A cooperative control method of intelligent connected vehicles in an interlaced area

Abstract

The application discloses a cooperative control method of intelligent networked vehicles in an interweaving area, which comprises the following steps: firstly, detecting various information required by the vehicle through an information detection module; secondly, delivering the information through a communication module; thirdly, determining the lane-changing behavior of the vehicle according to a set lane-changing model through a decision module; the lane-changing model comprises two modes, i.e., a free lane-changing mode and a forced lane-changing mode, the free lane-changing mode mainly considers the acceleration benefits of the ego vehicle and multiple rear vehicles, and the forced lane-changing mode mainly considers the safety of the ego vehicle; and finally, controlling the vehicle to complete the lane-changing action through a vehicle control module. Compared with the prior art, the application can make the intelligent networked vehicle consider the overall benefits while meeting the demand of the ego vehicle when driving in the interweaving area by increasing the yielding coefficient of the vehicle when calculating the lane-changing acceleration benefits of the ego vehicle and the multiple rear vehicles.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of intelligent transportation, and particularly relates to a cooperative control method of intelligent networked vehicles in an interweaving area. BACKGROUND

[0002] The intelligent networked vehicle is a car with the functions of environment perception, intelligent decision-making and automatic control, or interaction with external information, and even cooperative control.

[0003] The definition of the interweaving area is that two or more than two vehicle flows travel in the same general direction, and the road sections of the long sections of the expressway cross each other without the assistance of traffic control devices (except for guide signs). When the diverging section is followed by the merging section, or when the single-lane off-ramp is followed by the single-lane on-ramp, and the two are connected by a continuous auxiliary lane, an interweaving area is formed.

[0004] When the intelligent networked vehicle is not fully popularized, it will experience a long process of mixed driving of intelligent networked vehicles and manually driven vehicles (hereinafter referred to as mixed traffic flow). In the mixed traffic flow environment, due to the randomness of the manually driven vehicles and the immaturity of the intelligent networked vehicle technology, the decision-making behavior of the intelligent networked vehicle is very important. As a key traffic conversion node in the road system, the lane-changing interweaving phenomenon of the traffic flow is easy to cause traffic disorder and traffic congestion. Unlike other road infrastructure, the running characteristics of the interweaving area traffic flow are affected by the interaction of the three vehicle flows of the main line flow, the ramp entering flow and the main line exiting flow, and different flow combinations produce different categories of running characteristics; at this time, the control strategy of the intelligent networked vehicle is more complex. SUMMARY

[0005] The purpose of the application is to provide a cooperative control method of intelligent networked vehicles in an interweaving area, which increases the courtesy coefficient of the vehicle and can meet the needs of the ego vehicle while considering the overall benefits.

[0006] Technical scheme: The cooperative control method of intelligent networked vehicles in an interweaving area, comprising the following steps:

[0007] (1) collecting information of the intelligent networked vehicles and the manually driven vehicles, road information and environmental information;

[0008] (2) realizing information transmission between the intelligent networked vehicles, between the intelligent networked vehicles and the roadside intelligent agents, and between the intelligent networked vehicles and the cloud platform through the communication module;

[0009] (3) before all the vehicles enter the interweaving area, the cloud platform makes a decision by evaluating the traffic density of each lane and the ramp, and selects the lane through which the vehicle enters the interweaving area;

[0010] (4) After the intelligent connected vehicle enters the weaving area, the vehicle acceleration is calculated based on the intelligent driver model IDM vehicle following model, and the corresponding calculation is performed according to the multi-vehicle cooperative courtesy lane changing model based on the minimization of lane changing caused by overall braking MOBIL, and the lane changing behavior of the intelligent connected vehicle is decided.

[0011] Further, the vehicle information collected in step (1) includes the speed, acceleration, horizontal coordinate, vertical coordinate, vehicle length, and vehicle width of the vehicle.

[0012] Further, the road information collected in step (1) includes the lane ID and traffic density of each lane where the vehicle is located.

[0013] Further, the collected environmental information in step (1) is the traffic control information of the weaving area.

[0014] Further, the step (3) is implemented as follows:

[0015] If the vehicle needs to leave the main line, it will change into the right lane in advance and enter the weaving area from the right lane. If the vehicle does not need to leave the main line, the cloud platform will determine the traffic density of the main line and the ramp. When the traffic density of the main line is less than the critical density of the main line and the traffic density of the ramp is greater than the critical density of the ramp, the vehicle will change into the left lane in advance and enter the weaving area from the left lane, reducing the impact on the remaining vehicles in the weaving area. If the traffic density does not meet the above conditions, the vehicle will not change lanes and directly enter the weaving area from the original lane.

[0016] Further, the IDM vehicle following model based vehicle acceleration calculation in step (4) is implemented as follows:

[0017] Construct a vehicle information matrix:

[0018]

[0019] Where: n is the vehicle number; V is the vehicle speed; a is the vehicle acceleration; h is the vehicle horizontal coordinate; z is the vehicle vertical coordinate; l is the vehicle length; b is the vehicle width; I is the vehicle lane ID; k is the vehicle lane traffic density; d is the vehicle lane traffic control information;

[0020] IDM vehicle following model based vehicle acceleration calculation:

[0021]

[0022]

[0023] Where: a cis the acceleration of the ego vehicle after lane changing; α is the maximum acceleration of the ego vehicle; V is the current speed of the ego vehicle; V0 is the desired speed of the ego vehicle; δ is the acceleration index; ΔV is the speed difference between the ego vehicle and the front vehicle; S is the distance between the ego vehicle and the front vehicle; S * (V, ΔV) is the desired following distance; S0 is the minimum distance; T is the safe headway; β is the comfortable deceleration.

[0024] Further, the process of constructing the lane changing model of the vehicle in step (4) is as follows:

[0025] When the intelligent connected vehicle needs to change lanes while driving in the interlaced area, the intelligent connected vehicle will calculate the acceleration benefit after lane changing, and if the threshold is reached, the intelligent connected vehicle will change to the target lane:

[0026]

[0027] wherein: Y is the lane changing benefit of the vehicle; is the acceleration of the ego vehicle after lane changing; α is the maximum acceleration of the ego vehicle; V is the current speed of the ego vehicle; V0 is the desired speed of the ego vehicle; δ is the acceleration index; ΔV is the speed difference between the ego vehicle and the front vehicle; S is the distance between the ego vehicle and the front vehicle; S c is the acceleration of the ego vehicle before lane changing; p is the courtesy coefficient; e is the number of vehicles within the communication range of the vehicle in the current lane; is the acceleration of the ith vehicle in the target lane after lane changing; α ni is the acceleration of the ith vehicle in the target lane before lane changing; λ i is the acceleration benefit weight of the ith vehicle in the target lane; f is the number of vehicles within the communication range of the vehicle in the target lane; is the acceleration of the ith vehicle in the original lane after lane changing; α oi is the acceleration of the ith vehicle in the original lane before lane changing; μ i is the acceleration benefit weight of the ith vehicle in the original lane; ΔA is the lane changing benefit threshold;

[0028] If the threshold is not reached, the intelligent connected vehicle continues to drive, calculates the acceleration benefit after lane changing, until the intelligent connected vehicle enters the forced lane changing area;

[0029] In the forced lane changing area, the lane changing logic of the intelligent connected vehicle will be switched to forced lane changing, and the acceleration benefit after lane changing will not be calculated, and only the safety of the lane changing of the vehicle will be judged by the following formula:

[0030]

[0031] wherein: S f is the distance between the ego vehicle and the front vehicle after the ego vehicle stabilizes after lane changing; x f is the longitudinal distance between the ego vehicle and the front vehicle in the target lane before lane changing; v c is the speed of the ego vehicle; t l is the time taken by the ego vehicle to reach the desired speed after lane changing; t cis the time used for the ego vehicle to change lane; α c is the acceleration of the ego vehicle; v f is the speed of the front vehicle in the target lane; X f is the minimum distance; S r is the distance between the ego vehicle and the rear vehicle after the ego vehicle changes lane; x r is the longitudinal distance between the ego vehicle and the rear vehicle in the target lane before the ego vehicle changes lane; v r is the speed of the rear vehicle in the target lane;

[0032] When the above two inequalities are satisfied, it is considered that the safety of the vehicle reaches a threshold; if the safety of the vehicle reaches the safety threshold, the vehicle changes into the target lane; if the safety threshold is not reached, it is determined whether the vehicle behind the target lane is an intelligent connected vehicle;

[0033] If the vehicle behind the target lane is an intelligent connected vehicle, the rear vehicle in the target lane slows down to assist the intelligent connected vehicle to change lane;

[0034] If the vehicle behind the target lane is a human-driven vehicle, the ego vehicle slows down and continues to determine whether it is safe to change lane and find a lane changing opportunity.

[0035] Further, the yielding coefficient p is:

[0036]

[0037] wherein ω1 is the first weight; x1 is the distance of the ego vehicle from the ramp exit; ω2 is the second weight; x2 is the distance of the ego vehicle from the nearest large vehicle after changing lane; and x3 is the distance of the ego vehicle from the nearest large vehicle before changing lane.

[0038] Further, the acceleration benefit weight of the ith rear vehicle in the target lane is:

[0039]

[0040] wherein x 4i is the distance between the ith rear vehicle in the target lane and the ego vehicle.

[0041] Further, the acceleration benefit weight of the ith rear vehicle in the original lane is:

[0042]

[0043] wherein x 5i is the distance between the ith rear vehicle in the original lane and the ego vehicle.

[0044] Compared with the prior art, the present application has the beneficial effects that: the present application increases the yielding coefficient of the vehicle when calculating the acceleration benefits of the ego vehicle and the multiple rear vehicles; the introduction of the yielding coefficient enables the system to more humanly consider the influence on the rear vehicles when making decisions on acceleration, deceleration or speed maintenance, avoids unnecessary acceleration and deceleration, and leaves more sufficient reaction time and safety space for the rear vehicles; the present application can enable the intelligent connected vehicle to consider the overall benefits while meeting the needs of the ego vehicle when driving in the weaving area. BRIEF DESCRIPTION OF DRAWINGS

[0045] Figure 1 Flowchart of the present application;

[0046] Figure 2 Schematic diagram of the lane changing scene in the weaving area in the embodiment of the present application. DETAILED DESCRIPTION

[0047] The present application will be further described in detail below with reference to the accompanying drawings.

[0048] As Figure 1 shown, the present application proposes a cooperative control method of intelligent connected vehicles in the weaving area, which includes the following steps:

[0049] Step S1, collect the information of the intelligent connected vehicles and human-driven vehicles, road information and environmental information.

[0050] The collected vehicle information includes: the speed, acceleration, horizontal coordinate, vertical coordinate, vehicle length and vehicle width of the vehicle; the collected road information includes: the lane ID where the vehicle is located and the traffic density of each lane; the collected environmental information includes: the traffic control information of the weaving area.

[0051] Step S2, realize the communication between the intelligent connected vehicles, between the intelligent connected vehicles and the roadside intelligent agents, and between the intelligent connected vehicles and the cloud platform through the communication module, and transmit the information collected in step S1.

[0052] Step S3, the decision module operates to make decisions based on the information obtained in steps S1 and S2 and the multi-vehicle cooperative yielding lane changing model based on the minimization of overall braking induced by lane change (MOBIL), and determine the lane changing behavior of the vehicle;

[0053] Step S3.1, before all vehicles enter the weaving area, the cloud platform makes decisions by evaluating the traffic density of each lane and the ramp, and selects the lane through which the vehicle enters the weaving area;

[0054] For example Figure 2The illustrated interlaced area is an example. If the vehicle needs to leave the main line, it will change into the right lane in advance, and enter the interlaced area from the right lane. If the vehicle does not need to leave the main line, the cloud platform will judge the traffic density of the main line and the ramp. When the traffic density of the main line is less than the critical density of the main line and the traffic density of the ramp is greater than the critical density of the ramp, the vehicle will change into the left lane in advance, and enter the interlaced area from the left lane, reducing the influence on the remaining vehicles in the interlaced area. If the traffic density does not meet the above conditions, the vehicle will not change lanes and directly enter the interlaced area from the original lane. The result of the decision is communicated to all vehicles in the interlaced area through the variable information board.

[0055] Step S3.2, after the intelligent connected vehicle enters the interlaced area, the vehicle-mounted CPU calculates according to the IDM vehicle following model and the MOBIL-based multi-vehicle cooperative courtesy lane changing model to make decisions on the lane changing behavior of the intelligent connected vehicle.

[0056] Construct a vehicle information matrix:

[0057]

[0058] Where: n is the vehicle number; V is the vehicle speed; a is the vehicle acceleration; h is the vehicle horizontal coordinate (perpendicular to the road direction); z is the vehicle vertical coordinate (along the road direction); l is the vehicle length; b is the vehicle width; I is the vehicle lane ID; k is the vehicle lane traffic density; d is the vehicle lane traffic control information.

[0059] According to the Intelli driving model (IDM) vehicle following model, the intelligent connected vehicle acceleration is calculated, and the formula is as follows:

[0060]

[0061] Where: a c is the vehicle acceleration; a is the maximum acceleration of the vehicle; V is the current vehicle speed; V0 is the desired vehicle speed; d is the acceleration index; AV is the speed difference between the vehicle and the front vehicle; S is the vehicle distance between the vehicle and the front vehicle; S * (V, AV) is the desired following distance; S0 is the minimum distance; T is the safe headway; b is the comfortable deceleration.

[0062] Construct a multi-vehicle cooperative courtesy lane changing model based on MOBIL, as follows:

[0063] When the intelligent connected vehicle needs to change lanes in the interlaced area, the intelligent connected vehicle will calculate the acceleration benefit after lane changing. If the threshold is reached, the vehicle will change to the target lane.

[0064]

[0065] Wherein: Y is the vehicle lane-changing benefit; is the acceleration of the ego vehicle after lane-changing; a c is the acceleration of the ego vehicle before lane-changing; e is the number of vehicles in the communication range of the ego vehicle in the current lane, and the range is 200 meters; is the acceleration of the ith vehicle following the ego vehicle in the target lane after lane-changing; a ni is the acceleration of the ith vehicle following the ego vehicle in the target lane before lane-changing; l i is the acceleration benefit weight of the ith vehicle following the ego vehicle in the target lane; f is the number of vehicles in the communication range of the ego vehicle in the target lane, and the range is 200 meters; is the acceleration of the ith vehicle following the ego vehicle in the original lane after lane-changing; a oi is the acceleration of the ith vehicle following the ego vehicle in the original lane before lane-changing; m i is the acceleration benefit weight of the ith vehicle following the ego vehicle in the original lane; DA is the lane-changing benefit threshold; p is the courtesy coefficient, and is:

[0066]

[0067] Wherein: w1 is the first term weight; x1 is the distance of the ego vehicle from the ramp exit; w2 is the second term weight; x2 is the distance of the ego vehicle from the nearest large vehicle after lane-changing; x3 is the distance of the ego vehicle from the nearest large vehicle before lane-changing; a vehicle with a length greater than 6 meters is defined as a large vehicle.

[0068]

[0069] Wherein: x 4i is the distance between the ith vehicle following the ego vehicle and the ego vehicle in the target lane; x 5i is the distance between the ith vehicle following the ego vehicle and the ego vehicle in the original lane.

[0070] If the threshold is not reached, the intelligent connected vehicle continues to drive, calculates the acceleration benefit after lane-changing, and until the intelligent connected vehicle enters the forced lane-changing area; the range of 50 meters before the exit ramp is set as the forced lane-changing area.

[0071] In the forced lane-changing area, the lane-changing logic of the intelligent connected vehicle will be switched to forced lane-changing, and the acceleration benefit after lane-changing will no longer be calculated, and the safety of vehicle lane-changing will only be judged by the following formula:

[0072]

[0073]

[0074] Wherein: S f is the distance between the ego vehicle and the front vehicle after the ego vehicle stabilizes after lane-changing; x f is the longitudinal distance between the ego vehicle and the front vehicle in the target lane before lane-changing; v c is the speed of the ego vehicle; tl is the time used for the ego vehicle to reach the desired speed after changing lanes; t c is the time used for the ego vehicle to change lanes; a c is the acceleration of the ego vehicle; v f is the speed of the front vehicle in the target lane; X f is the minimum vehicle distance; S r is the distance between the ego vehicle and the rear vehicle after the ego vehicle stabilizes after changing lanes; x r is the longitudinal distance between the ego vehicle and the rear vehicle in the target lane before changing lanes; v r is the speed of the rear vehicle in the target lane.

[0075] When both inequalities are satisfied, the safety of the intelligent connected vehicle is considered to reach a threshold value; if the safety of the intelligent connected vehicle reaches the safety threshold, the intelligent connected vehicle changes into the target lane, and if the safety threshold is not reached, it is determined whether the vehicle behind the target lane is an intelligent connected vehicle.

[0076] If the vehicle behind the target lane is an intelligent connected vehicle, the rear vehicle in the target lane slows down to assist the ego vehicle (intelligent connected vehicle) to change lanes.

[0077] If the vehicle behind the target lane is a human-driven vehicle, the ego vehicle slows down and continues to determine whether it is safe to change lanes and find a lane-changing opportunity.

[0078] The above embodiments are only to illustrate the technical concept and characteristics of the present application, and the purpose is to enable those skilled in the art to understand the content of the present application and implement it, and cannot limit the protection scope of the present application. Any equivalent transformation or modification made in accordance with the spirit and essence of the present application shall be covered within the protection scope of the present application.

Claims

1.A method for cooperative control of intelligent connected vehicles in an interlaced area, characterized in that, The method comprises the following steps: (1) collecting information of intelligent connected vehicles and human-driven vehicles, road information and environmental information; (2) realizing information transmission among intelligent connected vehicles, between intelligent connected vehicles and roadside intelligent agents and between intelligent connected vehicles and a cloud platform through a communication module; (3) before all vehicles enter an interlaced area, the cloud platform makes a decision by evaluating traffic density at each lane and ramp and selecting a lane for the vehicles to enter the interlaced area; (4) after the intelligent connected vehicles enter the interlaced area, a vehicle following model based on an intelligent driver model (IDM) is used to calculate vehicle acceleration, and a multi-vehicle cooperative courtesy lane-changing model based on minimization of overall braking (MOBIL) is used for corresponding calculation to make a decision on lane-changing behavior of the intelligent connected vehicles; The multi-vehicle cooperative courtesy lane-changing model in step (4) realizes the following process: When the intelligent connected vehicles need to change lanes while driving in the interlaced area, the intelligent connected vehicles calculate the acceleration benefit after lane changing, and if the threshold is reached, the intelligent connected vehicles change to the target lane: Y = a + b * p + c * e + d * f + g * h + i * j + k * l + m * n + o * p + q * r + s * t + u * v + w * x + y * z + aa * bb + cc * dd + ee * ff + gg * hh + ii * jj + kk * ll + mm * nn + oo * pp + qq * rr + ss * tt + uu * vv + ww * xx + xx * yy + yy * zz + aaa * bbb + ccc * ddd + eee * fff + ggg * hhh + iii * jjj + kkk * lll + mmm * nnn + ooo * ppp + qqq * rrr + sss * ttt + uuu * vvv + wwww * xxx + xxx * yyy + zzz * zzz is the acceleration of the ith vehicle in the target lane after the lane change; a c is the acceleration of the ith vehicle in the target lane before the lane change; p is the courtesy coefficient; e is the number of vehicles in the communication range of the vehicle in the current lane; is the acceleration of the ith vehicle in the target lane after the lane change; a ni is the acceleration of the ith vehicle in the target lane before the lane change; λ i is the acceleration of the ith vehicle in the target lane before the lane change; f is the number of vehicles in the communication range of the vehicle in the target lane; is the acceleration of the ith vehicle in the target lane after the lane change; a oi is the acceleration of the ith vehicle in the target lane before the lane change; μ i is the acceleration of the ith vehicle in the target lane before the lane change; ΔA is the lane change benefit threshold; If the threshold is not reached, the intelligent connected vehicles continue to drive and calculate the acceleration benefit after lane changing until the intelligent connected vehicles enter a forced lane-changing area; In the forced lane-changing area, the lane-changing logic of the intelligent connected vehicles is switched to forced lane-changing, and the acceleration benefit after lane changing is no longer calculated, but the safety of vehicle lane changing is judged by the following formula: Wherein: S f is the distance between the ego vehicle and the front vehicle in the target lane after the ego vehicle has stabilized after changing lanes; x f is the longitudinal distance between the ego vehicle and the front vehicle in the target lane before changing lanes; v c is the speed of the ego vehicle; t l is the time taken by the ego vehicle to reach the desired speed after changing lanes; t c is the time taken by the ego vehicle to change lanes; a c is the acceleration of the ego vehicle; v f is the speed of the front vehicle in the target lane; X f is the minimum vehicle distance; S r is the distance between the ego vehicle and the rear vehicle in the target lane after the ego vehicle has stabilized after changing lanes; x r is the longitudinal distance between the ego vehicle and the rear vehicle in the target lane before changing lanes; v r is the speed of the rear vehicle in the target lane; When the above two inequalities are both satisfied, it is considered that the safety of the vehicle reaches the threshold; if the safety of the vehicle reaches the safety threshold, the vehicle changes into the target lane; if the safety threshold is not reached, it is judged whether the vehicle behind the target lane is an intelligent connected vehicle; If the vehicle behind the target lane is an intelligent connected vehicle, the vehicle behind the target lane slows down to assist the intelligent connected vehicle in lane changing; If the vehicle behind the target lane is a human-driven vehicle, the vehicle slows down and continues to judge whether the lane changing is safe and find a lane changing opportunity; The courtesy coefficient p is: Wherein: ω1 is the weight of the first term; x1 is the distance of the vehicle from the ramp exit; ω2 is the weight of the second term; x2 is the distance of the vehicle from the nearest large vehicle after lane changing; x3 is the distance of the vehicle from the nearest large vehicle before lane changing; The acceleration benefit weight of the ith vehicle behind the target lane is: wherein: x 4i is the distance between the ith vehicle behind the target lane and the ego vehicle; The acceleration benefit weight of the ith vehicle behind the original lane is: wherein: x 5i is the distance between the ith vehicle behind the original lane and the ego vehicle. 2.The method of claim 1, wherein, The vehicle information collected in step (1) includes speed, acceleration, horizontal coordinate, vertical coordinate, vehicle length and vehicle width of the vehicle. 3.The method of claim 1, wherein, The road information collected in step (1) includes lane ID and traffic density of each lane. 4.The method of claim 1, wherein, The environmental information collected in step (1) is interlaced area traffic control information. 5.The method of claim 1, wherein, The realization process of step (3) is as follows: If the vehicle needs to leave the main line, it will change into the right lane in advance and enter the weaving area from the right lane; if the vehicle does not need to leave the main line, the cloud platform will judge the traffic density of the main line and the ramp, and when the traffic density of the main line is less than the critical density of the main line and the traffic density of the ramp is greater than the critical density of the ramp, the vehicle will change into the left lane in advance and enter the weaving area from the left lane, thereby reducing the influence on the remaining vehicles in the weaving area; if the traffic density does not meet the above conditions, the vehicle will not change lanes and directly enter the weaving area from the original lane. 6.The method of claim 1, wherein, The process of calculating the vehicle acceleration based on the IDM vehicle following model in step (4) is as follows: Construct a vehicle information matrix: Where: n is the vehicle number; V is the vehicle speed; a is the vehicle acceleration; h is the vehicle horizontal coordinate; z is the vehicle vertical coordinate; l is the vehicle length; b is the vehicle width; I is the vehicle lane ID; k is the vehicle lane traffic density; d is the vehicle lane traffic control information; Calculate the intelligent networked vehicle acceleration based on the IDM vehicle following model: wherein: a c is the acceleration of the ego vehicle; a is the maximum acceleration of the ego vehicle; V is the current speed of the ego vehicle; V0 is the desired speed of the ego vehicle; δ is an acceleration exponent; ΔV is the speed difference between the ego vehicle and the preceding vehicle; S is the distance between the ego vehicle and the preceding vehicle; S * (V, ΔV) is the desired following distance; S0 is the minimum distance; T is the safe headway; β is the comfortable deceleration.

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