CN110941901A

CN110941901A - Autonomous driving method and system

Info

Publication number: CN110941901A
Application number: CN201911172410.3A
Authority: CN
Inventors: 郭伟伟; 谭墍元; 李洋洋; 毛琰; 李颖宏; 郭子慧
Original assignee: North China University of Technology; Research Institute of Highway Ministry of Transport
Current assignee: Beijing Municipal Commission Of Transport; North China University of Technology; Research Institute of Highway Ministry of Transport
Priority date: 2019-11-26
Filing date: 2019-11-26
Publication date: 2020-03-31
Anticipated expiration: 2039-11-26
Also published as: CN110941901B

Abstract

The invention relates to an autonomous driving method and a system thereof, wherein a following model is constructed by analyzing the following characteristic of an automatic driving vehicle, and a lane changing characteristic of the automatic driving vehicle, lane changing model simulation and manual driving vehicle lane changing effect are analyzed, and the lane changing behavior is restrained by introducing speed bearing degree and space allowance degree to construct a matching model by combining the influence factor of lane changing of the automatic driving vehicle, the lane changing characteristic and the characteristics of an autonomous driving simulation platform, and a lane changing preparation model is constructed by confirming a lane changing space; after the lane changing behavior is generated, a lane changing speed control model based on the two-lane front-driving vehicle and a track optimization model based on the lane changing space are constructed according to the size of the current lane changing actual space and the actual condition of information processing time delay, and the running parameters generated by the autonomous driving model are ensured to be accurately fitted with the actual traffic scene of the automatic driving vehicle.

Description

Autonomous driving method and system

Technical Field

The invention belongs to the field of autonomous driving, and particularly relates to an autonomous driving method and an autonomous driving system.

Background

In recent years, the rapid increase in the number of automobiles causes traffic problems to be increasingly severe, and the cause of drivers is a major cause of traffic accidents. The autonomous driving technology can effectively avoid partial accident risks of drivers, and can bring huge social and economic benefits due to development of the autonomous driving technology, so that the autonomous driving technology can become an effective way for solving the current road traffic problem.

The autonomous driving vehicle realizes vehicle information acquisition, behavior decision and self control through three systems of perception-decision-execution. There are three approaches to the research of autonomous driving techniques: firstly, based on the research of real vehicle testing, the modification of manually driven vehicles enables the manually driven vehicles to have an autonomous driving function, and the autonomous driving vehicle control model, the related information perception technology and the like are tested and verified in the actual traffic environment, and the method can truly reflect the problems and difficulties of the autonomous driving technology, but has high testing cost, difficult security guarantee and poor experimental repeatability; secondly, based on the research of the traditional microscopic simulation tool, the method researches the operation effect of the vehicle model macroscopically and microscopically, but the vehicle models of most simulation tools mainly simulate artificial driving behaviors, which has a certain difference with the actual requirements and actual application of autonomous driving and can not provide driving experience in a simulation environment; thirdly, based on the research of a driving simulator, the method realizes the simulation of real road scenes and vehicle parameters through UC-winRoad, can enable researchers and passengers to test and experience autonomous driving technology under a virtual reality simulation environment, simulates information perception, vehicle control and the like of actual autonomous driving to a greater extent, and although the method cannot replace actual vehicle testing, the method has certain reliability and practicability for the test and verification of a new model and a new method.

Therefore, the invention provides an autonomous driving behavior model adaptive to a driving simulator, which mainly comprises an autonomous driving following model and an autonomous driving lane changing model.

The running characteristics of road vehicles are researched from different angles by the existing various following models, and each model has the advantages and characteristics of the model. For example, the stimulus-response model can clearly reflect the characteristics of the following vehicle and has a simple model form, but the stimulus-response model and the following vehicle are considered to have an influence relationship no matter how far the distance between the front vehicle and the rear vehicle is; the optimal speed model can truly describe the macroscopic traffic flow characteristics, but the safe vehicle distance cannot be ensured when the speed difference is too large; the safe distance model is most widely used, but most of the time, the driver does not keep driving at a safe distance, so that the model does not conform to the actual distance, and the like. Under the influence of the variability of traffic environment, the autonomous driving vehicle needs to have good performance in order to realize safe and stable driving in an actual road traffic scene. Therefore, the application of the traditional vehicle behavior model to the autonomous driving vehicle needs to be further improved by combining the actual situation.

The lane changing behavior is always the key point of the research on the driving behavior, and the research on the lane changing model of the vehicle has achieved abundant results. Due to different research angles and emphasis points of researchers, the lane changing model is diversified in types. However, the existing vehicle lane change model is more in pursuit of vehicle driving benefits or fitting reproduction of a real vehicle lane change track, and sometimes neglects the performance of the vehicle and the requirements of passengers. The research angle and the use tool of the researcher are different, the target of each model is different, and the applicability of the model is different. In addition, the complexity of part of models is high, so that the problems that parameters are difficult to calibrate, information operation consumes long time and the like exist.

Disclosure of Invention

In order to solve the above technical problems, an object of the present invention is to provide an autonomous driving method and system.

According to an aspect of the present invention, there is provided an autonomous driving method including the steps of:

collecting driving data of an autonomous vehicle, inputting the driving data into an autonomous driving model to generate operating parameters of the autonomous vehicle for the autonomous vehicle to automatically operate based on the operating parameters,

the autonomous driving model comprises an autonomous driving following model and an autonomous driving lane changing model,

wherein the content of the first and second substances,

the autonomous driving following model is constructed on the basis of different driving states of the autonomous driving vehicle divided by a set behavior threshold;

the autonomous driving lane change model is configured on the basis of a matching model constructed according to speed bearing degree and space allowance degree, a lane change preparation model, a lane change speed control model and a track optimization model which are respectively configured and generated by taking a lane change space as a constraint condition, and the incidence relation among the matching model, the lane change preparation model, the lane change speed control model and the track optimization model, wherein the matching model is used for representing whether the running data of the autonomous driving vehicle matches the execution condition of lane change operation.

Further, the set behavior threshold comprises a safety threshold and a comfort threshold,

the different driving states of the autonomous vehicle are divided into a free driving state, a transition driving state and a following driving state based on a magnitude preset relationship between the end time distance of the autonomous vehicle and the safety threshold value and the comfort threshold value,

wherein the safety threshold is configured based on a first preset relationship between the safety threshold and a time period required for the autonomous vehicle to receive the travel instruction information until the corresponding instruction operation is executed, a travel speed of the autonomous vehicle, a maximum deceleration value, a travel safety compensation time, and a travel speed of a preceding driven vehicle, that is, the safety threshold of the autonomous vehicle:

wherein, T₁To a safety threshold, t_cObtaining a duration, v, required to perform an operation from information for the autonomous vehicle_cIs the running speed, v, of the autonomous vehicle_hFor the speed of a preceding driven vehicle of the autonomous vehicle, b_s△ t is the driving safety compensation time for the maximum deceleration value of the autonomous vehicle, the magnitude of which is related to the accuracy of the detection information of the sensing device.

The comfort threshold is configured based on a second preset relationship between the comfort threshold and a time period required for the autonomous vehicle to receive the travel instruction information until the corresponding instruction operation is performed, a travel speed of the autonomous vehicle, a travel safety compensation time, and a travel speed of a preceding driven vehicle, that is, the comfort threshold of the autonomous vehicle:

wherein, T₂Is a comfort threshold.

Further, the autonomous driving following model is constructed based on a preset relationship between the time interval of the end of the autonomous driving vehicle and the safety threshold and the comfort threshold and a corresponding relationship between the predicted acceleration and the predicted acceleration in different driving states, and is used for representing the corresponding relationship between the driving data of the autonomous driving vehicle and the different driving states.

Further, the autonomous driving following model comprises a free driving state following model, a following driving state following model and a conversion driving state following model,

wherein the content of the first and second substances,

the free-driving state following model is configured based on an attribution preset relation between the acceleration of the automatic driving vehicle and a preset acceleration range, a magnitude preset relation between the driving speed and the expected speed, and a third preset relation between the acceleration, the driving speed, the expected speed and the predicted acceleration, and is used for representing the corresponding relation between the driving data of the automatic driving vehicle and the free-driving state, and the free-driving state following model is specifically as follows:

wherein, a_m(t) acceleration at time t, v of the autonomous vehicle_c(t) is the speed of the autonomous vehicle at time t, V_extIs the desired speed of the autonomous vehicle, a₀(t + τ) is the acceleration of the autonomous vehicle at time t + τ in the free-driving state, λ is the proportionality coefficient, and τ is the time delay of the autonomous vehicle.

The following driving state following model is configured based on a fourth preset relationship between the acceleration of the autonomous vehicle, the respective positions of the autonomous vehicle and the preceding vehicle, the minimum safety distance between the autonomous vehicle and the preceding vehicle, and the predicted acceleration, and is used for representing a corresponding relationship between the driving data of the autonomous vehicle and the following driving state, wherein in the following driving state, the acceleration of the autonomous vehicle approaches the acceleration of the preceding vehicle, and the following driving state following model is specifically:

a₁(t+τ)＝min{a_acc(t),η((x_h(t)-x_c(t))-S₀(t))}

wherein, a₁(t + τ) is the acceleration of the autonomous vehicle at time t + τ in the following state, a_acc(t) acceleration of the autonomous vehicle approaching the lead vehicle, η scaling factor, x_c(t) and x_h(t) is-the location of the autonomous vehicle and the preceding vehicle at time t, S, respectively₀(t) is the minimum safe vehicle distance of the driven vehicles before and after the moment t of the autonomous vehicle.

The converted driving state following model is configured based on a fifth preset relationship among the acceleration, the running speed of the autonomous vehicle, the running speed of a preceding driven vehicle and a set behavior threshold difference value of the autonomous vehicle, and is used for representing a corresponding relationship between the running data of the autonomous vehicle and the converted driving state. The following model for converting the driving state specifically comprises the following steps:

wherein, a₂(t + τ) is the acceleration of the autonomous vehicle at time t + τ in the transition driving regime, a_m(t) acceleration at time t, v of the autonomous vehicle_c(t) and v_h(t) the speeds of the autonomous vehicle and the previously driven vehicle at time t, respectively,

△t_mthe difference between the behavior thresholds divided for the running state.

Further, the matching model is constructed by using a constraint function that the speed tolerance of the automatic driving vehicle is not less than a speed tolerance threshold value and the space tolerance is not less than a space tolerance threshold value.

The lane change generation constraints are:

wherein u is_thAs a threshold value of the bearing degree of the speed, δ_thIs a spatial allowance threshold. Different thresholds are set according to different experimental scenes and different types of automatic driving vehicles.

Further, the speed tolerance is configured based on a first corresponding relationship between the speed tolerance and a preset threshold, a first preset magnitude relationship between an actual running speed and an expected speed of the autonomous vehicle, and an association relationship between the first corresponding relationship and the first preset magnitude relationship, and a speed tolerance between the speed tolerance and a previous specific time, an actual running speed of the autonomous vehicle, a sixth preset magnitude relationship between an expected speed and a time delay, a second preset magnitude relationship between an actual running speed and an expected speed of the autonomous vehicle, and an association relationship between the sixth preset magnitude relationship and the second preset magnitude relationship, wherein a time interval between the previous specific time and a current time is the same as a time duration of the time delay, and the speed tolerance is specifically:

wherein u (t) is the speed tolerance of the autonomous vehicle at time t; u (t- τ) is the speed bearing of the autonomous vehicle at time t- τ; v. of_sIs the current actual travel speed of the autonomous vehicle.

The space allowance is configured based on a second corresponding relation between the space allowance and a preset threshold, a third size preset relation between a current lane change space and a minimum distance required for lane change, an association relation between the second corresponding relation and the third size preset relation, and a space allowance at a previous specific time, a seventh preset relation between the current lane change space and the minimum distance required for lane change, a fourth size preset relation between the current lane change space and the minimum distance required for lane change, and an association relation between the seventh preset relation and the fourth size preset relation, wherein time intervals at the previous specific time and the current time are the same as the duration of time delay. The space allowance specifically is as follows:

wherein δ (t) is the space allowance at time t of the autonomous vehicle; δ (t- τ) is the space allowance at time t- τ of the autonomous vehicle; d_dA current lane change space for the automatic driving vehicle at the moment t; d_minA minimum spacing required for lane change at time t for the autonomous vehicle.

Further, the configuration of the generated lane change preparation model by taking the lane change space as a constraint condition comprises the following steps:

acquiring a first distance from the automatic driving vehicle to the driving vehicle in front of the lane where the automatic driving vehicle is located, a second distance from the automatic driving vehicle to the driving vehicle in front of the adjacent lane and a third distance from the automatic driving vehicle to the driving vehicle behind, and configuring a first safety distance from the automatic driving vehicle to the driving vehicle in front of the lane where the automatic driving vehicle is located, a second safety distance from the automatic driving vehicle to the driving vehicle in front of the adjacent lane and a third safety distance from the automatic driving vehicle behind;

and generating a constraint condition according to a fifth size preset relation between the first distance and the first safety distance, a sixth size preset relation between the second distance and the second safety distance, a seventh size preset relation between the third distance and the third safety distance, and an incidence relation among the fifth size preset relation, the sixth size preset relation and the seventh size preset relation, and further configuring and generating the lane change preparation model. The lane change space of the automatic driving vehicle needs to meet the following requirements:

wherein D is_cl(t)，D_mh(t)，D_ch(t) is the distance of the autonomous vehicle from the driven vehicle behind the adjacent lane, the driven vehicle in front, and the driven vehicle in front of the own lane at time t, respectively.

Further, the first safety distance is configured based on an eighth preset relationship between the maximum length and the acceleration of the autonomous vehicle and the relative speed of the autonomous vehicle and the driven vehicle behind the adjacent lane, an eighth preset relationship between the traveling speed of the autonomous vehicle and the traveling speed of the driven vehicle behind the adjacent lane, an association relationship between the eighth preset relationship and the eighth preset relationship, a ninth preset relationship between the first safety distance and the maximum length of the autonomous vehicle, a tenth preset relationship between the traveling speed of the autonomous vehicle and the traveling speed of the driven vehicle behind the adjacent lane, and an association relationship between the ninth preset relationship and the tenth preset relationship, that is, it is ensured that the autonomous vehicle C and the driven vehicle ML (i.e., the driven vehicle behind the adjacent lane) do not issue In case of collision, the safety distance needs to satisfy:

wherein, △ D_clFor the distance, v, between the autonomous vehicle C and the driven vehicle ML at the start of the lane change_cAnd v_mlSpeeds, a, of the autonomous vehicle C and the driven vehicle ML, respectively, at the start of lane change_mcFor acceleration of the autonomous vehicle C, L_sIs the maximum length of the autonomous vehicle C;

the second safe distance is configured based on a ninth preset relationship between the second safe distance and the maximum length, the acceleration, the safe following distance of the autonomous vehicle and the relative speed of the autonomous vehicle and the vehicle driven in front of the adjacent lane, namely, the autonomous vehicle C and the vehicle driven MH (namely, the vehicle driven in front of the adjacent lane) are ensured not to collide, and the safe distance needs to satisfy:

wherein v is_cAnd v_mhSpeeds, a, of the autonomous vehicle C and the driven vehicle MH, respectively, at the start of a lane change_mcFor acceleration of autonomous vehicles C, S_dSafe following distance, L, for autonomous vehicle C_sIs the maximum length of the autonomous vehicle C;

the third safety distance is configured based on a tenth preset relation between the maximum length and the acceleration of the autonomous vehicle, the relative speed of the autonomous vehicle and the vehicle driven in front of the lane where the autonomous vehicle is located, and the lane change time length, that is, the autonomous vehicle C and the vehicle driven CH (that is, the vehicle driven in front of the lane where the autonomous vehicle is located) are ensured not to collide, and the safety distance needs to satisfy:

wherein, △ D_chDistance, v, between the automatically driven vehicle C and the driven vehicle at the start of lane change_cAnd v_chSpeeds, t, of the autonomous vehicle C and the driven vehicle CH, respectively_aDuration of lane change, a_mcFor acceleration of the autonomous vehicle C, L_sIs the maximum length of the autonomous vehicle C.

Further, configuring a generated lane change speed control model by taking a lane change space as a constraint condition, comprising:

and based on lane change space constraint, realizing lane change speed control of the automatic driving vehicle by means of different influence degrees on the front driving vehicle in the lane change process so as to generate the lane change speed control model.

The lane change speed control of the automatic driving vehicle is realized by the difference of the influence degrees of the front driving vehicles in the lane change process, and the lane change speed control specifically comprises the following steps:

configuring an expected driving speed of the autonomous vehicle when changing lanes based on the driving speeds of the autonomous vehicle in front of the lane in which the autonomous vehicle is located and the driving vehicles in front of the adjacent lane;

and constructing the lane change speed control model by combining the expected driving speed during lane change with the transverse predicted displacement of the automatic driving vehicle to realize the control of the lane change speed of the automatic driving vehicle.

Further, configuring the expected driving speed of the autonomous vehicle when changing lanes based on the driving speeds of the autonomous vehicle in the lane ahead of the autonomous vehicle and the driving vehicle in the adjacent lane ahead comprises:

configuring the expected driving speed of the automatic driving vehicle during lane changing based on the preset relation between the expected driving speed and the transverse displacement, the transverse displacement of lane changing under non-special conditions and the driving speeds of the driving vehicle in front of the lane where the automatic driving vehicle is located and the driving vehicle in front of the adjacent lane, wherein the expected driving speed during lane changing is as follows:

wherein v is_ot(t) is the expected running speed of the automatic driving vehicle at the time t, y (t) is the transverse displacement of the automatic driving vehicle at the time t, d is the transverse displacement for lane changing of the automatic driving vehicle under the condition of no special use, v (t) is the transverse displacement of the automatic driving vehicle at the time t, d is 3.5m_mh(t) and v_ch(t) are the speeds of the driving vehicle MH and the driving vehicle CH, respectively, at time t.

The lateral predicted displacement of the autonomous vehicle is configured based on a preset relationship between the lateral predicted displacement and the lateral displacement, lateral velocity and lateral acceleration of the autonomous vehicle, and satisfies:

where y (t + τ) and y (t) are the lateral displacements of the autonomous vehicle at times t + τ and t, respectively, and v_k(t) is the lateral velocity of the autonomous vehicle, a_k(t) is the lateral acceleration of the autonomous vehicle at time t.

Considering the target speed of the vehicle at the time t in combination with the constraint of the running safety and stability of the vehicle, the acceleration is as follows:

wherein, t_timeThe simulation step length of the autonomous driving simulation platform is shown.

Satisfy the vehicle and trade the restraint in the space of saying to and under the security restraint prerequisite when trading the way, the speed control model of changing the way based on two lane front cars is:

a_c(t+τ)＝min{a_c(t+τ),a_mc(t+τ)}。

further, configuring the generated trajectory optimization model by taking the lane change space as a constraint condition, comprising:

based on the constraint condition of the lane changing space, the lane changing system is configured by the incidence relation between the lateral acceleration value of the automatic driving vehicle and the lane changing time length and the lane changing track, the influence degree on the lane changing time length and the lane changing track, the incidence relation between the longitudinal acceleration value of the automatic driving vehicle and the lateral required space and the lane changing track of the automatic driving vehicle, and the influence degree on the lateral required space and the lane changing track.

Further, the track optimization control taking the lane change space as the constraint condition comprises the following steps:

s1, collecting the driving data of the automatic driving vehicle, wherein the driving data comprises: the present autonomous vehicle information: v. of_c(t)、v_k(t)、a_c(t)，a_k(t); peripheral driving vehicle information: v. of_ml(t)、v_mh(t)、v_ch(t); and (3) interactive information: d_cl(t)、D_mh(t)、D_ch(t)；

S2, predicting the behavior of the autonomous vehicle based on the driving data, and if the autonomous vehicle and the neighboring driven vehicles are both driven at a constant speed or at a uniform acceleration at time t, predicting the speed of the autonomous vehicle at time t + τ as:

wherein v is_c(t + τ) and v_k(t + τ) is the transverse and longitudinal predicted speed of the automatic driving vehicle at the moment of t + τ respectively;

the automatic driving vehicle space prediction:

wherein D is_clτ，D_mhτ，D_chτThe longitudinal distance between the automatic driving vehicle predicted for the time t + tau and the driving vehicles behind the adjacent lane, the driving vehicles in front and the driving vehicles in front of the automatic driving vehicle predicted for the time t + tau;

s3, according to whether the predicted longitudinal distance between the automatic driving vehicle and the driving vehicle behind the adjacent lane, the front driving vehicle and the driving vehicle in front of the self-lane meets the corresponding safe distance, and whether the predicted transverse displacement meets the requirement of the corresponding safe transverse displacement, the lane changing operation of the automatic driving vehicle is judged, and the lane changing requirement is that:

and S4, if so, continuing to execute the step S1, if not, adjusting the acceleration of the automatic driving vehicle to adjust the lane changing track to enable the driving data of the automatic driving vehicle to meet the lane changing requirement of the step S3, and if not, abandoning the lane changing.

Further, the safe lateral displacement amount is configured and generated based on a preset relationship between the predicted lateral displacement amount and the width of the autonomous vehicle or the magnitude of the lateral displacement amount at the time of lane change.

Wherein adjusting the acceleration of the autonomous vehicle to adjust the lane change trajectory such that the driving data of the autonomous vehicle meets the lane change requirement of step S3 comprises:

adjusting a longitudinal acceleration of the autonomous vehicle to satisfy a longitudinal acceleration constraint condition, adjusting a lateral acceleration value of the autonomous vehicle to satisfy a lateral acceleration constraint condition,

wherein the longitudinal acceleration constraint condition is based on a predicted longitudinal distance between the autonomous vehicle and a driven vehicle in front of its own lane, a predicted relative speed of the autonomous vehicle with respect to a driven vehicle in front of its own lane at a lane change start time, a lane change duration, an eleventh preset relationship between the maximum lengths of the autonomous vehicles, an eleventh preset relationship between a predicted lateral displacement amount and a width of the autonomous vehicle and an associated relationship between the eleventh preset relationship and the eleventh preset relationship, and a twelfth preset relationship between a predicted relative speed of the autonomous vehicle with respect to a driven vehicle behind its adjacent lane at a lane change start time, a predicted longitudinal distance between the autonomous vehicle and a driven vehicle behind its adjacent lane and a maximum length of the autonomous vehicle, a twelfth preset relationship between the predicted lateral displacement and the width of the autonomous vehicle, the lateral displacement during lane change, a correlation between the twelfth preset relationship and the twelfth preset relationship, a thirteenth preset relationship between the relative speed of the autonomous vehicle with respect to the autonomous vehicle in front of the adjacent lane at the start of lane change, the longitudinal distance between the autonomous vehicle and the autonomous vehicle in front of the adjacent lane, the safe follow-up distance of the autonomous vehicle, the maximum length, a thirteenth preset relationship between the predicted lateral displacement and the lateral displacement during lane change, and a correlation between the thirteenth preset relationship and the thirteenth preset relationship are configured, that is, the method for determining the lane change is a method for determining the lane change in the autonomous vehicle

The lateral acceleration constraint condition is based on a maximum acceleration value of the autonomous vehicle, a lateral displacement amount during lane change, a lateral predicted speed, a longitudinal predicted speed, a predicted acceleration, a predicted lateral displacement, a predicted travel speed of a vehicle driven in front of a lane where the autonomous vehicle is located, and predicted relative speeds of the vehicle driven in front of the lane where the autonomous vehicle is located and an adjacent lane in time

The preset relationship between the delays is configured, namely:

according to another aspect of the present invention, there is provided an autonomous driving system, including:

an operation parameter generation unit configured to acquire driving data of an autonomous vehicle, input the driving data into an autonomous driving model to generate operation parameters of the autonomous vehicle for the autonomous vehicle to automatically operate based on the operation parameters,

a model construction unit, wherein the autonomous driving model comprises an autonomous driving following model and an autonomous driving lane changing model,

wherein the content of the first and second substances,

Compared with the prior art, the invention has the following beneficial effects:

according to the autonomous driving method and the autonomous driving system, the following characteristic of the autonomous driving vehicle is analyzed, the following model of the autonomous driving vehicle is constructed by combining the simulation effect of the traditional model under the environment of the autonomous driving simulation platform and the running characteristic of the manually driven vehicle, and the following state of the vehicle is divided by analyzing the selected behavior threshold; and secondly, constructing an autonomous following model based on multi-mode selection according to the characteristics of the automatic driving vehicle in different states. On the basis of the existing research, by analyzing the lane change characteristic of the automatic driving vehicle, and the lane change effect of the traditional lane change model simulation and the manual driving vehicle under the environment of the autonomous driving simulation platform, and combining the influence factors, the lane change characteristic and the characteristics of the autonomous driving simulation platform of the automatic driving vehicle, the speed bearing degree and the space allowance degree are introduced to restrain the lane change behavior of the automatic driving vehicle so as to construct a matching model, and the lane change space of the automatic driving vehicle is confirmed so as to construct a lane change preparation model; after the lane change behavior is generated, a lane change speed control model based on a dual-lane front-driving vehicle and a track optimization model based on a lane change space are constructed according to the size of the current lane change actual space and the actual condition of information processing time delay of the automatic driving vehicle, and the models are combined with the running characteristics of driving, following and lane change in an actual variable traffic environment during construction, so that the running parameters generated by the autonomous driving model are ensured to be accurately attached to the actual traffic scene of the automatic driving vehicle, and the defects that the constructed model is large in difference with the actual traffic scene and cannot be directly used are thoroughly overcome.

Drawings

FIG. 1 is a schematic diagram of a lane change potential collision of an autonomously driven vehicle according to a second embodiment;

FIG. 2 is a schematic view illustrating a relationship between a vehicle and a rear vehicle in an adjacent lane according to a second embodiment;

FIG. 3 is a schematic view illustrating a positional relationship between a vehicle and a front vehicle in an adjacent lane according to a second embodiment;

FIG. 4 is a schematic view illustrating a positional relationship between a host vehicle and a front vehicle of a host vehicle lane according to a second embodiment;

FIG. 5 is a flowchart illustrating a local optimization control of an autonomously driven vehicle according to a second embodiment;

FIG. 6 is a diagram illustrating the effect of the speed following of the autonomous driving vehicle under the information delay in the second embodiment;

FIG. 7 is a diagram illustrating the effect of the vehicle's starting phase speed after information delay according to the second embodiment;

FIG. 8 is a diagram illustrating the effect of the difference between the vehicle speed before and after the vehicle start phase under the information delay in the second embodiment;

FIG. 9 is a diagram illustrating the effect of the distance between the front and rear vehicles at the starting stage of the vehicle with information delay according to the second embodiment;

FIG. 10 is a diagram illustrating the speed operation effect of the following phase of the vehicle with delayed information according to the second embodiment;

fig. 11 is a diagram illustrating the effect of the speed difference between the front and rear vehicles at the following stage of the vehicle with information delay in the second embodiment;

fig. 12 is a diagram illustrating an effect of the distance between the vehicle before and after the following phase of the vehicle in the second embodiment with information delay;

FIG. 13 is a diagram illustrating the driving effect of the speed of the vehicle under the distance detection error in the second embodiment;

FIG. 14 is a graph showing the effect of the difference in speed between the front and rear vehicles under the distance detection error in the second embodiment;

FIG. 15 is a diagram illustrating the effect of the distance between the front and rear vehicles in the case of distance detection errors in the second embodiment;

FIG. 16 is a diagram illustrating the effect of the front-rear headway under the distance detection error in the second embodiment;

FIG. 17 is a diagram illustrating the track-changing effect of the vehicle under the error of the speed detection of the leading vehicle in the second embodiment;

FIG. 18 is a diagram illustrating the lane change effect of the vehicle under the error of the speed detection of the leading vehicle in the second embodiment;

fig. 19 is a diagram illustrating the effect of lane change headway of the vehicle under the speed detection error of the vehicle ahead in the second embodiment.

Detailed Description

In order to better understand the technical scheme of the invention, the invention is further explained by combining the specific embodiment and the attached drawings of the specification.

The first embodiment is as follows:

the embodiment provides an autonomous driving method, which comprises the following steps:

wherein the content of the first and second substances,

the autonomous driving following model is constructed based on different driving states of the automatic driving vehicle divided by a set behavior threshold value, the set behavior threshold value comprises a safety threshold value and a comfort threshold value, the different driving states of the automatic driving vehicle are divided into a free driving state, a conversion driving state and a following driving state based on the preset relation between the time distance of the end part of the automatic driving vehicle and the safety threshold value and the comfort threshold value,

wherein, T₂Is a comfort threshold.

The autonomous driving following model is constructed on the basis of the preset size relation between the end time interval of the autonomous driving vehicle and the safety threshold and the comfort threshold and the corresponding relation between the predicted acceleration and the predicted acceleration in different driving states, and is used for representing the corresponding relation between the driving data of the autonomous driving vehicle and the different driving states.

wherein the content of the first and second substances,

a₁(t+τ)＝min{a_acc(t),η((x_h(t)-x_c(t))-S₀(t))}

wherein, a₁(t + τ) is the acceleration of the autonomous vehicle at time t + τ in the following state, a_acc(t) acceleration of the autonomous vehicle approaching the lead vehicle, η scaling factor, x_c(t) and x_h(t) the position of the autonomous vehicle and the preceding vehicle at time t, S₀(t) is the minimum safe separation of vehicles driven by the autonomous vehicle before and after time t.

wherein, a₂(t + τ) is the acceleration of the autonomous vehicle at time t + τ in the transition driving regime, a_m(t) acceleration at time t, v of the autonomous vehicle_c(t) and v_h(t) speeds of the autonomous vehicle and of the preceding vehicle at time t, △ t, respectively_mA difference between the behavior thresholds divided for the driving state;

The lane change generation constraints are:

The speed tolerance is configured based on a first corresponding relation between the speed tolerance and a preset threshold, a first magnitude preset relation between an actual running speed and an expected speed of the autonomous vehicle, an association relation between the first corresponding relation and the first magnitude preset relation, and a speed tolerance between the speed tolerance and a previous specific time, an actual running speed of the autonomous vehicle, a sixth preset relation between the expected speed and a time delay, a second magnitude preset relation between the actual running speed and the expected speed of the autonomous vehicle, and an association relation between the sixth preset relation and the second magnitude preset relation, wherein a time interval between the previous specific time and a current time is the same as a time duration of the time delay, and the speed tolerance is specifically:

The first safety distance is configured based on an eighth preset relationship between the maximum length and the acceleration of the autonomous vehicle and the relative speed of the autonomous vehicle and the driven vehicle behind the adjacent lane, an eighth preset relationship between the travel speed of the autonomous vehicle and the travel speed of the driven vehicle behind the adjacent lane, an association relationship between the eighth preset relationship and the eighth preset relationship, a ninth preset relationship between the first safety distance and the maximum length of the autonomous vehicle, a tenth preset relationship between the travel speed of the autonomous vehicle and the travel speed of the driven vehicle behind the adjacent lane, and an association relationship between the ninth preset relationship and the tenth preset relationship, that is, the autonomous vehicle C and the driven vehicle ML (i.e., the driven vehicle behind the adjacent lane) are ensured not to collide, the safe distance needs to meet the following requirements:

wherein, △ D_chDistance, v, between the automatically driven vehicle C and the driven vehicle at the start of lane change_cAnd v_chAre respectively self-drivingSpeed, t, of vehicle C and of driving vehicle CH_aDuration of lane change, a_mcFor acceleration of the autonomous vehicle C, L_sIs the maximum length of the autonomous vehicle C.

The method for configuring the generated lane change speed control model by taking a lane change space as a constraint condition comprises the following steps of:

and based on lane change space constraint, realizing lane change speed control of the automatic driving vehicle by means of different influence degrees on the front driving vehicle in the lane change process so as to generate the lane change speed control model. The lane change speed control of the automatic driving vehicle is realized by the difference of the influence degrees of the front driving vehicles in the lane change process, and the lane change speed control specifically comprises the following steps:

(1) configuring an expected driving speed of the automatic driving vehicle when the lane is changed based on the driving speeds of the automatic driving vehicle in the lane where the automatic driving vehicle is located and the driving speeds of the automatic driving vehicle in the adjacent lane, specifically comprising:

wherein v is_ot(t) is the expected running speed of the automatic driving vehicle at the time t, y (t) is the transverse displacement of the automatic driving vehicle at the time t, d is the transverse displacement for lane changing of the automatic driving vehicle under the condition of no special use, v (t) is the transverse displacement of the automatic driving vehicle at the time t, d is 3.5m_mh(t) and v_ch(t) the speeds of the driving vehicle MH and the driving vehicle CH at time t, respectively;

(2) and constructing the lane change speed control model by combining the expected driving speed during lane change with the transverse predicted displacement of the automatic driving vehicle to realize the control of the lane change speed of the automatic driving vehicle. Wherein the predicted lateral displacement of the autonomous vehicle is configured based on a predetermined relationship between the predicted lateral displacement and lateral velocity and lateral acceleration of the autonomous vehicle, and satisfies:

a_c(t+τ)＝min{a_c(t+τ),a_mc(t+τ)}。

based on the constraint condition of the lane changing space, the lane changing system is configured by the incidence relation between the lateral acceleration value of the automatic driving vehicle and the lane changing time length and the lane changing track, the influence degree on the lane changing time length and the lane changing track, the incidence relation between the longitudinal acceleration value of the automatic driving vehicle and the lateral required space and the lane changing track of the automatic driving vehicle, and the influence degree on the lateral required space and the lane changing track. The track optimization control taking the lane change space as the constraint condition comprises the following steps:

s1, collecting automatic driveTravel data for traveling a vehicle, the travel data including: the present autonomous vehicle information: v. of_c(t)、v_k(t)、a_c(t)，a_k(t); peripheral driving vehicle information: v. of_ml(t)、v_mh(t)、v_ch(t); and (3) interactive information: d_cl(t)、D_mh(t)、D_ch(t)；

the automatic driving vehicle space prediction:

s4, if yes, continuing to execute step S1, and if not, adjusting the acceleration of the autonomous vehicle to adjust the lane change trajectory so that the driving data of the autonomous vehicle meets the lane change requirement of step S3, specifically comprising: adjusting a longitudinal acceleration of the autonomous vehicle to satisfy a longitudinal acceleration constraint condition, adjusting a lateral acceleration of the autonomous vehicle to satisfy a lateral acceleration constraint condition,

And configuring and generating the safe transverse displacement based on the preset relation between the predicted transverse displacement and the width of the automatic driving vehicle or the transverse displacement during lane changing.

The lateral acceleration constraint condition is configured based on a preset relationship among a maximum acceleration value of the autonomous vehicle, a lateral displacement amount during lane changing, a lateral predicted speed, a longitudinal predicted speed, a predicted acceleration, a predicted lateral displacement, a predicted travel speed of a driving vehicle in front of a lane where the autonomous vehicle is located, and predicted relative speeds and time delays of the driving vehicle in front of the lane where the autonomous vehicle is located and an adjacent lane, namely:

if adjusting the longitudinal acceleration and the lateral acceleration of the autonomous vehicle still does not meet the lane change requirement,

the lane change is aborted.

The present embodiment provides an autonomous driving system, including:

wherein the content of the first and second substances,

It should be understood that the steps of the autonomous driving method described above correspond to sub-units described in the autonomous driving system. Thus, the operations and features described above for the system and the units included therein are equally applicable to the above method and will not be described again here.

Example two

The same features of this embodiment and the first embodiment are not described again, and the different features of this embodiment and the first embodiment are:

the autonomous driving model is constructed by the following steps:

division of vehicle states in autonomous driving following model

The division of the vehicle state considers the requirements of the vehicle on stability, safety and comfort in the driving process, the headway is used as a division threshold value, and the value is determined by considering the influence of the peripheral vehicle and the change of self acceleration.

In order to ensure that the autonomous driving vehicle does not collide with surrounding vehicles in the driving process, a certain time interval with a front vehicle is required to be met, namely the autonomous driving vehicle acquires a required time interval from information acquisition when the autonomous driving vehicle adopts braking to enable the vehicle to carry out acceleration and deceleration operation to stop the vehicle or the speed of the autonomous driving vehicle is the same as that of the front vehicle, and the expression is as follows:

t_s＝t_c+t_d+△t (1-1)

in the formula: t is t_sThe safe headway is obtained; t is t_cAcquiring the time length required for executing the operation from the information for the autonomous driving vehicle, wherein the size of the time length depends on the performance index of the vehicle; t is t_dRequired for the autonomously driven vehicle to run at the same speed as the preceding vehicle or stop at a certain decelerationThe duration, △ t, is the driving safety compensation time, and the value is set for avoiding the collision between the vehicle and the preceding vehicle due to certain error in the self environment sensing ability during the driving process of the vehicle, and is related to the accuracy of the detection information of the sensing device.

The behavior threshold value is selected by taking the self acceleration and deceleration change degree of the autonomous driving vehicle caused by the motion change of the front vehicle as an influence factor. The method selects the safety threshold and the comfort threshold to divide the running state of the vehicle based on the factors such as the acceleration and deceleration change degree of the autonomous driving vehicle, the influence degree of the preceding vehicle, the driving safety and stability degree and the like.

The headway that should be maintained when the safety and driving stability of an autonomously driven vehicle are met during driving is taken as the safety threshold of the vehicle, t_dThe following were confirmed:

in the formula: v. of_cIs the travel speed of the autonomously driven vehicle; v. of_hFor autonomously driving the speed of the vehicle ahead, v if there is no ahead_h＝0；b_sIs the maximum deceleration value of the autonomous driving vehicle.

The safety threshold T₁Comprises the following steps:

the vehicle head time interval required by the deceleration running to stop of the vehicle or the same speed with the front vehicle at the deceleration value meeting the requirements of safety, stability and comfort of the vehicle running is taken as a comfortable threshold value of the vehicle, and at the moment, t is t_dThe following formula:

in the formula: b_mDeceleration at which comfort requirements are met for an autonomously driven vehicle.

Then comfort threshold T₂Comprises the following steps:

the autonomous driving vehicle driving state is then divided into a free driving state, a transition driving state, and a following driving state, as follows:

wherein Q represents a state of the vehicle, wherein 1 represents a following driving state, 2 represents a transition driving state, and 3 represents a free driving state; t is_tRepresenting the headway of the vehicle at time t.

Assuming that the vehicle keeps a constant-speed running state at the time t, the following steps are carried out:

here, △ d is the distance difference between the front and rear vehicles at time t.

Second, construction of autonomous driving following model

(1) The free driving state: the state is that the autonomous driving vehicle is in a state that no front vehicle exists or the autonomous driving vehicle is far away from the front vehicle when the autonomous driving vehicle runs. At this time, the vehicle is hardly affected by the change in the behavior of the preceding vehicle. The driving constraint of the vehicle in the state is less, the requirements of the vehicle on driving safety, stability and comfort are only required to be met, the riding comfort of passengers and the stability of vehicle operation are considered in the construction of the model of the vehicle, and the free driving state following model needs to be met:

in the formula: a is_m(t) is the acceleration of the vehicle at time t; v_extIs the desired speed of the vehicle.

Secondly, considering the requirement of the vehicle on the speed during running, the expected speed is introduced into the control of the acceleration value of the vehicle, and the following model of the free driving state of the vehicle needs to meet the following requirements:

a₀(t+τ)＝min{a_m(t),λ(V_ext-v_c(t))} (2-2)

in the formula: a is₀(t + τ) is the acceleration of the vehicle at time t + τ in the free-driving state; λ is a proportionality coefficient; τ is the time delay of the vehicle.

(2) Following driving state:

the speed of the vehicle in the following state tends to travel at the same speed as the preceding vehicle, and the speed thereof changes as the speed of the preceding vehicle changes. The invention mainly considers the factors of the safety, the stability, the conformity with the actual situation and the like of the vehicle running.

Firstly, the distance between the vehicle and the front vehicle in the state is relatively short, the influence of the front vehicle is very obvious, and the most basic safe driving requirement of the vehicle needs to be met, namely:

x_h-x_c-L_h≥0 (2-3)

in the formula: x is the number of_hThe distance the front vehicle is braked; x is the number of_cThe distance of the vehicle when braking; l is_hFor the length of the front vehicle body, 5m is generally taken.

Wherein:

in the formula: x is the number of_c(t)，x_h(t) the positions of the vehicle and the preceding vehicle at the time t, respectively; v. of_c(t)，v_h(t) the speed of the vehicle and the speed of the preceding vehicle at time t, respectively; b_s，b_hMaximum deceleration of the host vehicle and the front vehicle respectively; v. of_c(t+τ)，v_h(t + τ) is the speed of the host vehicle and the preceding vehicle, respectively, at time t + τ.

By sorting the above formula, one can obtain:

in order to ensure safe driving of the vehicle, the following requirements are met:

S₀(t+τ)＝max{S(t+τ),v_h(t)τ+L} (2-6)

in the formula: s₀(t + tau) is the minimum safe distance between the front vehicle and the rear vehicle at the moment of t + tau; s (t + tau) is the safe inter-vehicle distance to be satisfied at t + tau.

Next, since the autonomously driven vehicle is expected to be equal to the speed of the preceding vehicle during traveling, an acceleration control function related to the speed of the preceding vehicle is constructed during acceleration traveling, that is:

in the formula: a is_acc(t) is the acceleration of the vehicle as it approaches the front; a is_sThe maximum acceleration value of the vehicle.

In summary, the model of the autonomous driving vehicle in the following state is:

a₁(t+τ)＝min{a_acc(t),η((x_h(t)-x_c(t))-S₀(t))} (2-8)

in the formula: a is₁(t + τ) is the acceleration of the vehicle at time t + τ in the following state, and η is a proportionality coefficient.

(3) And (3) switching the driving state: the method is characterized in that a conversion driving state is added to the vehicle state division, and the purpose is to give a certain adjustment time to the autonomous driving vehicle in the driving state conversion process, so that the phenomenon of sudden speed change of the vehicle in a simulation platform is avoided. In addition, the change of the state of the vehicle in the driving process is a continuous and gradual process, so that the vehicle running effect can be more practical by increasing the vehicle conversion driving state. The control of the state mainly realizes the linking effect in the vehicle state conversion process, and ensures the stability of vehicle operation, and the control function is as follows:

in the formula: a is₂(t + τ) for switching drivesAcceleration of vehicle at time t + T in running state △ t_mThe difference between the behavior thresholds classified for the vehicle state.

To sum up, the acceleration value of the autonomous driving vehicle at the time of t + τ in the following driving process, that is, the following model of the converted driving state is:

wherein, a_c(t + τ) is the acceleration of the autonomously driven vehicle at time t + τ.

The autonomous driving following model has the effects: the running effect of the autonomous driving model is tested by adopting information transmission delay and information detection errors commonly existing in the current autonomous driving technology. The value range of the information transmission delay time is [0s,2s ], the simulation data of the front 140s of the autonomous vehicle is intercepted and analyzed, the speed running effect of the complete process of the following behavior of the autonomous vehicle is shown in fig. 6, and the specific change conditions of the vehicle motion under different information delays are respectively shown in fig. 7-fig. 9 (the black line is the front vehicle speed). It is seen from the vehicle launch phase of fig. 7 that the maximum speed of the autonomously driven vehicle increases first and then decreases with increasing delay. It is found from fig. 8 that the vehicle speed difference before and after the take-off phase decreases and then increases as the delay increases. It is found from fig. 9 that the front-rear inter-vehicle distance decreases with an increase in delay, and the rate of decrease is also faster. When the vehicle is driven by the driver autonomously without delay, the vehicle can be accelerated at a higher efficiency, and the vehicle can be driven at a stable distance from the front vehicle quickly after entering a converted driving state. The operation of the autonomously driven vehicle with different information delays when the autonomously driven vehicle is in a following state is shown in fig. 10-12. It is found from fig. 10 that the speed of the autonomous vehicle changes most smoothly and with a small change width without delay during traveling, and the speed changes with an increasing delay in an increasing direction, resulting in a decrease in the traveling stability of the autonomous vehicle. It is found from fig. 11 that the magnitude of the change of the speed difference of the autonomous driving vehicle is large when the delay is small and large, and the speed difference at the delay [0.5s,0.7s ] is closer to the state without time delay. It is found from fig. 12 that when the delay is small or large, the headway is small, and the headway at the delay of [0.5s,0.7s ] is close to the state of the vehicle without the delay. Information delays can have an impact on the stability of autonomous vehicle operation. The running states of the vehicles under different information delays are different, the autonomous driving vehicle following model in the invention shows better stability, and especially in the [0.5s,0.7s ] interval, the running state index of the autonomous driving vehicle is better, and the similarity with the running state under the non-time delay state is highest.

And when the information delay is 0.5s, setting a distance detection error and a front vehicle speed error to carry out simulation evaluation on the running safety and stability of the autonomous driving vehicle. The distance error is set to [0,0] m, [ -1,1] m, [ -4,4] m, [ -7,7], [ -10,10] m; the speed error of the leading vehicle is set to [0,0] m/s, [ -0.5,0.5] m/s, [ -1,1] m/s, [ -1.5,1.5] m/s, [ -2.0,2.0] m/s, [ -3.0,3.0] m/s. And randomly generating error values in the error interval to carry out a simulation experiment. Fig. 13-16 show that distance detection errors have a significant impact on safety and stability. It is found from fig. 13 and 14 that the speed variation tendency of the autonomously driven vehicle has better consistency with the preceding vehicle when the distance detection error is small, and the consistency decreases when the distance detection error increases to a certain degree. It is found from fig. 15 and 16 that the detection distance error causes a small inter-vehicle distance when the vehicle travels at a high speed and a large inter-vehicle distance when the vehicle travels at a low speed, which is not in accordance with the actual situation. In general, the autonomous driving vehicle following model of the invention shows good running characteristics within a certain distance detection error range, which also accords with the development trend of continuous progress of detection technology and precision in reality.

Third, generating restraint by changing lanes of autonomous driving vehicle

Lane changing of a vehicle is influenced by many factors and can be summarized into two aspects: firstly, when the current road driving environment is not full, if the driving speed is always lower than the expected driving speed, the lane changing operation is executed after the insufficient emotion is accumulated to a certain degree, or the lane changing operation has to be executed because of the requirement of a vehicle driving path; secondly, the driving state of the lane where the vehicle is located is poor, if the speed of the vehicle changes frequently, the driving state of the adjacent lane is obviously superior to that of the current lane, and the driver performs lane changing operation.

In conclusion, whether the autonomous driving vehicle executes the lane changing operation needs to have a complete constraint condition, so that the operation of the autonomous driving vehicle accords with the characteristics of real vehicle operation. Through the analysis, the lane change operation of the autonomous driving vehicle mainly considers two factors of the speed and the lane change space of the autonomous driving vehicle to construct the lane change generation constraint of the vehicle, and introduces the concept of speed bearing degree and space permissivity.

The speed tolerance represents the accumulated amount of the vehicle running speed which cannot reach the expected speed and is accumulated and increased along with time, and the tolerance is as follows:

wherein u (t) is the speed bearing degree of the vehicle at the time t; u (t-tau) is the speed bearing degree of the vehicle at the t-tau moment; v. of_sIs the current actual speed of the vehicle.

The space permission degree represents the time length which can be continued by the lane change execution space when the vehicle runs, the longer the duration is, the higher the lane change occurrence of the space is, and the permission degree is as follows:

δ (t) is the space allowance of the vehicle at the moment t; delta (t-tau) is the space allowance at the moment t-tau of the vehicle; d_dThe current lane changing space of the vehicle at the time t; d_minThe minimum distance required for changing lanes of the vehicle at the time t; t is the sampling time interval.

The matching model is then:

wherein u is_thAs a threshold value of the bearing degree of the speed, δ_thIs a spatial allowance threshold. Different threshold values are set according to different experimental scenes and vehicle types.

Fourth, confirmation of lane changing space between autonomous driving vehicle C and adjacent vehicle

The lane changing behavior of the autonomous driving vehicle has certain requirements on the traffic environment of the surrounding roads, and the lane changing operation can be executed only when the lane changing space-time requirements of the vehicle are met. The invention confirms the space required by lane changing of the vehicle by analyzing the lane changing requirement of the vehicle. The influence of the front vehicle of the lane, the front vehicle of the adjacent lane and the rear vehicle of the adjacent lane on the vehicle is mainly considered.

As shown in fig. 1, when an autonomous driving vehicle performs lane change, three collision constraint conditions exist, and the lane change operation can be performed safely only if space constraints of the autonomous driving vehicle are satisfied. To this end, the present invention validates its lane change collision constraints.

(1) ML safety distance confirmation from rear vehicle in adjacent lane

As shown in fig. 2, to ensure that the vehicle C does not collide with the vehicle ML, it is necessary to satisfy:

D_s-D_ml-L_s+△D_cl>0 (4-1)

in the formula: d_s，D_mlThe moving distances of the vehicle C and the vehicle ML along the road are respectively; l is_sThe maximum length of the vehicle C is obtained by taking the maximum value of the diagonal angle of the vehicle body as the vehicle rotates at a certain angle in the lane changing process of the vehicle, △ D_mlThe distance between two vehicles is the initial time of lane change.

Wherein:

△D_cl＝x_c-x_ml(4-2)

in the formula: x is the number of_c，x_mlRespectively, the location of vehicle C and vehicle ML along the road.

If the vehicle ML runs at a constant speed during lane changing, in order to avoid collision of the vehicle, the vehicle C is positioned in front of the vehicle ML when the speed of the vehicle C is greater than or equal to the speed of the vehicle ML.

If v is_ml>v_cThe method comprises the following steps:

let the acceleration of the vehicle C be a_mcWherein a is_mc∈[0,2]When the space in the text is confirmed, take a_mc＝2m/s²Then it accelerates for a required time period t_mlComprises the following steps:

in the formula: v. of_c，v_mlThe speeds of the vehicle C and the vehicle ML at the lane change starting moment respectively;

meanwhile, according to the collision position of the vehicle in fig. 11, the collision position is about the middle position of the whole lane change process, and the lane change duration is about the whole lane change duration t_hHalf, the time required for the acceleration phase needs to be satisfied: t is t_ml<0.5t_h。

Then:

if v is_ml≤v_cThe method comprises the following steps:

△D_cl≥L_s(4-5)

in summary, the safety distance needs to satisfy:

(2) safe distance confirmation with MH of front vehicle in adjacent lane

As shown in fig. 3, to ensure that the vehicle C does not collide with the vehicle MH, it is necessary to satisfy:

D_mh-D_s-L_s+△D_mh>S_d(4-7)

in the formula: d_mhDistance traveled by vehicle MH along the road △ D_mhThe relative distance between the lane change starting time of the vehicle C and the vehicle MH; s_dThe safe following distance of the vehicle C.

Wherein:

△D_mh＝x_mh-x_c(4-8)

in the formula: x is the number of_mhThe position of the vehicle MH along the road is the starting point of the lane change.

After the lane change is finished, the speed of the vehicle C and the speed of the vehicle MH tend to be consistent. Assuming that the vehicle MH is running at a constant speed and the vehicle C is running at an accelerated speed during lane change, the speed of the vehicle C tends to be equal to or less than the speed of the vehicle MH before reaching the collision position.

Then, its safety distance needs to satisfy:

in the formula: v. of_mhThe speed of the vehicle MH is the start time of the lane change.

(3) Safety distance confirmation from the preceding vehicle CH of the own lane

As shown in fig. 4, to ensure that the vehicle C does not collide with the vehicle CH, it is necessary to satisfy:

D_ch-D_s-L_s+△D_ch>0 (4-10)

in the formula: d_chFor car CH at t_aDistance traveled along the road in time △ D_chThe distance between the vehicle C and the vehicle CH is the initial time of lane change;

wherein:

△D_ch＝x_ch-x_c(4-11)

in the formula: x is the number of_chThe position of the vehicle CH along the road at the start of lane change.

If the vehicle CH moves at a constant speed during lane changing, the vehicle CH moves at a time t_aThe inner vehicle C is positioned behind the front vehicle of the lane, and t_aThe value is determined by the transverse displacement of the vehicle, and the transverse displacement is larger than w/2m at the moment, wherein w is the width of the vehicle body.

Then, its safety distance needs to satisfy:

to sum up, the lane change preparation model needs to be satisfied

In the formula: d_cl(t)，D_mh(t)，D_chAnd (t) the distances between the vehicle and the rear vehicle and the front vehicle of the adjacent lane and the distance between the vehicle and the front vehicle of the lane at the time t are respectively shown.

Fifthly, constructing a lane change speed control model based on the two-lane front vehicle

When the vehicle changes lanes, not only the vehicle state information of the vehicle but also the surrounding environment state information need to be considered, and meanwhile, the state information and the lane change space in the future time interval need to be judged in advance. After the vehicle generates a track changing instruction, the autonomous driving vehicle safely and effectively controls the track changing track through a set model according to the surrounding environment state information of the autonomous driving vehicle. The invention takes the left lane change of the vehicle as an example to explain the control strategy and the model.

In the lane changing process of the autonomous driving vehicle, the lane changing of the vehicle is divided into longitudinal movement, namely, along the X-axis direction and transverse movement, namely, along the Y-axis direction. The duration of lane change of the vehicle is controlled by the lateral movement, and the size of the lane change space is controlled by the longitudinal movement. Further, the acceleration variation of the autonomously driven vehicle satisfies:

in the formula: a is_maxAn upper maximum acceleration limit for an autonomously driven vehicle, the magnitude of which is related to vehicle performance; a is_x，a_kRespectively, vehicle longitudinal and lateral acceleration.

According to the characteristics of an autonomous driving vehicle in the lane changing process, in order to avoid the problem that the speed of the vehicle changes too much due to the fact that the speeds of two-lane front vehicles are different, a lane changing speed control model based on the two-lane front vehicles is provided. The model is based on the vehicle lane changing space constraint, realizes the vehicle speed control through the difference of the influence degree of the front vehicle in the vehicle lane changing process, and assumes that:

(1) the influence degree of the front vehicle of the lane on the autonomous driving vehicle at the initial lane changing time is 100%, and the influence of the front vehicle of the target lane is 0%;

(2) when the lane change is finished, the influence degree of the front vehicle of the lane on the autonomous driving vehicle is 0%, and the influence of the front vehicle of the target lane is 100%;

(3) the degree of influence changes in proportion to the amount of lateral displacement of the vehicle.

Based on the above assumptions, the expected travel speed when the vehicle changes lanes is:

in the formula: v. of_ot(t) is the expected running speed of the vehicle at time t; y (t) is the transverse displacement of the vehicle at the time t;

wherein:

in the formula: a is_k(t) is the lateral acceleration of the vehicle at time t.

in the formula: t is t_timeThe simulation step length of the autonomous driving simulation platform is shown.

a_c(t+τ)＝min{a_c(t+τ),a_mc(t+τ)} (5-5)

sixthly, optimizing the track based on the lane changing space to construct a track optimization model

In the lane changing process of the autonomous driving vehicle, the lane changing requirement can be met in an initial state due to the influence of surrounding vehicles, and the lane changing requirement cannot be met in the lane changing execution process. Therefore, when the vehicle changes lanes, whether the lane changing space meets the lane changing requirement needs to be detected in real time, the lane changing can be directly carried out according to the original lane changing scheme when the requirement is met, and when the requirement cannot be met, the model parameters or the control strategy of the vehicle needs to be adjusted in time to adapt to the lane changing space in the current state.

Therefore, in order to ensure the safe execution of the lane change of the vehicle and improve the self-adjustment and track optimization capability of the vehicle, the invention constructs a track optimization method based on the lane change space, so that the whole lane change process of the vehicle realizes real-time feedback optimization control, and the control flow is shown in fig. 5.

Under the premise of meeting the lane change constraint condition, the track optimization control of the autonomous driving vehicle is mainly realized by adjusting the transverse acceleration value a of the autonomous driving vehicle_kThe track of the vehicle is adjusted by changing the lane changing time of the vehicle; adjusting longitudinal acceleration value a of vehicle_cThe required space in the transverse direction of the vehicle is changed to adjust the lane changing track. And if the adjusted variables still cannot meet the lane changing requirement, executing the operation of giving up the lane changing and returning to the original lane for following driving. The feedback regulation control for the vehicle follows the following principles:

(1) the lane changing process of the vehicle needs to shorten the lane changing time as much as possible on the basis of meeting the safety and stability of the vehicle;

(2) the longitudinal speed of the vehicle keeps uniform motion or uniform acceleration motion.

(3) The change of the acceleration and speed values of the transverse motion of the vehicle generally satisfies the symmetrical distribution.

According to the analysis result of the simulation data of the driver, the transverse displacement amount of the vehicle is gathered to be about 3.5m in the driving lane changing process, so that d is taken to be 3.5m and is taken as the transverse displacement amount of the lane changing under the condition that the vehicle is not special. For the selection of the lane changing time length of the vehicle, the lane changing time length is selected as t to ensure that the lane changing of the vehicle meets the requirements of rapidity and stability_m∈[3,5]And s. The acceleration value at the lane change starting moment is as follows:

in the formula, t_mThe lane change time of the vehicle is 4s at the initial moment; a is_hIs the vehicle lateral acceleration.

Based on this, the track optimization control based on the lane changing space is as follows:

s1, real-time acquisition of vehicle information:

the vehicle information: v. of_c(t)、v_k(t)、a_c(t)，a_k(t); peripheral vehicle information: v. of_ml(t)、v_mh(t)、v_ch(t); and (3) interactive information: d_cl(t)、D_mh(t)、D_ch(t)，

Wherein the content of the first and second substances,

v_c(t)、v_k(t) is the longitudinal speed and the lateral speed of the vehicle at time t,

a_c(t)，a_k(t) represents the longitudinal acceleration and lateral acceleration of the vehicle at time t,

v_ml(t)、v_mh(t)、v_ch(t) the speeds of the rear vehicle ML, the front vehicle MH and the front vehicle CH in the adjacent lane at the moment t,

D_cl(t)，D_mh(t)，D_ch(t) the distances between the vehicle and the rear vehicle and the front vehicle of the adjacent lane and the distance between the vehicle and the front vehicle of the lane at the time t are respectively;

s2, vehicle behavior prediction:

assuming that the host vehicle and the neighboring vehicles are both running at a constant speed or at a uniform acceleration at time t, the host vehicle speed at time t + τ is predicted to be:

in the formula: v. of_c(t+τ)，v_k(t + τ) are the lateral and longitudinal predicted distances of the vehicle at the time t + τ, respectively;

the vehicle space prediction:

in the formula: d_clτ，D_mhτ，D_chτAnd predicting the longitudinal distance between the vehicle and the rear vehicle, the front vehicle and the front vehicle of the adjacent lane at the moment of t + tau.

S3, vehicle operation judgment:

and judging whether the vehicle running requirement is met or not according to the state information of the vehicle after the predicted time tau.

S4, vehicle motion adjustment:

if the vehicle state satisfies the S3 constraint, proceed to S1; if the constraint of S3 is not satisfied, the control value is adjusted.

According to the condition that the constraint condition is not met in the S3, a feedback regulation mechanism is introduced, and the specific idea is as follows:

first, adjust a_mcThe constraint to S3 is satisfied;

next, it is established that a after the adjustment is satisfied according to the formulas (4-18), (4-19), (4-20) and (4-23)_mcIn the case of values, the lateral acceleration value of the vehicle confirms:

s5, vehicle control execution:

if it is

The method comprises the following steps:

a_h＝a_k(t+τ)

(6-7)

if a_h≥a_maxAnd if so, giving up the lane change.

The autonomous driving lane change model has the effects that: and setting the detection errors of the front vehicle speeds of the two lanes in four intervals of (0, 0) m/s, [ -0.5,0.5] m/s, [ -1.0,1.0] m/s and [ -1.5,1.5] m/s for the autonomous driving lane changing model to carry out simulation. The simulation finds that the detection error of the speed of the front vehicle has influence on the running stability and the safety of the lane changing process of the autonomous driving vehicle. It is found from fig. 17 that the change of the detection error of the speed of the leading vehicle directly affects the size of the space required for lane changing and the lane changing track. From fig. 18, the speed of the autonomously driven vehicle increases with the error, and the variation range of the speed also increases slowly. From fig. 19, the headway increases as the front vehicle speed detection error increases. Overall, the autonomous driving vehicle speed variance and standard deviation exhibit fluctuating changes as the error changes, while the speed and acceleration exhibit a decreasing and increasing trend. The lane changing model of the autonomous driving vehicle shows good running characteristics within a certain detection error range.

EXAMPLE III

The features of this embodiment that are the same as those of the second embodiment are not described again, and the features of this embodiment that are different from those of the second embodiment are:

the autonomous driving model is adapted to the UC-winRoad driving simulator.

The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by a person skilled in the art that the scope of the invention as referred to in the present application is not limited to the embodiments with a specific combination of the above-mentioned features, but also covers other embodiments with any combination of the above-mentioned features or their equivalents without departing from the inventive concept. For example, the features described above have similar functions to (but are not limited to) those disclosed in this application.

Claims

1. An autonomous driving method, characterized by comprising the steps of:

wherein the content of the first and second substances,

2. The autonomous driving method of claim 1, wherein the set behavior thresholds include a safety threshold, a comfort threshold,

wherein the safety threshold is configured based on a first preset relationship between the safety threshold and a time period required for the autonomous vehicle to receive the travel instruction information until the corresponding instruction operation is performed, a travel speed of the autonomous vehicle, a maximum deceleration value, a travel safety compensation time, and a travel speed of a preceding driven vehicle,

the comfort threshold is configured based on a second preset relationship between the comfort threshold and a time period required by the autonomous vehicle to receive the travel instruction information until the corresponding instruction operation is executed, a travel speed of the autonomous vehicle, a travel safety compensation time, and a travel speed of a preceding driven vehicle.

3. The autonomous driving method of claim 1, wherein the autonomous driving follow-up model is constructed based on a preset relationship between a tip time distance of the autonomous vehicle and the safety threshold value and the comfort threshold value, and a corresponding relationship between a predicted acceleration and a predicted acceleration in the different driving states, and is used for representing a corresponding relationship between driving data of the autonomous vehicle and the different driving states.

4. The autonomous driving method of claim 1,

the matching model is constructed by taking the speed tolerance of the automatic driving vehicle not less than a speed tolerance threshold value and the space tolerance not less than a space tolerance threshold value as constraint functions.

5. The autonomous driving method according to claim 4, wherein the speed tolerance is configured based on a first correspondence relationship between the speed tolerance and a preset threshold value, a first magnitude preset relationship between an actual travel speed and an expected speed of the autonomous vehicle and a correlation relationship between the first correspondence relationship and the first magnitude preset relationship, and a correlation relationship between the speed tolerance and a speed tolerance at a previous specific time, a sixth preset relationship between an actual travel speed and an expected speed of the autonomous vehicle and a time delay, a second magnitude preset relationship between an actual travel speed and an expected speed of the autonomous vehicle and a correlation relationship between the sixth preset relationship and the second magnitude preset relationship, wherein a time interval between a previous specific time and a current time is the same as a duration of the time delay,

the space allowance is configured based on a second corresponding relation between the space allowance and a preset threshold, a third size preset relation between a current lane change space and a minimum distance required for lane change, an association relation between the second corresponding relation and the third size preset relation, and a space allowance at a previous specific time, a seventh preset relation between the current lane change space and the minimum distance required for lane change, a fourth size preset relation between the current lane change space and the minimum distance required for lane change, and an association relation between the seventh preset relation and the fourth size preset relation, wherein time intervals at the previous specific time and the current time are the same as the duration of time delay.

6. The autonomous driving method of claim 5, wherein configuring the generated lane-change preparatory model with a lane-change space as a constraint comprises:

and generating a constraint condition according to a fifth size preset relation between the first distance and the first safety distance, a sixth size preset relation between the second distance and the second safety distance, a seventh size preset relation between the third distance and the third safety distance, and an incidence relation among the fifth size preset relation, the sixth size preset relation and the seventh size preset relation, and further configuring and generating the lane change preparation model.

7. The autonomous driving method of claim 1, wherein configuring the generated lane change speed control model with a lane change space as a constraint comprises:

8. The autonomous driving method of claim 1, wherein controlling lane change speed of the autonomous vehicle by varying the degree of influence of a previously driven vehicle during a lane change comprises:

9. The autonomous driving method of claim 1, wherein configuring the generated trajectory optimization model with the lane change space as a constraint comprises:

10. An autonomous driving system, comprising:

wherein the content of the first and second substances,