Hybrid vehicle group consistency control method considering heterogeneous spacing strategy and delay compensation under digital twin network

Abstract

The invention relates to a hybrid vehicle group consistency control method considering a heterogeneous spacing strategy and delay compensation under a digital twin network, and belongs to the field of intelligent vehicles and intelligent traffic. According to the method, a mixed traffic scene including various heterogeneous vehicles is considered, a mixed vehicle group information flow topological structure is designed, and a mixed vehicle group is divided into a plurality of subgroups; designing a space strategy between heterogeneous vehicles in the hybrid vehicle group, and respectively establishing a network-connected automatic vehicle model, an automatic driving vehicle model and an improved traditional person driving vehicle model in the hybrid vehicle group; and constructing a digital twinborn body for each heterogeneous vehicle in the hybrid vehicle group, forming a digital twinborn body of the subgroup by the digital twinborn bodies corresponding to the heterogeneous vehicles in the same subgroup, and establishing a hybrid vehicle group cooperative consistency control strategy based on a time delay compensation mechanism under a digital twinborn network. According to the method, the consistent stability of cooperative driving between the sub-groups and between the heterogeneous vehicles in the sub-groups can be ensured, and the heterogeneous vehicles can be ensured to quickly track different expected vehicle distances.

Description

Technical Field

[0001] This invention belongs to the field of intelligent vehicles and intelligent transportation, and relates to a method for consistency control of mixed vehicle groups under a digital twin network that considers heterogeneous spacing strategy and delay compensation. Background Technology

[0002] With the rapid development of artificial intelligence, the Internet of Things, and communication technologies, hybrid transportation systems composed of autonomous vehicles, connected autonomous vehicles, and traditional human-driven vehicles are gradually becoming the main form of future road transportation. This hybrid transportation mode can not only improve the utilization efficiency of road resources, but also significantly reduce traffic accident rates and alleviate traffic congestion, making it a key link in realizing smart cities and sustainable transportation. However, the introduction of mixed vehicle groups has also brought unprecedented technical challenges, especially in the cooperative control of heterogeneous vehicles. How to ensure that vehicle groups maintain efficient, safe, and stable operation in dynamic environments has become a core issue that urgently needs to be addressed in the field of intelligent transportation.

[0003] In mixed-traffic swarms, vehicles are diverse, with significant differences in their control capabilities, information acquisition methods, and dynamic characteristics. Connected autonomous vehicles, relying on advanced sensors and vehicle-to-everything (V2X) technology, can achieve real-time information exchange between multiple vehicles, characterized by rapid response and high control precision. While autonomous vehicles possess a certain degree of autonomous decision-making ability, they typically rely on pre-set perception systems, with their response speed and control flexibility falling between connected autonomous vehicles and traditional human drivers. Traditional human drivers, on the other hand, primarily rely on the driver's experience and judgment, are significantly influenced by human factors, and have longer reaction times and inherent uncertainties. This heterogeneity makes it difficult to achieve unified control during cooperative driving, especially when vehicles are tracking the desired distance. Due to the different safety requirements and performance limitations of various vehicle types, adopting a uniform spacing strategy may prevent connected and autonomous vehicles from fully utilizing their advantages, while traditional human drivers may struggle to adapt to excessively short distances, leading to oscillations within the swarm or unstable following.

[0004] Furthermore, the coordinated control of mixed vehicle groups faces significant challenges due to latency. In real-world traffic environments, heterogeneous latency, including communication delays, sensor processing delays, and driver reaction delays, is prevalent and varies across different vehicle types. While connected and autonomous vehicles can reduce some latency through onboard systems, the instability of communication links can still introduce variable latency. Traditional human drivers experience more significant and random reaction time delays due to physiological and psychological factors. These heterogeneous latency issues disrupt the synchronization of vehicle group control, leading to untimely vehicle status updates and delayed acceleration or deceleration commands, thus affecting the overall stability of the vehicle group. In high-speed scenarios, even small delays can accumulate and rapidly amplify into safety risks, such as rear-end collisions or traffic flow disruptions.

[0005] In existing technologies, research on vehicle group cooperative control largely focuses on homogeneous vehicle platoons, such as fully connected or fully autonomous vehicle fleets, achieving consistency among vehicles through methods like linear feedback control and model predictive control. However, these methods often exhibit limitations when applied to mixed vehicle groups: firstly, they fail to adequately consider the differences in spacing strategies caused by vehicle heterogeneity, easily leading to decreased tracking performance for some vehicles due to "one-size-fits-all" control parameters; secondly, traditional control methods often assume ideal communication conditions, lacking sufficient compensation for heterogeneous delays and struggling to guarantee system robustness in delayed environments. Although some studies have attempted to introduce adaptive control or delay-tolerant algorithms, these are typically limited to single vehicle types or simplified scenarios, failing to effectively address the complex dynamic interactions and uncertainties in mixed vehicle groups.

[0006] Digital twin technology, as a cutting-edge approach that has emerged in recent years, enables data-driven high-fidelity simulation and real-time optimization by constructing virtual mappings of physical entities, providing new ideas for the control of complex systems. In the transportation sector, digital twins have been initially applied to vehicle monitoring and route planning, but in-depth exploration is still lacking in the cooperative control of mixed-traffic groups. In particular, how to utilize digital twin networks to integrate heterogeneous vehicle information, design adaptive spacing strategies, and compensate for heterogeneous latency remains an unsolved problem. Summary of the Invention

[0007] In view of this, the purpose of this invention is to provide a mixed vehicle group consistency control method under a digital twin network that considers heterogeneous spacing strategy and delay compensation. The method triggers control actions based on the relative distance, speed and other state information between vehicles to adjust the position, speed and acceleration of heterogeneous vehicles, so as to ensure that heterogeneous vehicles in the mixed vehicle group can quickly track their desired vehicle spacing and achieve a stable driving state.

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] A consensus control method for mixed-traffic groups considering heterogeneous spacing strategies and delay compensation in a digital twin network includes:

[0010] Considering mixed traffic scenarios involving various heterogeneous vehicles, design a mixed traffic vehicle group information flow topology and divide the mixed traffic vehicle group into multiple subgroups;

[0011] Design a spacing strategy between heterogeneous vehicles in a mixed vehicle group, and establish connected autonomous vehicle model, autonomous vehicle model and improved traditional human-driven vehicle model in the mixed vehicle group according to the randomness of the driver, the uncertainty of driving and the influence of heterogeneous time delay.

[0012] A digital twin is constructed for each heterogeneous vehicle in a mixed-traffic vehicle group. The digital twins corresponding to each heterogeneous vehicle in the same subgroup constitute the digital twin of the subgroup. A collaborative consistency control strategy for the mixed-traffic vehicle group based on a time delay compensation mechanism is established under the digital twin network.

[0013] Furthermore, consider a mixed traffic group consisting of M connected autonomous vehicles, Z autonomous vehicles, and R traditional human-driven vehicles, all moving forward together along a single lane. M + Z + R = N, where N is the total number of vehicles in the mixed traffic group.

[0014] Connected autonomous vehicles use a multi-vehicle-following topology, while autonomous vehicles and traditional human-driven vehicles use a single-vehicle-following topology. Therefore, the information flow topology of a mixed vehicle group is as follows:

[0015] G p =({H1,H2,…,H n ,…H N},Ω),N∈Z +

[0016] In the formula, H n Let H represent a finite and non-empty set of vehicle nodes. n = 0 indicates that the nth vehicle is a connected autonomous vehicle, H n =1 indicates that the nth vehicle is an autonomous vehicle, H n =2 indicates that the nth vehicle is driven by a traditional human; Ω represents the set of edges consisting of ordered pairs of vehicle nodes. This indicates that vehicle node n obtains information data from vehicle node m.

[0017] Furthermore, the mixed traffic group is divided into multiple subgroups. In each subgroup, except for the lead vehicle which is a connected autonomous vehicle, the remaining vehicles are either autonomous vehicles or traditional human-driven vehicles.

[0018] Furthermore, a spacing strategy is designed between heterogeneous vehicles in a mixed traffic group. The expected distances for connected autonomous vehicles, autonomous vehicles, and traditional human drivers are different, and the expected distances for each heterogeneous vehicle satisfy the following formula:

[0019]

[0020] In the formula, and Let these represent the expected vehicle spacing for connected autonomous vehicles, autonomous vehicles, and traditional human-driven vehicles in the l-th subgroup, respectively; heterogeneous vehicle spacing is defined as:

[0021]

[0022] In the formula, and These represent the errors in tracking the expected distance between vehicles, respectively, for connected autonomous vehicles, autonomous vehicles, and traditional human-driven vehicles. and These represent the positions of the i-th connected autonomous vehicle, the i-th autonomous vehicle, and the i-th traditional human-driven vehicle in the i-th subgroup, respectively. This indicates the position of the (i-1)th vehicle in the l-th subgroup, # represents a traditional human-driven vehicle, an autonomous vehicle, or a connected autonomous vehicle, and len represents the length of the vehicle.

[0023] Furthermore, longitudinal dynamic models of heterogeneous vehicles within the subgroup are established in real space; among them, the connected autonomous vehicle model is represented as:

[0024]

[0025] In the formula, This represents the control input of the leading connected autonomous vehicle in the l-th subgroup; and These represent the position, velocity, and acceleration of the leading connected autonomous vehicle in the l-th subgroup, respectively. and Let represent the position, velocity, and acceleration of the leading connected autonomous vehicle in the j-th subgroup, respectively; Let represent the expected distance between the leading connected autonomous vehicle in the l-th subgroup and the leading connected autonomous vehicle in the j-th subgroup. L represents the expected vehicle spacing between the leading connected autonomous vehicle in the l-th subgroup and the last vehicle in the (l-1)-th subgroup. sub,j Indicates the length of the j-th subgroup; and τ represents the constant control gain for the position, velocity, and acceleration of the leading connected autonomous vehicle in the l-th subgroup; l,0 Indicates communication delay; Δt represents the sampling period;

[0026] The autonomous vehicle model is represented as:

[0027]

[0028] In the formula, This represents the control input of the i-th autonomous vehicle in the l-th subgroup; and Let represent the position, velocity, and acceleration of the i-th autonomous vehicle within the l-th subgroup, respectively; and Let represent the position, velocity, and acceleration of the (i-1)th vehicle in the l-th subgroup, respectively; and This represents the constant control gain for the position, velocity, and acceleration of the autonomous vehicle within the l-th subgroup; θ represents the expected vehicle spacing within the l-th subgroup; l,iLet be the perception delay of the i-th autonomous vehicle in the l-th subgroup;

[0029] The improved traditional human-driven driving model is represented as follows:

[0030]

[0031] In the formula, This represents the control input of the i-th autonomous vehicle in the l-th subgroup; Let represent the speed of the i-th conventional human-driven vehicle in the l-th subgroup; a, b, c, and f represent the maximum acceleration, comfortable deceleration, acceleration exponent, and speed exponent, respectively. This represents the optimal distance between the i-th traditional human-driven vehicle and the vehicle in front of it within the l-th subgroup; This represents the actual distance between the i-th traditional human-driven vehicle and the vehicle in front of it within the l-th subgroup. β represents the expected distance between vehicles in the i-th traditional human-driven vehicle within the l-th subgroup; β represents the driver's sensitivity coefficient to the position of the vehicle in front. This represents the reaction time delay of the i-th conventional human-driven vehicle within the l-th subgroup; v des Indicates the desired speed of the vehicle; δ a This represents the acceleration noise generated by the driver's random actions; d0 and T represent the minimum distance and the expected time interval between vehicles, respectively. This represents the speed difference between the i-th traditional human-driven vehicle and its adjacent vehicle within the l-th subgroup. This represents the speed of the (i-1)th vehicle within the l-th subgroup.

[0032] Furthermore, in real space, a state update model for the i-th vehicle within the l-th subgroup is established:

[0033]

[0034] In the formula, This represents the acceleration of the i-th vehicle within the l-th subgroup. This represents the control input of the i-th vehicle within the l-th subgroup. This represents the speed of the i-th vehicle within the l-th subgroup. This indicates the position of the i-th vehicle within the l-th subgroup, and # represents a connected autonomous vehicle, an autonomous vehicle, or a traditional human-driven vehicle; where the control input for a traditional human-driven vehicle is directly used as acceleration.

[0035] Furthermore, based on the constructed connected autonomous vehicle model, autonomous driving vehicle model, traditional human-driven vehicle model, and state update model, a digital twin is constructed for each heterogeneous vehicle in the mixed vehicle group. The digital twins corresponding to each heterogeneous vehicle within the same subgroup constitute the digital twin of the subgroup. Under the digital twin network, the state of the leading connected autonomous vehicle in each subgroup in the real space is taken as the driving state of the subgroup, and a consistency control input and state update model for each subgroup is constructed accordingly. At the same time, consistency constraints are set for heterogeneous vehicles and subgroups to ensure that the driving behavior of the mixed vehicle group reaches consistency.

[0036] The consistency control input for the subgroup is represented as:

[0037]

[0038] In the formula, U l (t) represents the control input of the l-th subgroup; P j (t), V j (t) and A j (t) represents the longitudinal position, velocity, and acceleration of the j-th subgroup, respectively, P l (t), V l (t) and A l (t) represents the longitudinal position, velocity, and acceleration of the l-th subgroup, respectively; S lj,des Let represent the expected distance between the leading connected autonomous vehicle in the l-th subgroup and the leading connected autonomous vehicle in the j-th subgroup. K P K V and K A τ represents the control gain of the subgroup; τ represents the communication delay.

[0039] In real space, the state update model for the l-th subgroup is represented as:

[0040]

[0041] In the formula, P l (t), V l (t) and A l (t) represents the longitudinal position, velocity, and acceleration of the l-th subgroup, respectively.

[0042] The consistency constraints include:

[0043] Consistency constraints for heterogeneous vehicles:

[0044]

[0045] Driving state constraints for heterogeneous vehicles within a subgroup:

[0046]

[0047] In the formula, v min and v max Let a represent the minimum speed and maximum speed of the heterogeneous vehicle, respectively. min and a max Let u represent the minimum and maximum accelerations of the heterogeneous vehicle, respectively. min and u max These represent the minimum and maximum control inputs for heterogeneous vehicles, respectively.

[0048] Consistency constraints for subgroups:

[0049]

[0050] The driving state constraints of the subgroup:

[0051]

[0052] The beneficial effects of this invention are as follows:

[0053] (1) This invention achieves high-precision real-time mapping and prediction of the operating status of mixed-vehicle groups by constructing a digital twin network. The digital twin, as a virtual copy of the real vehicle, can continuously synchronize vehicle data from the physical world and evaluate the effectiveness of control strategies in advance through simulation. This virtual-real fusion framework enables the system to adaptively cope with complex factors such as driver random operations, vehicle dynamic uncertainties, and communication delays, thereby significantly improving the robustness and accuracy of control. The digital twin network not only provides a collaborative management platform with a global perspective for mixed-vehicle groups but also effectively offsets the negative impact of heterogeneous delays on stability through a delay compensation mechanism, ensuring the timeliness and reliability of control commands and avoiding the risk of vehicle oscillation or loss of control due to delays.

[0054] (2) This invention fully considers the differences in characteristics of different vehicle types and designs a heterogeneous spacing strategy, allowing connected autonomous vehicles, autonomous vehicles, and traditional human drivers to track their respective optimized desired vehicle spacing. The heterogeneous spacing strategy avoids a "one-size-fits-all" spacing setting, retaining the advantages of connected and autonomous vehicles in terms of rapid response and shorter distances, while also taking into account the longer reaction time and greater safety margin required by traditional human drivers. Through subgroup partitioning and optimization of the information flow topology, the system decomposes the mixed traffic group into multiple easily manageable sub-units, with the leading connected autonomous vehicle acting as the coordination core, driving heterogeneous vehicles within the subgroup to achieve rapid and stable state synchronization. This not only significantly improves the efficiency of vehicles tracking desired vehicle spacing but also ensures that heterogeneous vehicles can quickly converge to a stable state during dynamic processes such as acceleration and deceleration, reducing the propagation of disturbances within the vehicle group, thereby improving the smoothness and continuity of the overall traffic flow.

[0055] (3) This invention ensures a high degree of consistency in the cooperative driving behavior between subgroups and between heterogeneous vehicles within subgroups by setting multi-level consistency constraints. The control strategy under the digital twin network can dynamically adjust the vehicle state, enabling the mixed vehicle group to maintain a safe distance while achieving coordinated and unified key parameters such as speed and acceleration. This consistent stability not only reduces the probability of collisions between vehicles but also enhances the system's ability to respond to emergencies, such as obstacles ahead or traffic congestion, allowing the vehicle group to respond collectively and adjust in an orderly manner, avoiding chain reaction accidents.

[0056] Overall, this invention provides a novel approach to solving the collaborative control problem of mixed-vehicle groups in the context of intelligent transportation. By leveraging the advanced nature of digital twin technology and the flexibility of adaptive control algorithms, it achieves collaborative optimization of efficiency, safety, and stability in mixed-vehicle groups.

[0057] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0058] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein:

[0059] Figure 1 This is a schematic flowchart of a mixed-traffic group consistency control method provided in an embodiment of the present invention;

[0060] Figure 2 This is a schematic diagram illustrating the spatial relationship between subgroups and vehicles in a mixed traffic vehicle group.

[0061] Figure 3 A schematic diagram of a digital twin network for mixed-traffic vehicle groups;

[0062] Figure 4 This is a schematic diagram of a delay compensation system based on the Smith predictor.

[0063] Figure 5 Simulation results for heterogeneous vehicles tracking different desired vehicle spacings. Detailed Implementation

[0064] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0065] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual pictures. They should not be construed as limiting the invention. To better illustrate the embodiments of the invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0066] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," "front," and "rear" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present invention. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0067] like Figure 1 As shown, this is an embodiment of the present invention providing a method for consensus control of mixed traffic groups in a digital twin network, considering heterogeneous spacing strategies and delay compensation. The method is described as follows:

[0068] 1. Considering a mixed traffic scenario involving various heterogeneous vehicles, design the information flow topology of the mixed traffic group and divide the mixed traffic group into multiple subgroups.

[0069] like Figure 2 As shown, consider a large mixed traffic group consisting of M connected autonomous vehicles, Z autonomous vehicles, and R traditional human-driven vehicles, all moving forward together along a single lane, where M + Z + R = N, and N represents the total number of vehicles.

[0070] Connected autonomous vehicles use a multi-vehicle-following topology, while autonomous vehicles and traditional human-driven vehicles use a single-vehicle-following topology. Therefore, the information flow topology of a mixed vehicle group can be defined as follows:

[0071] G p=({H1,H2,…,H n ,…H N},Ω),N∈Z +

[0072] In the formula, H n Let H represent a finite and non-empty set of vehicle nodes. If the nth vehicle is a connected autonomous vehicle, then H... n =0; if the nth vehicle is an autonomous vehicle, then H n =1; if the nth car is driven by a traditional person, then H n =2; Ω represents the edge set of ordered pairs consisting of vehicle nodes. side This indicates that vehicle node n can obtain information data from vehicle node m.

[0073] Based on the characteristics of heterogeneous vehicles, a large mixed traffic vehicle group is logically divided into subgroups according to the lead vehicle being a connected autonomous vehicle, followed by autonomous vehicles or traditional human-driven vehicles. Within each subgroup, only the lead vehicle is a connected autonomous vehicle, while the rest are autonomous vehicles or traditional human-driven vehicles.

[0074] 2. Design a heterogeneous spacing strategy, and consider the randomness of drivers, the uncertainty of driving, and the impact of heterogeneous time delay. Establish improved traditional human-driven vehicle models, connected autonomous vehicle models, and autonomous vehicle models in mixed vehicle groups, and analyze the cooperative driving relationship between subgroups and between heterogeneous vehicles within subgroups.

[0075] Specifically as follows:

[0076] 1. Design heterogeneous spacing strategy

[0077] In mixed traffic, connected and autonomous vehicles have more sensitive reaction capabilities and shorter reaction times compared to traditional human drivers, allowing them to brake more quickly. Furthermore, connected vehicles possess communication capabilities, enabling them to acquire more information than autonomous vehicles, thus allowing them to brake faster. Therefore, the expected distances tracked by connected vehicles, autonomous vehicles, and traditional human drivers differ and can satisfy the following inequality:

[0078]

[0079] In the formula, and Let represent the expected vehicle spacing for connected autonomous vehicles, autonomous vehicles, and traditional human-driven vehicles in the l-th subgroup, respectively. Then, the heterogeneous vehicle spacing is defined as:

[0080]

[0081] In the formula, and These represent the errors in tracking the expected distance between vehicles, respectively, for connected autonomous vehicles, autonomous vehicles, and traditional human-driven vehicles. and These represent the positions of the i-th connected autonomous vehicle, the i-th autonomous vehicle, and the i-th traditional human-driven vehicle in the i-th subgroup, respectively. This indicates the position of the (i-1)th vehicle in the l-th subgroup, # represents a traditional human-driven vehicle, an autonomous vehicle, or a connected autonomous vehicle, and len represents the length of the vehicle.

[0082] 2. Establish a longitudinal dynamics model for heterogeneous vehicles within a subgroup.

[0083] (1) In real space, establish the leading connected autonomous vehicle model in the subgroup.

[0084] The leader vehicle in the l-th subgroup is a connected automated vehicle (AAV), and its ID i = 0. The leader AAV in the l-th subgroup can obtain the status information of the leader AAVs in other subgroups through vehicle-to-vehicle communication. The control input of the leader AAV in the l-th subgroup can be expressed as:

[0085]

[0086] In the formula, and These represent the position, velocity, and acceleration of the leading connected autonomous vehicle in the l-th subgroup, respectively. and Let represent the position, velocity, and acceleration of the leading connected autonomous vehicle in the j-th subgroup, respectively; Let represent the expected distance between the leading connected autonomous vehicle in the l-th subgroup and the leading connected autonomous vehicle in the j-th subgroup. L represents the expected distance between the leading connected autonomous vehicle in the l-th subgroup and the last vehicle (traditional human-driven or autonomous vehicle) in the (l-1)-th subgroup. sub,j Represents the length of the j-th subgroup, such as Figure 2 As shown. and τ represents the constant control gain for the position, velocity, and acceleration of the leading connected autonomous vehicle in the l-th subgroup. l,0 Δt represents the communication delay; Δt represents the sampling period.

[0087] (2) In real space, establish autonomous vehicle models in subgroups.

[0088] When the i-th vehicle in the l-th subgroup is an autonomous vehicle, these autonomous vehicles lack communication capabilities. Therefore, the autonomous vehicles in the l-th subgroup can only obtain the state information of their adjacent vehicles based on onboard sensors and adjust their own vehicle state accordingly to track the driving state of the vehicles ahead. The control input of the i-th autonomous vehicle in the l-th subgroup can be expressed as:

[0089]

[0090] In the formula, and Let represent the position, velocity, and acceleration of the i-th autonomous vehicle within the l-th subgroup, respectively; and These represent the position, velocity, and acceleration of the (i-1)th vehicle (connected autonomous vehicle, autonomous vehicle, or traditional human-driven vehicle) within the l-th subgroup, respectively. and This represents the constant control gain for the position, velocity, and acceleration of any autonomous vehicle within the l-th subgroup; θ represents the expected vehicle spacing for any autonomous vehicle within the l-th subgroup; l,i Let be the perception delay of the i-th autonomous vehicle within the l-th subgroup.

[0091] (3) In real space, establish an improved traditional human driving model in a subgroup.

[0092] To more accurately describe traditional human driving behavior, an improved intelligent driving model is proposed by considering the driver's sensitivity, randomness, and reaction time delay. Specifically, the input of the i-th traditional human vehicle in the l-th subgroup can be represented as follows:

[0093]

[0094] In the formula, Let represent the speed of the i-th conventional human-driven vehicle in the l-th subgroup; a, b, c, and f represent the maximum acceleration, comfortable deceleration, acceleration exponent, and speed exponent, respectively. Let represent the optimal distance between the i-th traditional human-driven vehicle and the vehicle in front within the l-th subgroup. This represents the actual distance between the i-th traditional human-driven vehicle and the vehicle in front of it within the l-th subgroup. β represents the expected distance between vehicles in the i-th traditional human-driven vehicle within the l-th subgroup; β represents the driver's sensitivity coefficient to the position of the vehicle in front. This represents the reaction time delay of the i-th conventional human-driven vehicle within the l-th subgroup; v des Indicates the desired speed of the vehicle; δ a This represents the acceleration noise generated by the driver's random actions; d0 and T represent the minimum distance and the expected time interval between vehicles, respectively. This represents the speed difference between the i-th traditional human-driven vehicle and its adjacent vehicle within the l-th subgroup. This represents the speed of the vehicle adjacent to the i-th traditional human-driven vehicle in the l-th subgroup.

[0095] (4) State update of heterogeneous vehicles in the subgroup.

[0096] In real space, the updated state of the i-th car within the l-th subgroup can be represented as:

[0097]

[0098] In the formula, This represents the acceleration of the i-th vehicle within the l-th subgroup. This represents the control input of the i-th vehicle within the l-th subgroup. This represents the speed of the i-th vehicle within the l-th subgroup. This represents the position of the i-th vehicle within the l-th subgroup.

[0099] Because traditional human-driven vehicles use a modified car-following model, their control input is directly used as acceleration, i.e. Used to update the vehicle's status.

[0100] Third, to eliminate the impact of heterogeneous latency on the driving stability of mixed-traffic vehicle groups, a collaborative consistency control strategy for mixed-traffic vehicle groups is constructed based on a latency compensation mechanism under a digital twin network.

[0101] Specifically as follows:

[0102] 1. Construct a digital twin network for mixed-traffic vehicle groups

[0103] Digital twin technology maps physical entities in the real world to a virtual space for digital modeling and simulation. In a mixed traffic system, the real space mainly includes the road environment and heterogeneous vehicle entities, with different vehicles using different perception capabilities to acquire vehicle state data to build a digital twin network. The virtual space maps the road environment and vehicle entities in the real space into a virtual model to obtain a virtual model of the real environment. Based on the acquired vehicle state data, a cooperative consistency strategy for mixed-traffic groups is established in the virtual space and fed back to the heterogeneous vehicle entities in the real space to adjust the driving states of the heterogeneous vehicles, such as... Figure 3 As shown.

[0104] from Figure 3 As can be seen, the digital twin network encompasses the road environment and heterogeneous vehicles in the real space, as well as the heterogeneous vehicles in the virtual space. These vehicles acquire driving status data of neighboring vehicles through their respective perception capabilities and update their own digital twin models based on the status data at different times. The mixed-vehicle group digital twin network consists of multiple subgroups of digital twins. Each subgroup contains a lead connected autonomous vehicle, several autonomous vehicles, and several traditional human-driven vehicles. The connected autonomous vehicle indirectly leads the digital twins of other vehicles within each subgroup. The digital twin of the i-th heterogeneous vehicle in the l-th subgroup can be represented as follows:

[0105] DT l,i ={S l,i ,I l,i}

[0106] In the formula, S l,i This represents the driving state data (e.g., speed, acceleration) of the i-th heterogeneous vehicle within the l-th subgroup, used to update its own digital twin. l,i This refers to the information update data and information resource data (such as communication resources and computing resources) during the digital twin update process.

[0107] 2. Design a delay compensation mechanism

[0108] To avoid the impact of heterogeneous time delays on the stability of mixed-vehicle groups, a Smith predictor is used to eliminate heterogeneous time delays in the closed-loop control system, such as... Figure 4 As shown.

[0109] 3. Establishing a cooperative consistency control strategy for mixed-traffic vehicle groups based on a time delay compensation mechanism under a digital twin network.

[0110] In a digital twin network, since the state of the leading connected autonomous vehicle within each subgroup in the real space can be considered as the driving state of the subgroup, the consistency control input between subgroups in the virtual space is approximately equal to the control input of the leading connected autonomous vehicle. Based on a digital twin of heterogeneous vehicles, the consistency control input of the l-th subgroup can be expressed as:

[0111]

[0112] In the formula, P j (t), V j (t) and A j (t) represents the longitudinal position, velocity, and acceleration of the j-th subgroup, respectively, P l (t), V l (t) and A l (t) represents the longitudinal position, velocity, and acceleration of the l-th subgroup, respectively; S lj,des Let represent the expected distance between the leading connected autonomous vehicle in the l-th subgroup and the leading connected autonomous vehicle in the j-th subgroup. K P K V and K A τ represents the control gain of the subgroup; τ represents the communication delay.

[0113] Since the driving state of the subgroup is close to that of the leading connected autonomous vehicle in the subgroup, the control gain of the subgroup is approximately equal to the control gain of the leading connected autonomous vehicle in the subgroup.

[0114] In real space, the update state of the l-th subgroup can be represented as:

[0115]

[0116] In the formula, P l (t), V l (t) and A l (t) represents the longitudinal position, velocity, and acceleration of the l-th subgroup, respectively.

[0117] According to the subgrouping rules, the driving state of the leading connected autonomous vehicle in each subgroup is close to the driving state of each subgroup. Therefore, the state of the subgroup can be assigned to the leading connected autonomous vehicle in the subgroup. Thus, the driving state of the leading connected autonomous vehicle in the l-th subgroup can be further expressed as:

[0118]

[0119] 4. Set consistency constraints

[0120] To ensure consistent driving behavior among mixed vehicle groups, consistency constraints are set for heterogeneous vehicles and subgroups.

[0121] The consistency constraint for heterogeneous vehicles is expressed as follows:

[0122]

[0123] The driving state constraints of heterogeneous vehicles within a subgroup can be described as follows:

[0124]

[0125] In the formula, v min and v max Let a represent the minimum speed and maximum speed of the heterogeneous vehicle, respectively. min and a max Let u represent the minimum and maximum accelerations of the heterogeneous vehicle, respectively. min and u max These represent the minimum and maximum control inputs for heterogeneous vehicles, respectively.

[0126] The consistency constraint of a subgroup can be expressed as:

[0127]

[0128] The driving state constraints of the subgroup can be represented as follows:

[0129]

[0130] Figure 5The experimental results of an example of the present invention are shown. As can be seen from the figure, heterogeneous vehicles can quickly track their desired vehicle spacing, further verifying the effectiveness of the method provided by the present invention.

[0131] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for consistency control of mixed platoon under digital twin network considering heterogeneous spacing strategy and delay compensation, characterized in that, The method comprises: Considering a mixed traffic scene including multiple heterogeneous vehicles, designing a mixed platoon information flow topology, and dividing the mixed platoon into multiple sub-platoons; Designing a spacing strategy between the heterogeneous vehicles in the mixed platoon, and respectively establishing a connected automatic vehicle model, an automatic driving vehicle model and an improved traditional human driving vehicle model in the mixed platoon according to randomness of drivers, uncertainty of driving and influence of heterogeneous time delays; Constructing a digital twin for each heterogeneous vehicle in the mixed platoon, the digital twins corresponding to the heterogeneous vehicles in the same sub-platoon constituting a digital twin of the sub-platoon, and establishing a mixed platoon cooperative consistency control strategy based on a time delay compensation mechanism under a digital twin network.

2. The method of claim 1, wherein, Considering a mixed platoon composed of M connected automatic vehicles, Z automatic driving vehicles and R traditional human driving vehicles, the mixed platoon jointly travels forward along a single lane, M+Z+R=N, N being the total number of vehicles in the mixed platoon; The connected automatic vehicles use a multi-vehicle-ahead following topology, and the automatic driving vehicles and the traditional human driving vehicles use a vehicle-ahead following topology, so that the mixed platoon information flow topology is: G p = (H1, H2,..., H n ,..., H N ), Ω), N ∈ Z + where H n denotes a finite and non-empty set of vehicle nodes, H n = 0 means that the nth vehicle is a connected automated vehicle, H n = 1 means that the nth vehicle is an automated vehicle, H n = 2 means that the nth vehicle is a traditional human-driven vehicle; Ω denotes a set of ordered pairs of edges composed of vehicle nodes, edge denotes that vehicle node n obtains information data from vehicle node m.

3. The method of claim 2, wherein, In the mixed platoon, multiple sub-platoons are divided, and in each sub-platoon, except for a leading vehicle being a connected automatic vehicle, the rest of the vehicles are automatic driving vehicles or traditional human driving vehicles.

4. The method of claim 3, wherein, A spacing strategy between the heterogeneous vehicles in the mixed platoon is designed, and the expected vehicle distances of the connected automatic vehicles, the automatic driving vehicles and the traditional human driving vehicles are different, and the expected vehicle distances of the heterogeneous vehicles satisfy the following formula: where, and respectively represent the desired inter-vehicle distance of the connected and automated vehicles, the automated vehicles and the traditional human-driven vehicles in the lth subgroup; the heterogeneous inter-vehicle distance is defined as: wherein, and denote the error of the desired inter-vehicle distance tracking by the connected and automated vehicle, the automated vehicle and the traditional human-driven vehicle, respectively, and denote the position of the i-th connected and automated vehicle, the automated vehicle and the traditional human-driven vehicle in the l-th subgroup, respectively, denote the position of the i-1-th vehicle in the l-th subgroup, # denotes a traditional human-driven vehicle or an automated vehicle or a connected and automated vehicle, and len denotes the length of the vehicle.

5. The method of claim 4, wherein, In a real space, a longitudinal dynamics model of each heterogeneous vehicle in a sub-platoon is established; wherein, a connected automatic vehicle model is represented as: wherein, denotes the control input of the lead CAV in the lth subgroup; and denote the position, velocity and acceleration of the lead CAV in the lth subgroup, respectively; and denote the position, velocity and acceleration of the lead CAV in the jth subgroup, respectively; denotes the desired inter-vehicle distance between the lead CAV in the lth subgroup and the lead CAV in the jth subgroup, denotes the desired inter-vehicle distance between the lead CAV in the lth subgroup and the trail CAV in the l-1th subgroup, L sub,j denotes the length of the jth subgroup; and denote the position, velocity and acceleration constant control gains of the lead CAV in the lth subgroup; τ l,0 denotes the communication delay; Δt denotes the sampling period; An automatic driving vehicle model is represented as: wherein, represents the control input of the i-th autonomous vehicle in the l-th subgroup; and respectively represent the position, velocity and acceleration of the i-th autonomous vehicle in the l-th subgroup; and respectively represent the position, velocity and acceleration of the i-1-th vehicle in the l-th subgroup; and represents the constant control gain of the position, velocity and acceleration of the autonomous vehicle in the l-th subgroup; represents the desired inter-vehicle distance of the autonomous vehicle in the l-th subgroup; θ l,i is the perception delay of the i-th autonomous vehicle in the l-th subgroup; An improved traditional human driving vehicle model is represented as: wherein, denotes the control input of the ith autonomous vehicle in the lth subgroup; denotes the speed of the ith conventional human-driven vehicle in the lth subgroup;a, b, c, and f represent the maximum acceleration, the comfortable deceleration, the acceleration exponent, and the speed exponent, respectively; denotes the optimal inter-vehicle distance between the ith conventional human-driven vehicle and its preceding vehicle in the lth subgroup; denotes the actual inter-vehicle distance between the ith conventional human-driven vehicle and its preceding vehicle in the lth subgroup, denotes the desired inter-vehicle distance of the ith conventional human-driven vehicle in the lth subgroup;β represents the sensitivity coefficient of the driver to the position of the preceding vehicle; denotes the reaction time delay of the ith conventional human-driven vehicle in the lth subgroup;v des denotes the desired speed of the vehicle;δ a denotes the acceleration noise generated by the random operation of the driver;d0 and T represent the minimum distance and the desired time interval between vehicles, respectively; denotes the speed difference between the ith conventional human-driven vehicle and its preceding adjacent vehicle in the lth subgroup, denotes the speed of the i-1th vehicle in the lth subgroup.

6. The method of claim 5, wherein, In the real space, a state update model of the i-th vehicle in the l-th sub-platoon is established as: wherein, denotes the acceleration of the ith vehicle in the lth subgroup, denotes the control input of the ith vehicle in the lth subgroup, denotes the speed of the ith vehicle in the lth subgroup, denotes the position of the ith vehicle in the lth subgroup, denotes a connected autonomous vehicle or an autonomous vehicle or a traditional human-driven vehicle; wherein the control input of the traditional human-driven vehicle is directly taken as the acceleration.

7. The method of claim 6, wherein, According to the constructed connected automatic vehicle model, the automatic driving vehicle model, the traditional human driving vehicle model and the state update model, a digital twin is constructed for each heterogeneous vehicle in the mixed platoon, the digital twins corresponding to the heterogeneous vehicles in the same sub-platoon constituting a digital twin of the sub-platoon; under a digital twin network, a state of a leading connected automatic vehicle of each sub-platoon in the real space is taken as a driving state of the sub-platoon, and based on this, a consistency control input and a state update model of each sub-platoon are constructed; meanwhile, consistency constraint conditions of the heterogeneous vehicles and the sub-platoons are set to make the driving behaviors of the mixed platoon consistent.

8. The method of claim 7, wherein, The consistency control input of the sub-platoon is represented as: where U l (t) represents the control input of the lth subgroup; P j (t), V j (t) and A j (t) represent the longitudinal position, velocity and acceleration of the jth subgroup, respectively; P l (t), V l (t) and A l (t) represent the longitudinal position, velocity and acceleration of the lth subgroup, respectively; S lj,des represents the desired spacing between the lead connected automated vehicle in the lth subgroup and the lead connected automated vehicle in the jth subgroup, K P , K V and K A represent the control gain of the subgroup; τ represents the communication time delay; In the real space, the state update model of the l-th sub-platoon is represented as: where P l (t), V l (t), and A l (t) represent the longitudinal position, velocity, and acceleration of the lth subgroup, respectively.

9. The method of claim 7, wherein, The consistency constraint conditions include: The consistency constraint condition of the heterogeneous vehicle: The driving state constraint condition of the heterogeneous vehicle in the sub-platoon: where v min and v max denote the minimum and maximum speed of the heterogeneous vehicle, respectively, a min and a max denote the minimum and maximum acceleration of the heterogeneous vehicle, respectively, u min and u max denote the minimum and maximum control input of the heterogeneous vehicle, respectively. The consistency constraint condition of the sub-platoon: Driving state constraint condition of the subgroup:

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