Rationality analysis method for planning scheme of dedicated lanes for connected autonomous driving vehicles

Abstract

The present invention provides a method for analyzing the rationality of a dedicated lane planning scheme for a networked autonomous driving vehicle, including: considering the types of front and rear vehicles and the characteristics of CAV vehicle formations to divide different following modes, and according to the different headway times in the mixed traffic flow corresponding to different following modes; establishing a macro probability model to calculate the average headway time of vehicles under mixed traffic conditions and the corresponding road capacity; based on the study of road capacity under mixed traffic scenarios, deriving a road section impedance function suitable for mixed traffic flows; assuming that after setting up a dedicated lane, the lane selection of CAV travelers follows the user equilibrium condition to obtain the proportion of CAV travelers who choose the dedicated lane; and obtaining the rationality judgment condition of the dedicated lane planning scheme by comparing the total travel cost before and after setting up the CAV dedicated road section. The present invention proposes conditions for judging the rationality of the dedicated lane planning scheme for a section where a dedicated road is not set up.

Description

Technical Field

[0001] The present invention provides a method for analyzing the rationality of a dedicated lane planning scheme for a networked autonomous driving vehicle, and belongs to the technical field of autonomous driving. Background Art

[0002] With the development of intelligent network technology and autonomous driving technology, connected and autonomous vehicles (CAVs) are recognized as the future direction of vehicle development and have great potential in improving traffic safety and traffic efficiency. Compared with traditional human-driven vehicles (HVs), CAVs have faster reaction time and can maintain a smaller safe headway with the vehicle in front. Relying on real-time communication and coordinated control, CAVs can drive in formation on the road, which will further shorten the headway between each other. Therefore, CAVs can significantly improve the traffic capacity of the road. However, due to the limitations of the level of technological development and people's acceptance, CAVs will not completely replace HVs in a short period of time, and the two types of vehicles will share urban road resources for a long time in the future.

[0003] In mixed traffic scenarios, considering the types of vehicles in front and behind and the characteristics of CAV vehicle formation, a variety of different following modes can be divided. Since different following modes correspond to different safe headway times, the proportion of different types of vehicles and their spatial distribution on the road directly affect the average headway time, vehicle density and road capacity. In summary, the road capacity in mixed traffic scenarios is no longer a fixed value, but a random variable related to the proportion of different types of vehicles and their spatial distribution on the road. In addition, due to the uncertainty of the driving behavior of manually driven vehicles, HVs will seriously interfere with the passage and formation of CAVs in mixed traffic scenarios. In order to give full play to the technical advantages of CAVs and improve the traffic efficiency of road sections, CAVs and mixed traffic can be separated by setting up CAV dedicated lanes, reducing the mutual interference between CAVs and HVs, and promoting CAV formation. However, the lane planning scheme of CAV dedicated lanes will directly change the lane selection of travelers and the total travel cost of all vehicles on the road section. Unreasonable planning schemes will not only fail to improve traffic efficiency but will increase traffic congestion. How to simply and conveniently analyze the rationality of CAV dedicated lane planning schemes is a major problem that needs to be solved urgently. To this end, the present invention proposes a reasonable planning and analysis method for dedicated lanes for connected autonomous driving vehicles in scenarios where HVs and CAVs are mixed.

[0004] In the prior art, Levin et al. (Levin, MW, & Boyles, SD (2016). A multiclass cell transmission model for shared human and autonomous vehicle roads. Transportation Research Part C: Emerging Technologies, 62, 103-116.) and Liu et al. (Liu, Z., & Song, Z. (2019). Strategic planning of dedicated autonomous vehicle lanes and autonomous vehicle / toll lanes in transportation networks. Transportation Research Part C: Emerging Technologies, 106, 381-403.) both assume that the distribution of different types of vehicles on lanes is completely random, that is, the distribution of the two types of vehicles on lanes is a Bernoulli process, and does not consider CAV vehicle formation. However, in actual scenarios, CAVs tend to travel in platoons, that is, they rely on advanced connected autonomous driving technology to achieve coordinated control between vehicles, and travel in platoons on the road through autonomous lane changing, acceleration (deceleration) and other operations. Therefore, the two types of vehicles are not completely randomly distributed on the road, which leads to serious errors in the calculation of mixed road capacity by Technology 1.

[0005] Although the study by Ghiasi et al. (Ghiasi, A., Hussain, O., Qian, Z. S., & Li, X. (2017). A mixed traffic capacity analysis and lane management model for connected automated vehicles: A Markov chain method. Transportation Research Part B: Methodological, 106, 266-292.) took into account the formation tendency of CAVs on the road and defined and introduced the indicator of formation strength to qualitatively describe the aggregation of CAVs on the road, this indicator has no specific mathematical expression. It only gives the maximum and minimum values ​​of the indicator corresponding to the queue strength when all CAVs are in the platoon or when all CAVs are not in the platoon, which cannot accurately and quantitatively describe the formation of CAVs on the lane.

[0006] At present, some studies focus on the impact of dedicated lanes for connected autonomous vehicles on the travel efficiency of the road network and the optimization of the layout of dedicated lanes. By establishing traffic distribution models under ordinary lanes and dedicated lanes, the solution algorithm of the model is given, the mixed equilibrium flow state under different lane configuration modes is studied, and the optimal lane configuration scheme is determined. For example, Zhou Zhaoming et al. (Zhou Zhaoming, Huang Zhongxiang, Yuan Jianbo, & Li Pan. (2021). Traffic flow optimization based on different lane modes of unmanned vehicles. Journal of Chang'an University (Natural Science Edition).) assumed that unmanned vehicles, vehicles equipped with advanced traveler information systems (ATIS) and ordinary driving vehicles select paths according to the system optimal mode, user equilibrium mode and random user equilibrium mode respectively, and established traffic distribution models under ordinary lanes and special lanes respectively. The model was solved by the method of successive averages (MSA), and the capacity of ordinary lanes and unmanned driving special lanes at different speeds was analyzed and determined. The influence of model parameters such as information quality level, travel demand, and market penetration rate on the average travel time was analyzed through examples; on the basis of determining the values ​​of various parameters, the mixed equilibrium flow state under different lane configuration modes was studied.

[0007] This technology does not take into account the different headway times corresponding to different following modes in mixed traffic environments. The capacity of mixed traffic roads is not a fixed value, but a random variable that depends on the proportion of the two types of vehicles and the spatial distribution of the two types of vehicles on the road. The model proposed by this technology is to optimize the planning and layout of CAV lanes from the road network level. It is necessary to establish a complex mathematical planning model and solve it to obtain a planning solution. The method is too complicated and difficult to calculate, making it difficult to use in actual traffic planning.

[0008] In addition, the main goal is to improve the traffic capacity of the road section, and the layout plan of optimized dedicated lanes is proposed. By constructing a lane management model, the traffic capacity of the road section is maximized, and the layout strategy of dedicated lanes is studied. In addition, there are studies that use simulation technology to study the impact of dedicated lane layout on the traffic capacity of the road section. For example, Chen et al. (Chen, D., Ahn, S., Chitturi, M., & Noyce, DA (2017). Towards vehicle automation: Roadway capacity formulation for traffic mixed with regular and automated vehicles. Transportation research part B: methodological, 100, 196-221.) studied the impact of different lane management schemes on the traffic flow of the road section based on the mixed road capacity calculation model they developed, such as the coexistence scheme of networked automatic vehicle dedicated lanes and manual vehicle dedicated lanes, the coexistence scheme of networked automatic vehicle dedicated lanes and mixed lanes, and the coexistence scheme of manual vehicle dedicated lanes and mixed lanes, and developed a set of general models to determine the effective domain of different lane management schemes.

[0009] First, we assume that all CAVs appear in the form of convoys, and that CAV convoys exist periodically, making the traffic flow periodic. Each cycle consists of an n-CAV convoy and m-HVs. We obtain the road capacity formula consisting of HV and CAV convoys. We then study the capacity of two-lane highways under different lane models. We further develop a general formula that includes all lane policies considered to determine how CAVs should be allocated on lanes given traffic demand, CAV penetration, and CAV efficiency gain. Finally, we extend the formula to general multi-lane highways.

[0010] This technology assumes that CAV fleets exist periodically, and each cycle consists of an n-CAV fleet and m-HV fleets, thereby obtaining a road capacity formula for mixed traffic flow. This is inconsistent with the random distribution of HVs and CAVs on lanes in actual situations, and does not take into account the formation tendency of CAV vehicles traveling on the road. Summary of the invention

[0011] Aiming at the mixed driving scenario of HV and CAV, considering that the minimum safe distance that can be maintained between mixed vehicles is related to the types of front and rear vehicles and the vehicle formation scenario, an indicator is introduced to quantitatively describe the formation of CAVs during road driving, and a more universal calculation formula for the traffic capacity of mixed sections is proposed. On this basis, for a given road section and CAV lane planning scheme, a method is provided for traffic planners to quickly judge whether the CAV lane planning scheme is reasonable. This method is simple and easy to operate, and does not require a complicated modeling and solving process.

[0012] The specific technical solution is:

[0013] The rationality analysis method of the planning scheme of the dedicated lane for connected autonomous driving vehicles includes the following steps:

[0014] Step 1. Consider the types of front and rear vehicles and the characteristics of CAV vehicle formation to divide different following modes, and then use different headway time in mixed traffic flow to correspond to different following modes;

[0015] Let the maximum convoy size be s vehicles. That is, when the convoy size exceeds s vehicles, the subsequent CAVs will form a new convoy. According to different following modes, there are five different headway times in mixed traffic flow. Different headway times correspond to the vehicle driving forms on the road:

[0016] (1)h 11 It indicates the headway time of an HV following another HV;

[0017] (2)h 21 It indicates the headway time of the CAV following the HV;

[0018] (3)h 12 It indicates the headway time of the HV following the CAV;

[0019] (4)h 22 It represents the headway time of vehicles in the same CAV queue;

[0020] (5)h 22′ It indicates the headway time of CAVs following the largest CAV formation;

[0021] Step 2. Establish a macro probability model to calculate the average headway time of vehicles under mixed traffic conditions and the corresponding road capacity;

[0022] Step 2.1 The proportion of each headway in the mixed flow is:

[0023] p 11 +p 12 =p H (1)

[0024] p 21 +p22′ +p 22 =1-p H =p C (2)

[0025]

[0026]

[0027]

[0028] Among them, p H 、p C denote the penetration rates of HV and CAV, respectively; represents the CAV formation ratio, that is, the ratio of the number of CAVs that form a formation with the preceding vehicle to all CAVs on the road; p 11 、p 12 They represent the probability of an HV following another HV and the probability of an HV following a CAV, respectively; p 22 、p 22′ 、p 21 They are respectively the probability that a CAV follows another CAV in a CAV queue; the probability that a CAV follows the tail of a CAV queue; and the probability that a CAV follows an HV.

[0029] Step 2.2 Calculate the average headway time and the corresponding road capacity;

[0030] Let HV be the minimum safe headway that the following vehicle can maintain, that is, h 12 =h 11 ;

[0031] The calculation formula for the average headway time of the lane is:

[0032]

[0033] The average capacity under mixed traffic flow environment is obtained as follows:

[0034]

[0035] Step 3. Based on the research on road capacity under mixed traffic scenarios, derive the road section impedance function suitable for mixed traffic flow;

[0036] Let the free travel time t of each lane on a city road with a speed limit be 0 The same, there are m lanes and N vehicles. According to equations (8), (10), and (11), the impedance of the mixed traffic section without dedicated lanes, the CAV dedicated lane after dedicated lanes, and the mixed traffic section after dedicated lanes are calculated respectively. The specific calculation formula is as follows:

[0037] No dedicated lanes:

[0038] When no CAV lanes are set up, all lanes are mixed lanes that allow HVs and CAVs to pass through, and the travel time of vehicles is obtained by the Federal Highway Administration (BPR) function:

[0039]

[0040] Where, t represents the travel time of the section without dedicated road; t 0 represents the free flow time of CAV and HV on the road section; c 0 Indicates the lane capacity when it is pure HV; represents the conversion factor of CAV-HV; N(p C ρ+p H ) represents the mixed traffic flow converted into standard HV traffic flow; α and β represent two parameters in the BPR function.

[0041] At the same time, the total travel time of vehicles without CAV dedicated lanes is:

[0042]

[0043] After setting up the dedicated lane:

[0044] After setting up the CAV lane, CAVs can drive on both the dedicated lane and the ordinary lane, but HVs can only drive on the mixed lane. Assuming that the proportion of CAVs driving on the dedicated lane to all CAVs is λ, the travel time of vehicles after setting up the dedicated lane can be obtained as follows:

[0045] (1) Calculation of travel time on CAV lanes

[0046]

[0047] Where n represents the number of CAV lanes; t′ represents the travel time of the CAV lane;

[0048] represents the CAV-HV conversion factor of the dedicated lane; p′ C represents the penetration rate of CAVs on the dedicated lane, with a value of 1; Indicates the proportion of CAV formations on the CAV-only lane, with a value of 1.

[0049] (2) Calculation of travel time for mixed traffic lanes

[0050]

[0051] Where t″ represents the travel time of the mixed lane after the CAV lane is set up;

[0052] Indicates the CAV-HV conversion coefficient of the mixed lane after the dedicated lane is set up; p″ C ″ represents the CAV penetration rate of mixed traffic lanes after setting up dedicated lanes; Indicates the proportion of CAVs in mixed lanes after dedicated lanes are set up.

[0053] At the same time, the total travel time of vehicles without CAV dedicated lanes is:

[0054] T C =Np C λt′+N(1-p C λ)t″ (12)

[0055] Step 4. Assuming that after the dedicated lane is set up, the lane selection of CAV travelers follows the user equilibrium condition to obtain the proportion of CAV travelers who choose the dedicated lane;

[0056] User Equilibrium (UE) condition:

[0057] In order to obtain the proportion of CAVs entering the dedicated lanes, the lane selection of CAV travelers is made to follow the UE equilibrium principle, that is, when equilibrium is reached, the lanes used have equal and minimum travel costs for CAV travelers. At the same time, according to the actual situation, there must be CAVs on the dedicated lanes, satisfying one of the following two conditions:

[0058] Case 1: When equation (13) is satisfied, the travel time of the dedicated lane for CAV travelers is less than the travel time of the mixed lane. At this time, all CAV travelers will choose to travel on the dedicated lane (λ = 1 at this time), that is, t′<t″.

[0059] (mn)p C h 22 -n(1-p C )h 11 <0 (13)

[0060] Case 2: If case 1 is not satisfied, it means that the travel time of the dedicated lane for CAV travelers is equal to the travel time of the mixed lane, that is, t″=t′. At this time:

[0061] Step 5. The rationality judgment condition of the dedicated lane planning scheme is obtained by comparing the total travel cost of the CAV dedicated road section; in the mixed driving environment of HV and CAV, the rationality judgment condition of the dedicated lane planning scheme is:

[0062] T>T C (14)

[0063] After setting up the CAV lane in step 4, the conditions for judging the rationality of the CAV lane planning scheme are obtained for the two situations of the travel time of the CAV lane and the mixed traffic section.

[0064] Case 1: For CAV travelers, the travel time of the dedicated lane is shorter than the travel time of the ordinary mixed lane. In this case, when the CAV penetration rate and the lane planning scheme (including the number of CAV lanes and the number of mixed lanes) satisfy formula (15), it means that the planning scheme of the dedicated lane is reasonable.

[0065]

[0066] in,

[0067] Case 2: If the parameters do not satisfy Case 1, it means that for CAV travelers, the travel time of the dedicated lane is equal to the travel time of the ordinary mixed lane. In this case, when the CAV penetration rate and the lane planning scheme (including the number of CAV lanes and the number of mixed lanes) satisfy formula (16), it means that the planning scheme of the dedicated lane is reasonable.

[0068] n(p C ρ+1-p C )-mp C λρ′>0 (16)

[0069] in,

[0070] Alternatively, step 5 considers that the minimum safe distance that can be maintained between mixed vehicles is related to the types of front and rear vehicles and the vehicle formation scenario. The given judgment conditions are applicable to more common traffic scenarios. The following simplifies the judgment conditions given in step 5 to make it more suitable for traffic planning scenarios.

[0071] Assume that CAV formation is not considered and the headway between CAVs is h 22 , the time distances between other train heads are equal, that is, h 11 =h 12 =h 21 , the conversion factor of CAV-HV is:

[0072]

[0073] According to the above content, the rationality judgment conditions of the simplified dedicated lane planning scheme are as follows:

[0074] Case 1: For CAV travelers, the travel time of the dedicated lane is less than the travel time of the ordinary mixed lane. In this case, when the CAV penetration rate and the lane planning scheme (including the number of CAV lanes and the number of mixed lanes) satisfy formula (18), it means that the planning scheme of the dedicated lane is reasonable.

[0075]

[0076] Case 2: If the parameters do not satisfy Case 1, it means that for CAV travelers, the travel time of the dedicated lane is equal to the travel time of the ordinary mixed lane. In this case, when the CAV penetration rate and the lane planning scheme (including the number of CAV lanes and the number of mixed lanes) satisfy formula (19), it means that the planning scheme of the dedicated lane is reasonable.

[0077]

[0078] in,

[0079] The specific technical effects of the present invention are:

[0080] (1) The present invention proposes a more universal calculation formula for the average lane capacity in a mixed driving environment of HVs and CAVs. Considering that the minimum safe distance that can be maintained between mixed vehicles is related to the types of front and rear vehicles and the vehicle formation scenario, five different headway intervals are defined according to different following modes. The two indicators of the proportion of different types of vehicles and the proportion of vehicles participating in the formation among connected autonomous driving vehicles (referred to as the formation ratio) are used to quantitatively describe the distribution of different vehicle following modes on mixed roads, and a macro probability model is established to calculate the average headway interval of lanes and the corresponding average capacity under mixed conditions.

[0081] (2) The present invention proposes conditions for judging the rationality of the dedicated lane planning scheme for sections without CAV dedicated lanes in mixed traffic scenarios. Based on the study of the traffic capacity of mixed traffic roads, the impedance function of the section applicable to different types of lanes is derived; when the CAV penetration rate and lane planning scheme (the specific number of ordinary lanes and CAV dedicated lanes) are known, by comparing the total travel costs before and after setting up CAV dedicated lanes, conditions for judging the rationality of the dedicated lane planning scheme for sections without dedicated lanes are proposed. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] Figure 1 Schematic diagram of vehicle headway in different following modes of the present invention. DETAILED DESCRIPTION

[0083] The specific technical solution of the present invention is described in conjunction with the accompanying drawings.

[0084] The rationality analysis method of the planning scheme of the dedicated lane for connected autonomous driving vehicles includes the following steps:

[0085] Step 1. Consider the types of vehicles in front and behind and the characteristics of CAV platooning to divide different following modes, and according to the different headway time in mixed traffic flow corresponding to different following modes:

[0086] Considering that CAVs tend to travel in platoons on the road, since the CAVs in the platoon communicate with the lead vehicle in real time, the longer the platoon is, the more unstable the information transmission is. Therefore, there is a maximum size limit for the CAV platoon on the same lane, and the maximum platoon size is s vehicles. That is, when the platoon size exceeds s vehicles, the subsequent CAVs will form a new platoon. According to different following modes, there are five different headway times in mixed traffic flow. Different headway times correspond to the vehicle driving forms on the road, such as Figure 1 shown.

[0087] (1)h 11 It indicates the headway time of an HV following another HV;

[0088] (2)h 21 It indicates the headway time of the CAV following the HV;

[0089] (3)h 12 It indicates the headway time of the HV following the CAV;

[0090] (4)h 22 It represents the headway time of vehicles in the same CAV queue;

[0091] (5)h 22′ It represents the headway time of a CAV following the largest CAV formation (the example given in the figure assumes that the maximum formation length is 4 vehicles).

[0092] Step 2. Establish a macro probability model to calculate the average headway time of vehicles under mixed traffic conditions and the corresponding road capacity;

[0093] Step 2.1 The proportion of each headway in the mixed flow is:

[0094] p 11 +p 12 =p H (1)

[0095] p 21 +p 22′ +p 22 =1-p H =p C (2)

[0096]

[0097]

[0098]

[0099] Among them, p H 、p C denote the penetration rates of HV and CAV, respectively; represents the CAV formation ratio, that is, the ratio of the number of CAVs that form a formation with the preceding vehicle to all CAVs on the road; p 11 、p 12 They represent the probability of an HV following another HV and the probability of an HV following a CAV, respectively; p 22 、p 22′ 、p 21 They are respectively the probability that a CAV follows another CAV in a CAV queue; the probability that a CAV follows the tail of a CAV queue; and the probability that a CAV follows an HV.

[0100] Step 2.2 Calculate the average headway time and the corresponding road capacity;

[0101] Assume that the minimum safe headway that HV can maintain as the following vehicle is equal, that is, h 12 =h 11 .

[0102] The calculation formula for the average headway time of the lane is:

[0103]

[0104] Therefore, the average capacity under mixed traffic flow environment is:

[0105]

[0106] Step 3. Based on the research on road capacity under mixed traffic scenarios, derive the road section impedance function suitable for mixed traffic flow;

[0107] Since the addition of CAV makes the calculation of road section travel cost more complicated, the present invention provides a calculation formula for the travel time of the mixed lanes and CAV dedicated lanes before and after the dedicated lanes in the new mixed traffic flow environment. Assuming that on an urban road with a speed limit, the free travel time t 0 The same, there are m lanes and N vehicles. According to equations (8), (10), and (11), the impedance of the mixed traffic section without dedicated lanes, the CAV dedicated lane after dedicated lanes, and the mixed traffic section after dedicated lanes can be calculated respectively. The specific calculation formula is as follows:

[0108] No dedicated lanes:

[0109] When no CAV lanes are set up, all lanes are mixed lanes that allow HVs and CAVs to pass through, and the travel time of vehicles is obtained by the Federal Highway Administration (BPR) function:

[0110]

[0111] Where, t represents the travel time of the section without dedicated road; t 0 represents the free flow time of CAV and HV on the road section; c 0 Indicates the lane capacity when it is pure HV; represents the conversion factor of CAV-HV; N(p C ρ+p H ) represents the mixed traffic flow converted into standard HV traffic flow; α and β represent two parameters in the BPR function.

[0112] At the same time, the total travel time of vehicles without CAV dedicated lanes is:

[0113]

[0114] After setting up the dedicated lane:

[0115] After setting up the CAV lane, CAVs can drive on both the dedicated lane and the ordinary lane, but HVs can only drive on the mixed lane. Assuming that the proportion of CAVs driving on the dedicated lane to all CAVs is λ, the travel time of vehicles after setting up the dedicated lane can be obtained as follows:

[0116] (1) Calculation of travel time on CAV lanes

[0117]

[0118] Where n represents the number of CAV lanes; t′ represents the travel time of the CAV lane;

[0119] represents the CAV-HV conversion factor of the dedicated lane; p′ C represents the penetration rate of CAVs on the dedicated lane, with a value of 1; Indicates the proportion of CAV formations on the CAV-only lane, with a value of 1.

[0120] (2) Calculation of travel time for mixed traffic lanes

[0121]

[0122] Where t″ represents the travel time of the mixed lane after the CAV lane is set up;

[0123] Indicates the CAV-HV conversion coefficient of the mixed lane after the dedicated lane is set up; p″C ″ represents the CAV penetration rate of mixed traffic lanes after setting up dedicated lanes; Indicates the proportion of CAVs in mixed lanes after dedicated lanes are set up.

[0124] At the same time, the total travel time of vehicles without CAV dedicated lanes is:

[0125] T C =Np C λt′+N(1-p C λ)t″ (12)

[0126] Step 4. Assuming that after the dedicated lane is set up, the lane selection of CAV travelers follows the user equilibrium condition to obtain the proportion of CAV travelers who choose the dedicated lane;

[0127] User Equilibrium (UE) condition:

[0128] In order to obtain the proportion of CAVs entering the dedicated lanes, it is assumed that the lane selection of CAV travelers follows the UE equilibrium principle, that is, when equilibrium is reached, the lanes used have equal and minimum travel costs for CAV travelers. At the same time, combined with the actual situation, there must be CAVs on the dedicated lanes, which should meet one of the following two conditions:

[0129] Case 1: When equation (13) is satisfied, the travel time of the dedicated lane for CAV travelers is less than the travel time of the mixed lane. At this time, all CAV travelers will choose to travel on the dedicated lane (λ = 1 at this time), that is, t′<t″.

[0130] (mn)p C h 22 -n(1-p C )h 11 <0 (13)

[0131] Case 2: If case 1 is not satisfied, it means that the travel time of the dedicated lane for CAV travelers is equal to the travel time of the mixed lane, that is, t″=t′.

[0132] Step 5. The rationality judgment condition of the dedicated lane planning scheme is obtained by comparing the total travel cost of the CAV dedicated road section; in the mixed driving environment of HV and CAV, the rationality judgment condition of the dedicated lane planning scheme is:

[0133] T>T C (14)

[0134] After setting up the CAV lane in step 4, the conditions for judging the rationality of the CAV lane planning scheme are obtained for the two situations of the travel time of the CAV lane and the mixed traffic section.

[0135] Case 1: For CAV travelers, the travel time of the dedicated lane is shorter than the travel time of the ordinary mixed lane. In this case, when the CAV penetration rate and the lane planning scheme (including the number of CAV lanes and the number of mixed lanes) satisfy formula (15), it means that the planning scheme of the dedicated lane is reasonable.

[0136]

[0137] in,

[0138] Case 2: If the parameters do not satisfy Case 1, it means that for CAV travelers, the travel time of the dedicated lane is equal to the travel time of the ordinary mixed lane. In this case, when the CAV penetration rate and the lane planning scheme (including the number of CAV lanes and the number of mixed lanes) satisfy formula (16), it means that the planning scheme of the dedicated lane is reasonable.

[0139] n(p C ρ+1-p C )-mp C λρ′>0 (16)

[0140] in,

[0141] Step 5 considers that the minimum safe distance that can be maintained between mixed vehicles is related to the types of front and rear vehicles and the vehicle formation scenario. The given judgment conditions are applicable to more common traffic scenarios. The following simplifies the judgment conditions given in step 5 to make them more suitable for traffic planning scenarios.

[0142] Assume that CAV formation is not considered and the headway between CAVs is h 22 , the time distances between other train heads are equal, that is, h 11 =h 12 =h 21 , the conversion factor of CAV-HV is:

[0143]

[0144] According to the above content, the rationality judgment conditions of the simplified dedicated lane planning scheme are as follows:

[0145] Case 1: For CAV travelers, the travel time of the dedicated lane is less than the travel time of the ordinary mixed lane. In this case, when the CAV penetration rate and the lane planning scheme (including the number of CAV lanes and the number of mixed lanes) satisfy formula (18), it means that the planning scheme of the dedicated lane is reasonable.

[0146]

[0147] Case 2: If the parameters do not satisfy Case 1, it means that for CAV travelers, the travel time of the dedicated lane is equal to the travel time of the ordinary mixed lane. In this case, when the CAV penetration rate and the lane planning scheme (including the number of CAV lanes and the number of mixed lanes) satisfy formula (19), it means that the planning scheme of the dedicated lane is reasonable.

[0148]

[0149] in,

Claims

1. A rationality analysis method for planning a dedicated lane for connected autonomous driving vehicles, characterized by: The following steps are involved: Step 1. Consider the types of front and rear vehicles and the characteristics of CAV vehicle formation to divide different following modes, and then use different headway time in mixed traffic flow to correspond to different following modes; Step 2. Establish a macro probability model to calculate the average headway time of vehicles under mixed traffic conditions and the corresponding road capacity; Step 3. Based on the research on road capacity under mixed traffic scenarios, derive the road section impedance function suitable for mixed traffic flow; Step 4. Assuming that after the dedicated lane is set up, the lane selection of CAV travelers follows the user equilibrium condition to obtain the proportion of CAV travelers who choose the dedicated lane; Step 5. By comparing the total travel costs before and after setting up the CAV dedicated road section, the rationality judgment conditions of the dedicated road planning scheme are obtained.

2. The rationality analysis method for the planning scheme of the networked autonomous driving vehicle dedicated lane according to claim 1 is characterized in that: In step 1, the maximum convoy size is set to s vehicles; that is, when the convoy size exceeds s vehicles, the subsequent CAVs will form a new convoy; according to different following modes, there are five different headway times in the mixed traffic flow, and the different headway times correspond to the vehicle driving forms on the road: (1)h 11 It indicates the headway time of an HV following another HV; (2)h 21 It indicates the headway time of the CAV following the HV; (3)h 12 It indicates the headway time of the HV following the CAV; (4)h 22 It represents the headway time of vehicles in the same CAV queue; (5)h 22′ Indicates the headway time of a CAV following the largest CAV formation.

3. The rationality analysis method for the planning scheme of the networked autonomous driving vehicle dedicated lane according to claim 2 is characterized in that: Step 2 specifically includes the following sub-steps: Step 2.1 The proportion of each headway in the mixed flow is: p 11 +p 12 =p H (1) p 21 +p 22′ +p 22 =1-p H =p C (2) Among them, p H 、p C denote the penetration rates of HV and CAV, respectively; represents the CAV formation ratio, that is, the ratio of the number of CAVs that form a formation with the preceding vehicle to all CAVs on the road; p 11 、p 12 They represent the probability of an HV following another HV and the probability of an HV following a CAV, respectively; p 22 、p 22′ 、p 21 They are the probability that a CAV follows another CAV in a CAV queue; the probability that a CAV follows the tail of a CAV queue; and the probability that a CAV follows an HV. Step 2.2 Calculate the average headway time and the corresponding road capacity; Let HV be the minimum safe headway that the following vehicle can maintain, that is, h 12 =h 11 ; The calculation formula for the average headway time of the lane is: The average capacity under mixed traffic flow environment is obtained as follows:

4. The rationality analysis method for planning a dedicated lane for a networked autonomous driving vehicle according to claim 3 is characterized in that: In step 3, specifically: Assume that on an urban road with a speed limit, the free travel time t0 of each lane is the same, there are m lanes and N vehicles in total; the impedance of the mixed traffic section without a dedicated lane, the CAV dedicated lane after the dedicated lane is set, and the mixed traffic section after the dedicated lane is set is calculated according to equations (8), (10), and (11). The specific calculation formula is as follows: No dedicated lanes: When no CAV lanes are set up, all lanes are mixed lanes that allow HVs and CAVs to pass through. The travel time of vehicles is obtained by the BPR function of the Federal Highway Administration of the United States: In the formula, t represents the travel time of the section without dedicated road; t0 represents the free flow time of CAV and HV on the road section; c0 represents the lane capacity when there are only HVs; represents the conversion factor of CAV-HV; N(p C ρ+p H ) represents the mixed traffic flow converted into the standard HV traffic flow; α and β represent two parameters in the BPR function; At the same time, the total travel time of vehicles without CAV dedicated lanes is: After setting up the dedicated lane: After setting up the CAV lane, CAVs can drive on both the dedicated lane and the ordinary lane, but HVs can only drive on the mixed lane. Assuming that the proportion of CAVs driving on the dedicated lane to all CAVs is λ, the travel time of vehicles after setting up the dedicated lane is as follows: (1) Calculation of travel time on CAV lanes Where n represents the number of CAV lanes; t′ represents the travel time of the CAV lane; represents the CAV-HV conversion factor of the dedicated lane; p′ C represents the penetration rate of CAVs on the dedicated lane, with a value of 1; Indicates the proportion of CAV formations on the CAV-only lane, with a value of 1; (2) Calculation of travel time for mixed traffic lanes Where t″ represents the travel time of the mixed lane after the CAV lane is set up; represents the CAV-HV conversion coefficient of the mixed lane after the dedicated lane is set; p′ C ′ represents the CAV penetration rate of the mixed lane after setting up the dedicated lane; It indicates the proportion of CAV formation in mixed lanes after setting up dedicated lanes; At the same time, the total travel time of vehicles after setting up the CAV lane is: T C =Np C λt′+N(1-p C λ)t″ (12)。 5. The rationality analysis method for planning a dedicated lane for a networked autonomous driving vehicle according to claim 4 is characterized in that: The user equilibrium condition described in step 4 is: In order to obtain the proportion of CAVs entering the dedicated lanes, the lane selection of CAV travelers is made to follow the UE equilibrium principle, that is, when equilibrium is reached, the lanes used have equal and minimum travel costs for CAV travelers. At the same time, according to the actual situation, there must be CAVs on the dedicated lanes, satisfying one of the following two conditions: Case 1: When equation (13) is satisfied, the travel time of the dedicated lane for CAV travelers is less than the travel time of the mixed lane. At this time, all CAV travelers will choose to travel on the dedicated lane. At this time, λ=1, that is, t′<t″; (m-n)p C h 22 -n(1-p C )h 11 <0 (13) Case 2: If case 1 is not satisfied, it means that the travel time of the dedicated lane for CAV travelers is equal to the travel time of the mixed lane, that is, t″=t′. At this time:

6. The rationality analysis method for planning a dedicated lane for a networked autonomous driving vehicle according to claim 5 is characterized in that: Specific judgment conditions in step 5: In a mixed driving environment of HVs and CAVs, the rationality judgment conditions for the dedicated lane planning scheme are: T>T C (14) After setting up the CAV lane in step 4, for the two situations where the travel time of the CAV lane and the mixed traffic section exists, the conditions for judging the rationality of the CAV lane planning scheme are obtained respectively: Case 1: For CAV travelers, the travel time of the dedicated lane is shorter than the travel time of the ordinary mixed lane. In this case, when the CAV penetration rate and the lane planning scheme satisfy formula (15), it means that the planning scheme of the dedicated lane is reasonable. in, Case 2: If the parameters do not meet the requirements of Case 1, it means that for CAV travelers, the travel time of the dedicated lane is equal to the travel time of the ordinary mixed lane. In this case, when the CAV penetration rate and the lane planning scheme meet formula (16), it means that the planning scheme of the dedicated lane is reasonable. n(p C p+1-p C )-mp C λρ′>0 (16) in, 7. The method for rationality analysis of a dedicated lane planning scheme for a networked autonomous driving vehicle according to claim 5, characterized in that: The simplified judgment condition in step 5 is: Without considering CAV formation, the headway between CAVs is h 22 , the other headways are equal, that is, h 11 =h 12 =h 21 , the conversion factor of CAV-HV is: The rationality judgment conditions of the simplified dedicated lane planning scheme are: Case 1: For CAV travelers, the travel time of the dedicated lane is shorter than the travel time of the ordinary mixed lane. In this case, when the CAV penetration rate and the lane planning scheme satisfy equation (18), it means that the planning scheme of the dedicated lane is reasonable. Case 2: If the parameters do not meet the requirements of Case 1, it means that for CAV travelers, the travel time of the dedicated lane is equal to the travel time of the ordinary mixed lane. In this case, when the CAV penetration rate and the lane planning scheme meet formula (19), it means that the planning scheme of the dedicated lane is reasonable. in,

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