CN116434603A - Automatic driving fleet horizontal and vertical synchronous safety control method based on SSM - Google Patents
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Abstract
The invention discloses an automatic driving fleet horizontal and vertical synchronous safety control method based on SSM, which comprises the following steps: firstly, aiming at curve scenes in an intelligent networking environment, after vehicles in an automatic driving motorcade receive front and rear vehicle running information and road state information; then constructing an automatic driving vehicle safety control target according to the strategy of utilizing the SSM and the distance between the motorcades; and then, combining model predictive control with vehicle dynamics to dynamically program and control the path selection of the vehicle in real time. The invention can ensure the efficiency of the automatic driving motorcade in the curve scene and improve the safety of the automatic driving motorcade.
Description
Technical Field
The invention relates to an intelligent traffic synchronous safety control method, in particular to an automatic driving fleet transverse and longitudinal synchronous safety control method based on SSM.
Background
The prior researches show that more than 90 percent of vehicle collision accidents are caused by human errors. Furthermore, according to the data of the united states department of transportation research and innovation technology administration, it is possible to reduce about 80% of vehicle collision accidents annually based on the automated driving vehicle technology. Because the automatic driving motorcade can accurately sense the surrounding environment, the reaction time is negligible, and the automatic driving motorcade is not influenced by distraction and fatigue driving, can coordinate the safe and compact running of a plurality of vehicles, and improves the traffic efficiency and the safety. However, when the outside of the automatic driving fleet interferes, it is difficult to maintain a preset vehicle spacing, increasing the risk of collision. Currently, most autopilot fleet control algorithms assume that the vehicle is traveling on a straight road, such as adaptive cruise control and coordinated adaptive cruise control. However, on curved roads, not only the longitudinal direction in which the autonomous vehicle is traveling, but also the dynamic change in the lateral direction should be considered. Furthermore, the risk of collision of the motorcade is higher on curved roads than on straight roads. There is little research in the safety control of autopilot fleets that address external disturbances on curved roads.
On curved roads, autonomous vehicles need not only have the ability to track a predetermined path, but also need to avoid collisions and reduce the risk of collisions caused by external disturbances. For longitudinal and lateral synchronous control of an autopilot fleet, most current algorithms employ hierarchical classification to address this problem: the trajectory is first planned taking into account only the position and speed of the autonomous vehicle and then the planned trajectory is tracked using a simple feedback controller. In order to achieve reliability and safe maneuver of an autonomous vehicle, researchers have proposed a number of motion planning strategies to optimize the path or trajectory of the autonomous vehicle over various roads by tracking the underlying feedback controllers. However, the above studies either incorporate a risk indicator (e.g., minimum time interval, minimum deceleration) into the control target to reduce the risk of collision, or consider a safety constraint (e.g., minimum safety interval) to ensure a sufficient distance interval between vehicles. For example, a learner has proposed a roll horizon control method for an autopilot system that provides a mechanism to minimize the safety risk of an autopilot vehicle under traffic disturbances. With this mechanism, an alternate safety measure (SSM) may be easily incorporated into an autonomous vehicle safety control objective. Among the various SSMs, a Time To Collision (TTC) and its comprehensive index, such as a time to exposure collision (TET), a time to integral collision (TIT), a rear end collision risk index (RCRI), a difference in space and parking Distance (DSS), a deceleration rate to avoid collision (DRAC), and a post intrusion time (PET), have been used for automated driving vehicle safety assessment. While SSM has been used to evaluate the safety impact of autonomous vehicles, no study has been made to directly use SSM as a control target for autonomous fleet trajectory optimization.
In summary, in the track optimization of the automatic driving fleet under the curve situation, the key technology of the optimal transverse and longitudinal synchronous safety control of the SSM is directly considered to be researched.
Disclosure of Invention
The invention aims to: the invention aims to provide an automatic driving fleet transverse and longitudinal synchronous safety control method based on SSM, which improves the safety of vehicles in the automatic driving fleet in a curve scene on the premise of not losing the running efficiency of the vehicles.
The technical scheme is as follows: the invention relates to a transverse and longitudinal synchronous safety control method for an automatic driving motorcade, which comprises the following steps:
s1, obtaining road information of a curve scene;
s2, acquiring initial running states of all vehicles in a motorcade;
s3, setting a vehicle safety distance control strategy in a motorcade, and controlling the motorcade by using a fixed headway strategy;
s4, taking a time period from the current moment to a set value as a prediction range of model prediction control, and designing sampling time and control time;
s5, designing a vehicle control objective function based on the selected SSM index and the designed vehicle target headstock distance;
s6, solving an optimal solution of a vehicle control objective function by using quadratic programming;
s7, controlling all vehicles by taking the first solution of all vehicles in the motorcade as control input according to the optimal solution obtained in the step S6;
s8, updating all vehicle running states;
and S9, if the vehicles do not all pass through the curve scene, repeating the steps S3 to S8 until all the vehicles pass through the curve scene.
Further, in step S3, a vehicle safety distance control strategy in the fleet is set, and the fleet control is performed by using the fixed headway strategy as follows:
s31, the lateral-longitudinal state of the vehicle i satisfies the following equation:
wherein X is the longitudinal displacement of the central point of the automatic driving vehicle i; y is the lateral displacement of the i central point of the automatic driving vehicle，
Is a derivative thereof; v x Representing the longitudinal speed, v, of the centre point of an autonomous vehicle i y A lateral speed representing the centre point of the autonomous vehicle i; psi represents the direction angle of the centre point of the autonomous vehicle i,/->
Is a derivative thereof; r represents the yaw rate of the centre point of the autonomous vehicle i,/->
Is a derivative thereof; beta is the slip angle of the autonomous vehicle i, < >>
Is a derivative thereof; f (F) xr Representing the longitudinal force of the rear wheel of the autonomous vehicle i, F yf Representing the lateral force of the front wheels of the autonomous vehicle i; f (F) yr Representing the lateral force of the rear wheels of the autonomous vehicle i; m is the mass of the vehicle; i z Yaw inertia as a center point; l (L) r Representing the distance from the center point of the autonomous vehicle to the rear wheels; l (L) f Representing the distance from the center point of the autonomous vehicle to the front wheels; delta represents the steering angle of the autonomous vehicle i, < >>
For its derivative, δ' represents the ideal steering angle of the autonomous vehicle i; f (F) x Representing the longitudinal force of the autonomous vehicle i +.>
For its derivative, F' x Indicating the ideal longitudinal force of the autonomous vehicle i. Wherein, delta 'and F' x Control parameters entered for the vehicle.
S32, solving a vehicle state equation, wherein the expression of the vehicle state equation is as follows:
dx＝fdt+G·udt
wherein, the liquid crystal display device comprises a liquid crystal display device,
x＝[X Yψβr v x v y δF x ] T
G＝[0 0 0 0 0 0 0 10 10] T
s33, selecting a fixed headway strategy, wherein the expression is as follows:
τ * v i,x (t)+l f +l r ＝(β i-1,y (t)-β i,y (t))×R
wherein τ * Representing the inter-vehicle head spacing; r represents the radius of curvature of the curve.
Further, in step S5, the step of designing the objective function based on the selected SSM index and the designed vehicle target head space includes:
s51, determining a state constraint condition of the vehicle:
-23deg≤δ≤23deg
|F′ x |≤8600N
s52, the vehicle control target is expressed as:
min q(x,u)＝α 1 (τ * v i,x (t)+l f +l r -(β i-1,y (t)-β i,y (t))×R-s 0 ) 2 +α 2 d 2
+α 3 (v i,x (t)-v i-1,x (t)) 2 +α 4 (SSM * )
wherein alpha is 1 、α 2 、α 3 、α 4 Is a weight coefficient; s is(s) 0 Is the rest distance; d is the geometric influence coefficient of the curve; SSM (secure storage management) * For control targets designed according to the SSM selected.
Further, when the time to collision TTC with the preceding vehicle is selected as the control target, since TTC is expressed as:
then
SSM * TTC ＝(v i,x (t)-v i-1,x (t)) 2 -((β i-1,y (t)-β i,y (t))×R-(l f +l r )) 2 。
Further, when the collision avoidance deceleration DRAC is selected as the control target, since the DRAC is expressed as:
wherein p is i (t) represents the position of the vehicle i; l represents the vehicle length.
Then
SSM * DRAC ＝(v i,x (t)-v i-1,x (t)) 2 -(β i-1,y (t)-β i,y (t))×R。
Further, when the emergency deceleration collision potential index PICUD is selected as the control target, since the PICUD is expressed as:
wherein a represents acceleration; Δt represents a sampling time interval;
then there are:
compared with the prior art, the invention has the following remarkable effects:
according to the invention, the SSM is utilized to design the objective function of the vehicle in the vehicle team, so that the vehicle team can be automatically driven to run in the curve scene under the cooperative environment of the vehicle and the road, and the transverse and longitudinal synchronous and effective safety control of the vehicle team is realized.
Drawings
FIG. 1 is a schematic view of the lateral and longitudinal variables of a vehicle according to the present invention;
FIG. 2 is a flow chart of the present invention;
FIG. 3 (a) is a schematic diagram showing the simulation effect of the spatial displacement variation of the center point of the autonomous vehicle according to the present invention,
FIG. 3 (b) is a schematic diagram showing the simulation effect of the longitudinal displacement variation of the center point of the autonomous vehicle according to the present invention,
FIG. 3 (c) is a schematic diagram showing the simulation effect of the lateral displacement variation of the center point of the autonomous vehicle according to the present invention,
FIG. 3 (d) is a schematic diagram showing the simulation effect of the change of the direction angle of the center point of the autonomous vehicle according to the present invention,
FIG. 3 (e) is a schematic diagram showing the simulation effect of the change in slip angle of the autonomous vehicle according to the present invention,
FIG. 3 (f) is a schematic diagram showing the effect of simulation of the longitudinal speed variation of the center point of the autonomous vehicle of the present invention,
FIG. 3 (g) is a schematic diagram showing the simulation effect of the lateral velocity variation of the center point of the autonomous vehicle of the present invention,
FIG. 3 (h) is a schematic diagram showing the simulation effect of the yaw rate variation of the center point of the autonomous vehicle of the present invention,
FIG. 3 (i) is a schematic view showing the effect of simulation of the change in steering angle of the autonomous vehicle of the present invention,
fig. 3 (j) is a schematic diagram showing the effect of simulation of the longitudinal force variation of the autonomous vehicle according to the present invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings. The embodiments described below by referring to the drawings are exemplary only for explaining the present invention and are not to be construed as limiting the present invention.
It should be noted that the terms like "upper", "lower", "left", "right", "front", "rear", and the like are also used for descriptive purposes only and are not intended to limit the scope of the invention in which the invention may be practiced, but rather the relative relationship of the terms may be altered or modified without materially altering the teachings of the invention.
Aiming at the condition that an automatic driving fleet runs in a curve scene in an intelligent networking environment, the invention firstly utilizes curve information and an initial state of a vehicle according to a V2V and V2I communication technology, then utilizes a selected SSM (Surrogate Safety Measures to replace a safety measure) index and a vehicle fixed-head time interval strategy to formulate an objective function, dynamically plans the path selection of an emergency vehicle through the idea of model prediction control rolling time domain, and improves the safety of the vehicle in the automatic driving fleet in the curve scene on the premise of not losing the running efficiency of the vehicle.
FIG. 1 is a schematic view of a vehicle transverse and longitudinal variable according to an embodiment of the present invention; fig. 2 is a frame diagram of a transverse and longitudinal synchronous safety control method of an automatic driving fleet. The method and the device are suitable for the situation that the safety optimization track of the automatic driving fleet is dynamically planned in the curve scene through equipment such as a server.
The invention relates to a transverse and longitudinal synchronous safety control method for an automatic driving motorcade, which comprises the following steps:
And 2, acquiring initial running states of all vehicles in the motorcade, wherein the initial running states comprise state parameters such as vehicle positions, speeds and the like.
Step 3, setting a vehicle safety distance control strategy in a motorcade, and selecting a fixed headway strategy to control the motorcade; the specific implementation steps are as follows:
step 31, the motion state of the vehicle i is changed as shown in fig. 1, and the lateral-longitudinal state thereof satisfies the formulas (1) to (9):
wherein X is the longitudinal displacement of the central point of the automatic driving vehicle i; y is the lateral displacement of the central point of the automatic driving vehicle i,
is a derivative thereof; v x Representing the longitudinal speed, v, of the centre point of an autonomous vehicle i y A lateral speed representing the centre point of the autonomous vehicle i; psi represents the direction angle of the centre point of the autonomous vehicle i,/->
Is a derivative thereof; r represents the yaw rate of the centre point of the autonomous vehicle i,/->
Is a derivative thereof; beta is the slip angle of the autonomous vehicle i, < >>
Is a derivative thereof; f (F) xr Representing the longitudinal force of the rear wheel of the autonomous vehicle i, F yf Representing the lateral force of the front wheels of the autonomous vehicle i; f (F) yr Representing the lateral force of the rear wheels of the autonomous vehicle i; m is the mass of the vehicle; i z Yaw inertia as a center point; l (L) r Representing the distance from the center point of the autonomous vehicle to the rear wheels; l (L) f Representing the distance from the center point of the autonomous vehicle to the front wheels; delta represents the steering angle of the autonomous vehicle i, < >>
For its derivative, δ' represents the ideal steering angle of the autonomous vehicle i; f (F) x Representing the longitudinal force of the autonomous vehicle i +.>
For its derivative, F' x Indicating the ideal longitudinal force of the autonomous vehicle i. Wherein, delta 'and F' x Control parameters entered for the vehicle.
Step 32, according to equations (1) - (9), the vehicle state equation may be listed as:
dx＝fdt+G·udt (10)
wherein, the liquid crystal display device comprises a liquid crystal display device,
x＝[X Y ψ β r v x v y δ F x ] T
G＝[0 0 0 0 0 0 0 10 10] T
where T represents the matrix transpose.
Step 33, under the condition of step 32, the fixed headway strategy of the ith vehicle and the ith-1 vehicle is:
τ * v i,x (t)+l f + l r ＝(β i-1,y (t)-β i,y (t))×R (11)
wherein τ * Representing the inter-vehicle head spacing; v i,x (t) represents the longitudinal speed of the i-th autonomous vehicle; l (L) r Representing the distance from the center point of the autonomous vehicle to the rear wheels; l (L) f Representing the distance from the center point of the autonomous vehicle to the front wheels; beta i,y (t) represents the lateral slip angle of the autonomous vehicle i; r represents the radius of curvature of the curve.
And 4, taking a time period from the current moment to 10 seconds as a prediction range of model prediction control, and designing sampling time and control time to be 1 second.
step 51, determining a state constraint condition of the vehicle:
-23deg≤δ≤23deg (12)
|F′ x |≤8600N (13)
the vehicle control target may be expressed as:
min q(x,u)＝α 1 (τ * v i,x (t)+l f +l r -(β i-1,y (t)-β i,y (t))×R-s 0 ) 2 +α 2 d 2 +α 3 (v i,x (t)-v i-1,x (t)) 2 +α 4 (SSM * ) (14)
wherein alpha is 1 、α 2 、α 3 、α 4 Is a weight coefficient; s is(s) 0 Is the rest distance; d is the geometric influence coefficient of the curve; SSM (secure storage management) * For control targets designed according to the SSM selected.
For example: when TTC (Time-to-Collision Time with a preceding vehicle) is selected as a control target, since TTC is expressed as:
then
SSM*TTC＝(v i,x (t)-v i-1,x (t)) 2 -((β i-1,y (t)-β i,y (t))×R-(l f + l r )) 2 (16)
When a DRAC (Deceleration Rate to Avoid a Crash), collision avoidance deceleration, is selected as the control target, since the DRAC is expressed as:
wherein p is i (t) represents the position of the vehicle i; v i (t) represents the speed of the vehicle i; v i-1 (t) represents the speed of the vehicle i-1; l represents the vehicle length.
Then
SSM * DRAC ＝(v i,x (t)-v i-1,x (t)) 2 -(β i-1,y (t)-β i,y (t))×R (18)
When the PICUD (Potential Index for Collision with Urgent Deceleration, emergency deceleration collision potential index) is selected as the control target, since the PICUD is expressed as:
wherein a represents acceleration; Δt represents the sampling time interval.
Then
And 6, solving an optimal solution of the vehicle objective function by using quadratic programming.
And 8, repeating the steps 1-7, and updating all the running states of the vehicles.
And 9, if the vehicles do not all pass through the curve scene, namely, the vehicles all reach the end point, repeating the steps 3 to 8 until all the vehicles pass through the curve scene.
Simulation experiment:
parameters of a fleet of 6 autonomous vehicles are shown in table 1.
Table 1 6 simulation data design for fleet of autonomous vehicles
Table 2 shows the results of three fixed headway strategies (τ=0.5 s, τ=1.0 s, τ=1.5 s) for intelligent networked vehicles (CAV, connected and Autonomous Vehicle), and the results of fleet safety metrics (minimum TTC value, maximum DRAC value, minimum PICUD value) and fleet stationary state (full range speed change variance, full range acceleration change variance) under different SSM control.
TABLE 2 safety Performance of CAV fleet on circular road under different headway strategies
Fig. 3 (a) - (j) are schematic diagrams of simulation effects at three different SSMs (TTC, DRAC, PICUD).
Therefore, after the invention is adopted, the safety of the automatic driving motorcade in a curve scene can be improved to a certain extent. Compared with a fleet which does not consider SSM, the safety index is improved, and the speed and acceleration variance is relatively small, namely, the fleet can ensure a smoother driving process.
The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above examples, and all technical solutions belonging to the concept of the present invention belong to the protection scope of the present invention. It should be noted that modifications and adaptations to the invention without departing from the principles thereof are intended to be within the scope of the invention as set forth in the following claims.
Claims (6)
1. An automatic driving fleet horizontal and vertical synchronous safety control method based on SSM is characterized by comprising the following steps:
s1, obtaining road information of a curve scene;
s2, acquiring initial running states of all vehicles in a motorcade;
s3, setting a vehicle safety distance control strategy in a motorcade, and controlling the motorcade by using a fixed headway strategy;
s4, taking a time period from the current moment to a set value as a prediction range of model prediction control, and designing sampling time and control time;
s5, designing a vehicle control objective function based on the selected SSM index and the designed vehicle target headstock distance;
s6, solving an optimal solution of a vehicle control objective function by using quadratic programming;
s7, controlling all vehicles by taking the first solution of all vehicles in the motorcade as control input according to the optimal solution obtained in the step S6;
s8, updating all vehicle running states;
and S9, if the vehicles do not all pass through the curve scene, repeating the steps S3 to S8 until all the vehicles pass through the curve scene.
2. The SSM-based automatic driving fleet horizontal-vertical synchronization safety control method according to claim 1, wherein in step S3, a vehicle safety distance control strategy in the fleet is set, and the step of performing fleet control with a fixed headway strategy is as follows:
s31, the lateral-longitudinal state of the vehicle i satisfies the following equation:
wherein X is the longitudinal displacement of the central point of the automatic driving vehicle i; y is the lateral displacement of the central point of the automatic driving vehicle i;
is a derivative thereof; v x Representing the longitudinal speed, v, of the centre point of an autonomous vehicle i y A lateral speed representing the centre point of the autonomous vehicle i; psi represents the direction angle of the centre point of the autonomous vehicle i,/->
Is a derivative thereof; r represents the yaw rate of the centre point of the autonomous vehicle i,/->
Is a derivative thereof; beta is the slip angle of the autonomous vehicle i, < >>
Is a derivative thereof; f (F) xr Representing the longitudinal force of the rear wheel of the autonomous vehicle i, F yf Representing the lateral force of the front wheels of the autonomous vehicle i; f (F) yr Representing the lateral force of the rear wheels of the autonomous vehicle i; m is the mass of the vehicle; i z Yaw inertia as a center point; l (L) r Representing the distance from the center point of the autonomous vehicle to the rear wheels; l (L) f Representing the distance from the center point of the autonomous vehicle to the front wheels; delta represents the steering angle of the autonomous vehicle i, < >>
For its derivative, δ' represents the ideal steering angle of the autonomous vehicle i; f (F) x Representing the longitudinal force of the autonomous vehicle i +.>
For its derivative, F' x Indicating the ideal longitudinal force of the autonomous vehicle i. Wherein, delta 'and F' x Control parameters entered for the vehicle.
S32, solving a vehicle state equation, wherein the expression of the vehicle state equation is as follows:
dx＝fdt+G·udt
wherein, the liquid crystal display device comprises a liquid crystal display device,
x＝[X Y ψ β r v x v y δ F x ] T
G＝[0 0 0 0 0 0 0 10 10] T
s33, selecting a fixed headway strategy, wherein the expression is as follows:
τ * v i,x (t)+l f +l r ＝(β i-1,y (t)-β i,y (t))×R
wherein τ * Representing the inter-vehicle head spacing; v i,x (t) represents the longitudinal speed of the i-th autonomous vehicle; beta i,y (t) represents the lateral slip angle of the autonomous vehicle i; beta i-1,y (t) represents the lateral slip angle of the autonomous vehicle i-1; r represents the radius of curvature of the curve.
3. The SSM-based automatic fleet horizontal-to-vertical synchronization safety control method according to claim 2, wherein in step S5, the step of designing an objective function based on the selected SSM index and the design vehicle target head-to-head distance comprises:
s51, determining a state constraint condition of the vehicle:
-23deg≤δ≤23deg
|F′ x |≤8600N
s52, the vehicle control target is expressed as:
min q(x,u)＝α 1 (τ * v i,x (t)+l f +l r -(β i-1,y (t)-β i,y (t))×R-s 0 ) 2 +α 2 d 2 +α 3 (v i,x (t)-v i-1,x (t)) 2 +α 4 (SSM * )
wherein alpha is 1 、α 2 、α 3 、α 4 Is a weight coefficient; s is(s) 0 Is the rest distance; d is the geometric influence coefficient of the curve; v i-1,x (t) represents the longitudinal speed of the i-1 st autonomous vehicle; SSM (secure storage management) * For control targets designed according to the SSM selected.
4. The SSM-based automatic fleet horizontal-to-vertical synchronization safety control method according to claim 3, wherein when the time to collision TTC with the preceding vehicle is selected as the control target, the TTC is expressed as:
then
SSM * TTC ＝(v i,x (t)-v i-1,x (t)) 2 -((β i-1,y (t)-β i,y (t))×R-(l f +l r )) 2 。
5. A SSM-based automatic fleet horizontal-to-vertical synchronization safety control method according to claim 3, characterized in that when the collision avoidance deceleration DRAC is selected as the control target, the following is expressed as DRAC:
wherein p is i (t) represents the position of the vehicle i; p is p i-1 (t) represents the position of the vehicle i-1; v i (t) represents the speed of the vehicle i; v i-1 (t) represents the speed of the vehicle i-1; l represents the vehicle length.
Then
SSM * DRAC ＝(v i,x (t)-v i-1,x (t)) 2 -(β i-1,y (t)-β i,y (t))×R。
6. The SSM-based automatic fleet horizontal-to-vertical synchronization safety control method according to claim 3, wherein when the emergency deceleration collision potential index PICUD is selected as the control target, since the PICUD is expressed as:
wherein a represents acceleration; Δt represents a sampling time interval;
then
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