether there is abnormal data in the vehicle map information. If so, execute step S52; if not, directly execute step S303.

[0151] In the specific implementation of step S51, the map signal processing submodule under the predictive cruise control module PCC21 in the VCU20 judges the POSITION message in the vehicle map signal. Specifically, it obtains the POSITION message in the current vehicle map signal, that is, the POSITION message of the current preset period, and the POSITION message of each preset period sent by the same vehicle before; then, it summarizes the periods in which the Offset cancellation function appears from all the POSITION messages of the same vehicle, and judges whether the Offset cancellation function remains unchanged for more than N POSITION periods. If so, it indicates that the POSITION message is abnormal, that is, the cause of the abnormality is the loss of the POSITION message; otherwise, it indicates that the POSITION message is normal.

[0152] When determining that the POSITION message is abnormal, determining that the abnormal data of the POSITION message is an Offset cancellation function that remains unchanged for more than N POSITION cycles.

[0153] Optionally, if the POSITION message in the current vehicle map signal is the first POSITION message sent by the vehicle, it is not processed and the next vehicle map signal sent by the ADAS map is continued to be received.

[0154] It should be noted that N is preset by technicians based on multiple experiments or experience, and can generally be set to 2.

[0155] Next, the map signal processing submodule under the predictive cruise control module PCC21 in the VCU20 processes the PROFILE SHORT message in the vehicle map signal. Specifically, it determines whether the function Value0 in the PROFILE SHORT message is within a preset range. If not, it indicates that the PROFILE SHORT message is abnormal; otherwise, it indicates that the PROFILE SHORT message is normal.

[0156] It should be noted that the preset range is set by technicians based on multiple experiments or experience, and can generally be set between -15% and 15%.

[0157] Optionally, in addition to the above-mentioned method for exception handling, the PROFILE SHORT message can also be processed in other ways, obtaining the POSITION SHORT message in the current vehicle map signal and the POSITION SHORT message in the previous vehicle map signal of the same vehicle, subtracting the value corresponding to the function Value0 in the previous POSITION message from the value corresponding to the function Value0 in the current POSITION SHORT message to obtain a first value; determining whether the first value is within a first range; if not, it indicates that the PROFILE SHORT message is abnormal; otherwise, it indicates that the PROFILE SHORT message is normal.

[0158] It should be noted that the first range is preset by technicians based on multiple experiments or experience, and can generally be set between -5% and 5%.

[0159] When it is determined that the PROFILE SHORT message is abnormal, the abnormal data of the PROFILE SHORT message is determined to be the corresponding function Value0.

[0160] Optionally, when the POSITION SHORT message in the current vehicle map signal is the first POSITION SHORT message sent by the vehicle, it can be directly processed using the previous method.

[0161] Step S52: Deleting abnormal data in the vehicle map signal.

[0162] In the specific implementation of step S52, abnormal data in the vehicle position information POSITION message, i.e., the Offset compensation function that remains unchanged for more than N POSITION cycles, and the function Value0 in the PROFILE SHORT message that is not within the preset range are eliminated.

[0163] Step S53: If the vehicle position information POSITION message in the vehicle map signal contains abnormal data, a new POSITION Offset compensation function is calculated based on the vehicle position information POSITION message and the vehicle's real-time speed integral.

[0164] In the specific implementation of step S53, in view of the presence of abnormal data in the vehicle position information POSITION message in the vehicle map signal, an offset function Offset before the POSITION message is lost, that is, a normal offset function before the message is lost, and the real-time speed of the current vehicle are obtained; then, the corresponding relationship between the preset vehicle speed and the vehicle speed integral is found to determine the real-time vehicle speed integral corresponding to the real-time vehicle speed; then, the product of the Offset before the POSITION message is lost and the real-time vehicle speed integral is calculated to obtain a new POSITION Offset offset function.

[0165] Step S54: Filling the vehicle position information PROFILE message with data based on the new POSITION Offset cancellation function to obtain a processed vehicle map signal.

[0166] In the specific implementation of step S14, a new POSITION Offset offset function is used to fill in the position deleted in the above step S53 to fill the vehicle position information PROFILE message with data to obtain a processed vehicle map signal.

[0167] Step S55: If there is abnormal data in the PROFILE SHORT message of the vehicle map signal, determine the previous normal function Value0.

[0168] In the specific implementation of step S55, if abnormal data is present in the PROFILE SHORT message of the short road characteristic information in the vehicle map signal, a search is first performed to determine whether a function value within a preset range is present in the previous PROFILE SHORT message. If so, it is determined to be the previous normal function value 0. If not, the previous previous PROFILE SHORT message is continuously traversed until the previous normal function value 0 is determined.

[0169] Step S56: Filling the short road feature information PROFILESHORT message with data based on the value corresponding to the previous normal function to obtain a processed vehicle map signal.

[0170] In the specific implementation of step S56, the normal value at the previous moment is maintained to fill the position deleted in the above step S55, thereby filling the short road feature information PROFILE SHORT message with data to obtain a processed vehicle map signal.

[0171] Step S303: reconstructing the vehicle map signal to obtain a reconstructed map corresponding to the vehicle;

[0172] Step S304: Constraining roads within a preset range of the vehicle according to the reconstructed map corresponding to the vehicle and the driving data to obtain constraint data;

[0173] Step S305: determining a target gear curve and a target vehicle speed curve based on the current vehicle driving data, the reconstructed map corresponding to the vehicle, and the dimension reduction data;

[0174] Step S306: Controlling the vehicle to perform cruise control based on the target gear curve and the target vehicle speed curve.

[0175] It should be noted that the specific implementation process of step S303 to step S306 is the same as the specific implementation process of step S202 to step S205 described above, and they can be referenced to each other.

[0176] In an embodiment of the present invention, the vehicle map signal is first processed, abnormal data is deleted, and the data is filled. The map is then reconstructed to obtain a reconstructed map constructed from multiple slope values. Roads are then classified by slope value, and the corresponding driving behavior is determined based on the types of two consecutive road segments, incorporating human driver experience. The driving behavior is then used to constrain the vehicle speed in the state space, effectively reducing the state space. This results in reduced-dimensional data based on the speeds and corresponding gears after different constraints, thereby reducing the dimensionality of the optimization problem and improving computational efficiency. The target gear curve and target speed curve for cruise control are predicted using the current vehicle's driving data, the corresponding reconstructed map, and the reduced-dimensional data. Predictive cruise control is then performed based on the target gear curve and target speed curve. This improves the effectiveness of predictive cruise control.

[0177] Based on the cruise control method shown in the above embodiment of the present invention, the embodiment of the present invention also shows a cruise control device, such as Figure 4 As shown, the device includes:

[0178] The acquisition unit 401 is configured to acquire a corresponding vehicle map signal in real time based on an advanced driver assistance system (ADAS) map during vehicle driving;

[0179] A map reconstruction unit 402 is configured to perform map reconstruction on the vehicle map signal to obtain a reconstructed map corresponding to the vehicle;

[0180] A dimensionality reduction processing unit 403 is configured to process the pre-acquired driving data and the reconstructed map to obtain dimensionality-reduced data;

[0181] A vehicle speed and gear planning unit 404 is configured to determine a target gear curve and a target vehicle speed curve based on the current vehicle's driving data, the reconstructed map corresponding to the vehicle, and the dimension reduction data;

[0182] The control unit 405 is configured to control the vehicle to perform cruise control based on the target gear curve and the target vehicle speed curve.

[0183] The specific principles and execution processes of each unit in the cruise control device disclosed in the above embodiment of the present invention are the same as the corresponding contents in the cruise control method provided in the above embodiment of the present invention. Please refer to the corresponding parts of the cruise control method disclosed in the above embodiment of the present invention, and no further details will be given here.

[0184] In an embodiment of the present invention, a map is first reconstructed to obtain a reconstructed map constructed from multiple slope values. Next, roads are classified by slope value, and corresponding driving behaviors are determined based on the types of two consecutive road segments, incorporating human driver experience. Driving behaviors are then used to constrain vehicle speeds in a state space, effectively reducing the state space. This results in reduced-dimensional data based on the speeds and corresponding gears after different constraints, thereby reducing the dimensionality of the optimization problem and improving computational efficiency. The target gear curve and target speed curve for cruise control are predicted using the current vehicle's driving data, the corresponding reconstructed map, and the reduced-dimensional data. Predictive cruise control is then performed based on the target gear curve and target speed curve. This improves the effectiveness of predictive cruise control.

[0185] Optionally, the cruise control device according to the above embodiment of the present invention further includes:

[0186] The signal preprocessing unit is used for performing map reconstruction on the vehicle map signal before further comprising:

[0187] The vehicle map signal is processed to obtain a processed vehicle map signal.

[0188] The step of processing the vehicle map signal to obtain a processed vehicle map signal includes:

[0189] If it is determined that abnormal data exists in the vehicle map information, deleting the abnormal data in the vehicle map signal;

[0190] If the vehicle location information message in the vehicle map signal contains abnormal data, a new offset function is calculated based on the vehicle location information message and the vehicle's real-time speed integral;

[0191] Filling the vehicle location information message with data based on the new cancellation function to obtain a processed vehicle map signal;

[0192] If abnormal data exists in the short road characteristic information message in the vehicle map signal, determining a previous normal function;

[0193] The short road characteristic information message is filled with data based on the value corresponding to the previous normal function to obtain a processed vehicle map signal.

[0194] Optionally, based on the cruise control device shown in the embodiment of the present invention, a map reconstruction unit that performs map reconstruction on the vehicle map signal to obtain a reconstructed map corresponding to the vehicle includes:

[0195] If the reconstruction state of the current map of the vehicle is reconstruction start, latching the short road feature information message in the vehicle map signal;

[0196] Based on the real-time vehicle map signal and the latched short road characteristic information message, a map within a preset range of the vehicle is reconstructed to obtain a reconstructed map corresponding to the vehicle.

[0197] Optionally, based on the cruise control device shown in the embodiment of the present invention, a dimensionality reduction processing unit is provided for processing the pre-acquired driving data and the reconstructed map to obtain dimensionality reduction data, including:

[0198] Collect driving data of different vehicles within a preset historical time period;

[0199] constructing state space data within a preset range of the vehicle based on the driving data;

[0200] Processing the reconstructed map corresponding to the vehicle and the driving data to obtain constraint data;

[0201] optimizing the velocity in the state-space data based on the constraint data to update the state-space data;

[0202] The state space data is used as dimension-reduced data.

[0203] Optionally, based on the cruise control device shown in the embodiment of the present invention, a dimensionality reduction processing unit is configured to obtain constraint data by processing the reconstructed map corresponding to the vehicle and the driving data, including:

[0204] Determining the road type of each road section divided by the preset range of the vehicle according to the slope value of the multi-dimensional numerical value in the reconstructed map corresponding to the vehicle;

[0205] The road type is constrained according to the driving data to obtain constraint data, where the constraint data is the driving behavior of the current road section.

[0206] Optionally, based on the cruise control device shown in the embodiment of the present invention described above, a vehicle speed and gear planning unit that processes the driving data of the current vehicle, the reconstructed map corresponding to the vehicle, and the dimensionality reduction data to determine a target gear curve and a target vehicle speed curve includes:

[0207] Determining current fuel consumption based on basic vehicle information in the current vehicle's driving data and a current road slope determined in a reconstructed map corresponding to the vehicle;

[0208] Determining an initial vehicle speed and an initial gear position at a current waypoint, as well as an initial vehicle speed and an initial gear position at a previous waypoint, based on the dimensionality reduction data and the reconstructed map corresponding to the vehicle and the driving data of the current vehicle;

[0209] Constructing a state transition cost based on the current fuel consumption, the initial vehicle speed and initial gear at the current waypoint, the initial vehicle speed and initial gear at the previous waypoint, and the current vehicle's driving data;

[0210] Predicting the cost of each waypoint starting from the vehicle's current position based on the state transition cost;

[0211] A target vehicle speed curve is constructed according to the vehicle speed index in the predicted cost of each waypoint, and a target gear curve is constructed according to the gear index in the cost of each waypoint.

[0212] An embodiment of the present application provides an electronic device, which includes a processor and a memory, wherein the memory is used to store cruise control program code and data, and the processor is used to call program instructions in the memory to execute the steps shown in the cruise control method in the above embodiment.

[0213] An embodiment of the present invention provides a vehicle, wherein a storage medium includes the electronic device provided in the above-mentioned embodiment of the present application, and the electronic device is used to execute the cruise control method disclosed in the embodiment of the present application.

[0214] Each embodiment in this specification is described in a progressive manner. The same or similar parts between the embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments. In particular, for system or system embodiments, since they are basically similar to method embodiments, the description is relatively simple. For relevant parts, refer to the partial description of the method embodiment. The system and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. A person of ordinary skill in the art can understand and implement it without expending creative work.

[0215] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0216] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A cruise control method, characterized in that: The method comprises: During vehicle driving, the corresponding vehicle map signal is obtained in real time based on the Advanced Driver Assistance System (ADAS) map; Reconstructing the vehicle map signal to obtain a reconstructed map corresponding to the vehicle; Processing the pre-acquired driving data and the reconstructed map to obtain dimension-reduced data; Determining a target gear curve and a target vehicle speed curve based on the current vehicle driving data, the reconstructed map corresponding to the vehicle, and the dimensionality reduction data; The vehicle is controlled to perform cruise control based on the target gear curve and the target vehicle speed curve.

2. The method according to claim 1, characterized in that Before reconstructing the vehicle map signal, the method further includes: The vehicle map signal is processed to obtain a processed vehicle map signal.

3. The method according to claim 2, characterized in that Processing the vehicle map signal to obtain a processed vehicle map signal includes: If it is determined that abnormal data exists in the vehicle map information, deleting the abnormal data in the vehicle map signal; If the vehicle location information message in the vehicle map signal contains abnormal data, a new offset function is calculated based on the vehicle location information message and the vehicle's real-time speed integral; Filling the vehicle location information message with data based on the new cancellation function to obtain a processed vehicle map signal; If abnormal data exists in the short road characteristic information message in the vehicle map signal, determining a previous normal function; The short road characteristic information message is filled with data based on the value corresponding to the previous normal function to obtain a processed vehicle map signal.

4. The method according to claim 1, wherein Performing map reconstruction on the vehicle map signal to obtain a reconstructed map corresponding to the vehicle includes: If the reconstruction state of the current map of the vehicle is reconstruction start, latching the short road feature information message in the vehicle map signal; Based on the real-time vehicle map signal and the latched short road characteristic information message, a map within a preset range of the vehicle is reconstructed to obtain a reconstructed map corresponding to the vehicle.

5. The method according to claim 1, characterized in that Processing is performed based on the pre-acquired driving data and the reconstructed map to obtain dimensionality-reduced data, including: Collect driving data of different vehicles within a preset historical time period; constructing state space data within a preset range of the vehicle based on the driving data; Processing the reconstructed map corresponding to the vehicle and the driving data to obtain constraint data; optimizing the velocity in the state-space data based on the constraint data to update the state-space data; The state space data is used as dimension-reduced data.

6. The method according to claim 5, characterized in that Constraint data is obtained by processing the reconstructed map corresponding to the vehicle and the driving data, including: Determining the road type of each road section divided by the preset range of the vehicle according to the slope value of the multi-dimensional numerical value in the reconstructed map corresponding to the vehicle; The road type is constrained according to the driving data to obtain constraint data, where the constraint data is the driving behavior of the current road section.

7. The method according to claim 1, characterized in that Determining a target gear curve and a target vehicle speed curve based on the current vehicle driving data, the reconstructed map corresponding to the vehicle, and the dimension reduction data, including: Determining current fuel consumption based on basic vehicle information in the current vehicle's driving data and a current road slope determined in a reconstructed map corresponding to the vehicle; Determining an initial vehicle speed and an initial gear position at a current waypoint, as well as an initial vehicle speed and an initial gear position at a previous waypoint, based on the dimensionality reduction data and the reconstructed map corresponding to the vehicle and the driving data of the current vehicle; Constructing a state transition cost based on the current fuel consumption, the initial vehicle speed and initial gear at the current waypoint, the initial vehicle speed and initial gear at the previous waypoint, and the current vehicle's driving data; Predicting the cost of each waypoint starting from the vehicle's current position based on the state transition cost; A target vehicle speed curve is constructed according to the vehicle speed index in the predicted cost of each waypoint, and a target gear curve is constructed according to the gear index in the cost of each waypoint.

8. A cruise control device, characterized in that: The device comprises: An acquisition unit, configured to acquire a corresponding vehicle map signal in real time based on an advanced driver assistance system (ADAS) map while the vehicle is traveling; a map reconstruction unit, configured to perform map reconstruction on the vehicle map signal to obtain a reconstructed map corresponding to the vehicle; a dimensionality reduction processing unit, configured to process the pre-acquired driving data and the reconstructed map to obtain dimensionality-reduced data; a vehicle speed and gear planning unit, configured to determine a target gear curve and a target vehicle speed curve based on the current vehicle's driving data, the reconstructed map corresponding to the vehicle, and the dimension reduction data; A control unit is used to control the vehicle to perform cruise control based on the target gear curve and the target vehicle speed curve.

9. An electronic device, characterized in that: The electronic device includes a processor and a memory, wherein the memory is used to store program codes and data generated by data, and the processor is used to call program instructions in the memory to execute the cruise control method according to any one of claims 1 to 7.

10. A vehicle, characterized in that: The vehicle includes a stored program, wherein when the program is running, the device where the storage medium is located is controlled to execute the cruise control method according to any one of claims 1 to 7.

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