CN111391832A - Vehicle self-adaptive cruise control method and system based on information sharing - Google Patents

Vehicle self-adaptive cruise control method and system based on information sharing Download PDF

Info

Publication number
CN111391832A
CN111391832A CN202010165726.6A CN202010165726A CN111391832A CN 111391832 A CN111391832 A CN 111391832A CN 202010165726 A CN202010165726 A CN 202010165726A CN 111391832 A CN111391832 A CN 111391832A
Authority
CN
China
Prior art keywords
vehicle
lane
speed
information
self
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202010165726.6A
Other languages
Chinese (zh)
Inventor
赵万忠
陈青云
李琳
徐灿
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Nanjing University of Aeronautics and Astronautics
Original Assignee
Nanjing University of Aeronautics and Astronautics
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Nanjing University of Aeronautics and Astronautics filed Critical Nanjing University of Aeronautics and Astronautics
Priority to CN202010165726.6A priority Critical patent/CN111391832A/en
Publication of CN111391832A publication Critical patent/CN111391832A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
    • B60W30/14—Adaptive cruise control
    • B60W30/143—Speed control

Landscapes

  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Traffic Control Systems (AREA)

Abstract

The invention discloses an information sharing-based adaptive cruise control method and system, wherein the control method comprises the steps of obtaining the current running state of a self-vehicle and the positions and motion states of objects around the self-vehicle, interacting the state information with the surrounding vehicles, then performing importance screening on the state information and environment information of the surrounding vehicles, obtaining possible interference information generated on the self-vehicle, inputting the interference information into a vehicle-mounted CPU (central processing unit) for decision making, and then braking by a control execution module; the self-adaptive speed control of the vehicle is realized.

Description

Vehicle self-adaptive cruise control method and system based on information sharing
Technical Field
The invention relates to an information sharing-based adaptive cruise control method and system, and belongs to the field of advanced vehicle driving assistance control and vehicle active safety.
Background
Different from a complete unmanned system, the application of a high-grade auxiliary driving function on a vehicle is more and more extensive, and a passenger vehicle with an L2 grade auxiliary driving system is started to appear on the market at present.
With the arrival of the 5G era, an efficient non-delay communication mode becomes possible, and in a road traffic environment, communication and information sharing between vehicles can better transmit information and improve the accuracy of the information. In the field of advanced driver assistance, information sharing of vehicle-to-vehicle interconnection is certainly a future development trend. An adaptive cruise system for a vehicle, which may also be referred to as active cruise, is an important application of current assisted driving systems, similar to conventional cruise control, and includes a radar sensor, a digital signal processor and a control module. In an adaptive cruise system, the system uses a low power radar or infrared beam to obtain the exact position of the leading vehicle, and if the leading vehicle is found to slow down or a new target is detected, the system sends an execution signal to the engine or the brake system to reduce the speed of the vehicle, so that the vehicle and the leading vehicle maintain a safe driving distance. When the front road obstacle is cleared, the speed is accelerated to be recovered to the set speed, and the radar system can automatically monitor the next target. The active cruise control system replaces a driver to control the speed of the vehicle, and frequent cancellation and setting of cruise control are avoided. The self-adaptive cruise system is suitable for various road conditions, when the self-adaptive cruise function is started, the vehicle can automatically control the running speed of the vehicle according to the road environment in front of the current lane, feet of a driver are liberated, and a more relaxed driving mode is provided for the driver.
However, the current adaptive cruise system mainly relies on a millimeter wave sensor in front of the vehicle to acquire traffic information of a road in front. But information for a vehicle further ahead that affects the vehicle ahead is not available subject to the sensor detection range and the obstacle. The adaptive cruise system of the vehicle can only effectively react when the front vehicle has obvious state change, so that the control of the control system has certain hysteresis in the actual working process, and relatively urgent braking operation can be adopted when some working conditions are met. The safety and the comfort of the vehicle are negatively influenced to a certain extent.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention aims to provide an information sharing-based adaptive cruise system and a control method thereof, so as to solve the problem of vehicle adaptive cruise speed control under complex road conditions in the prior art. The method can determine the scheme of actively adjusting the vehicle speed of the vehicle under the condition that the vehicle in the surrounding lane interferes with the vehicle, and provides a safe and convenient self-adaptive cruise system and a control method thereof for a driver.
In order to achieve the above purpose, the invention adopts the following technical scheme:
the invention firstly provides a vehicle self-adaptive cruise control method based on information sharing, which comprises the following steps:
1) the current running state of the self-vehicle is obtained through a self-vehicle state sensing module, and the position and the motion state of an object around the vehicle are obtained through an environment sensing module;
2) sharing the data information acquired in the step 1 to surrounding vehicles within a certain range through a vehicle-vehicle real-time communication module (the data information is set within a range of 200m under the condition of a common expressway, and is set within a range of 50m under the condition of a common urban road), and receiving the information acquired by the surrounding vehicles within the certain range;
3) and (4) performing importance screening on the self state information and the environmental information of the surrounding vehicles acquired by the vehicle-vehicle communication module, and rejecting the information of the vehicles which can not interfere with the vehicles. Then, the screened data of the surrounding vehicles and the data acquired by the vehicles are integrated and input to a control decision module;
4) and the control decision module comprehensively analyzes the state data of surrounding vehicles possibly generating interference and the self vehicle and the acquired environmental information. The possibility that the surrounding vehicles cut into the lane where the vehicles are located is judged, meanwhile, the influence of the cut-in of the lane on the vehicles is analyzed, the optimal vehicle speed control strategy of the vehicles is obtained through comprehensive analysis of the factors, and the optimal vehicle speed control strategy is converted into a control signal which can be received by an actuator.
5) And the actuator controls the brake or the throttle of the vehicle according to the acquired control signal so as to complete the adjustment of the adaptive cruise speed of the vehicle.
Further, the state of the vehicle obtained by the vehicle state sensing module in the step 1) includes the current speed, the longitudinal acceleration, the heading angle and the yaw rate of the vehicle. The environment sensing module acquires the position and the motion state of an object around the vehicle, wherein the position and the relative speed of the surrounding vehicle relative to a vehicle coordinate system, the number of a lane where the surrounding vehicle is located and the position of the lane line.
Further, the information interacted through the vehicle-vehicle communication module in the step 2) includes information acquired by the vehicle and surrounding vehicles through respective vehicle state sensing modules and environment sensing modules. The communication module shares the information among vehicles within a certain range so as to facilitate the work of the auxiliary safety system of each vehicle.
Further, the vehicles possibly interfering with the host vehicle in the step 3) include a vehicle in front of the lane where the host vehicle is located and a vehicle in front of the host vehicle in two lanes adjacent to the lane where the host vehicle is located.
Further, the comprehensive analysis of the state data of the surrounding vehicles and the host vehicle which may interfere with the host vehicle and the acquired environmental information in the step 4) includes the following steps:
41) firstly, preprocessing acquired data, analyzing information obtained by surrounding vehicles through vehicle-vehicle communication module transmission, and comprising the following steps:
411) respectively acquiring the current speed of a vehicle i in front of an adjacent lane
Figure BDA0002407382040000031
Wherein i ∈ { L, R } represents the left adjacent lane and the right adjacent lane, respectively, the longitudinal distance of the vehicle from the vehicle in front of the vehicle and optionally in the lane change lane is obtained simultaneously
Figure BDA0002407382040000032
And relative longitudinal vehicle speed
Figure BDA0002407382040000033
Where j ∈ { F }i,LFi,LRi,RFi,RRiAnd (c) respectively representing vehicles in front of the lane where the vehicle i is located and in front of and behind the optional left and right lane-changing lanes. And simultaneously calculating the expected time of collision of the corresponding vehicle to the vehicle:
Figure BDA0002407382040000034
42) then, the predicted time TTC of collision between the vehicle and the surrounding vehicle obtained as described above is usedjThe vehicle state prediction module input to the vehicle-mounted CPU analyzes the future motion state of the vehicle-mounted CPU, and is a conventional built-in module of the vehicle-mounted CPU, such as an ADAS processor (S32V234) introduced in 2015 in series S32V of NXP company;
the method comprises the following specific steps:
421) presetting the current state of the vehicle as an original lane keeping state, using the information as the input of a vehicle prediction module, and obtaining the possibility that the vehicle selects the lane where the vehicle is located as a lane changing lane to carry out a lane changing event X by the prediction module according to the input and the output:
Figure BDA0002407382040000035
in the formula
Figure BDA0002407382040000036
A line vector M ∈ R representing the predicted time of collision between the vehicle i and surrounding vehicles and the current acceleration of the vehicle1×nIs a parameter matrix.
422) When P (X | u)t) And when the lane change possibility threshold value D is larger than the set lane change possibility threshold value D, the vehicle is considered to have the possibility of entering the lane where the vehicle is located. Calculating the probability density of the average acceleration of the vehicle in the lane changing process:
Figure BDA0002407382040000041
in the formula f (u)t)＝NG,G＝[TTCij]m×1A line vector N ∈ R representing the predicted time of collision between the vehicle i and surrounding vehicles1×mIs a parameter matrix.
423) Further calculating an average acceleration expectation for the vehicle:
Figure BDA0002407382040000042
43) according to the average acceleration expectation of the vehicle, calculating the influence of the vehicle on the vehicle, and specifically comprising the following steps:
431) calculating the possibility of collision between the own vehicle and the vehicle within the following time T:
432) if it is
Figure BDA0002407382040000043
There is a certain risk that the own vehicle collides with the vehicle, in which
Figure BDA0002407382040000044
Is the current longitudinal distance, Δ x, between the vehicle and the host vehiclesafeIs a set safety distance.
44) If the system judges that the collision risk exists, the speed of the vehicle needs to be reduced, and the specific steps are as follows:
441) the required braking deceleration of the vehicle is first calculated:
Figure BDA0002407382040000045
442) and converting the calculated braking deceleration into a control signal and transmitting the control signal to the braking controller.
443) During the whole braking process, the system can circularly judge the collision risk between the vehicle and the target vehicle and calculate the required braking deceleration, if the calculated deceleration is larger than the currently executed deceleration, the larger deceleration is adopted, and if the calculated deceleration is smaller than the currently executed deceleration, the current braking deceleration is kept unchanged until the vehicle speed of the vehicle is smaller than the target vehicle speed.
45) When the vehicle analyzes that the adjacent lane vehicle has no interference influence on the vehicle, the adopted vehicle speed control strategy is as follows:
451) obtaining the current speed of a vehicle
Figure BDA0002407382040000046
Obtaining the vehicle speed limit v of the current road section through the vehicle-mounted navigation equipmentmaxSet cruising speed vcurCurrent speed of vehicle located in front of same lane
Figure BDA0002407382040000047
Distance from the vehicle to the preceding vehicle
Figure BDA0002407382040000048
452) The speed of the current vehicle is controlled, and the acceleration is as follows:
Figure BDA0002407382040000051
in the formula, lambda is an allowable speed error and is generally set to be less than 5 km/h; a isx1For a given acceleration, the upper limit of the value is generally not higher than 0.1g, ax2For a set braking acceleration, generally not exceeding 0.2 g; t issafeFor collision safety times, generally greater than 4 s;
Figure BDA0002407382040000052
for safety factor, generally less than 0.4 is set; in specific applications, the specific values of the above parameters can be set according to specific situations.
Further, the step 5) of controlling a brake or a throttle of the vehicle according to the acquired control signal so as to complete the adjustment of the adaptive cruise speed of the vehicle comprises the following steps:
51) the brake controller controls the hydraulic valve to provide corresponding pressure for the brake calipers of each wheel according to the obtained brake signal, so as to brake the vehicle until the vehicle speed reaches the cruising speed and then stop braking;
52) the throttle control area controls the opening of the throttle according to the obtained acceleration signal, and the vehicle speed is increased until the cruise vehicle speed is reached, and then the acceleration is stopped.
The invention further provides an adaptive cruise system applied to the control method of the vehicle adaptive cruise based on the information sharing, which comprises the following components:
1) the environment perception module: the system mainly comprises a plurality of millimeter wave radars and cameras, and is used for acquiring the position and motion state data of objects around the vehicle;
the millimeter wave radar is generally arranged at the front side and two sides of the vehicle body to acquire the positions of objects in front of the vehicle and in the left and right directions; the camera is generally installed at the top of the automobile body and mainly obtains image data in front of the automobile, and the installation mode of the camera and the installation mode can be installed according to corresponding product specifications or actual needs.
2) The self-vehicle state sensing module: the system mainly comprises an integrated inertial navigation module and is used for acquiring accurate motion state data of a vehicle;
the integrated inertial navigation module functions are inertial and GPS navigation systems that measure motion, position, and direction, and are well established commercial modules in the art, such as RT2000, a product of Oxts, uk.
3) Vehicle-vehicle real-time communication module: the system consists of a signal receiver, a signal transmitter and a signal processor;
the communication module is used for acquiring information sent by vehicles in a certain range around, and sharing the state information of the vehicles to the communication modules carried by the vehicles around, and a commercially available mature communication module, such as the vehicle-mounted module 4G L TE communication module of the Xinfei brand of the product of Shenzhen Shangfeng Wei science and technology Limited company, can be used for realizing the signal receiving function in specific application.
4) A control decision module: the vehicle-mounted CPU is internally packaged with a program code which comprehensively analyzes the interference degree of the vehicles in the surrounding lanes on the vehicle according to the information obtained by the environment sensing module, the vehicle state sensing module and the vehicle-vehicle real-time communication module and gives a speed control decision.
5) The control execution module: according to the signals sent by the control decision module, the brake caliper and a control valve for controlling the hydraulic pressure of the caliper are included; the control execution module regulates the cruising speed of the vehicle by controlling a brake hydraulic valve and a throttle valve of the vehicle;
the environment sensing module, the self-vehicle state sensing module, the vehicle-vehicle real-time communication module and the control execution module are respectively connected with the control decision module through a vehicle-mounted CAN bus so as to finish the summarization and execution of information data.
Compared with the control method of the conventional cruise system, the cruise control method has the following beneficial effects:
1. the invention realizes the analysis of the interference of the vehicle to the vehicle according to the environmental information obtained by the vehicle in the surrounding lanes under the road traffic environment, and performs cruise speed control on the vehicle, thereby greatly increasing the safety of the self-adaptive cruise auxiliary driving of the vehicle and the adaptability to complex traffic conditions.
2. The self-adaptive cruise system decision method fully considers the possible future motion state of the vehicles in the adjacent lanes, and carries out speed control decision on the vehicles in advance before danger occurs, so that the dangerous condition is avoided.
Drawings
FIG. 1 is a schematic diagram of vehicle-to-vehicle information interaction;
fig. 2 is a control flow diagram of the vehicle adaptive cruise system based on information sharing.
Detailed Description
In order to facilitate understanding of those skilled in the art, the present invention will be further described with reference to the following examples and drawings, which are not intended to limit the present invention.
The embodiment is a control flow method of a vehicle adaptive cruise system based on information sharing, which is described by taking a tesla model3 as an example:
the millimeter wave and the camera installed on the vehicle model are used as components, the information obtained by the components is processed by a built-in intelligent driving program, and the obtained information is transmitted to the vehicle-mounted CPU. An adaptive cruise system (vehicle H) based on information sharing, comprising the following components:
1) the environment perception module: the system comprises a millimeter wave radar and a camera, and is used for acquiring the position and motion state data of objects around a vehicle;
in the embodiment, millimeter waves and a camera configured by a tesla model3 are used as components of an environment sensing module, the positions of objects in front of a vehicle and in the left and right directions and image data in front of the vehicle are acquired, information acquired by the components is processed by an intelligent driving program built in the vehicle, and the acquired information is transmitted to a vehicle-mounted CPU.
2) The self-vehicle state sensing module: this embodiment uses a sensing module configured as a tesla model3 vehicle model to measure inertia and GPS navigation system for motion, position, and direction; in practical applications, an RT2000 series integrated inertial navigation module from Oxts, uk may also be used.
3) Vehicle-vehicle real-time communication module: the system consists of a signal receiver, a signal transmitter and a signal processor;
the communication module is used for acquiring information sent by vehicles within a certain range around (in the embodiment, the range is set to be 200m because the vehicles are in the running working condition of an expressway, and the specific range can be determined according to actual application requirements), and meanwhile, the state information of the vehicles is shared to the communication modules carried by the surrounding vehicles;
the signal receiver acquires electromagnetic wave information transmitted from the outside, the signal processor processes the acquired information to distinguish the source of the signal and the content of the signal, and meanwhile, the signal processor integrates the information acquired by the state sensor module and the environment sensing module of the self-vehicle and the signal transmitter sends the information in the form of the electromagnetic wave.
4) A control decision module: i.e., an in-vehicle CPU, the present embodiment uses a tesla model3 model CPU;
the CPU is internally packaged with a program code which comprehensively analyzes the interference degree of the vehicles of the surrounding lanes on the self-vehicle according to the information obtained by the environment sensing module, the self-vehicle state sensing module and the vehicle-vehicle real-time communication module and gives a speed control decision;
5) the control execution module: the brake system comprises a brake caliper and a control valve for controlling hydraulic pressure of the caliper, wherein the control valve is installed according to a conventional method and a product specification in the field, and a control execution module adjusts the cruising speed of a vehicle by controlling a brake hydraulic valve and a throttle valve of the vehicle;
the environment sensing module, the self-vehicle state sensing module, the vehicle-vehicle real-time communication module and the control execution module are respectively connected with the control decision module through a vehicle-mounted CAN bus so as to finish the summarization and execution of information data.
Referring to fig. 1, a vehicle H in road traffic is regarded as the above-described own vehicle that turns on the information-sharing-based vehicle adaptive cruise system. With the adaptive cruise system on, the vehicles with which the vehicle has an interactive relationship include vehicle a, vehicle B, and vehicle D. The information obtained by the vehicle H includes the self information and the environmental information received by the sensor mounted on the vehicle H. Meanwhile, the vehicle H can receive self information and environmental information received by sensors mounted on vehicles, which are possibly interacted with the vehicle A, the vehicle B and the vehicle D, so that a basis is provided for the decision of the adaptive cruise system.
The control method flow of the information sharing-based vehicle adaptive cruise system (vehicle H) is shown in FIG. 2, and the specific steps are as follows:
1) firstly, the current speed of the vehicle H is obtained through a loaded environment sensing module and a loaded state sensing module
Figure BDA0002407382040000081
Current acceleration
Figure BDA0002407382040000082
Longitudinal distance between vehicle A, vehicle B and vehicle D and vehicle H
Figure BDA0002407382040000083
And
Figure BDA0002407382040000084
2) then the vehicle-vehicle communication module interactively obtains the information of the vehicle A, the vehicle B and the vehicle D according to the interaction condition of the vehicle H, including the current vehicle speeds of the three vehicles
Figure BDA0002407382040000085
And
Figure BDA0002407382040000086
distance between vehicle A and preceding vehicle C
Figure BDA0002407382040000087
Distance between vehicle A and vehicle D of adjacent lane
Figure BDA0002407382040000088
Distance of vehicle B from vehicle D of adjacent lane
Figure BDA0002407382040000089
3) And (4) performing importance screening on the self state information and the environmental information of the surrounding vehicles acquired by the vehicle-vehicle communication module, and rejecting the information of the vehicles which can not interfere with the vehicles. Then, the screened data of the surrounding vehicles and the data acquired by the vehicles are integrated and input to a control decision module;
the vehicles which may cause interference to the own vehicle include a vehicle located in front of a lane in which the own vehicle is located and a vehicle located in front of the own vehicle in a distance between two lanes adjacent to the lane in which the own vehicle is located.
4) The comprehensive analysis of the state data of the surrounding vehicles and the self vehicle which may generate interference and the acquired environmental information comprises the following steps:
41) firstly, preprocessing acquired data, analyzing information obtained by surrounding vehicles through vehicle-vehicle communication module transmission, and comprising the following steps:
411) respectively acquiring the current speed of a vehicle i in front of an adjacent lane
Figure BDA00024073820400000810
Wherein i ∈ { L, R } represents the left adjacent lane and the right adjacent lane, respectively, the longitudinal distance of the vehicle from the vehicle in front of the vehicle and optionally in the lane change lane is obtained simultaneously
Figure BDA00024073820400000811
And relative longitudinal vehicle speed
Figure BDA00024073820400000812
Where j ∈ { F }i,LFi,LRi,RFi,RRiRepresents the vehicle in front of the lane where the vehicle i is located and in front of the optional left-right lane-changing lane and behind the vehicle i; the information obtained above is transmitted to a control decision module to firstly calculate the predicted time of collision between the vehicle A and the vehicle B and surrounding vehicles:
Figure BDA0002407382040000091
42) then, the predicted time TTC of collision between the vehicle and the surrounding vehicle obtained as described above is usedjThe future motion state of the vehicle is input to a vehicle state prediction module to be analyzed, and the specific steps are as follows:
421) presetting the current state of the vehicle as an original lane keeping state, using the information as the input of a vehicle prediction module, and obtaining the possibility that the vehicle selects the lane where the vehicle is located as a lane changing lane to carry out a lane changing event X by the prediction module according to the input and the output:
Figure BDA0002407382040000092
in the formula
Figure BDA0002407382040000093
A line vector M ∈ R representing the predicted time of collision between the vehicle i and surrounding vehicles and the current acceleration of the vehicle1×nIs a parameter matrix.
In this embodiment, a vehicle a is taken as an example, and the two situations are divided:
the first condition is as follows: when the lane change probability of the vehicle B is smaller than the threshold value and the lane change probability of the vehicle A is larger than the threshold value D, the vehicle A is taken as an object, and the average longitudinal acceleration probability density of the vehicle A in the lane change process is further analyzed:
Figure BDA0002407382040000094
in the formula f (u)t)＝NG,G＝[TTCij]m×1A line vector N ∈ R representing the predicted time of collision between the vehicle i and surrounding vehicles1×mIs a parameter matrix.
Further calculating an average acceleration expectation for the vehicle:
Figure BDA0002407382040000095
according to the average acceleration expectation of the vehicle A, analyzing the influence of the vehicle on the vehicle, and firstly calculating the possibility that the vehicle collides with the vehicle within the next time T, wherein the specific steps are as follows:
calculating the possibility of collision between the own vehicle and the vehicle within the following time T:
is calculated to obtain
Figure BDA0002407382040000096
There is a risk of collision with the vehicle a. Therefore, the speed reduction control is performed on the self vehicle, and the braking speed reduction is as follows:
Figure BDA0002407382040000097
converting the calculated braking deceleration into a control signal and transmitting the control signal to a braking controller; the brake controller adjusts the brake pressure according to the received brake deceleration signal to enable the vehicle to decelerate according to the deceleration given by the signal until the speed of the vehicle is less than the target vehicle speed;
case two: it was found that lane change probability for car B was less than the threshold, and lane change probability for car a was also less than the threshold.
Obtaining the current speed of a vehicle
Figure BDA0002407382040000101
Vehicle speed limit v of current road sectionmaxSet cruising speed vcurAnd the current speed of the vehicle located in front of the same lane
Figure BDA0002407382040000102
The speed of the current vehicle is controlled, and the acceleration is as follows:
Figure BDA0002407382040000103
in the formula, lambda is an allowable speed error and is set to be 5 km/h; a isx1For a set acceleration, set to 0.1g, ax2For a set braking acceleration, set to 0.2 g; t issafeThe collision safety time is set to 4 s;
Figure BDA0002407382040000104
setting the safety coefficient to 0.4; (in specific applications, the specific values of the above parameters can be set according to specific situations).
And converting the calculated acceleration into a control signal and transmitting the control signal to the throttle valve controller.
The brake controller adjusts the opening of the brake throttle valve according to the received acceleration signal to accelerate the vehicle according to the acceleration given by the signal until the vehicle speed of the vehicle reaches the target vehicle speed.
In the above embodiment, the tesla model3 is used as a system platform, a communication module (a wing-in-vehicle module 4G L TE communication module, shenzhen, yokun wegian science and technology limited) is additionally installed on the basis of the original components in the vehicle model, and the control method in the patent is completed according to the above process.

Claims (2)

1. A vehicle self-adaptive cruise control method based on information sharing is characterized by comprising the following specific steps:
1) acquiring the current running state of the vehicle and the positions and motion states of objects around the vehicle;
the current running state of the self-vehicle comprises the current speed, the longitudinal acceleration, the course angle and the yaw rate of the self-vehicle; the position and motion state of the object around the vehicle comprise the position and relative speed of the surrounding vehicle relative to a vehicle coordinate system, the number of a lane where the surrounding vehicle is located and the position of the surrounding vehicle relative to a lane line;
2) sharing the data information acquired in the step 1) to surrounding vehicles, and receiving the information acquired by the surrounding vehicles;
the method comprises the following steps of (1) performing importance screening on self state information and environment information of surrounding vehicles, and rejecting information of the vehicles which cannot interfere with the vehicles; then inputting the screened information which possibly interferes the own vehicle into a vehicle-mounted CPU;
the vehicles which possibly interfere with the self-vehicle comprise a vehicle in front of a lane where the self-vehicle is located and a vehicle in front of the self-vehicle in two lanes adjacent to the lane where the self-vehicle is located;
4) the vehicle-mounted CPU comprehensively analyzes the state data of surrounding vehicles possibly generating interference with the vehicle and the acquired environmental information:
41) firstly, the vehicle-mounted CPU preprocesses the data acquired in the step 3) and analyzes the information obtained by the surrounding vehicles through the vehicle-vehicle communication module, and the method comprises the following steps:
411) respectively acquiring vehicles in front of adjacent lanesi current vehicle speed
Figure FDA0002407382030000011
Wherein i ∈ { L, R } represents the left adjacent lane and the right adjacent lane, respectively, while obtaining the longitudinal distance from the vehicle in front of the vehicle and optionally in the lane change lane
Figure FDA0002407382030000012
And relative longitudinal vehicle speed
Figure FDA0002407382030000013
Where j ∈ { F }i,LFi,LRi,RFi,RRiRepresents the vehicle in front of the lane where the vehicle i is located and in front of the optional left-right lane-changing lane and behind the vehicle i; and simultaneously calculating the expected time of collision of the corresponding vehicle to the vehicle:
Figure FDA0002407382030000014
42) the obtained predicted time TTC of collision between the vehicle and the surrounding vehiclejInputting the vehicle-mounted CPU to analyze the future motion state of the vehicle-mounted CPU, and specifically comprising the following steps of:
421) presetting the current state of the vehicle as an original lane keeping state, using the information as the input of a vehicle prediction module, and obtaining the possibility that the vehicle selects the lane where the vehicle is located as a lane changing lane to carry out a lane changing event X by the prediction module according to the input and the output:
Figure FDA0002407382030000021
in the formula
Figure FDA0002407382030000022
A line vector M ∈ R representing the predicted time of collision between the vehicle i and surrounding vehicles and the current acceleration of the vehicle1×nIs a parameter matrix;
422) when P (X | u)t) Big (a)And at the set lane change possibility threshold value D, considering that the vehicle has the possibility of entering the lane where the vehicle is located by changing the lane, wherein the probability density of the average acceleration of the vehicle in the lane changing process is as follows:
Figure FDA0002407382030000023
in the formula f (u)t)＝NG,G＝[TTCij]m×1A line vector N ∈ R representing the predicted time of collision between the vehicle i and surrounding vehicles1×mIs a parameter matrix;
423) further calculating an average acceleration expectation for the vehicle:
Figure FDA0002407382030000024
43) according to the average acceleration expectation of the vehicle, calculating the influence of the vehicle on the vehicle, and specifically comprising the following steps:
431) calculating the possibility of collision between the own vehicle and the vehicle within the following time T:
432) if it is
Figure FDA0002407382030000025
There is a risk that the own vehicle collides with the vehicle, in which
Figure FDA0002407382030000026
Is the current longitudinal distance, Δ x, between the vehicle and the host vehiclesafeIs a set safe distance;
44) if the system judges that the collision risk exists, the speed of the vehicle needs to be reduced, and the specific steps are as follows:
441) the required braking deceleration of the vehicle is first calculated:
Figure FDA0002407382030000027
442) converting the calculated braking deceleration into a control signal and transmitting the control signal to a braking controller;
443) in the braking process, the vehicle-mounted CPU circularly judges the collision risk between the vehicle and the target vehicle and calculates the required braking deceleration, if the calculated deceleration is larger than the currently executed deceleration, the larger deceleration is adopted, and if the calculated deceleration is smaller than the currently executed deceleration, the current braking deceleration is kept unchanged until the vehicle speed of the vehicle is smaller than the vehicle speed of the target vehicle;
45) when the vehicle analyzes that the adjacent lane vehicle has no interference influence on the vehicle, the adopted vehicle speed control strategy is as follows:
451) obtaining the current speed of a vehicle
Figure FDA0002407382030000028
Obtaining the vehicle speed limit v of the current road section through the vehicle-mounted navigation equipmentmaxSet cruising speed vcurCurrent speed of vehicle located in front of same lane
Figure FDA0002407382030000031
Distance from the vehicle to the preceding vehicle
Figure FDA0002407382030000032
452) The speed of the current vehicle is controlled, and the acceleration is as follows:
Figure FDA0002407382030000033
where λ is the allowable speed error, ax1For a set acceleration, ax2For a set braking acceleration, TsafeIn order to achieve a collision-safe time,
Figure FDA0002407382030000034
a safety factor is set;
5) the vehicle brake controller controls the hydraulic valve to provide corresponding pressure for the brake calipers of each wheel according to the brake signals obtained in the step 4) to brake the vehicle;
or the vehicle throttle controller controls the throttle opening according to the acceleration signal obtained in the step 4) to improve the vehicle speed.
2. An adaptive cruise system based on an information sharing vehicle adaptive cruise control method is characterized by comprising the following modules:
the environment perception module: the system comprises a plurality of millimeter wave radars and cameras;
the self-vehicle state sensing module: the system comprises an integrated inertial navigation module;
vehicle-vehicle real-time communication module: the device comprises a signal receiver, a signal transmitter and a signal processor;
a control decision module: namely a vehicle-mounted CPU;
the control execution module: the brake caliper comprises a brake caliper and a control valve for controlling hydraulic pressure of the caliper;
the environment sensing module, the self-vehicle state sensing module, the vehicle-vehicle real-time communication module and the control execution module are respectively connected with the control decision module through a vehicle-mounted CAN bus.
CN202010165726.6A 2020-03-11 2020-03-11 Vehicle self-adaptive cruise control method and system based on information sharing Pending CN111391832A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010165726.6A CN111391832A (en) 2020-03-11 2020-03-11 Vehicle self-adaptive cruise control method and system based on information sharing

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010165726.6A CN111391832A (en) 2020-03-11 2020-03-11 Vehicle self-adaptive cruise control method and system based on information sharing

Publications (1)

Publication Number Publication Date
CN111391832A true CN111391832A (en) 2020-07-10

Family

ID=71424710

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010165726.6A Pending CN111391832A (en) 2020-03-11 2020-03-11 Vehicle self-adaptive cruise control method and system based on information sharing

Country Status (1)

Country Link
CN (1) CN111391832A (en)

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112896140A (en) * 2021-03-03 2021-06-04 李解 Hybrid vehicle with lane-changing collision avoidance system
CN112977447A (en) * 2021-03-01 2021-06-18 恒大新能源汽车投资控股集团有限公司 Vehicle control method, device, equipment and storage medium
CN113561977A (en) * 2021-09-22 2021-10-29 国汽智控(北京)科技有限公司 Vehicle adaptive cruise control method, device, equipment and storage medium
CN113992709A (en) * 2021-10-12 2022-01-28 北京理工新源信息科技有限公司 Intelligent vehicle-mounted early warning terminal equipment based on 5G communication

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112977447A (en) * 2021-03-01 2021-06-18 恒大新能源汽车投资控股集团有限公司 Vehicle control method, device, equipment and storage medium
CN112896140A (en) * 2021-03-03 2021-06-04 李解 Hybrid vehicle with lane-changing collision avoidance system
CN113561977A (en) * 2021-09-22 2021-10-29 国汽智控(北京)科技有限公司 Vehicle adaptive cruise control method, device, equipment and storage medium
CN113992709A (en) * 2021-10-12 2022-01-28 北京理工新源信息科技有限公司 Intelligent vehicle-mounted early warning terminal equipment based on 5G communication
CN113992709B (en) * 2021-10-12 2023-11-03 北京理工新源信息科技有限公司 Intelligent vehicle-mounted early warning terminal equipment based on 5G communication

Similar Documents

Publication Publication Date Title
CN110497908B (en) Autonomous driving system and control logic for intelligent vehicle control using sensor fusion
CN107346612B (en) Vehicle anti-collision method and system based on Internet of vehicles
CN107867283B (en) Integrated FCW/ACC/AEB system based on prediction model and vehicle
CN111391832A (en) Vehicle self-adaptive cruise control method and system based on information sharing
EP3446941B1 (en) Vehicle driving support apparatus
EP3699051A1 (en) Vehicle control device
CN102673545B (en) Vehicle driving support apparatus
EP3699047A1 (en) Vehicle control apparatus
EP3741639A1 (en) Vehicle control device
US11938924B2 (en) Driving assistance control apparatus for vehicle, driving assistance control system for vehicle, and driving assistance control method for vehicle
US20190263425A1 (en) Travel control apparatus, travel control method, and non-transitory computer-readable storage medium
CN110329259B (en) Vehicle automatic following system and method based on multi-sensor fusion
US11358595B2 (en) Vehicle control system, vehicle control method, and storage medium
CN103269935A (en) Vehicular driving assist apparatus, method, and vehicle
CN111127920A (en) Vehicle-mounted communication-based rear-end collision prevention early warning and control method and system
EP3741638A1 (en) Vehicle control device
CN112406820B (en) Multi-lane enhanced automatic emergency braking system control method
EP3738849A1 (en) Vehicle control device
CN109501798B (en) Travel control device and travel control method
US11479220B2 (en) Adaptive cruise control
US20220080967A1 (en) Vehicle control device, vehicle control method, and non-transitory computer readable storage medium
Ding et al. Research on safety lane change warning method based on potential angle collision point
US11772653B2 (en) Vehicle control device, vehicle control method, and non-transitory computer readable storage medium
CN113830084B (en) Control method based on active collision avoidance of multi-lane vehicle and vehicle
CN115892024A (en) Automobile driving scene recognition control system, method and storage medium

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
RJ01 Rejection of invention patent application after publication

Application publication date: 20200710

RJ01 Rejection of invention patent application after publication