A driving control method and system

Abstract

The application discloses a driving control method applied to a vehicle with a V2X vehicle-mounted unit, wherein, during highway driving, a host vehicle can calculate whether there is a collision risk between the host vehicle and remote vehicles in the same lane and adjacent lanes in real time according to messages from a roadside unit and other remote vehicles; if there is no collision risk, adaptive cruise control is performed in combination with a current vehicle speed and a set expected vehicle speed; if there is a collision risk, automatic lane changing detection is started; if there is no collision risk in lane changing detected according to a lane changing early warning algorithm, automatic lane changing is performed, and lane keeping and adaptive cruise control are performed on the changed lane; if there is a collision risk in automatic lane changing, the automatic lane changing is cancelled, the current lane is kept, and ACC cruise control is performed to keep a safe distance from vehicles in front and behind in the same lane. The application further discloses a corresponding system. By implementing the application, full automatic driving under various weather conditions can be realized, and the application has high safety and practicability.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automatic driving, in particular to a driving control method and system based on V2X vehicle-road cooperation. BACKGROUND

[0002] At present, driving mainly relies on cameras and radars and other sensors for environment perception, but in the case of visual obstruction, rainy and foggy days and other harsh environments, these sensors may not be accurately identified, or even fail to function; at the same time, in the vehicle following part, such as the ACC function, most vehicles cannot well realize the ACC function in the case of a curve, and in the lane keeping part, due to the inaccurate lane line identified by the camera, it may cause unsuccessful lane transverse control and lane matching in bad weather, resulting in unsatisfactory lane keeping function; at the same time, in the vehicle lane changing control, most lane changes mainly consider detecting whether the vehicle is safe at the beginning of lane changing, and less consider whether there is a collision danger in real time during the lane changing process; and for whether there is a collision danger between the vehicle lane changing process and the surrounding vehicles, most consider collision warning in straight roads, but less consider collision warning in complex roadways such as curves.

[0003] With the rapid development of vehicle networking technology V2X (Vehicle to Everything), the ability of vehicles to perceive the outside world based on V2X technology is becoming stronger and stronger. Due to the high reliability and low delay of V2X, intelligent driving based on V2X is getting more and more attention. Compared with traditional vehicle environment perception schemes such as cameras and radars, V2X is less affected by environmental changes and can still work stably in visual obstruction, rainy and foggy days and other harsh environments. With the development of vehicle networking technology V2X, real-time perception and data interaction of man-vehicle-road-cloud based on V2X technology is possible in some highways (such as closed areas such as expressways), and driving based on V2X technology lane following and autonomous lane changing function becomes possible. Using V2X technology for vehicle-road cooperative control, it is possible for vehicles to still work stably in visual obstruction, rainy and foggy days and other harsh environments, so it is of great significance to study V2X vehicle-road cooperative driving. SUMMARY

[0004] The technical problem to be solved by the present application is to provide a driving control method and system which can realize full automatic driving and has high safety and practicality.

[0005] To solve the above technical problems, as one aspect of the present application, a driving control method is provided, which is applied to a vehicle with a V2X vehicle-mounted unit, and includes the following steps:

[0006] Step S10, the vehicle receives the V2I message sent by the road side unit in real time, and receives the V2V message sent by the remote vehicle in the predetermined range around the vehicle;

[0007] Step S11, according to the V2I message and each V2V message, the state information of each specific remote vehicle in front and behind the vehicle in the same lane or adjacent lane is determined, and the safety distance, distance value per unit time and collision time sequence between the vehicle and each specific remote vehicle are determined;

[0008] Step S12, according to the safety distance and distance value per unit time, it is determined whether there is a collision risk between the specific remote vehicle in front and behind the vehicle in the same lane and the vehicle; if there is no collision risk, the vehicle continues to perform lane keeping and adaptive cruise in combination with the current vehicle speed and the set expected vehicle speed;

[0009] Step S13, if the vehicle and each specific remote vehicle in the same lane have a collision risk, or the difference between the current vehicle speed and the preset expected vehicle speed exceeds the predetermined threshold, the automatic lane changing detection is started, the optimal lane changing trajectory and the optimal lane changing time of the automatic lane changing to the adjacent lane on both sides are obtained according to the optimal lane changing trajectory calculation, and it is determined whether there is a risk of automatic lane changing to the adjacent lane on both sides according to the optimal lane changing time and the collision time sequence;

[0010] Step S14, when it is determined that there is no risk of automatic lane changing to at least one side of the adjacent lane, one side of the adjacent lane is selected, the expected front wheel steering angle and longitudinal vehicle speed are obtained, the optimal lane changing trajectory is tracked, the automatic lane changing operation is performed, and the lane keeping and adaptive cruise are performed on the changed lane according to the set expected vehicle speed;

[0011] Step S15, when it is determined that both sides of the automatic lane changing have risks, the automatic lane changing detection is closed, the vehicle speed of the vehicle is automatically adjusted to keep a safe distance between the vehicle and the specific remote vehicle in front and behind the vehicle in the same lane, and the lane keeping and adaptive cruise are performed on the current lane.

[0012] The V2I message sent by the road side unit is a local map message, which at least includes road information, lane ID number and speed limit information; the V2V message includes vehicle state information of each vehicle in the predetermined distance range, and the vehicle state information includes vehicle speed, vehicle position coordinates, steering wheel steering angle, vehicle heading angle and acceleration information.

[0013] The step S11 further comprises:

[0014] The road information, lane ID number in the V2I message are combined with each V2V message to perform coordinate translation transformation on remote vehicles in a predetermined range around the vehicle, and all remote vehicle sequences in the same lane and adjacent lanes of the vehicle are screened out according to the relative positions of the remote vehicles relative to the vehicle, and each remote vehicle with the closest distance to the vehicle in front of or behind the vehicle in the same lane or adjacent lane is identified as a specific remote vehicle.

[0015] The safety threshold value and the distance value per unit time between each specific remote vehicle and the vehicle in each time interval are obtained through iterative calculation by a vector method according to the state information of each specific remote vehicle, and if the distance value per unit time is less than or equal to the corresponding safety threshold value, the iterative calculation of the corresponding specific remote vehicle is stopped, and the collision time sequence of the specific remote vehicle stopped from iteration is obtained.

[0016] The step S12 further comprises:

[0017] When the distance value per unit time between the specific remote vehicle with the closest distance in the same lane and the vehicle is less than or equal to the corresponding safety threshold value, it is determined that the specific remote vehicle on the same lane and the vehicle have a collision risk, including that the specific remote vehicle in front of the vehicle and the vehicle have a collision risk, and that the specific remote vehicle behind the vehicle and the vehicle have a collision risk.

[0018] The step S13 further comprises:

[0019] After starting the automatic lane changing detection, lane changing detection is performed on each adjacent lane, and the optimal lane changing trajectory and the optimal lane changing time for changing lanes to the adjacent lane are obtained by using the fusion of a 5th order polynomial and a genetic algorithm.

[0020] When the distance value per unit time of each specific remote vehicle on the adjacent lane is greater than the corresponding safety threshold value, and there is no collision time sequence or all collision time sequences are greater than the optimal lane changing time, it is determined that there is no risk in changing lanes to the adjacent lane.

[0021] The step S15 further comprises:

[0022] If it is determined that there is a risk in changing lanes to both adjacent lanes, the automatic lane changing detection is turned off.

[0023] When the specific remote vehicle in front of the vehicle on the same lane and the vehicle have a collision risk, the vehicle is automatically controlled to slow down and follow the specific remote vehicle in front; when the specific remote vehicle behind the vehicle on the same lane and the vehicle have a collision risk, the vehicle is automatically controlled to accelerate and avoid the specific remote vehicle behind.

[0024] Lane keeping and adaptive cruise control are performed on the current lane.

[0025] Correspondingly, another aspect of the present application also provides a driving control system applied to a vehicle with a V2X vehicle-mounted unit, which comprises:

[0026] an information receiving unit configured to receive V2I messages transmitted by a roadside unit in real time and receive V2V messages transmitted by remote vehicles within a predetermined range around the vehicle;

[0027] a specific remote vehicle state determining unit configured to determine, according to the V2I messages and each V2V message, state information of each remote vehicle closest to the vehicle in front of and behind the vehicle in the same lane or adjacent lanes, and a safety distance between the vehicle and each remote vehicle, a distance value per unit time, and a collision time sequence;

[0028] a collision risk identification processing unit configured to determine, according to the safety distance and the distance value per unit time, whether there is a collision risk between each specific remote vehicle in front of and behind the vehicle in the same lane and the vehicle; if there is no collision risk, the vehicle continues to perform lane keeping and adaptive cruise in combination with a current vehicle speed and a set desired vehicle speed;

[0029] an automatic lane changing detection unit configured to start automatic lane changing detection when it is detected that there is a collision risk between the vehicle and each specific remote vehicle in front of and behind the vehicle in the same lane, or a difference between a current vehicle speed of the vehicle and a preset desired vehicle speed exceeds a predetermined threshold, and to determine, according to an optimal lane changing trajectory and an optimal lane changing time obtained by calculating the optimal lane changing trajectory, whether there is a risk of automatic lane changing to either side adjacent lane;

[0030] an automatic lane changing processing unit configured to, when the automatic lane changing detection unit determines that there is no risk of automatic lane changing to at least one side adjacent lane, select one side adjacent lane, obtain a desired front wheel steering angle and a longitudinal vehicle speed, track the optimal lane changing trajectory, perform automatic lane changing operation, and perform lane keeping and adaptive cruise on the changed lane at a set desired vehicle speed;

[0031] a vehicle speed adjusting unit configured to, when the automatic lane changing detection unit determines that there is a risk of automatic lane changing to both sides, turn off the automatic lane changing detection, and automatically adjust a vehicle speed of the vehicle to keep a safety distance between the vehicle and each specific remote vehicle in front of and behind the vehicle in the same lane, and then perform lane keeping and adaptive cruise on the current lane.

[0032] The V2I messages transmitted by the roadside unit are local map messages, which at least include road information, lane ID number, and speed limit information; the V2V messages include vehicle state information of each vehicle within a predetermined distance range, and the vehicle state information includes vehicle speed, vehicle position coordinates, steering wheel steering angle, vehicle heading angle, and acceleration information.

[0033] The specific remote vehicle state determining unit further comprises:

[0034] a specific remote vehicle selection unit configured to perform coordinate translation transformation on remote vehicles within a predetermined range around the ego vehicle based on road information and lane ID numbers in the V2I message and each V2V message, and select all remote vehicle sequences in the same lane and adjacent lanes of the ego vehicle according to relative positions of each remote vehicle relative to the ego vehicle, and identify each remote vehicle in the same lane or adjacent lanes closest to the ego vehicle as a specific remote vehicle;

[0035] a vector calculation processing unit configured to obtain a corresponding safety threshold value between each specific remote vehicle and the ego vehicle and a distance value per unit time within each time interval by iterative calculation using a vector method based on state information of each specific remote vehicle, and stop iterative calculation of a corresponding specific remote vehicle if the distance value per unit time is less than or equal to the corresponding safety threshold value, and obtain a collision time sequence of the specific remote vehicle for which iterative calculation is stopped.

[0036] The collision risk identification processing unit further comprises:

[0037] a same-lane risk judgment unit configured to determine whether there is a collision risk between the remote vehicles in front of and behind the ego vehicle in the same lane and the ego vehicle based on the collision time sequence;

[0038] a same-lane maintenance processing unit configured to continue lane keeping and adaptive cruise control of the ego vehicle based on a current vehicle speed and a set desired vehicle speed if the same-lane risk judgment unit determines that there is no collision risk in the same lane.

[0039] The automatic lane change detection unit further comprises:

[0040] an optimal trajectory calculation unit configured to perform lane change detection on each adjacent lane after starting automatic lane change detection, and obtain an optimal trajectory for changing lanes to the adjacent lane and an optimal lane change time T opt ;

[0041] an adjacent-lane risk discrimination unit configured to determine that there is no risk in changing lanes to the adjacent lane if the distance value per unit time of each specific remote vehicle in the adjacent lane is greater than the corresponding safety threshold value, and there is no collision time sequence or the collision time sequence is greater than the optimal lane change time.

[0042] The vehicle speed adjustment unit further comprises:

[0043] a lane change detection closing unit configured to close automatic lane change detection if the adjacent-lane risk discrimination unit determines that there is a risk in changing lanes to both adjacent lanes.

[0044] The adjustment holding unit is used to automatically control the vehicle to decelerate and follow the specific distant vehicle in front when there is a risk of collision between the vehicle and a specific distant vehicle in front when the automatic lane change detection is turned off; to automatically control the vehicle to accelerate and avoid the specific distant vehicle behind when there is a risk of collision between the vehicle and a specific distant vehicle behind; and to perform lane keeping and adaptive cruise control in the current lane.

[0045] Implementing the embodiments of the present invention has the following beneficial effects:

[0046] This invention provides a driving control method and system. While the vehicle is in motion, based on V2X technology, and combining V2I messages sent by roadside units with V2V messages from surrounding vehicles, it can calculate in real time whether there is a collision risk between the vehicle and distant vehicles in the same or adjacent lanes. If there is no collision risk, the vehicle performs adaptive cruise control based on its current speed and a set desired speed. If the current speed differs significantly from the set desired speed, or if there is a forward or rearward collision with a distant vehicle in the same lane, automatic lane changing is activated. If there is no collision risk according to the lane change warning algorithm, the vehicle performs automatic lane changing while maintaining lane keeping and adaptive cruise control at the set desired speed. If automatic lane changing poses a collision risk, the lane change is automatically canceled, and the vehicle maintains ACC cruise control in the current lane, maintaining a safe following distance from the distant vehicle in the same lane, or accelerating urgently to avoid a rearward collision. Implementing this invention enables fully automated driving.

[0047] Meanwhile, the driving control method and system provided by this invention can be used in various road shapes and weather conditions, and have high safety and practicality. Attached Figure Description

[0048] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, obtaining other drawings based on these drawings without creative effort still falls within the scope of the present invention.

[0049] Figure 1 This is a schematic diagram of the main flow of an embodiment of a driving control method provided by the present invention;

[0050] Figure 2 for Figure 1 The diagram illustrates the application scenarios involved.

[0051] Figure 3 This is a schematic diagram of the vector coordinates of the relative position of the remote vehicle to the main vehicle in an embodiment of the present invention;

[0052] Figure 4 A vector analysis diagram related to the collision risk calculation of the host vehicle and the remote vehicle driving on a curve for an embodiment of the present application;

[0053] Figure 5 Another vector analysis diagram related to the collision risk calculation of the host vehicle and the remote vehicle driving on a curve for an embodiment of the present application;

[0054] Figure 6 A schematic diagram of vehicle lane changing for an embodiment of the present application;

[0055] Figure 7 A motion trajectory diagram of a vehicle changing lane from the outer side to the inner side during driving on a curve for an embodiment of the present application;

[0056] Figure 8 A motion trajectory diagram of a vehicle changing lane from the inner side to the outer side during driving on a curve for an embodiment of the present application;

[0057] Figure 9 A flowchart of solving an optimal lane changing trajectory based on a genetic algorithm for an embodiment of the present application;

[0058] Figure 10 A more detailed flowchart of a driving control method for an embodiment of the present application;

[0059] Figure 11 A structural schematic diagram of an embodiment of a driving control system provided by the present application;

[0060] Figure 12 A structural schematic diagram of a specific remote vehicle state determination unit in Figure 11 ;

[0061] Figure 13 A structural schematic diagram of a collision risk identification processing unit in Figure 11 ;

[0062] Figure 14 A structural schematic diagram of an automatic lane changing detection unit in Figure 11 ;

[0063] Figure 15 A structural schematic diagram of a vehicle speed adjustment unit in Figure 11 . DETAILED DESCRIPTION

[0064] To make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings.

[0065] As shown in Figure 1 , a main flowchart of an embodiment of a driving control method provided by the present application is shown.Figures 2 to 10 As shown, the method of the present application is applied in a vehicle with a V2X on-board unit (OBU), and in particular, can refer to the application environment diagram shown in Figure 2 which a road side unit (RSU) broadcasts its surrounding road conditions and road information, traffic light information, map information, etc. through V2I messages to vehicles; while vehicles communicate with each other through V2V messages, mainly sending or receiving the position, speed, direction, etc. of surrounding vehicles. In this embodiment, the method comprises the following steps:

[0066] Step S10, the host vehicle (HV) receives the V2I messages sent by the road side unit in real time, and receives the V2V messages sent by the remote vehicles (RV) within a predetermined range (such as 800 meters); more specifically, the V2I messages sent by the road side unit are local map messages, which at least include road information, lane ID number, speed limit information; the V2V messages include vehicle state information of each vehicle within a predetermined distance range, which includes vehicle speed V, vehicle position coordinates position (X, Y, 0) (currently considering projection to the same plane), steering wheel angle St RV, vehicle heading angle H RV , acceleration information a, etc.; the host vehicle can be an automatically driven vehicle in highway driving.

[0067] Step S11, according to the V2I messages and each V2V message, determine the state information of each specific remote vehicle closest to the host vehicle in the same lane or adjacent lane in front and behind, and the safety distance between the host vehicle and each specific remote vehicle, the distance value per unit time and the collision time sequence;

[0068] In a specific example, the step S11 further comprises:

[0069] Step S110, using the road information and lane ID number in the V2I messages, combining each V2V message, performing coordinate translation transformation on the remote vehicles within a predetermined range, and according to the relative position of each remote vehicle relative to the host vehicle, screening out all remote vehicle sequences in the same lane and adjacent lanes of the host vehicle; and identifying each remote vehicle closest to the host vehicle in the same lane or adjacent lane as a specific remote vehicle;

[0070] Step S111, according to the state information of each specific remote vehicle, the corresponding safety threshold and distance value per unit time between the host vehicle and each specific remote vehicle are obtained through iterative calculation by vector method; if there is a distance value per unit time less than or equal to the corresponding safety threshold, stop the iterative calculation of the corresponding specific remote vehicle, and at the same time obtain the collision time sequence of the specific remote vehicle that stops iteration.

[0071] More specifically, the above step S110 is realized by the following principle.

[0072] First, by using the road information (the lane ID number where the vehicle is on the road) sent by the RSU vehicle, the RV vehicles within the 360° range in the HV lane and the adjacent lane are identified through target vehicle identification calculation;

[0073] In combination Figure 3 As shown in the figure, the RV is first translated by coordinate transformation, and the RV is in the orientation of the HV in the local coordinate system of the HV. The formula of coordinate transformation is:

[0074]

[0075] Where, X HV , X RV represents the horizontal coordinates of the mass centers of HV and RV in the global coordinates; Y HV Y RV represents the longitudinal coordinates of the mass centers of HV and RV in the global coordinates, x RV>HV and y RV>HV are the coordinate values of the RV relative to the HV coordinate system; θ is the compass angle obtained from the GNSS of the HV vehicle, and the counterclockwise direction is positive.

[0076] Then the relative orientation of the RV to the HV can be determined, and a flag is set for each remote vehicle, as shown in Table 1 below:

[0077] Table 1 Orientation of RV relative to HV

[0078] Judgment condition Orientation of RV relative to HV Orientation number flag -1 ≤ y RV>HV ≤ 1 && x RV>HV ≥ 0]]> Front 1 <![CDATA[y RV>HV >1&&x RV>HV ≥0]]> Right front 2 [["y RV>HV ["-1 && x RV>HV [">=0 Left front 3 -1 ≤ y RV>HV ≤ 1 && x RV>HV ≤ 0]]> Rear 4 [["y RV>HV <-1 && x RV>HV ≤ 0]]] Left rear 5 [["y[" RV>HV [">1&&x[" RV>HV ["≤0]]][" "]] Right rear 6 y RV>HV ≤-1 && -1 ≤ x RV>HV ≤1]]> Left side 7 [["y[" RV>HV ["≥1 && -1 ≤ x[" RV>HV ["≤1]]][" Right side 8

[0079] In combination with the above table and according to the lane information sent by the RSU, the RV vehicle sequence in the same lane and adjacent lane of the HV can be screened, and the remote vehicle closest to the vehicle in front or behind in the same lane or adjacent lane is identified to determine the specific remote vehicle;

[0080] And step S111 needs to use the vehicle 360-degree identification and vehicle collision warning algorithm based on vector method to realize, specifically, as Figure 4 The speed of the remote vehicle RV and the host vehicle in the curve is V HV , V RV , the steering wheel angle is St HV , St RV , and the vehicle heading angle is H HV ; wherein H RV is the vehicle heading angle, which is the angle between the vehicle heading direction and the Y axis of the earth coordinate system, and the counterclockwise direction is positive; α HV , α RVis the steering angle of HV and RV, the steering angle is positive in clockwise direction and negative in anticlockwise direction; V is the vector vehicle speed At B1 as the starting point, α HV is the steering angle of HV and RV, the steering angle is positive in clockwise direction and negative in anticlockwise direction; V is the vector vehicle speed At B1 as the starting point, α is the projection of RV on HV where A1 is the projection point; the purpose is to find the distance of RV from HV in unit time (i.e. DCPA n ).

[0081] where the projection is calculated as follows:

[0082]

[0083] where θ1 is the angle between vector and .

[0084] Here

[0085] Therefore

[0086] Therefore

[0087] At n = 1, the velocity of RV relative to HV is To find the closest distance of HV to RV, a typical mathematical problem is formed: the shortest distance of a point HV outside a line segment to the line segment : no matter where the point HV is on the line segment , formula (4) is established, so the coefficient is set:

[0088]

[0089]

[0090] The physical meaning represented by it is: if A1 is on the vector , then the point is the closest distance point (CPA1) of RV relative to HV at the first cycle n = 1, and the vector is DCPA1; if A1 is on the extension of , DCPA1 can be represented by ; if A1 is on the extension of , DCPA1 can be represented by .

[0091] Figure 5This indicates that when HV and RV are driving on a curve, RV relative to HV at n=3, according to CPA3, where This represents the vector relative to vehicle HV at n=3, that is, at a unit time interval Δt=1s. and They are equal in size but opposite in direction; therefore, when n=1, the coordinates of B1 and P1 have the coordinates of point B1:

[0092]

[0093] Then the coordinates of P1 are:

[0094]

[0095] When n = n

[0096] B n Point coordinates based on the GPS coordinate system (global coordinate system):

[0097]

[0098] in:

[0099]

[0100] Let α be the initial velocity and acceleration vector of RV; RV0 The initial steering angle is given by the national standard for V2X application layer, which specifies that the steering wheel angle St can be obtained from the vehicle bus. Therefore, the RV wheel steering angle is... i RV This is the steering gear ratio of the RV.

[0101] P n Point coordinates based on the GPS coordinate system (global coordinate system):

[0102]

[0103] in:

[0104] Let α be the initial velocity and acceleration vector of HV; HV,0 The initial steering angle is given by the national standard for V2X application layer, which specifies that the steering wheel angle St can be obtained from the vehicle bus. Therefore, the HV wheel steering angle is... i HV This is the steering gear ratio of the RV.

[0105] Based on the vector method for B n Point, P nThe point analysis is independent of the type of path the vehicle is on. Therefore, based on the different positions of the RV within the HV and combined with the local map information sent by the RSU, the risk of collision between the HV and surrounding vehicles can be calculated in real time. For example... Figure 4 This indicates the process of HV changing lanes on a curve. During the lane change, it is necessary to consider whether there is a risk of collision between HV and surrounding RV1, RV2, RV3, RV4, RV5, and RV6. HV can only change lanes if there is no risk of collision with surrounding vehicles.

[0106] Therefore, the safety distance model between HV and RV in this invention is as follows:

[0107] When V RV >0

[0108]

[0109] When V RV =0

[0110]

[0111] Among them, V rel The relative speeds of the HV and RV.

[0112] To calculate the potential collision risk between HV and surrounding RV1, RV2, RV3, RV4, RV5, and RV6, taking RV1, which is in front of HV and in the same lane, as an example, the following calculation is performed: Figure 5 It can be seen that the resultant velocity of RV relative to HV at n=1 is... When n=2, it is When n=3, it is Will Projected onto vectors respectively Above; due to Vehicle speed vector with HV They are equal in magnitude but opposite in direction; therefore, within each time interval Δt (set Δt = 1s), at n = n (the value of n can be actually calibrated):

[0113] When V RV When >0,

[0114]

[0115] When V RV When = 0,

[0116]

[0117] in, for and The included angle; is an angle between and is an angle between and

[0118] In vector calculation, if DCPA n ≤d w,n , the calculation is stopped, and it can be concluded that the HV is in danger of forward collision after T warning time. According to formulas (5) and (6), at the nth calculation, the following can be obtained:

[0119]

[0120]

[0121]

[0122]

[0123]

[0124] According to the vector calculation of the forward RV1 collision time, the vector calculation methods of RV2, RV3, RV4, RV5, RV6 and the HV are the same.

[0125] In step S12, whether there is a collision risk between the specific remote vehicle in front of and behind the vehicle in the same lane and the vehicle is determined according to the safety distance and the distance value per unit time. If there is no collision risk, the vehicle continues to keep the lane and perform adaptive cruise control in combination with the current vehicle speed and the set expected vehicle speed.

[0126] In a specific example, the step S12 further includes:

[0127] When the distance value per unit time between the specific remote vehicle closest to the vehicle in the same lane and the vehicle is less than or equal to the corresponding safety threshold (i.e., DCPA n ≤d w,n ), it is determined that there is a collision risk between the specific remote vehicle in the same lane and the vehicle, including a collision risk between the specific remote vehicle in front of the vehicle and the vehicle, and a collision risk between the specific remote vehicle behind the vehicle and the vehicle.

[0128] Specifically, after the vector calculation in the foregoing step S111, if it is determined that the condition DCPA n ≤d w,n ​​&& (RV and HV are in the same lane) && (flag == 1 || 2 || 3) are met, it is determined that the specific RV in front of the HV in the same lane has a collision risk; if it is determined that the condition DCPA n ≤d w,n && (RV and HV are in the same lane) && (flag == 4 || 5 || 6) are met, it is determined that the specific RV behind the HV in the same lane has a collision risk.

[0129] Step S13, if the vehicle and each specific RV in the same lane have a collision risk, or the difference between the current vehicle speed and the preset expected speed exceeds the predetermined threshold, the automatic lane changing detection is started, the optimal lane changing trajectory and the optimal lane changing time of automatic lane changing to the two adjacent lanes are calculated according to the optimal lane changing trajectory, and whether there is a risk of automatic lane changing to the two adjacent lanes is determined according to the optimal lane changing time and the collision time sequence;

[0130] In a specific example, the step S13 further comprises:

[0131] After starting the automatic lane changing detection, the lane changing detection is performed for each adjacent lane, and the optimal lane changing trajectory and the optimal lane changing time of lane changing to the adjacent lane are obtained by using the optimal lane changing trajectory calculation of the fusion of the 5th order polynomial and the genetic algorithm;

[0132] It is determined that there is no risk of lane changing to the adjacent lane when it is determined that the distance value corresponding to each specific RV in the adjacent lane per unit time is greater than the corresponding safety threshold d w,n , and there is no collision time sequence or the collision time sequence is greater than the optimal lane changing time T opt .

[0133] The principle of the optimal lane changing trajectory planning of the fusion of the 5th order polynomial and the genetic algorithm involved in the application will be introduced as follows:

[0134] The following two advantages are obtained by using high-order polynomials as ideal reference lane changing trajectories: first, the high-order polynomial trajectory curve is smoother, and the first and second derivatives are continuous and smooth functions, so that the control process will not have sudden changes; second, the high-order polynomial trajectory curve has smaller high-frequency components, and the system can easily compensate and control it through a feedback system.

[0135] The 5th order polynomial lane changing trajectory expression used in the application is:

[0136] The longitudinal displacement x(t) and the transverse displacement y(t) are functions of time t

[0137]

[0138] Where a0-a5 are longitudinal displacement trajectory undetermined coefficients, b0-b5 are transverse displacement trajectory undetermined coefficients initial state and target state State known, then the undetermined coefficients of formula (22) can be solved.

[0139] In the initial and end time of lane changing, the car driving state should tend to be stable, and will not produce acceleration, and will not produce transverse speed, so as to meet the kinematic characteristics of vehicle. Therefore, the initial state can be expressed as Where v xin represents the longitudinal initial state speed.

[0140] The target state can be expressed as Where v xfin represents the longitudinal speed after lane changing, L is the longitudinal displacement of lane changing process, and h represents the lane width, generally 3.75m. Assuming that t=0, the lane changing starts and the vehicle mass center is located at the coordinate origin at this time, and t=t0, the lane changing is completed, and formula (22) is obtained by substitution:

[0141]

[0142]

[0143] The second and third order derivatives of formula (22) are obtained, and the lane changing trajectory transverse and longitudinal speed acceleration, transverse and longitudinal acceleration based on quintic polynomial are obtained:

[0144]

[0145] Substitute formula (23) and (24) into formula (25), and the extreme value is obtained:

[0146]

[0147] Where a mx is the maximum value of trajectory longitudinal acceleration; a my is the maximum value of trajectory transverse acceleration. Since the lane width h is known, v xin can be obtained through the CAN bus of the HV vehicle, so the extreme value of acceleration is only related to v xfin , L and lane changing time t0. Therefore, the lane changing trajectory evaluation index J:

[0148]

[0149] Where a max represents the maximum acceleration of the vehicle, L max is the maximum value of the longitudinal displacement of lane changing, and t cmaxis the maximum value of the lane changing time; w1, w2, w3 are weight coefficients, and the relationship among them is: w1+w2+w3=1. L represents the longitudinal displacement of lane changing, and represents the influence on the traffic flow, and the smaller the value is, the smaller the influence is; t c represents the lane changing time of lane changing, and represents the lane changing efficiency, and the smaller the value is, the higher the lane changing efficiency is.

[0150] Therefore, the problem of lane changing trajectory optimization based on genetic algorithm is described as:

[0151]

[0152] The fitness function is brought into the genetic algorithm module for iterative optimization, and the corresponding v xfin , L and t c can be obtained under the condition of minimum J, and thus the complete boundary conditions are obtained, and the parameters of the 5th order polynomial are obtained, and finally the optimal lane changing trajectory is obtained.

[0153] At present, the research is also focused on straight lanes, and there is a lack of research on lane changing trajectory planning model on curved lanes. Therefore, through the analysis and comparison of curved lane changing and straight lane changing, the curved lane problem is converted into a straight lane problem, thereby simplifying the operation. The curved lane changing is different from the straight lane changing. On the straight lane, changing from A lane to B lane can be regarded as changing from B lane to A lane in mirror image, and the calculation method is completely the same. Since there is a difference in the curvatures of the two lanes on the curved lane, the cases are discussed:

[0154] (1) the starting lane is the outer lane, and the target lane is the inner lane

[0155] In order to study the lane changing trajectory planning model on curved lanes, the curved lane problem is converted into a straight lane problem through the analysis and comparison of curved lane changing and straight lane changing, so as to simplify the operation. The curved lane changing process of an intelligent vehicle is shown in Figure 7 , wherein the curved road section can be approximated as a circular arc road section, S1 and S2 are the center lines of the starting lane and the target lane respectively, θ is the included angle between the starting boundary line and the ending boundary line of the road section, H is the same curvature of the starting lane and the target lane, and R is the curvature radius of the starting lane, so the curvature radius of the target lane is R-h. A coordinate system is established with the mass center O at the starting moment of lane changing as the origin, and the mass center of the vehicle at the ending moment of lane changing is F. The whole lane changing process is to find a smooth curve between point O and point F, which is the curved lane changing trajectory.

[0156] The 5th order polynomial method is used to describe the lane changing trajectory. The biggest difference compared with the straight lane is that the state quantity (speed, acceleration, etc.) at the ending moment of lane changing is given with itself as the reference, and the coordinate system is established with the mass center of the vehicle at the starting moment of planning as the origin, so the coordinate transformation is needed, and the purpose is to transform the numerical value of the vehicle state quantity at the ending moment of lane changing into Figure 7The corresponding numerical value in the OXY coordinate system is:

[0157]

[0158] x fin = (R - h) sin θ (33)

[0159]

[0160]

[0161] y fin = R - (R - h) cos θ (36)

[0162]

[0163]

[0164] The coordinate conversion process of the target state quantity is combined with formulas (32) to (38), and thus the numerical values of the initial state and the target state in the OXY coordinate system are obtained. The solving method of the curve is the same as that of the straight curve.

[0165] (2) The starting lane is the inner lane, and the target lane is the outer lane

[0166] The process of the intelligent vehicle changing lanes from the inner lane to the outer lane on the curve is shown in Figure 8 . S1 and S2 are the center lines of the starting lane and the target lane, respectively. The angle between the starting boundary line and the ending boundary line of the road section is α. The starting lane and the target lane have the same center of curvature H. Unlike the previous case, if the radius of curvature of the starting lane is R, then the radius of curvature of the target lane is R + h due to the lane width h. The coordinate system is established with the mass center O at the beginning of the lane change as the origin, and the mass center of the vehicle at the end of the lane change is F. The entire lane changing process can be expressed as finding a smooth curve between points O and F, which is the curve lane changing trajectory.

[0167] Similarly, the mass center of the vehicle at the beginning of the planning is taken as the origin, so the coordinate transformation is performed. The purpose is to convert the numerical value of the vehicle state quantity at the end of the lane change into the corresponding numerical value in the OXY coordinate system. The conversion formula is:

[0168] x fin = (R + h) sin α (39)

[0169] x fin = v xfin cos α + v yfin sin α (40)

[0170]

[0171] y fin =R-(R+h)cosα (42)

[0172] y fin =v xfin sinα-v yfin cosα (43)

[0173]

[0174] Combining formulas (39) to (44) yields the coordinate transformation process of the target state variables, thus obtaining the initial state in the OXY coordinates. and target state The solution method for calculating the value on curves is the same as that for changing lanes on straights.

[0175] Therefore, in practical applications, it can be combined with Figure 9 As shown, the following steps are used to plan the optimal lane-changing trajectory based on a fusion of a 5th-order polynomial and a genetic algorithm:

[0176] Step S130: Obtain road information and RV information sent by the RSU based on the OBU equipped with V2X; simultaneously obtain the initial state at the moment of HV lane change in real time.

[0177] Step S131: Initially assign a longitudinal displacement of L = 50m for the lane change, and set the final velocity at the end of the lane change to v. xfin =v xin The lane change time is initially set to t. c =6s;

[0178] Step S132: Use a genetic algorithm to solve for the objective function based on formula (28) and find the function that satisfies the constraints 30≤L≤150, 0 <t c ≤12, 0.6v xin ≤v xfin ≤1.3v xin The optimal longitudinal displacement L that minimizes the lower objective function J opt Optimal lane change time T opt Lane change longitudinal speed v xfin_opt The weighting coefficients are set as w1 = 0.4, w2 = 0.3, and w3 = 0.3. If the constraints are met, the optimal lane-changing trajectory can be obtained; otherwise, proceed to step S133.

[0179] In step S133, a new population is generated by using the selection, selection, crossover, and mutation operations of the genetic algorithm. Then, the process proceeds to step S132 to solve the objective function J until the process terminates at the set number of iterations.

[0180] Specifically, if the far vehicles RV3 and RV4 of the left neighboring lane of the HV satisfy the following conditions: DCPA > d w && collision time it is indicated that there is no risk of lane-changing collision for the left neighboring lane, where the collision time The value of T warning can be the corresponding T opt obtained by the foregoing calculation, and T opt is the optimal lane-changing time obtained by the foregoing calculation.

[0181] If the far vehicles RV5 and RV6 of the right neighboring lane of the HV satisfy the following conditions:

[0182] DCPA > d w && collision time it is indicated that there is no risk of lane-changing collision for the right neighboring lane; similarly, where the collision time The value of T warning can be the corresponding T opt obtained by the foregoing calculation, and T opt is the optimal lane-changing time obtained by the foregoing calculation.

[0183] Step S14, when it is determined that there is no risk of automatic lane-changing to at least one side neighboring lane, a side neighboring lane is selected, and the expected front wheel steering angle and longitudinal vehicle speed are obtained, the optimal lane-changing trajectory is tracked, the automatic lane-changing operation is performed, and the lane keeping and adaptive cruise are performed on the changed lane at the set expected vehicle speed.

[0184] Specifically, the expected front wheel steering angle and longitudinal vehicle speed can be obtained based on model predictive control using a three-degree-of-freedom vehicle dynamics equation.

[0185] Step S15, when it is determined that there is a risk of automatic lane-changing to both sides, the automatic lane-changing detection is closed, the vehicle speed of the host vehicle is automatically adjusted to maintain a safe distance between the host vehicle and specific far vehicles in front and behind the host vehicle on the current lane, and the lane keeping and adaptive cruise are performed on the current lane.

[0186] In a specific example, the step S15 further includes:

[0187] If it is determined that there is a risk of lane-changing to both sides, the automatic lane-changing detection is closed.

[0188] When a specific far vehicle in front of the host vehicle has a risk of collision with the host vehicle, the host vehicle is automatically controlled to decelerate and follow the specific far vehicle in front; when a specific far vehicle behind the host vehicle has a risk of collision with the host vehicle, the host vehicle is automatically controlled to accelerate and avoid the specific far vehicle behind.

[0189] The lane keeping and adaptive cruise are performed on the current lane.

[0190] Correspondingly, another aspect of the present application also provides a driving control system 1 applied to a vehicle with a V2X vehicle-mounted unit, which comprises:

[0191] An information receiving unit 10 is configured to receive V2I messages sent by a roadside unit and V2V messages sent by remote vehicles within a predetermined range around the vehicle in real time during high-speed driving of the vehicle in autonomous driving;

[0192] A specific remote vehicle state determining unit 11 is configured to determine state information of each remote vehicle closest to the vehicle in front and behind in the same lane or adjacent lanes and a safety distance between the vehicle and each remote vehicle, a distance value per unit time and a collision time sequence according to the V2I messages and each V2V message;

[0193] A collision risk identification processing unit 12 is configured to determine whether there is a collision risk between the specific remote vehicle in front and behind in the same lane and the vehicle according to the safety distance and the distance value per unit time; if there is no collision risk, the vehicle continues to perform lane keeping and adaptive cruise in combination with a current vehicle speed and a set expected vehicle speed;

[0194] An automatic lane changing detection unit 13 is configured to start automatic lane changing detection when it is detected that there is a collision risk between the vehicle and the specific remote vehicle in front and behind in the same lane or the difference between the current vehicle speed of the vehicle and the preset expected vehicle speed exceeds a predetermined threshold, and to determine whether there is a risk of automatic lane changing to the two adjacent lanes according to an optimal lane changing trajectory and an optimal lane changing time obtained according to the optimal lane changing trajectory.

[0195] An automatic lane changing processing unit 14 is configured to select one adjacent lane and obtain an expected front wheel steering angle and a longitudinal vehicle speed when the automatic lane changing detection unit determines that there is no risk of automatic lane changing to at least one adjacent lane, track the optimal lane changing trajectory to perform automatic lane changing operation, and perform lane keeping and adaptive cruise on the changed lane at the set expected vehicle speed.

[0196] A vehicle speed adjusting unit 15 is configured to close the automatic lane changing detection and automatically adjust the vehicle speed of the vehicle to keep a safety distance between the vehicle and the specific remote vehicle in front and behind in the same lane when the automatic lane changing detection unit determines that there is a risk of automatic lane changing to both sides, and then perform lane keeping and adaptive cruise on the current lane.

[0197] The V2I messages sent by the roadside unit are local map messages, which at least include road information, lane ID number and speed limit information; the V2V messages include vehicle state information of each vehicle within a predetermined distance range, and the vehicle state information includes vehicle speed, vehicle position coordinates, steering wheel steering angle, vehicle heading angle and acceleration information.

[0198] The specific remote vehicle state determination unit 11 further comprises:

[0199] The specific remote vehicle selection unit 110 is configured to perform coordinate translation transformation on the remote vehicles within a predetermined range around the ego vehicle by using the road information and lane ID number in the V2I message in combination with each V2V message, and to filter out all remote vehicle sequences in the same lane and adjacent lanes of the ego vehicle according to the relative positions of the remote vehicles relative to the ego vehicle, and to identify each remote vehicle in the same lane or adjacent lane that is closest to the ego vehicle as a specific remote vehicle.

[0200] The vector calculation processing unit 111 is configured to obtain, by using a vector method, the corresponding safety threshold value between each specific remote vehicle and the ego vehicle and the distance value per unit time within each time interval according to the state information of each specific remote vehicle, and to stop the iteration calculation on the corresponding specific remote vehicle if the distance value per unit time is less than or equal to the corresponding safety threshold value, and to obtain the collision time sequence of the specific remote vehicle for which the iteration is stopped.

[0201] The collision risk identification processing unit 12 further comprises:

[0202] The ego lane risk judgment unit 120 is configured to determine, according to the collision time sequence, whether there is a collision risk between the remote vehicles in front of and behind the ego vehicle in the same lane and the ego vehicle.

[0203] The ego lane maintenance processing unit 121 is configured to continue lane keeping and adaptive cruise control of the ego vehicle in combination with the current vehicle speed and the set desired vehicle speed if the ego lane risk judgment unit determines that there is no collision risk in the ego lane.

[0204] The automatic lane change detection unit 13 further comprises:

[0205] The optimal trajectory calculation unit 130 is configured to perform lane change detection on each adjacent lane after starting automatic lane change detection, and to obtain the optimal trajectory for changing lanes to the adjacent lane and the optimal lane change time T by using a 5th-degree polynomial and a genetic algorithm. opt ;

[0206] The adjacent lane risk discrimination unit 131 is configured to determine that there is no risk in changing lanes to the adjacent lane if the distance value per unit time corresponding to each specific remote vehicle in the adjacent lane is greater than the corresponding safety threshold value, and there is no collision time sequence or the collision time sequence is greater than the optimal lane change time.

[0207] The vehicle speed adjustment unit 14 further comprises:

[0208] The lane changing detection closing unit 140 is configured to close the automatic lane changing detection when the lane changing risk judging unit determines that there is a risk of changing lanes to both sides.

[0209] The adjustment maintaining unit 141 is configured to automatically control the host vehicle to decelerate and follow a specific far vehicle in front of the current lane when there is a risk of collision between the specific far vehicle and the host vehicle, automatically control the host vehicle to accelerate and avoid the specific far vehicle behind the current lane when there is a risk of collision between the specific far vehicle and the host vehicle, and maintain the lane and perform adaptive cruise control on the current lane.

[0210] More details can be found in the foregoing description of the lane changing risk judging unit, which will not be described in detail here. Figures 1 to 10

[0211] The embodiment of the present application has the following beneficial effects:

[0212] The present application provides a driving control method and system. In the driving process, based on the V2X technology, the V2I messages sent by the roadside unit and the V2V messages of surrounding vehicles can be used to calculate in real time whether there is a risk of collision between the far vehicle in the same lane or adjacent lane and the host vehicle. If there is no risk of collision, the host vehicle performs adaptive cruise control in combination with the current vehicle speed and the set expected vehicle speed. If the current vehicle speed and the set expected vehicle speed differ greatly or the host vehicle has a forward or backward collision with the far vehicle in the same lane, the automatic lane changing function is started. If there is no risk of collision according to the lane changing warning algorithm, the host vehicle performs automatic lane changing and lane keeping and adaptive cruise control according to the set expected vehicle speed. If there is a risk of collision in automatic lane changing, the lane changing is automatically cancelled, and the host vehicle performs ACC cruise control in the current lane while keeping a safe distance from the forward far vehicle in the same lane, or the host vehicle performs emergency acceleration to avoid the backward collision vehicle. The present application can realize full-automatic driving.

[0213] Meanwhile, the driving control method and system provided by the present application can be used in various road shapes and weather conditions, and has high safety and practicability.

[0214] Those skilled in the art will understand that the embodiments of the present application can be provided as a method, device, or computer program product. Therefore, the present application can be in the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can be in the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program code.

[0215] ​The computer program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other processing devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other processing devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions specified in the flowchart block or blocks. Figure 1 The flowchart and / or block diagram in the variations disclosed herein illustrate the architecture, functionality, and operation of possible implementations of apparatuses and computer program products according to various embodiments. In this regard, each flowchart block and / or block in the flowcharts and / or block diagrams can represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical functions. It should also be noted that, in some alternative implementations, the flowchart blocks and / or blocks in the flowcharts and / or block diagrams can represent a Figure 1 The computer program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other processing devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other processing devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions specified in the flowchart block or blocks.

[0216] The above-described embodiments are merely given as non-limiting examples. It will be understood by those skilled in the art that various modifications, changes, additions and omissions can be made without departing from the spirit and essential characteristics of the application. Therefore, the scope of the present application should be determined not by the foregoing description but by the appended claims.

Claims

1. A driving control method applied to a vehicle having a V2X on-board unit, characterized by, The method comprises the following steps: Step S10, the vehicle receives the V2I message sent by the roadside unit and the V2V message sent by the remote vehicle within a predetermined range around the vehicle in real time; Step S11, according to the V2I message and each V2V message, the state information of each specific remote vehicle closest to the vehicle in front and behind in the same lane or adjacent lane is determined, and the safety distance, the distance value per unit time and the collision time sequence between the vehicle and each specific remote vehicle are determined; Step S12, according to the safety distance and the distance value per unit time, it is determined whether there is a collision risk between the specific remote vehicle in front and behind in the same lane and the vehicle; if there is no collision risk, the vehicle continues to perform lane keeping and adaptive cruise in combination with the current vehicle speed and the set expected vehicle speed; Step S13, if the vehicle and each specific remote vehicle in the same lane have a collision risk, or the difference between the current vehicle speed of the vehicle and the preset expected vehicle speed exceeds a predetermined threshold, the automatic lane changing detection is started, the optimal lane changing trajectory and the optimal lane changing time for automatic lane changing to the adjacent lane on both sides are obtained according to the optimal lane changing trajectory calculation, and whether there is a risk for automatic lane changing to the adjacent lane on both sides is determined according to the optimal lane changing time and the collision time sequence; Step S14, when it is determined that there is no risk for automatic lane changing to at least one adjacent lane, one adjacent lane is selected, the expected front wheel steering angle and the longitudinal vehicle speed are obtained, the automatic lane changing operation is performed according to the optimal lane changing trajectory, and the lane keeping and adaptive cruise are performed on the changed lane according to the set expected vehicle speed; Step S15, when it is determined that there is a risk for automatic lane changing to both sides, the automatic lane changing detection is closed, the vehicle speed of the vehicle is automatically adjusted to keep a safety distance between the vehicle and the specific remote vehicle in front and behind in the same lane, and the lane keeping and adaptive cruise are performed on the current lane.

2. The method of claim 1, wherein, The V2I message sent by the roadside unit is a local map message, which at least includes road information, lane ID number and speed limit information; the V2V message includes vehicle state information of each vehicle within a predetermined distance range, and the vehicle state information includes vehicle speed, vehicle position coordinates, steering wheel steering angle, vehicle heading angle and acceleration information.

3. The method of claim 2, wherein, The step S11 further comprises: The road information and lane ID number in the V2I message are used to perform coordinate translation transformation on the remote vehicles within a predetermined range around the vehicle in combination with each V2V message, and all remote vehicle sequences in the same lane and adjacent lanes of the vehicle are screened out according to the relative positions of each remote vehicle relative to the vehicle; and the remote vehicle closest to the vehicle in front and behind in the same lane or adjacent lane is identified as a specific remote vehicle; According to the state information of each specific remote vehicle, the corresponding safety threshold and distance value per unit time between the vehicle and each specific remote vehicle in each time interval are obtained through iterative calculation by vector method; if there is a distance value per unit time less than or equal to the corresponding safety threshold, the iterative calculation of the corresponding specific remote vehicle is stopped, and the collision time sequence of the specific remote vehicle stopped from iteration is obtained.

4. The method of claim 3, wherein, The step S12 further comprises: When it is determined that the distance value per unit time between the specific far vehicle closest to the ego vehicle on the same lane and the ego vehicle is less than or equal to the corresponding safety threshold, it is determined that there is a collision risk between the specific far vehicle on the lane and the ego vehicle, including that there is a collision risk between the specific far vehicle in front and the ego vehicle, and that there is a collision risk between the specific far vehicle behind and the ego vehicle.

5. The method of claim 4, wherein, The step S13 further includes: After starting the automatic lane changing detection, lane changing detection is performed on each adjacent lane, and the optimal lane changing trajectory calculation of the fusion of the 5th order polynomial and the genetic algorithm is used to obtain the optimal trajectory of the lane changing on the adjacent lane and the optimal lane changing time; When it is determined that the distance value per unit time corresponding to each specific far vehicle on the adjacent lane is greater than the corresponding safety threshold, and there is no collision time sequence or the collision time sequence is greater than the optimal lane changing time, it is determined that there is no risk in the lane changing to the adjacent lane.

6. The method of claim 5, wherein, The step S15 further includes: If it is determined that there is a risk in the lane changing to the adjacent lane, the automatic lane changing detection is closed; When there is a collision risk between the specific far vehicle in front of the lane and the ego vehicle, the ego vehicle is automatically controlled to slow down and follow the specific far vehicle in front; when there is a collision risk between the specific far vehicle behind the lane and the ego vehicle, the ego vehicle is automatically controlled to accelerate and avoid the specific far vehicle behind; Lane keeping and adaptive cruise are performed on the current lane. 7.A driving control system applied to a vehicle having a V2X on-board unit, characterized by, It includes: An information receiving unit is configured to receive V2I messages transmitted by a roadside unit in real time, and receive V2V messages transmitted by far vehicles within a predetermined range; A specific far vehicle state determining unit is configured to determine state information of each far vehicle closest to the ego vehicle in front of and behind on the same lane or adjacent lanes, and safety distance, distance value per unit time, and collision time sequence between the ego vehicle and each far vehicle according to the V2I messages and each V2V message; A collision risk identification processing unit is configured to determine whether there is a collision risk between the specific far vehicle in front of and behind on the same lane and the ego vehicle according to the safety distance and the distance value per unit time; if there is no collision risk, the ego vehicle continues to perform lane keeping and adaptive cruise in combination with the current vehicle speed and the set desired vehicle speed; An automatic lane changing detection unit is configured to start automatic lane changing detection when it is detected that there is a collision risk between the ego vehicle and the specific far vehicle in front of and behind on the same lane, or the difference between the current vehicle speed of the ego vehicle and the preset desired vehicle speed exceeds a predetermined threshold, and to obtain the optimal lane changing trajectory and the optimal lane changing time of the automatic lane changing to the adjacent lane according to the optimal lane changing trajectory calculation, and to determine whether there is a risk in the automatic lane changing to the adjacent lane according to the optimal lane changing time and the collision time sequence; An automatic lane changing processing unit is configured to select an adjacent lane when it is determined by the automatic lane changing detection unit that there is no risk in the automatic lane changing to at least one adjacent lane, to obtain a desired front wheel steering angle and a longitudinal vehicle speed, to track the optimal lane changing trajectory, to perform automatic lane changing operation, and to perform lane keeping and adaptive cruise on the changed lane according to the set desired vehicle speed. The vehicle speed adjusting unit is configured to, when the automatic lane changing detection unit determines that there is risk in automatic lane changing on both sides, close the automatic lane changing detection, automatically adjust the vehicle speed of the host vehicle, maintain a safe distance between the host vehicle and specific distant vehicles in front and behind the host vehicle in the current lane, and then perform lane keeping and adaptive cruise control on the current lane.

8. The system of claim 7, wherein, The V2I message sent by the road side unit is a local map message, which at least includes road information, lane ID number, and speed limit information; and the V2V message includes vehicle state information of each vehicle within a predetermined distance range, and the vehicle state information includes vehicle speed, vehicle position coordinates, steering wheel angle, vehicle heading angle, and acceleration information.

9. The system of claim 8, wherein, The specific distant vehicle state determining unit further includes: A specific distant vehicle selecting unit is configured to perform coordinate translation transformation on distant vehicles within a predetermined range around the host vehicle by using road information and lane ID number in the V2I message and combining each V2V message, and screen all distant vehicle sequences in the same lane and adjacent lanes of the host vehicle according to relative positions of the distant vehicles relative to the host vehicle, and identify each distant vehicle in front and behind the host vehicle in the same lane or adjacent lanes as a specific distant vehicle. A vector calculation processing unit is configured to obtain a corresponding safety threshold value and a distance value per unit time between each specific distant vehicle and the host vehicle within each time interval by iterative calculation through a vector method according to state information of each specific distant vehicle, and stop iterative calculation on a corresponding specific distant vehicle if the distance value per unit time is less than or equal to the corresponding safety threshold value, and obtain a collision time sequence of the specific distant vehicle for which the iterative calculation is stopped.

10. The system of claim 9, wherein, The collision risk identification processing unit further includes: A host lane risk determining unit is configured to determine whether there is a collision risk between the host vehicle and the distant vehicles in front and behind the host vehicle in the same lane according to the collision time sequence. A host lane maintaining processing unit is configured to continue lane keeping and adaptive cruise control of the host vehicle in combination with a current vehicle speed and a set desired vehicle speed if the host lane risk determining unit determines that there is no collision risk in the same lane.

11. The system of claim 10, wherein, The automatic lane changing detection unit further includes: An optimal trajectory calculation unit is configured to, after starting automatic lane changing detection, perform lane changing detection on each adjacent lane, and obtain an optimal trajectory for changing lanes to the adjacent lane and an optimal lane changing time by using an optimal lane changing trajectory calculation that combines a 5th-degree polynomial and a genetic algorithm. The lane changing detection closing unit is configured to close the automatic lane changing detection if the adjacent lane risk discriminating unit determines that there is risk in changing lanes to both adjacent lanes.

12. The system of claim 11, wherein, The vehicle speed adjusting unit further includes: The lane changing detection closing unit is configured to close the automatic lane changing detection if the adjacent lane risk discriminating unit determines that there is risk in changing lanes to both adjacent lanes. The adjusting holding unit is used for automatically controlling the host vehicle to decelerate and follow the specific far vehicle in front of the host vehicle lane when the specific far vehicle in front of the host vehicle lane has a collision risk with the host vehicle after the automatic lane changing detection is closed, automatically controlling the host vehicle to accelerate and drive away from the specific far vehicle behind the host vehicle lane when the specific far vehicle behind the host vehicle lane has a collision risk with the host vehicle, and performing lane holding and adaptive cruise on the host vehicle lane.

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