A control system for vehicle following distance

Abstract

The application discloses a kind of control systems of vehicle driving follow vehicle distance, comprising: driving environment data acquisition module: for collecting the basic driving data of specified vehicle, vehicle parameter, according to the basic driving data to determine extended driving data;Follow vehicle index determination module: including calculation model control unit, index definition unit, index calculation unit and index output unit, for determining and outputting follow vehicle index;The follow vehicle index includes: first safety distance, second safety distance, minimum safety distance and desired follow vehicle distance;Follow vehicle distance controller: for obtaining the desired follow vehicle distance, to vehicle speed controller output vehicle speed control demand, realize follow vehicle distance control.According to the above technical solution, the index related to follow vehicle distance can be calculated according to local conditions, and combined with the vehicle speed controller of automobile, the control of follow vehicle distance is realized, and the safety of vehicle follow driving and the utilization rate of road are improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent driving technology, and more specifically, to a control system for vehicle following distance. Background Technology

[0002] Following distance is a crucial factor in safe traffic, and intelligent control of following distance is an important means for intelligent driving technology to improve safety and road utilization. Current technologies often control following distance based on the vehicle's own speed or the speed of the vehicle in front, setting a fixed minimum safe distance. However, in reality, vehicle mass is not constant, especially for commercial vehicles, where mass can vary by several times. Different mass, road conditions, and weather conditions will alter a vehicle's braking distance, and the minimum safe distance should be adjusted accordingly.

[0003] Therefore, a control scheme is needed to achieve dynamic control of the following distance when the vehicle is in motion, taking into account the driving environment, weather, and vehicle status. Summary of the Invention

[0004] To achieve the above objectives, this application provides a vehicle following distance control system, comprising:

[0005] Driving environment data acquisition module: used to collect basic driving data and vehicle parameters of a specified vehicle, and determine extended driving data based on the basic driving data, including: driving conditions and vehicle weight; among which, driving data includes the vehicle's current speed and road friction coefficient;

[0006] The following indicator determination module includes a calculation model control unit, an indicator definition unit, an indicator calculation unit, and an indicator output unit, used to determine and output following indicators. These indicators include: a first safe distance, a second safe distance, a minimum safe distance, and a desired following distance. The indicator definition unit determines the correlation between these indicators, including: determining the minimum safe distance using the first and second safe distances, and calculating the desired following distance based on the minimum safe distance. The first safe distance refers to the safe distance determined by the driving conditions, and the second safe distance refers to the safe distance determined by the vehicle's overall weight.

[0007] Following distance controller: Used to obtain the desired following distance and output the vehicle speed control requirement to the vehicle speed controller to realize the following distance control;

[0008] The indicator calculation unit is used to calculate the expected following distance, and the specific algorithm is as follows:

[0009] S des =t h v+S0, where S des For the desired vehicle spacing, th S0 is the workshop time distance, and S0 is the minimum safety distance, where the workshop time distance t h It is generated by calculating the driving parameters of the vehicle in front.

[0010] Workshop time interval t h The specific algorithm is as follows:

[0011] t h =t0-c v v rel -c a a p

[0012] Where t0 is the initial workshop time distance, c v The correlation coefficient is greater than zero, c a a is a constant greater than zero. p It is the acceleration of the vehicle ahead in the future, v q It is the current speed of the vehicle in front, v rel Let v be the relative speed between this vehicle and the vehicle in front. The relative speed is calculated as follows: v rel =v q -v, where v is the current speed of the vehicle; the initial vehicle-to-vehicle distance is set in the simulation;

[0013] The current speed of the vehicle in front is obtained by onboard sensors in the driving environment data acquisition module, and the future acceleration of the vehicle in front is generated by the calculation model control unit to predict the future speed of the vehicle in front.

[0014] Furthermore, the basic driving data includes: driving time, motor speed, wheel rolling radius, total transmission ratio of the transmission system, air density, frontal area, and vehicle speed at the current moment;

[0015] Vehicle parameters also include: wheelbase;

[0016] Driving conditions include urban, suburban, and highway driving, and are generated by clustering of characteristic parameters, which include: average speed, speed standard deviation, average acceleration, average deceleration, acceleration time percentage, high-speed time percentage, medium-speed time percentage, and low-speed time percentage.

[0017] The indicator calculation unit is used to calculate the second safety distance, and the calculation method is as follows: S m =λ m *s, where S m As the second safety distance, λ m Let be the coefficient of mass affecting braking distance, and s be the vehicle braking distance, and:

[0018] s = 0.0034v + 0.00451v 2 , where v is the vehicle's current speed.

[0019] The minimum safe distance is calculated by the index calculation unit, and the specific calculation method is: S0 = max(S G S m ), where S G As the first safe distance, S m S0 is the second safe distance, and S0 is the minimum safe distance.

[0020] Furthermore, the indicator definition unit defines the correlation between following indicators, and also includes the optimization of following indicators, including: optimization of expected following distance, the optimization method being: S des0 =λ T S des , among which, S des0 This is the corrected expected following distance, λ T The weather influencing factor is used. The desired following distance optimization is calculated by the index calculation unit, and the weather influencing factor is obtained from the weather conditions acquired by the driving environment data acquisition module.

[0021] The current weather status includes light intensity and weather type;

[0022] The driving environment data acquisition module supports reading light intensity data and wiper intensity data from the vehicle bus; the weather type is generated based on the wiper intensity data.

[0023] The light intensity includes: strong, medium, weak, and dark; the weather type includes no rain or snow, moderate rain or snow, heavy rain or snow, and blizzard.

[0024] The weather influencing factors are extracted from a two-dimensional data factor table corresponding to light intensity and weather type based on the current weather conditions.

[0025] Furthermore, the calculation model control unit is also used to obtain the driving conditions of the preceding vehicle, and to match the neural network speed prediction model according to the driving conditions to predict the future speed of the preceding vehicle; the index calculation unit generates the time distance between the two vehicles based on the current speed of the current vehicle, the current speed of the preceding vehicle, and the future speed of the preceding vehicle.

[0026] Furthermore, the following distance controller includes: a vehicle distance acquisition unit, a fuzzy processing unit, and a control unit;

[0027] The vehicle distance acquisition unit is used to obtain the current actual output following distance from the vehicle speed sensor;

[0028] The vehicle speed calculation unit is used to determine the optimal real-time control parameters based on fuzzy rules and defuzzification operations. The optimal real-time control parameters are applied to the PID control model, and the target following speed is output based on the actual following distance and the desired following distance.

[0029] The control unit is used to output the target following speed to the vehicle speed controller.

[0030] According to the present invention, on-site environmental data and weather conditions can be integrated to calculate indicators related to following distance in a way that is appropriate to local conditions, and combined with the vehicle speed controller to achieve control of following distance, thereby improving the safety of vehicle following and the utilization rate of the road. Attached Figure Description

[0031] Figure 1 This is a schematic diagram of the vehicle following distance control system provided in an embodiment of the present invention;

[0032] Figure 2 This is a schematic diagram of a following distance controller provided according to an embodiment of the present invention. Detailed Implementation

[0033] This invention proposes a control system for following distance during vehicle operation. The system separates the functions of each module, enabling environmental data acquisition, following distance indicator definition and calculation, target distance determination, and inter-vehicle distance control. Specifically, based on the defined following distance indicator, environmental data and weather factors are collected, and the following distance indicator is calculated based on algorithm optimization. This invention separates data acquisition, indicator relationships, indicator calculation, and optimization into different module units, facilitating algorithm expansion. Finally, combined with vehicle speed control, it achieves the effect of safe driving.

[0034] The specific implementation of the present invention will now be described in detail with reference to the accompanying drawings.

[0035] Figure 1 The following distance control system is shown in the diagram, including:

[0036] P100 Driving Environment Data Acquisition Module: Used to collect basic driving data and vehicle parameters of a specified vehicle, and further determine extended driving data based on the basic driving data.

[0037] The driving environment data acquisition module connects to onboard sensors, distance sensors, and the vehicle bus system, and can collect basic driving data for a specified vehicle, including the vehicle's current speed, road friction coefficient, driving time, motor speed, wheel rolling radius, total transmission ratio, air density, frontal area, and current speed. Simultaneously, based on the vehicle bus, it can read data from the light sensor and wiper controller. The driving environment data acquisition module can also read light intensity data and wiper mode data from the vehicle bus to construct light intensity and weather type. In this invention, the extracted light intensity is broadly defined as: strong, medium, weak, and dark; and the weather type is classified as: no rain / snow, moderate rain / snow, heavy rain / snow, and blizzard.

[0038] Meanwhile, the driving environment data acquisition module can also obtain basic vehicle parameters, such as vehicle equipment weight and wheelbase.

[0039] Extended driving data can be calculated using the current vehicle speed and time. In this invention, the extended driving data includes at least the following eight characteristic parameters: average speed (km / h), speed standard deviation (km / h), and average acceleration (m / s²). 2 ), average deceleration (m / s) 2 The data includes acceleration time percentage, high-speed time percentage, medium-speed time percentage, and low-speed time percentage. The driving environment data acquisition module performs clustering calculations on the extended driving data and outputs driving condition data. In this invention, the eight feature parameters output three types of driving condition data: urban, suburban, and highway.

[0040] The driving environment data acquisition module collects basic driving data of the designated vehicle, and also obtains the current speed and time of the vehicle in front through the distance sensor to calculate the extended driving data of the vehicle in front and output the driving conditions of the vehicle in front.

[0041] On the other hand, the driving environment data acquisition module can obtain the vehicle mass through a mass identification method. In practical applications, there are multiple methods to obtain the vehicle mass, such as direct acquisition from onboard mass sensors or real-time calculation based on feedback signals from the vehicle signal bus. The calculation method adopts a method based on the vehicle's longitudinal dynamics model, which specifically includes the following steps:

[0042] 1) The motor torque is calculated using a longitudinal vehicle dynamics model that does not consider lateral dynamics and rotational mass:

[0043]

[0044] Where, η fd For the efficiency of the automotive transmission system;

[0045] By transforming formula (1) and ignoring the change in road slope angle, we obtain the following formula:

[0046]

[0047] Formula (2) embodies a linear relationship related to mass, expressed as: y=Φθ (3) where,

[0048] Where m is the vehicle mass, r w For the wheel rolling radius, α f Let α be the road slope angle and i be the road slope, then tan(α) f ) = i、 The derivative of velocity, i.e., the acceleration T at the current moment.m The motor speed is obtained through feedback from the vehicle bus, r is the wheel rolling radius, and i is the speed of the motor. fd The total transmission ratio of the transmission system is given by ρ, where ρ is the air density and C is the total transmission ratio. d For air drag coefficient, A f Let v be the frontal area, v be the vehicle's current speed, g be the gravitational acceleration, Φ1 and Φ2 be constants calculated using known vehicle variables, and θ1 and θ2 be unknown coefficients attempted to be estimated in the online estimator.

[0049] In this invention, multiple forgetting factors of recursive least squares (RLS) are used to select the unknown parameters of the system.

[0050] RLS transforms the parameter estimation problem into a minimization problem of a follower function, which can be expressed as:

[0051] In the formula, λ1 and λ2 are forgetting factors; for discrete systems, the solution of this function is obtained through the following formula:

[0052]

[0053] in:

[0054]

[0055] and:

[0056]

[0057] The unknown parameters θ1 and θ2 that need to be estimated are respectively represented by their estimated values. and By substituting and rearranging the matrix, we can obtain:

[0058]

[0059] An RLS with multiple forgetting factors was constructed using equations (4)-(7) to solve the linear equation shown in equation (3), and the mass m of the car could be estimated.

[0060] The operating conditions and quality are collected and determined in the P100 driving environment data acquisition module, and used for the calculation of the following indicators in the P110 following indicator determination module.

[0061] P110 Following Index Determination Module: Used to calculate and output following index based on basic driving data and extended driving data. The following index in this invention includes: first safe distance, second safe distance, minimum safe distance and expected following distance.

[0062] The P110 vehicle following indicator determination module includes the P111 calculation model control unit, the P112 indicator definition unit, the P113 indicator calculation unit, and the P114 indicator output unit.

[0063] First, the P112 indicator definition unit is used to determine the correlation between various following indicators;

[0064] include:

[0065] The minimum safe distance is determined by the first safe distance and the second safe distance, and the expected following distance is determined based on the minimum safe distance; wherein, the first safe distance refers to the safe distance determined by the driving conditions, and the second safe distance refers to the safe distance determined by the overall vehicle weight.

[0066] The first safe distance is generated by the neural network speed prediction model provided by the P111 calculation model control unit; the neural network speed prediction model in this unit is pre-trained for three types of working condition datasets.

[0067] After acquiring the operating condition data output by P110, the computational model control unit calls the corresponding neural network speed prediction model to predict the future speed of the vehicle ahead and obtain the following safety distance S for that operating condition. G .

[0068] The preceding vehicle data includes: preceding vehicle operating conditions, preceding vehicle current speed, preceding vehicle predicted speed, and preceding vehicle acceleration. The preceding vehicle current speed is obtained through onboard sensors in the driving environment data acquisition module. The preceding vehicle operating conditions can be obtained from the P110 driving environment data acquisition module. The preceding vehicle predicted speed is obtained through the calculation model control unit. Based on the above data, the preceding vehicle acceleration is calculated.

[0069] The remaining vehicle-following indicators are calculated by the P113 indicator calculation unit, specifically including:

[0070] 1) Second safety distance: This can be calculated from the vehicle mass output in P110. The calculation method is: S m =λ m *s, where S m As the second safety distance, λ m Let be the coefficient of mass affecting braking distance, and s be the vehicle braking distance, and:

[0071] s = 0.0034v + 0.00451v 2 v is the vehicle's current speed;

[0072] λ m =f(m), where f(m) is a function constraint with quality as the factor.

[0073] 2) The minimum safe distance is determined by the first safe distance and the second safe distance. The specific calculation method is: S0 = max(S G S m ), where S G As the first safe distance, S m S0 is the second safe distance, and S0 is the minimum safe distance.

[0074] 3) Expected following distance:

[0075] The calculation of the desired following distance involves data from both the target vehicle and the vehicle in front, both of which can be obtained through the P100 driving environment data acquisition module. The specific calculation method for the desired following distance is as follows:

[0076] S des =t h v+S0, where S des It is the expected vehicle spacing, t h S0 is the workshop time distance, and S0 is the minimum safety distance.

[0077] Among them, workshop time distance t h The index is generated by the index calculation unit based on the driving parameters of the vehicle in front. The specific algorithm is as follows:

[0078] t h =t0-c v v rel -c a a p ,

[0079] Where t0 is the initial workshop time distance, c v The correlation coefficient is greater than zero, c a a is a constant greater than zero. p It is the acceleration of the vehicle ahead in the future, v q It is the current speed of the vehicle in front, v rel Let v be the relative speed between this vehicle and the vehicle in front. The relative speed is calculated as follows: v rel =v q -v, where v is the current speed of the vehicle;

[0080] The initial workshop time interval is set in the simulation;

[0081] The current speed of the vehicle in front is obtained by onboard sensors in the driving environment data acquisition module, and the future acceleration of the vehicle in front is calculated and predicted by the calculation model control unit to generate the future speed of the vehicle in front.

[0082] The expected following distance calculated by the indicator calculation unit is output to the following distance controller P120 through the indicator output unit P114.

[0083] Considering that weather conditions and lighting also have a certain impact on following distance, the indicator output unit extracts the current weather conditions and optimizes the expected following distance before outputting it. In this application, when defining the correlation between following indicators, the indicator definition unit can also define the optimization of following indicators, including: expected following distance optimization, the optimization method being: S des0 =λ T S des , among which, S des0 This is the corrected expected following distance, λ T The weather influencing factor is used; the desired following distance optimization is calculated by the index calculation unit, and the weather influencing factor is obtained by the driving environment data acquisition module from the weather conditions.

[0084] Weather influencing factors are extracted from a two-dimensional data factor table corresponding to light intensity and weather type based on the current weather conditions. The two-dimensional data factor table is a pre-set table that defines weather factor parameters matching light intensity and weather type, as shown in the table below:

[0085]

[0086]

[0087] For example, in the case of weak light intensity and heavy rain, the extracted weather factor is λ. T11 .

[0088] Given that the driving environment of a vehicle can vary greatly, ranging from diverse public transportation environments to stable training or storage facilities, the data collection and definition of indicators based on weather conditions and operating conditions are not suitable for all driving environments. To address this, this invention separates the functions of driving environment data collection and following distance indicator relationships in the system structure, facilitating flexible adjustments to the algorithm and data during practical use. If high-speed driving conditions are not considered, only the P111 calculation model control unit needs adjustment; if the desired following distance does not need to consider weather factors, only the P112 indicator definition unit needs adjustment.

[0089] The P120 following distance controller includes a vehicle distance acquisition unit, a vehicle speed calculation unit, and a control unit. It calculates the target following speed of the vehicle and provides it to the vehicle's speed controller.

[0090] The vehicle distance acquisition unit is used to obtain the current actual output following distance from the vehicle distance sensor;

[0091] The vehicle speed calculation unit determines the optimal real-time control parameters based on fuzzy rules and defuzzification operations. These optimal real-time control parameters are applied to the PID control model, which outputs the target following speed based on the actual and desired following distance.

[0092] The formula for PID control can be expressed as:

[0093]

[0094] Where u(t) represents the output value of the PID control parameter, e(t) is the difference between the expected following distance and the actual output following distance, and t is the calculation time; the expected following distance comes from the following index output unit in the following index determination module, and the actual output following distance comes from the data collected separately by the vehicle distance acquisition unit.

[0095] The principle of PID controller is as follows Figure 2 As shown, the distance difference E and its change EC are used as inputs. Based on the control rules, corresponding fuzzy rules are formulated, and after defuzzification, real-time control parameters are output. K is selected. p K i K d As a real-time output variable, the PID controller receives the control parameters output by the fuzzy controller and adjusts the output control variable of the controlled object.

[0096] This invention proposes a control system for following distance during vehicle operation. At the system architecture level, data acquisition, algorithm determination, and calculation processes are separated to facilitate functional expansion and optimization. At the level of determining following distance-related parameters, on-site environmental data and weather conditions are integrated to calculate indicators related to following distance in a site-specific manner. This is then combined with the vehicle's speed controller to achieve following distance control, thereby improving vehicle following safety and road utilization.

[0097] The above-disclosed embodiments are merely a few specific examples of the present invention. However, the present invention is not limited thereto, and any variations that can be conceived by those skilled in the art should fall within the protection scope of the present invention.

Claims

1. A control system for vehicle following distance, characterized in that, include: Driving environment data acquisition module: used to collect basic driving data and vehicle parameters of a specified vehicle, and determine extended driving data based on the basic driving data, including: driving conditions and vehicle weight; wherein, the driving data includes the vehicle's current speed and road friction coefficient; The following indicator determination module includes a calculation model control unit, an indicator definition unit, an indicator calculation unit, and an indicator output unit, used to determine and output following indicators. The following indicators include: a first safe distance, a second safe distance, a minimum safe distance, and a desired following distance. The indicator definition unit is used to determine the correlation between the following indicators, including: determining the minimum safe distance using the first and second safe distances, and calculating the desired following distance based on the minimum safe distance. The first safe distance refers to the safe distance determined by the driving conditions, and the second safe distance refers to the safe distance determined by the vehicle's overall weight. Following distance controller: used to obtain the desired following distance, output the vehicle speed control requirement to the vehicle speed controller, and realize the following distance control; The indicator calculation unit is used to calculate the expected following distance, and the specific algorithm is as follows: S des =t h v+S0, where S des For the desired vehicle spacing, t h S0 is the workshop time distance, and S0 is the minimum safety distance, where the workshop time distance t h It is generated by calculating the driving parameters of the vehicle in front.

2. The control system according to claim 1, characterized in that, The workshop time interval t h It is generated by calculating the driving parameters of the vehicle in front. The specific algorithm is as follows: t h =t0-c v v rel -c a a p Where t0 is the initial workshop time distance, c v The correlation coefficient is greater than zero, c a a is a constant greater than zero. p It is the acceleration of the vehicle ahead in the future, v q It is the current speed of the vehicle in front, v rel Let v be the relative speed between this vehicle and the vehicle in front. The relative speed is calculated as follows: v rel =v q -v, where v is the current speed of the vehicle; The initial workshop time interval is set in the simulation.

3. The control system according to claim 1, characterized in that, The basic driving data includes: driving time, motor speed, wheel rolling radius, total transmission ratio of the transmission system, air density, frontal area, and vehicle speed at the current moment; The vehicle parameters also include: vehicle wheelbase; The driving conditions include urban, suburban and highway driving conditions, which are generated by clustering of feature parameters. The feature parameters include: average speed, speed standard deviation, average acceleration, average deceleration, acceleration time percentage, high speed time percentage, medium speed time percentage and low speed time percentage.

4. The control system according to claim 1, characterized in that, The index calculation unit is used to calculate the second safety distance, and the calculation method is as follows: S m =λ m *s, where S m As the second safety distance, λ m Let be the coefficient of mass affecting braking distance, and s be the vehicle braking distance, and: s = 0.0034v + 0.00451v 2 Where v is the vehicle's current speed.

5. The control system according to claim 1, characterized in that, The minimum safe distance is calculated by the index calculation unit, and the specific calculation method is: S0 = max(S G S m ), where S G As the first safe distance, S m S0 is the second safe distance, and S0 is the minimum safe distance.

6. The control system according to claim 1, characterized in that, The indicator definition unit defines the correlation between the following indicators, and also includes the optimization of the following indicators, including: optimization of the following distance, the optimization method being: S des0 =λ T S des , among which, S des0 This is the corrected expected following distance, λ T These are weather-related factors.

7. The control system according to claim 6, characterized in that, The optimized following distance is calculated by the indicator calculation unit, and the weather influence factor is obtained by the driving environment data acquisition module from the weather conditions.

8. The control system according to claim 1, characterized in that, The calculation model control unit obtains the driving conditions of the vehicle in front, and matches the neural network speed prediction model according to the driving conditions to predict the future speed of the vehicle in front. The indicator calculation unit generates the inter-vehicle time distance based on the current speed of the vehicle, the current speed of the vehicle in front, and the future speed of the vehicle in front.

9. The control system according to claim 1, characterized in that, The following distance controller includes: a vehicle distance acquisition unit, a fuzzy processing unit, and a control unit; The vehicle distance acquisition unit is used to obtain the current actual output following distance from the vehicle speed sensor; The vehicle speed calculation unit is used to determine the optimal real-time control parameters based on fuzzy rules and defuzzification operations. The optimal real-time control parameters are applied to the PID control model, and the target following speed is output based on the actual following distance and the desired following distance. The control unit is used to output the target following speed to the vehicle speed controller.

10. The control system according to claim 7, characterized in that, The current weather conditions include light intensity and weather type; The driving environment data acquisition module supports reading light intensity data and wiper intensity data from the vehicle bus; The weather type is generated based on the wiper intensity data; The light intensity includes: strong, medium, weak, and dark; the weather type includes no rain / snow, moderate rain / snow, heavy rain / snow, and blizzard. The weather influencing factors are extracted from a two-dimensional data factor table corresponding to light intensity and weather type based on the current weather conditions.

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