CN113085853B - Auxiliary driving system for actively dodging large-scale vehicle in lane - Google Patents
Auxiliary driving system for actively dodging large-scale vehicle in lane Download PDFInfo
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Abstract
The invention provides an auxiliary driving system for actively avoiding large vehicles in a lane, which comprises a sensing module, a decision-making module, a planning module, a tracking module and a controller, wherein the sensing module comprises an environment sensing sensor, the environment sensing sensor is connected to the input end of the decision-making module through signals, the decision-making module determines four states of the auxiliary driving system through the environment sensing sensor, the output end of the decision-making module is connected to the input end of the planning module through signals, the planning module is used for planning vehicle motion tracks in deviation and regression processes, and the output end of the planning module is connected to the input end of the tracking module through signals. The auxiliary driving system for actively avoiding the large-scale vehicle in the lane can realize the automatic target identification, decision making, active avoiding track planning and avoiding action executing functions in the scene of exceeding the large-scale vehicle, and can improve the side collision safety of the vehicle in the scene and the safety of drivers and passengers.
Description
Technical Field
The invention belongs to the technical field of active safety technology, advanced driving auxiliary systems, perception, decision, planning and tracking of automatic driving, and particularly relates to an auxiliary driving system for actively dodging large-scale vehicles in a lane.
Background
An Advanced Driver Assistance System (ADAS) collects road and environmental vehicle parameters in a vehicle running environment through a vehicle-mounted sensor, and identifies, detects and tracks a target object, so that the risk possibly encountered by the vehicle can be predicted, the motion state of the vehicle can be actively changed or a prompt is actively sent to a Driver, and the safety is improved. Common sensors used in the ADAS system include millimeter wave radar, laser radar, ultrasonic radar, cameras, and the like. According to an actuating mechanism controlled by an assistant driving system, an ADAS system can be divided into functions of ACC (adaptive cruise control), FCW (forward collision warning) and the like mainly aiming at longitudinal motion control, LKA (lane keeping), LDW (lane departure warning), ALC (automatic lane change) and the like aiming at lateral motion control, and TJA (traffic jam assistance), HWP (high-speed automatic driving) and the like combining the ACC and the FCW;
in the functions of LKA, LDW, ALC and the like for planning and controlling the lateral movement, the LKA and the LDW aim to control the self vehicle to keep running at the central position of a lane line, belong to lateral movement control in a self lane, and the ALC judges lane change intention based on sensing information and controls cross-lane lateral movement control of a vehicle changing lane;
for the scene that the self-vehicle surpasses the large-scale vehicle, because the large-scale vehicle has large vehicle width, large lateral position fluctuation range and a relatively obvious negative pressure area exists at the tail part of the vehicle, if the function of the self-vehicle LKA is activated, the self-vehicle is continuously kept at the central line position of the lane, the meeting distance between the self-vehicle and the large-scale vehicle in the surpassing process is small, the lateral collision risk exists, and the bad driving experience is easily brought to drivers and passengers. The introduction of the DWEL system of the present invention can fundamentally solve this problem. When the self-vehicle sensor detects a target scene exceeding the large vehicle, the DWEL system is started, the self-vehicle is controlled to deviate towards the opposite direction in advance (in the self-lane) when approaching the target, and the self-vehicle is controlled to return to the center line position of the lane after exceeding. The DWEL system is introduced, so that the stability and comfort of the vehicle are guaranteed, and meanwhile the meeting distance is increased based on environmental parameters, so that the side collision safety in the exceeding process is improved, and the 'feeling of security' of a driver is improved. Meanwhile, the tracks of the deviation process and the regression process are planned based on real-time environment parameters and a driving behavior map, so that the driving habit of a human driver is met, and the safety personification characteristic is realized.
Disclosure of Invention
In view of this, the present invention aims to provide a driving assistance system (DWEL) for actively evading large vehicles in a Lane, so as to solve the problems of lateral collision risk and poor driving experience for drivers and passengers when the LKA function of the vehicle is activated.
In order to achieve the purpose, the technical scheme of the invention is realized as follows:
an assistant driving system for actively dodging large vehicles in a lane comprises a sensing module, a decision-making module, a planning module, a tracking module and a controller, wherein the sensing module comprises an environment sensing sensor which senses the environment and acquires target-level information, the target-level information comprises target vehicle identification and environment parameters and is processed, the processed target-level information is transmitted to the decision-making module, the decision-making module receives the processed target-level information, carries out conversion operation on four states of the assistant driving system according to a state machine condition transfer list and determines the current state of the assistant driving system, the four states of the assistant driving system comprise an activated state, and the decision-making module processes the processed target-level information to obtain decision instruction parameters in the activated state, and then, the decision instruction parameters are transmitted to a planning module, the planning module receives the decision instruction parameters, the planning module plans the self vehicle to avoid the deviating track and the regression track of the large vehicle through a polynomial track planning method according to the current environmental parameters and the driving behavior map to obtain planning track parameters, the planning track parameters are transmitted to a tracking module, the tracking module receives the planning track parameters, controls and operates an executing mechanism of the vehicle to ensure that the self vehicle moves along a target track, and the environmental perception sensor, the decision module, the planning module and the tracking module are all in signal connection with a controller.
Furthermore, the environment perception sensor comprises a camera, a steering wheel torque sensor, a steering wheel angle sensor, an accelerator pedal sensor, a brake pedal sensor and a gyroscope, and the camera, the steering wheel torque sensor, the steering wheel angle sensor, the accelerator pedal sensor, the brake pedal sensor and the gyroscope are all in signal connection with the controller.
Further, the four states of the driving assistance system further include a shutdown state, a standby state and a fault state, the shutdown state is started to enter the standby state, the standby state is closed to enter the shutdown state, the standby state is activated to enter the activated state, the activated state is exited to enter the standby state, and when the system is in the standby state and the activated state, if a fault is detected, the system enters the fault state, the fault state is removed, and the system enters the shutdown state.
Further, the target level information processing operation comprises the following steps:
a1, judging whether the type of the target vehicle is a large vehicle by the sensing module, if so, carrying out the next step, otherwise, switching the next target vehicle, and re-entering the step A1;
a2, judging whether the longitudinal distance between the target vehicle and the self vehicle is within a threshold value delta Dx (150m) by a sensing module, if so, carrying out the next step, otherwise, switching the next target vehicle, and re-entering the step A1;
a3, judging whether the transverse distance of the target vehicle relative to the vehicle is within the range delta Dy by the sensing module1In (2-5 m), if yes, increasing 1 by Left _ Count, switching to the next target vehicle, and re-entering the step A1, otherwise, performing the next step;
a4, perception module judgmentWhether the transverse distance of the target-breaking vehicle relative to the vehicle is within the range of delta Dy2In (-5- — 2m), if yes, Right _ Count is increased by 1, the next target vehicle is switched, and the step A1 is entered again, otherwise, the next target vehicle is switched, and the step A1 is entered again; (ii) a
A5, when all target vehicles are subjected to the judgment process from a1 to a4, the sensing module judges whether Left _ Count is greater than 0 and Right _ Count is 0, if Left _ Count is greater than 0 and Right _ Count is 0, the target vehicle is located on the Left side, all information of the large vehicle with the minimum relative longitudinal distance is output, at the moment, the target scene requirement is met, otherwise, the next step is carried out;
a6, the sensing module determines whether Left _ Count is 0 and Right _ Count is greater than 0, if Left _ Count is 0 and Right _ Count is greater than 0, the target vehicle is located at the Right side, all information of the large vehicle with the smallest relative longitudinal distance is output, and the target scene requirement is met at this moment, otherwise, the next step is performed;
a7, the sensing module determines whether Left _ Count is 0 and Right _ Count is 0, if Left _ Count is 0 and Right _ Count is 0, no large target vehicle appears, the DWEL system is in a standby state, otherwise, the next step is performed;
a8, the sensing module determines whether Left _ Count >0 and Right _ Count >0, if Left _ Count >0 and Right _ Count >0, large vehicles are present on both sides of the road, and the DWEL system is in a standby state.
Further, the judgment process in the step a5 includes four possibility judgments, and the four possibility judgments respectively include going through only the step a1, going through the steps a1 and a2 in order, going through the steps a1, a2 and A3 in order, going through the steps a1, a2, A3 and a4 in order.
Further, the polynomial trajectory planning method for the deviation trajectory and the regression trajectory comprises the following parameters: TTO (time To overtake), tau1、τ2Δ y, TTO calculation formula, τ1And τ2The parameter setting mode and the Δ y calculation formula are as follows:
TTO＝Δx/(uSV-utarget)；
τ1＝0.0394·(uSV-utarget)+3.3159(s)；
τ2＝-0.0298·(uSV-utarget)+4.6306(s)；
wherein TTO refers to the time when the vehicle and the target vehicle both run at a constant speed according to the current speed and exceed the target vehicle, Deltax is the relative longitudinal distance between the head of the vehicle and the tail of the target vehicle, and usvIs the current speed of the vehicle, utargetThe current speed of the target vehicle is obtained. Tau is1And τ2Respectively, the duration of the deviation process and the regression process, Δ y being the maximum offset of the deviation process, x1And x2The distance between the host vehicle and the target large vehicle (the lateral length of the gap between the two vehicle bodies) and the distance between the host vehicle and the side lane line of the target large vehicle are respectively.
Further, the tracking module control operation comprises the steps of:
b1, the tracking module tracks the target track by PID control,
b2, introducing a Particle Swarm Optimization (PSO) algorithm and a preview model into the tracking module to realize self-adaptive setting PID control;
b3, the tracking module continuously iterates the states of all the particles to obtain the optimal position.
Compared with the prior art, the auxiliary driving system for actively dodging the large vehicle in the lane has the following advantages:
(1) the auxiliary driving system for actively avoiding the large-scale vehicle in the lane can realize the automatic target identification, decision making, active avoiding track planning and avoiding action executing functions in the scene of exceeding the large-scale vehicle, and can improve the side collision safety of the vehicle in the scene and the safety of drivers and passengers.
(2) The planning strategy made by the auxiliary driving system for actively dodging the large-scale vehicle in the dodging track planning link is artificially developed based on natural driving behavior data, and the function can be ensured to accord with the driving habits of human (Chinese).
(3) The dodging action executing function of the auxiliary driving system for actively dodging the large vehicle in the lane can be developed on the basis of a bottom layer executing mechanism of an LKA function, and the needed modification cost is low.
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The accompanying drawings, which are included to provide a further understanding of the invention, illustrate embodiments of the invention and together with the description serve to explain the invention and do not constitute a limitation of the invention. In the drawings:
fig. 1 is a schematic view of a target detected by a camera of an assistant driving system for actively dodging a large vehicle in a lane according to an embodiment of the present invention;
fig. 2 is a flow chart of determining a type of a target object in a perception link of an assistant driving system for actively dodging a large vehicle in a lane according to an embodiment of the present invention;
fig. 3 is a state machine transition diagram of an assistant driving system for actively dodging a large vehicle in a lane according to an embodiment of the present invention;
fig. 4 is a schematic diagram illustrating a trajectory planning of a deviation and regression process of an assistant driving system for actively avoiding a large vehicle in a lane according to an embodiment of the present invention;
fig. 5 is a flowchart illustrating a trajectory tracking process of an assistant driving system for actively evading a large vehicle in a lane according to an embodiment of the present invention;
FIG. 6 is a flow chart of an optimization algorithm of a particle swarm optimization for an assistant driving system for actively dodging a large vehicle in a lane according to an embodiment of the present invention;
FIG. 7 is a diagram of a result of a self-adaptive tuning PID trajectory tracking test of an auxiliary driving system for actively dodging a large vehicle in a lane based on a Prescan-Simulink simulation platform according to an embodiment of the present invention;
fig. 8 is a schematic diagram of an error of a self-adaptive setting PID trajectory tracking test result of an auxiliary driving system for actively dodging a large vehicle in a lane based on a PreScan-Simulink simulation platform according to an embodiment of the present invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
In the description of the present invention, it is to be understood that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on those shown in the drawings, and are used only for convenience in describing the present invention and for simplicity in description, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and thus, are not to be construed as limiting the present invention. Furthermore, the terms "first", "second", etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first," "second," etc. may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless otherwise specified.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood by those of ordinary skill in the art through specific situations.
The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
As shown in fig. 1, an assistant driving system (DWEL) for actively dodging a large vehicle in a lane includes a sensing module, a decision-making module, a planning module, a tracking module and a controller, wherein the sensing module includes an environmental sensing sensor, the environmental sensing sensor is in signal connection with an input end of the decision-making module, the decision-making module determines four states of the assistant driving system through an environmental sensing sensor signal, an output end of the decision-making module is in signal connection with an input end of the planning module, the planning module is used for planning a vehicle motion track in a deviation process and a regression process, an output end of the planning module is in signal connection with an input end of the tracking module, the decision-making module, the planning module and the tracking module are all in signal connection with the controller, the controller is an ECU, the system can actively sense the surrounding environment aiming at a risk scene that the vehicle exceeds the large vehicle, and plan a deviation track and a regression track of the vehicle dodging the large vehicle according to current environmental parameters and a driving behavior map The proper vehicle clearance in the overrunning process is ensured, and the driving safety is improved.
The environment perception sensor comprises a camera, a steering wheel torque sensor, a steering wheel angle sensor, an accelerator pedal sensor, a brake pedal sensor and a gyroscope, the camera, the steering wheel torque sensor, the steering wheel angle sensor, the accelerator pedal sensor, the brake pedal sensor and the gyroscope are all connected to the controller through signals, and the camera perception information comprises but is not limited to: object ID, type, relative distance, relative speed, left and right lane line information, and the like. The steering wheel torque sensor is used for detecting the torque applied to the steering wheel by a driver. The steering wheel angle sensor is used for detecting the steering wheel angle. The accelerator pedal sensor is used for detecting the opening degree of an accelerator pedal. The brake pedal sensor is used for detecting the angle of the brake pedal. The gyroscope is used for detecting the speed, the longitudinal acceleration and the lateral acceleration of the bicycle. If the target object in a certain longitudinal range meets the condition that only one-side adjacent lane (left adjacent lane or right adjacent lane) of the host vehicle has large vehicles, the current environment meets the target scene condition of the DWEL system, otherwise, the DWEL system is not started.
As shown in fig. 3, the four states of the driver assistance system include an OFF state (OFF), a STANDBY State (STANDBY), an ACTIVE state (ACTIVE), and a fault state (fault), and the transition between these four states is determined by the DWEL system decision module. And if the deviation process and the regression process are finished, the DWEL system exits and enters the standby state. When the system is in a standby state and an activated state, if faults are detected, the system enters a fault state, and the fault state is removed and enters a closed state.
The driving assistance system includes the following operation steps:
s1, the environment perception sensor conducts perception operation on the environment, identifies a target vehicle, obtains environmental parameters, processes target-level information, and then transmits the processed target-level information to the decision module; the identification of the target vehicle and the acquisition of the environmental parameters comprise relative motion parameters of the target vehicle, lane line information and the like;
s2, the decision module receives the processed target level information, performs conversion operation on four states of the DWEL system according to the state machine condition transfer list, determines the current state of the DWEL system, performs processing operation on the processed target level information to obtain decision instruction parameters in the activated state, and then transmits the decision instruction parameters (which refer to the decision instruction parameters output by the decision module) to the planning module; the current state of the DWEL system comprises four states of closing, standby, activation and failure;
s3, the planning module receives the decision instruction parameters, plans the deviation track and the regression track of the self vehicle for avoiding the large vehicle through a polynomial track planning method according to the current environment parameters and the driving behavior map, and transmits the planned track parameters to the tracking module;
s4, the tracking module receives the planned track parameters (the planned track parameters output by the planning module), controls and operates an executing mechanism of the vehicle, ensures that the vehicle moves along a target track, the executing mechanism of the vehicle is a steer-by-wire system, the DWEL system can identify the type of a target object and detect the transverse distance of a large vehicle encountered in the driving process through a camera, and the working process can be divided into two stages: 1 deviation stage: when the vehicle approaches the targetCalculating target transverse offset delta y and offset time tau based on information such as longitudinal and transverse distances of the target, distance between the vehicle and a lane line, vehicle speed of the vehicle, longitudinal relative vehicle speed of the target and the like1And regression time τ2And sending a bottom layer actuating mechanism to turn to realize deviation so as to keep a transverse safe distance; 2, a regression stage: and returning to the position of the center line of the lane after the target is surpassed. The function can further improve the lateral safety of the vehicle and the sense of security of drivers and passengers on the basis of systems such as ACC, LKS, TJA and HWP.
In addition, the core of the decision module is a state machine, as shown in fig. 3, the decision module includes four states of off, standby, active, and failure, the initial state of the decision module should be in the off state, the decision module enters the standby state when the driver turns on the DWEL system, the decision module enters the active state when determining that the target scene requirement is met after the sensing module processes the target level information, and the decision module enters the off state when the driver turns off the DWEL system when the decision module is in the standby state. And if and only if the decision module is in a standby state, the planning module plans the deviation track and the regression track of the self vehicle to avoid the large vehicle. And after the self-vehicle returning stage is completed, the decision module exits from the activated state and enters a standby state. When the decision-making module is in an activated state or a standby state and the system detects that the sensing module, the decision-making module, the planning module or the tracking module have faults, the decision-making module reports an error and enters a fault state until the system detects that the faults are eliminated, and the decision-making module enters a closed state.
As shown in fig. 2, the target level information processing operation in step S1 includes the steps of:
a1, judging whether the type of the target vehicle is a large vehicle (truck or bus) by a sensing module, if so, carrying out the next step, otherwise, switching the next target vehicle, and re-entering the step A1;
a2, judging whether the longitudinal distance between the target vehicle and the self vehicle is within a threshold value delta Dx (150m) by a sensing module, if so, carrying out the next step, otherwise, switching the next target vehicle, and re-entering the step A1;
a3, judging whether the transverse distance of the target vehicle relative to the vehicle is in the range by the sensing moduleΔDy1In (2-5 m), if yes, increasing 1 by Left _ Count, switching to the next target vehicle, and re-entering the step A1, otherwise, performing the next step;
a4, judging whether the transverse distance of the target vehicle relative to the vehicle is within the range delta Dy by the sensing module2In (-5- — 2m), if yes, Right _ Count is increased by 1, the next target vehicle is switched, and the step A1 is entered again, otherwise, the next target vehicle is switched, and the step A1 is entered again;
a5, when all target vehicles are subjected to the judgment process from a1 to a4, the sensing module judges whether Left _ Count is greater than 0 and Right _ Count is 0, if Left _ Count is greater than 0 and Right _ Count is 0, the target vehicle is located on the Left side, all information of the large vehicle with the minimum relative longitudinal distance is output, at the moment, the target scene requirement is met, otherwise, the next step is carried out;
a6, the sensing module determines whether Left _ Count is 0 and Right _ Count is greater than 0, if Left _ Count is 0 and Right _ Count is greater than 0, the target vehicle is located at the Right side, all information of the large vehicle with the smallest relative longitudinal distance is output, and the target scene requirement is met at this moment, otherwise, the next step is performed;
a7, the sensing module determines whether Left _ Count is 0 and Right _ Count is 0, if Left _ Count is 0 and Right _ Count is 0, no large target vehicle appears, the DWEL system is in a standby state, otherwise, the next step is performed;
a8, the sensing module determines whether Left _ Count >0 and Right _ Count >0, if Left _ Count >0 and Right _ Count >0, large vehicles are present on both sides of the road, and the DWEL system is in a standby state.
It should be noted that the auxiliary driving system for actively avoiding the large-scale vehicle in the lane is mainly designed for transverse decision planning and control of the vehicle, if a vehicle ahead exists in the lane, a driver should follow the vehicle ahead by himself, if the vehicle speed is low at the moment, the large-scale vehicle on the left side cannot be surpassed, the DWEL system is in an activated state, and TTO and tau of the planning module are passed through1As can be seen from the formula and definition of (TTO-tau), the system begins timing when it enters the active state and the time (TTO-tau) has elapsed1) Then start to enter the deviation process ifThe speed of the bicycle is less than that of the large vehicle on the left lane, then TTO<0, meaningless, the system will continue to remain activated but not perform the deviating action until the target vehicle exits the perception threshold range and the system returns to the standby state.
The judgment process in step a5 includes four possibility judgments, each of which includes going through only step a1, going through step a1, step a2 in order, going through step a1, step a2, step A3 in order, going through step a1, step a2, step A3, step a4 in order.
In an actual test, after target-level information of all target vehicles is obtained, assuming that 5 target vehicles exist, the first target vehicle firstly undergoes the step A1 to judge whether the first target vehicle belongs to a large-sized vehicle, if the first target vehicle belongs to the large-sized vehicle, the first target vehicle enters the step A2, if the first target vehicle does not belong to the large-sized vehicle, the first target vehicle stops at the step A1, and the other four target vehicles wait for the judgment process;
meanwhile, the second target vehicle enters step a1 to judge whether the vehicle belongs to a large vehicle, if the vehicle belongs to the large vehicle, the second target vehicle enters step a2 (if the vehicle does not belong to the large vehicle, the second target vehicle stops at step a1 to wait for the remaining three target vehicles to go through the judgment process), if the second target vehicle is in step a2 and the longitudinal distance of the second target vehicle relative to the vehicle is within a threshold value Δ Dx (20m), the second target vehicle enters step A3, and if the longitudinal distance of the second target vehicle relative to the vehicle is not within the threshold value Δ Dx (20m), the second target vehicle stops at step a2 to wait for the remaining three target vehicles to go through the judgment process;
and analogizing the judging processes of the third target vehicle, the fourth target vehicle and the fifth target vehicle in turn, and entering the step A5 to perform the next operation when all the target vehicles are subjected to the judging processes of the steps A1-A4.
As shown in fig. 4, the deviation trajectory and regression trajectory polynomial trajectory planning method in step S3 includes the following parameters: TTO (time To overtake), tau1、τ2Δ y, TTO calculation formula, τ1And τ2The parameter setting mode and the Δ y calculation formula are as follows:
TTO＝Δx/(uSV-utarget)；
τ1＝0.0394·(uSV-utarget)+3.3159(s)；
τ2＝-0.0298·(uSV-utarget)+4.6306(s)；
wherein TTO refers to the time when the vehicle and the target vehicle both run at a constant speed according to the current speed and the distance between the vehicle and the target vehicle exceeds the time of the target vehicle, delta x is the relative longitudinal distance between the head of the vehicle and the tail of the target vehicle, namely the longitudinal distance between the two vehicles, usvIs the current speed of the vehicle, utargetThe current speed of the target vehicle is obtained. Tau is1And τ2Respectively, the duration of the deviation process and the regression process, Δ y being the maximum offset of the deviation process, x1And x2Respectively, the lateral distance (lateral length of the gap between the vehicle bodies) between the host vehicle and the target large vehicle and the distance between the host vehicle and the side lane line of the target large vehicle, and further, τ1、τ2The three parameters of delta y are not constant values, but change in real time according to the surrounding environment of the self vehicle, such as: tau is1Increases with the increase of the longitudinal speed difference of the self-vehicle relative to the target large vehicle, tau2Decreases with the increase of the longitudinal speed difference of the host vehicle relative to the target large vehicle, tau1And τ2The parameter setting of (b) may be set based on a driving behavior map, an empirical formula, or the like: the system calculates TTO in real time after entering an activated state, and when TTO is less than tau1And TTO>At 0s, the deviation process is started and τ is maintained1Entering a regression process after time, wherein the regression process lasts for tau2The post DWEL system exits the active state.
The trajectory planning part of the deviation process and the regression process is carried out based on a polynomial trajectory planning method, the maximum deviation delta y of the deviation process is determined according to environmental parameters and expert experience, and the delta y is determined along with the lateral distance x between the self vehicle and the target large vehicle1(of the space between the two vehicle bodiesLateral length) of the vehicle, while decreasing with the distance x between the host vehicle and the side lane line of the target large vehicle2Is increased and decreased as shown in fig. 4.
On the basis, the initial and final state boundary conditions of the dodging track are introduced as follows, and polynomial parameters can be solved according to a polynomial track planning model:
y1(t)＝a0+a1t+a2t2+a3t3；
ylindicating the lateral trajectory of the vehicle, a0～a3The coefficients of the lateral trajectory planning model designed based on the cubic polynomial trajectory planning method have no physical significance, the calculation mode is listed in the formula, Δ y is the maximum offset of the deviation process, and t represents the duration of the deviation process.
As shown in fig. 5, the tracking module control operation in step S4 includes the steps of:
b1, the tracking module tracks the target track by PID control,
b2, introducing a Particle Swarm Optimization (PSO) algorithm and a preview model into the tracking module to realize self-adaptive setting PID control;
b3, the tracking module continuously iterates all the particle states to obtain the optimal position (optimal solution of the optimization problem); the input of the track tracking module of the DWEL system is the target track output by the planning module, and the output is the steering wheel turning angle. The track tracking control flow is shown in figure 5, the target track is tracked by adopting PID control, and a Particle Swarm Optimization (PSO) algorithm and a preview model are introduced on the basis of the traditional PID control to realize self-adaptive setting PID control so as to reduce the error and delay of the PID control. Wherein the content of the first and second substances,the particle swarm optimization algorithm is a swarm intelligence algorithm for solving the target optimization problem and has flexibility, robustness and self-organization. The particle swarm optimization has the core idea that: a population consisting of a plurality of particle individuals is constructed in a solving space of an optimization problem, the initial state of the particles is random and can move freely in the space, and the solution of the optimal problem is realized through the cyclic iterative optimization of all the particles in the population. Where the spatial state of each particle can be represented as xi＝[xi1,xi2......xiD]The dimension D of each particle is equal to the dimension of the solution space (D is 3 in this patent, and the solution dimension is pid three control parameters), and each particle has four features: the particle spatial position p, the particle motion velocity vector v, the fitness value fitness and the individual extreme value g of the particle. The space position of the particles represents the possible optimal solution of the optimization problem, the motion velocity vector of the particles represents the optimization direction and the gradient of the optimal solution, the fitness value represents the mapping value of each particle relative to the fitness function, and the individual extreme value represents the position of each particle which is closest to the optimal solution of the model in the optimization process. In addition, there is another optimization parameter z at the population level: and the group extremum represents the individual extremum closest to the optimal solution in the group of each iteration. In each iterative optimization process, the update formula of the speed and the position of the particle is as follows:
wherein w is the inertial weight, D is the [1, D ]]、rand1And rand2Respectively two random factors between 0 and 1, c1And c2Is the acceleration factor. The variation interval of the particle position and velocity can be artificially set as [ p ]min pmax]And [ v ]min vmax]. The dynamics of particle motion arise from three aspects: inertial force to keep the particles moving in the initial directionRealizing global search; secondly, the individual extreme attraction guides the particles to move towards the self historical optimal position and keeps the particles at the position, so that self-cognition is realized; and thirdly, the attraction of the extreme values of the population guides the particles to be separated from the initial movement direction and move towards the historical optimal positions of other particles, so that the population cognition is realized. The particle swarm optimization process is shown in figure 6.
In order to realize self-adaptive tuning PID control, the optimal p, i and d control parameters under certain initial working conditions (vehicle speed, transverse dodging distance and deviation process time) can be optimized based on a particle swarm optimization, wherein the particle state can be represented as Xi＝[Kp Ki Kd]And the fitness value corresponding to each particle is obtained by statistics of simulation test results, and the fitness value fitness comprises three parts: lag distance e between actual lateral displacement and target lateral displacement 1s after the deviation process starts1The lag distance e between the actual lateral displacement and the target lateral displacement at the end of the deviation process2And comparing the actual track with the maximum overshoot e of the lateral displacement of the target track after the regression process is finished3. After the optimal PID parameters under different initial working conditions are obtained, a PID truth table is formulated, and different PID parameters are selected under different initial working conditions (vehicle speed, transverse dodging distance and deviation process time), so that a track tracking algorithm capable of self-adapting adjustment under different working conditions is realized, and the robustness of a track tracking module is improved.
fitness＝e1+e2+e3；
e1＝|ytarget(t)-yreal(t)||t＝t0+1s；
e2＝|ytarget(t)-yreal(t)| |t＝t0+τ1；
e3＝|ytarget(t)-yreal(t)| |t＝t0+τ1+τ2；
Wherein the vertical lines in the formula represent the boundary conditions, i.e. e1Is at time t0Error at +1s, e2Is at time t0+τ1Error of time, e3Is at time t0+τ1+τ2An error in time; y istarget(t) represents the target trajectory lateral displacement value, y, output by the planning modulerealAnd (t) represents the lateral displacement value of the actual track of the vehicle in the simulation test.
The self-adaptive tuning PID track tracking system provided by the invention is subjected to simulation verification in a Prescan-Matlab-Simulink combined simulation platform, the result is shown in the attached figures 7 and 8, and the simulation result shows that the track tracking method provided by the invention has higher tracking precision in the scene aimed at by the invention and can meet the control requirement of the real vehicle.
In addition, in fig. 1, the camera sensor is a camera in which text appears, the field of view of the camera sensor and the detection range of the camera; OBJ1～OBJ4The method comprises the following steps that (1) a schematic diagram of a target object detected by a camera is shown, wherein (x, y) represents the relative longitudinal distance and the relative lateral distance of the target object;
in FIG. 2, OBJi(xi,yi) Representing the ith target vehicle and relative longitudinal distance and relative lateral distance information thereof;
in fig. 4, the left adjacent lane represents the first lane on the left side of the own vehicle, and the right adjacent lane represents the first adjacent lane on the right side of the own vehicle;
in fig. 5, the trajectory tracking error is a difference between the lateral displacement of the target trajectory and the lateral displacement of the actual trajectory, the controlled variable is a steering wheel angle, and the controlled object is a vehicle dynamics system;
in fig. 6, Y represents that the accuracy judgment is satisfied, and the optimization result is output. And if the N represents that the precision judgment is not met, continuously and iteratively updating the speed and the position of the particle.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (6)
1. The utility model provides an active driver assistance system who dodges large vehicle in lane which characterized in that: the system comprises a sensing module, a decision-making module, a planning module, a tracking module and a controller, wherein the sensing module comprises an environment sensing sensor which is used for sensing the environment and acquiring target-level information, the target-level information comprises target vehicle identification and environmental parameters and is processed and then is transmitted to the decision-making module, the decision-making module receives the processed target-level information, the four states of an auxiliary driving system are converted according to a state machine condition transfer list and determines the current state of the auxiliary driving system, the four states of the auxiliary driving system comprise an activated state, the decision-making module processes and operates the processed target-level information to obtain decision-making instruction parameters in the activated state, the decision-making instruction parameters are transmitted to the planning module, and the planning module receives the decision-making instruction parameters, the planning module plans a deviation track and a regression track of the self-vehicle for avoiding the large vehicle through a polynomial track planning method according to the current environment parameters and the driving behavior map to obtain planning track parameters, and transmits the planning track parameters to the tracking module, the tracking module receives the planning track parameters and controls and operates an executing mechanism of the vehicle to ensure that the self-vehicle moves along a target track, and the environment perception sensor, the decision module, the planning module and the tracking module are all in signal connection with the controller;
the target level information processing operation comprises the following steps:
a1, judging whether the type of the target vehicle is a large vehicle by the sensing module, if so, carrying out the next step, otherwise, switching the next target vehicle, and re-entering the step A1;
a2, judging whether the longitudinal distance between the target vehicle and the self vehicle is at the threshold value by the sensing module
In the interior of said container body,
if the value of the target vehicle is 150m, carrying out the next step, otherwise, switching to the next target vehicle, and re-entering the step A1;
a3, perception module judgmentWhether the transverse distance of the target-breaking vehicle relative to the self vehicle is in the range or not
In the interior of said container body,
if the range value is 2 m-5 m, increasing 1 for Left _ Count, switching to the next target vehicle, and re-entering the step A1, otherwise, performing the next step;
a4, judging whether the transverse distance of the target vehicle relative to the vehicle is in the range by the sensing module
In the interior of said container body,
if the range value is-5 m to-2 m, the Right _ Count is increased by 1, the next target vehicle is switched, and the step A1 is entered again, otherwise, the next target vehicle is switched, and the step A1 is entered again;
a5, when all target vehicles are subjected to the judging process of A1-A4, judging whether Left _ Count is greater than 0 and Right _ Count is =0 by a sensing module, if Left _ Count is greater than 0 and Right _ Count is =0, the target vehicles are located on the Left side, all information of the large-sized vehicle with the minimum relative longitudinal distance is output, the requirement of a target scene is met at the moment, and if not, the next step is carried out;
a6, judging whether Left _ Count =0 and Right _ Count >0 by the sensing module, if Left _ Count =0 and Right _ Count >0, locating the target vehicle on the Right side, outputting all information of the large vehicle with the minimum relative longitudinal distance, and at the moment, meeting the requirements of the target scene, otherwise, carrying out the next step;
a7, judging whether Left _ Count =0 and Right _ Count =0 by a perception module, if Left _ Count =0 and Right _ Count =0, a large target vehicle does not appear, and an auxiliary driving system for actively dodging the large vehicle in a lane is in a standby state, otherwise, the next step is carried out;
a8, the sensing module determines whether Left _ Count is greater than 0 and Right _ Count is greater than 0, if Left _ Count is greater than 0 and Right _ Count is greater than 0, large vehicles are present on both sides of the road, and the auxiliary driving system actively avoiding the large vehicles in the lane is in a standby state.
2. An in-lane active large vehicle avoidance pilot system according to claim 1, wherein: the environment perception sensor comprises a camera, a steering wheel torque sensor, a steering wheel corner sensor, an accelerator pedal sensor, a brake pedal sensor and a gyroscope, and the camera, the steering wheel torque sensor, the steering wheel corner sensor, the accelerator pedal sensor, the brake pedal sensor and the gyroscope are all in signal connection with the controller.
3. An in-lane active large vehicle avoidance pilot system according to claim 1, wherein: the four states of the auxiliary driving system further comprise a closing state, a standby state and a fault state, wherein the closing state is opened and enters the standby state, the standby state is closed and enters the closing state, the standby state is activated and enters the activation state, the activation state exits and enters the standby state, and when the system is in the standby state and the activation state, if faults are detected, the system enters the fault state, the fault state is removed, and the system enters the closing state.
4. An in-lane active large vehicle avoidance pilot system according to claim 1, wherein: the judgment process in step a5 includes four possibility judgments, each of which includes going through only step a1, going through step a1, step a2 in order, going through step a1, step a2, step A3 in order, going through step a1, step a2, step A3, step a4 in order.
5. An in-lane active large vehicle avoidance pilot system according to claim 1, wherein: the polynomial trajectory planning method comprises the following parameters: TTO,
、
、
TTO formula,
And
the parameter setting method,
The calculation formula is as follows:
τ 1 ＝ 0.0394 · (uSV-utarget)+3.3159(s) ；
τ 2 ＝ -0.0298 · (uSV-utarget)+4.6306(s) ；
wherein TTO refers to the time when the own vehicle and the target vehicle both run at a constant speed according to the current speed and exceed the target vehicle from the own vehicle,
is the relative longitudinal distance between the head of the bicycle and the tail of the target vehicle,
in order to obtain the current speed of the vehicle,
the current speed of the target vehicle is taken;
and
respectively the duration of the deviation process and the regression process,
for the maximum amount of deviation of the deviation process,
and
the distance between the self vehicle and the target large-sized vehicle is the lateral distance between the self vehicle and the target large-sized vehicle.
6. An in-lane active large vehicle avoidance pilot system according to claim 1, wherein: the tracking module control operation comprises the steps of:
b1, the tracking module tracks the target track by PID control,
b2, introducing a particle swarm optimization algorithm and a preview model into the tracking module to realize self-adaptive setting PID control;
b3, the tracking module continuously iterates the states of all the particles to obtain the optimal position.
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| WO2020135742A1 (en) * | 2018-12-29 | 2020-07-02 | 长城汽车股份有限公司 | Autonomous driving vehicle horizontal decision system and horizontal decision-making method |
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| EP1726513A1 (en) * | 2005-05-02 | 2006-11-29 | Iveco S.p.A. | Driving assistance system for lane keeping support, for lane change assistance, and for driver status monitoring for a vehicle |
| WO2020135742A1 (en) * | 2018-12-29 | 2020-07-02 | 长城汽车股份有限公司 | Autonomous driving vehicle horizontal decision system and horizontal decision-making method |
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