A method of adaptive cruise control for a vehicle platoon

Abstract

The application discloses a kind of platoon adaptive cruise control methods, comprising: step A: establish dynamic safety demand distance model, for describing the demand of intelligent networked driving platoon to safety spacing under networked environment;Combined with the speed, acceleration information of the vehicle in front of the queue inside, and the influence of different positions in the queue where the vehicle is located, introduce information transmission coefficient and position influence coefficient, establish dynamic safety demand distance strategy;Step B: improve cooperative adaptive cruise control model CACC based on dynamic safety demand distance model, update the headway strategy to the dynamic safety demand distance strategy;Introduce the delay of speed and spacing to simulate the time delay relationship between upper planning and bottom control;Step C: establish cooperative lane-changing control strategy based on adaptive spacing strategy, set relaxation spacing, recovery spacing, equilibrium spacing for different stages of vehicle forced lane-changing, adaptively change according to lane-changing stage indicator variable;Cooperative lane-changing optimization model is constructed to realize centralized control of platoon.

Description

Technical Field

[0001] This invention relates to the field of fleet cruise control technology, specifically designing an adaptive cruise control and cooperative lane-changing control method for autonomous vehicle platoons based on dynamic safety requirement distance. Background Technology

[0002] With advancements in intelligent connectivity and autonomous driving technologies, connected vehicles (CAVs) are widely recognized for their significant potential in improving traffic safety, efficiency, and fuel economy. CAV platoons, forming the Connected Truck Platoon (CATP), are considered multi-agent systems with a four-element architecture (node ​​dynamics, information flow topology, platoon geometry, and distributed controller). They utilize real-time information interaction to accurately perceive large-scale dynamic traffic environments and achieve platooning operation through driving model-driven and cooperative control. While cooperative adaptive cruise control, speed regulation, feedback control, and heuristic control are commonly applied in vehicle control, CATP inevitably faces various complex traffic situations, including acceleration / deceleration and braking on basic road sections, and lane changing and overtaking behaviors in weaving zones. Therefore, from a control system perspective, more appropriate control strategies are needed to improve the stability of the platoon under disturbances.

[0003] The disadvantages of existing technologies are as follows:

[0004] 1. Insufficient flexibility and adaptability: Existing headway strategies and truck platoon longitudinal control algorithms perform poorly in complex dynamic traffic environments. For example, while the constant spacing (CS) strategy can guarantee platoon stability, it performs poorly in dynamic traffic environments and lacks flexibility in dealing with complex driving conditions. Similarly, proportional-derivative (PD) control and model predictive control (MPC) algorithms also have limited performance in the face of external disturbances and system uncertainties.

[0005] 2. Insufficient utilization of information in the connected environment: Existing headway strategies such as CTH and VTH fail to fully utilize the information transmission characteristics of the intelligent connected environment, only considering the direct information of the vehicle in front. This results in these strategies failing to fully utilize the vehicle platoon information perceived in the connected environment, thus limiting the rapid response and precise control to dynamic changes in vehicles.

[0006] 3. Low real-time performance: In the research on longitudinal control of truck platoons, although some advanced control algorithms such as MPC and machine learning algorithms can theoretically provide higher accuracy and control effects, they generally perform poorly in real-time applications. High computational complexity and resource requirements make these algorithms difficult to apply effectively in traffic control with high real-time requirements.

[0007] 4. Insufficient Dynamic Adaptability: Existing research focuses on the creation of lane-changing timing and gaps, neglecting the impact of different stages of lane-changing in the split zone on CATP. Current lane-changing control strategies fail to fully consider the adaptive needs at different stages and often cannot effectively cope with the dynamic changes during lane-changing, resulting in deficiencies in adjusting safe distances and maintaining queue stability during lane-changing. Summary of the Invention

[0008] The objective of this invention is achieved through the following technical solutions.

[0009] Specifically, this invention provides a platoon adaptive cruise control method, comprising:

[0010] Step A: Establish a dynamic safety requirement distance model to describe the safety distance requirements of intelligent connected driving platoons in a connected environment; combine the speed and acceleration information of vehicles ahead in the platoon, as well as the influence of different positions of vehicles in the platoon, introduce information perception coefficient and position influence coefficient, and establish a dynamic safety requirement distance strategy.

[0011] Step B: Improve the Cooperative Adaptive Cruise Control (CACC) model based on the dynamic safety demand distance model, update the headway strategy to the dynamic safety demand distance strategy; introduce speed and spacing delay to simulate the time delay relationship between upper-level planning and lower-level control;

[0012] Step C: Establish a cooperative lane-changing control strategy based on adaptive spacing. Set relaxation spacing, recovery spacing, and equilibrium spacing for different stages of forced lane changing of vehicles, and adaptively change them according to the indicator variables of the lane-changing stage. Based on the needs of different stages in the lane-changing process, analyze the interrelationship between sub-objectives, establish the weight allocation of the objective function of each stage, and construct a cooperative lane-changing optimization model in combination with the constraints of vehicles in the queue to achieve centralized queue control.

[0013] The advantages of this invention are:

[0014] 1. This invention proposes a dynamic safety distance strategy for CATP (Continuous Availability-Adaptive Trip). Traditional headway strategies only consider information about the immediately preceding vehicle, failing to fully utilize information obtained in a connected environment. Therefore, this invention analyzes the correlation between the speed of the preceding vehicle and the target vehicle's speed, proposes an information perception coefficient and a position influence coefficient, and establishes a dynamic safety headway strategy. This strategy can perceive the speed information of all vehicles ahead within the queue, improving driving behavior response speed.

[0015] 2. This invention establishes an improved CACC control method based on dynamic safety requirement distance, while considering model stability. The MPC (Multi-Level Planning) is used as the upper-level planner, dynamically adjusting control parameters according to objectives such as trajectory tracking, fuel economy, and stability. PD (Dynamic Detection) control serves as the lower-level control model, achieving longitudinal control under vehicle dynamic constraints. Existing research often neglects the time delay relationship between the upper-level planning model and the lower-level controller; however, feedback delay has a significant impact on queuing stability and can even lead to system instability. This invention, based on frequency domain analysis, introduces speed delay and spacing delay to construct the system transfer function, derives queuing stability constraints, incorporates queuing stability into the sub-objective, and achieves stable queuing operation under complex conditions through dynamic parameter adjustment.

[0016] 3. This invention proposes an adaptive cooperative control strategy for lane changing in divergence zones, applicable to CATP (Conditional Auxiliary Traffic Flow). Existing research focuses on the creation of lane changing times and gaps, neglecting the impact of different stages of lane changing in divergence zones on CATP. Therefore, this invention proposes an adaptive spacing strategy, including relaxed spacing, restored spacing, and balanced spacing. This strategy can create spacing promptly upon detecting lane-changing vehicles and quickly restore stable queue operation after the vehicles leave the main line, reducing the impact of forced lane changing in divergence zones on queue efficiency and overall traffic flow efficiency. Attached Figure Description

[0017] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0018] Figure 1 A schematic diagram of the CATP navigator's operating conditions according to an embodiment of the present invention is shown.

[0019] Figure 2 A schematic diagram comparing driving behaviors with different headway strategies according to embodiments of the present invention is shown.

[0020] Figure 3 A comparative schematic diagram illustrating the improvement trend of the DSRTH strategy according to an embodiment of the present invention is shown.

[0021] Figure 4 A schematic diagram comparing the driving characteristics of different car-following models according to embodiments of the present invention is shown.

[0022] Figure 5 A schematic diagram showing the improvement effect of the improved CACC model according to an embodiment of the present invention is illustrated.

[0023] Figure 6A schematic diagram illustrating traffic flow rate results under different vehicle type ratios according to an embodiment of the present invention is shown.

[0024] Figure 7 A schematic diagram illustrating the impact of queue size on traffic efficiency according to an embodiment of the present invention is shown.

[0025] Figure 8 A schematic diagram of vehicle trajectories with different queue sizes is shown according to an embodiment of the present invention.

[0026] Figure 9 A schematic diagram of a flow diversion zone simulation environment according to an embodiment of the present invention is shown.

[0027] Figure 10 A schematic diagram illustrating the degree of improvement in average driving time according to an embodiment of the present invention is shown.

[0028] Figure 11 A schematic diagram illustrating the degree of improvement in average driving speed according to an embodiment of the present invention is shown.

[0029] Figure 12 A schematic diagram illustrating the average travel time distribution for different queue sizes according to an embodiment of the present invention is shown. Detailed Implementation

[0030] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0031] Cooperative autonomous truck platooning refers to a group of trucks traveling at close intervals, which can improve platoon fuel economy and safety while enhancing traffic efficiency. However, factors such as speed variations within the platoon and external traffic flow speed disturbances can easily lead to platoon instability, resulting in reduced traffic efficiency. To optimize speed control of cooperative autonomous truck platooning and better improve the stability, safety, and traffic efficiency of highway main sections and diversion zones, this invention proposes an improved adaptive cruise control model for cooperative autonomous truck platooning and a cooperative lane-changing control strategy for vehicles requiring lane changes in diversion zones. This model aims to improve platoon stability, fuel economy, and traffic efficiency while ensuring safety, and simultaneously improve the overall traffic flow.

[0032] To address the problems mentioned in the prior art, this invention proposes a dynamic safety demand distance, and based on this, an improved adaptive cruise control model is proposed. Furthermore, a cooperative lane-changing control strategy for vehicles requiring mandatory lane changes is proposed in the split-traffic zone. The technical solutions of this method are as follows:

[0033] (1) Establish a dynamic safety requirement distance model to describe the safety distance requirements of intelligent connected driving platoons in a connected environment. The target distance between following vehicles is an important reflection of ensuring safety and pursuing efficiency. Combining information such as the speed and acceleration of the vehicles in front within the platoon, as well as the influence of different positions of the vehicles in the platoon, an information perception coefficient and a position influence coefficient are introduced to establish a dynamic safety requirement distance strategy. The specific steps are as follows:

[0034] Step 1: The existing VTH spacing strategy is as follows:

[0035]

[0036] in, The desired safe distance; For the following car speed; This refers to the headway of the train. These are the coefficients to be calibrated; This represents the speed difference between the vehicle in front and the vehicle behind. This strategy is suitable for homogeneous manually driven vehicles. In a connected environment, using existing headway strategies can lead to excessive headway spacing, wasting road resources and increasing the frequency of vehicles merging from adjacent lanes, causing queue splitting and reducing information transmission efficiency. Therefore, considering the information of other vehicles within the vehicle's communication range is crucial for the headway strategy.

[0037] Step 2: Introduce the information perception coefficient and the location influence coefficient:

[0038] Because CAVs can acquire real-time acceleration information of surrounding vehicles through communication and perception technologies, and improve the headway strategy by introducing information perception coefficients and position influence coefficients, they can adapt to the vehicle characteristics of CATP.

[0039] Due to limitations in information transmission rules, some vehicles may be outside the communication range or lack network communication characteristics. To better utilize this information, an information perception coefficient is introduced. This reflects the topological structure of information transmission between vehicles. The value can be 0 or 1, where This indicates that the status information of vehicle k can be obtained. This indicates that the vehicle k status information could not be obtained.

[0040] Existing research indicates that the greater the distance between the vehicle ahead and the target vehicle, the less significant the impact of the former on the latter. To analyze the specific impact of the former on the target vehicle, a correlation analysis was performed on the state information of vehicles at different positions within the queuing and the target vehicle, based on the ACC Data intelligent driving dataset. The results show that vehicles farther away have a smaller impact on the target vehicle's speed, and these results are all significant. Therefore, considering vehicle position, a position influence coefficient is introduced, expressed as: The coefficient ranges from (0,1) and represents the degree of influence of vehicle k on the target vehicle.

[0041] The Dynamic Safety Requirement Time Headway (DSRTH) strategy is established as follows:

[0042]

[0043] In the formula, This indicates the speed difference between the target vehicle and the vehicle in front. It is the information perception coefficient of vehicle j; It is the position influence coefficient of vehicle j; It is the acceleration of vehicle j; , and These are the parameters to be calibrated. This indicates the effect of the speed difference with the vehicle in front. This indicates the effect of the acceleration of the vehicle ahead; Number the vehicle at the end of the queue.

[0044] Step 3: Parameter calibration:

[0045] DSRTH strategy involves , and Three parameters to be calibrated, among which The meaning is consistent with the meaning of the headway. This indicates the effect of the speed difference with the vehicle in front. This represents the effect of the acceleration of the vehicle ahead. Model parameters were calibrated using ACC data, and the experiment lasted 289.7 seconds. The dataset consists of 2897 frames, including vehicle speed and spacing information. All frames in the dataset were used for calibration. Particle Swarm Optimization (PSO) was used as the calibration method, and Root Mean Square Error (RMSE) was chosen to measure the error between the actual spacing and the DSRTH. The calibration results are as follows:

[0046]

[0047] (2) Improved Cooperative Adapted Cruise Control (CACC) model based on dynamic safety demand distance. Based on the existing CACC model, the headway strategy is updated to a dynamic safety demand distance strategy. Considering the impact of time delay on system stability, speed and spacing delays are introduced to simulate the time delay relationship between upper-level planning and lower-level control. To improve fuel economy, tracking accuracy, and stability, Model Predictive Control (MPC) is used to dynamically adjust the parameters of the lower-level controller. The specific steps are as follows:

[0048] Step 1: Improved CACC model considering time delay and dynamic security requirements:

[0049]

[0050] In the formula, It is gap error; and They are vehicles and The longitudinal position; t is the speed of the target vehicle; t is the headway. It updates the step size; and It is a control factor; It is the length of the vehicle; and These represent the spacing delay and the velocity delay, respectively.

[0051] Step 2: Dynamic parameter tuning based on MPC:

[0052] By combining constraints and the objective function, an optimization model is established within the MPC framework to dynamically tune the PD control parameters. This method can predict the queue behavior within a certain future time range and optimize the target vehicle's driving behavior based on the prediction results. The iteration step size of this model is 0.1s, where the prediction step size is set to... Control step size set to Using linear predictions for the future of vehicles The vehicle state within a step is predicted. The model is established as follows:

[0053] In the formula, J is the objective function. It is the speed tracking weight coefficient. It is the trajectory tracking weight coefficient. It is a fuel economy weighting coefficient. It is the queue stability weight coefficient. It is the speed of the target vehicle. For the desired safe distance, and Indicates vehicle and The vertical position, It is the length of the vehicle. It is a vehicle Information perception coefficient.

[0054] In the formula, and Indicates vehicle and The vertical position, It is the speed of the target vehicle. Indicates vehicle The acceleration; It is the vehicle's maximum acceleration. It is the vehicle's maximum deceleration, a parameter in the model. and All are set to 3 m / s²; It updates the step size; and It is a control factor; It is angular frequency.

[0055] Considering the dynamic changes of the vehicle and the environment, the state variables are defined as follows:

[0056]

[0057] The state update is defined as follows:

[0058]

[0059] (3) Establish a cooperative lane-changing control method based on an adaptive spacing strategy. Considering that some vehicles in the highway diversion zone need to leave the main line, a cooperative lane-changing strategy is established to actively create gaps for vehicles to make forced lane changes, while optimizing speed control to reduce the impact on traffic flow. Relaxation spacing, recovery spacing, and equilibrium spacing are proposed for different stages of forced lane changes, and are adaptively changed according to the indicator variables of the lane-changing stage. Based on the needs of different stages in the lane-changing process, the interrelationship between each sub-objective is analyzed, the weight allocation of the objective function of each stage is established, and combined with the constraints of vehicles in the queue, a cooperative lane-changing optimization model is constructed to achieve centralized queue control. The specific steps are as follows:

[0060] Step 1: Adaptive Spacing Strategy

[0061] The lane-changing process is broken down into three stages: relaxation, balancing, and recovery, and corresponding spacing is constructed accordingly.

[0062] Relaxation gap:

[0063]

[0064] In the formula, It is the remaining distance for vehicle k to change lanes; It refers to the number of lane changes; It is the difference between the position of the lane change sign and the position of the lane change point; The desire of lane-changing vehicle k to leave the current lane is defined. For vehicles exist Vehicle lane changing pressure is The expected distance between vehicles For vehicles Adjacent vehicles behind in the target lane; For vehicles Expected safe distance, To create a safe gap for CATP to switch lanes.

[0065] Equal spacing:

[0066] ,

[0067] In the formula For vehicles The expected value of restoring the equilibrium spacing is given by the formula, where, For vehicles Distance from the vehicle in front, To balance the spacing between queues, Number the lead vehicle in the convoy. Number the vehicle at the end of the queue.

[0068] Restoration spacing:

[0069]

[0070] In the formula It is a vehicle The desired recovery spacing is expected; For the first Vehicle and following vehicle The spacing, To balance the spacing between queues, For vehicles Expected safe distance.

[0071] To determine the stage of a CAT vehicle during lane-changing coordination, an indicator variable is introduced to describe the vehicle's progress in coordinated lane changing, with the following specific meanings:

[0072]

[0073] An adaptive adjustment mechanism is established based on the relationship between the current spacing and the required spacing. The algorithm flow is as follows:

[0074]

[0075] Step 2: Collaborative lane-changing optimization model:

[0076] In the formula, The distance between the target vehicle and the vehicle in front; The desired speed of the road; , , , , The weights of the sub-objectives in the objective function; To control the prediction step size for model prediction; The target vehicle is positioned at a balanced distance from the vehicle in front.

[0077] The aim is to minimize the joint cost function comprised of the safety, fuel, efficiency, tracking, and equilibrium costs of all vehicles. Since the objective function includes multiple objectives such as safety, fuel, efficiency, tracking, and equilibrium, the level of expectation of these objectives varies among vehicles at different stages of lane changing. Therefore, Multi-Criteria Decision Analysis (MCDA), which takes into account prior knowledge, is chosen, as it is suitable for solving multi-objective optimization problems in the cooperative lane-changing phase. Based on MCDA theory, the weighting coefficients are determined by the preferences for objectives at different stages of lane changing, and the coefficient values ​​are as follows:

[0078]

[0079] During the car-following phase, since the vehicle spacing is already safe and the probability of danger is low, a smaller safety weighting coefficient is used. Simultaneously, in car-following mode, the fuel weighting coefficient is increased to reduce the magnitude of vehicle speed changes, aiming to maintain stability and improve the platoon's fuel economy. Furthermore, overall traffic efficiency can be improved by adjusting the free-flow speed of the following vehicle. In this state, the platoon itself maintains relatively uniform small spacing, eliminating the need to separately monitor platoon balance.

[0080] During the spacing creation phase, the queue needs to accelerate and decelerate to create a larger gap for vehicles changing lanes to merge. The vehicle spacing will decrease sharply at the moment of lane change, so it is necessary to increase the focus on safety and reduce the pursuit of efficiency targets (free flow speed) in order to create spacing by deceleration.

[0081] During the recovery spacing phase, large gaps appear in the queue. Therefore, vehicles with large gaps need to accelerate as soon as possible to shorten the gaps and reach a balanced gap. At this time, increasing the weight of the balanced target can improve the recovery speed in the optimization process.

[0082] During the equilibrium phase, since the equilibrium spacing is for vehicles with large distances ahead, tracking the desired distance is no longer the primary objective. Instead, the focus shifts to enabling the vehicle spacing to recover to the equilibrium spacing as quickly as possible. During this phase, the equilibrium weight is increased.

[0083] Adaptive variable weighting is achieved by switching the weight coefficients of indicator variables at different stages of the lane-changing process. This adaptive variable weighting method can adjust the weights of different objectives according to different lane-changing stages to better reflect the current decision-making environment and needs, thereby improving the flexibility and adaptability of decision-making and enabling CATP to better cope with different decision-making situations and changes.

[0084] The beneficial effects of the technical solution of this invention are as follows:

[0085] 1) To describe the safety distance requirements of intelligent connected driving platoons, a dynamic safety requirement distance strategy was established. Compared with the traditional constant headway strategy and variable headway strategy, the platoon recovery stabilization time was shortened by 12.3% and 4.5%, respectively, which verified the advantages of the dynamic safety requirement distance strategy in response speed and platoon recovery stability.

[0086] 2) An improved Cooperative Adapted Cruise Control (CACC) model is proposed. The headway strategy is updated to a dynamic safety-demand distance strategy. Considering the impact of time delay on system stability, speed and spacing delays are introduced to simulate the time delay relationship between upper-level planning and lower-level control, and the queuing stability judgment condition is derived based on frequency domain analysis. To improve fuel economy, tracking accuracy, and stability, Model Predictive Control (MPC) is used to dynamically adjust the parameters of the lower-level controller. Compared with the Intelligent Driver Model (IDM) model and the original CACC model, the queuing recovery time is reduced by 19.4% and 3.2%, respectively.

[0087] 3) An adaptive spacing strategy for mandatory lane-changing behavior is established, and a multi-objective optimization collaborative lane-changing control method is proposed. This method can effectively advance the lane-changing time and quickly restore the stable operation of the queue after the lane change is completed, avoiding queue fragmentation caused by other vehicles changing lanes into the queue. This has a positive impact on traffic flow efficiency, and the improvement effect is particularly significant when the traffic flow is high. When the traffic flow is 1800 veh / h, the average travel time on the off-ramp is reduced by 19.6%, and the average travel speed is increased by 10.3%.

[0088] Example

[0089] 1) Evaluation of the effectiveness of dynamic security requirement distance strategy

[0090] To analyze the model performance of the dynamic safety requirement distance strategy applied to speed-changing scenarios, the reaction speed, speed change duration, and stability of the CATP (Continuous Ability Test) were used to evaluate the strategy's effectiveness. The experimental environment used the speed of the lead vehicle in the ACC Data as the execution condition, selecting a time period from 50 to 100 seconds, during which the speed exhibited significant changes. The speed, acceleration, and displacement changes of the lead vehicle are as follows: Figure 1 As shown.

[0091] To verify the effectiveness of the DSRTH strategy, IDM was used as the vehicle following model, and the results of the DSRTH strategy were compared with those of the CTH and VTH strategies. The headway strategy parameters in the table below are the results after calibration using the ACC Data dataset.

[0092] Different headway strategy parameter values

[0093]

[0094] To quantitatively analyze the characteristics of different headway strategies in terms of reaction speed, duration of speed change, and stability, three features were used for evaluation: the time of occurrence of the speed trough, the speed trough value, and the time for acceleration to recover to zero. The trends of vehicle speed and expected spacing at different positions within the queue are as follows: Figure 2 As shown.

[0095] The table below shows the improvement degree of the DSRTH strategy by quantitatively analyzing the time of occurrence of the vehicle speed trough, the speed trough value, and the time when acceleration returns to zero. A comparative analysis of driving behavior at different stages is also conducted.

[0096] Speed ​​maintenance period: The 50 to 60-second period represents driving conditions with a constant speed. During this period, the acceleration of the vehicle in front is zero, and the system is unaffected by the acceleration or speed difference of the vehicle in front. All three strategies maintain a consistent speed and are subject to... The influence of this leads to differences in the expected front-end distance. The results show that the DSRTH strategy has the shortest expected front-end distance and can maintain a smaller gap in the speed phase.

[0097] Comparison of evaluation indicators for different headway strategies

[0098]

[0099] Deceleration-Acceleration Period: The 60-80 second period represents the scenario of acceleration after deceleration. This scenario is mainly used to compare the response and tracking capabilities of different strategies to rapid speed changes by the vehicle in front. For the lead vehicle V1, due to the similar information obtained by different strategies, relatively consistent results were produced, with a consistent speed change trend. However, for the following vehicles V2-5, the DSRTH strategy responded more quickly than the VTH and CTH strategies. This difference was more pronounced in vehicles further back in the queue.

[0100] Furthermore, compared to the VTH strategy, the DSRTH strategy leads by 1-3 seconds in terms of speed change rate. Since the preceding vehicle's speed starts at 22 m / s, decelerates, and then accelerates before stabilizing at 20 m / s, the V5 speed trough is 3.0% higher than the CTH strategy and 1.8% higher than the VTH strategy. Compared to the CTH strategy, the time to reach the speed trough is reduced by 6.1%. Analyzing the improvement effects of the two strategies, the following vehicles (V3-V5) in the platoon exhibit a faster response under the DSRTH strategy than under both the CTH and VTH strategies, accompanied by a relatively smaller change in speed.

[0101] Velocity recovery period: The period of 80 to 100 seconds represents the stage where the queue recovers to a stable state. During this stage, the velocity recovery time is primarily used as the evaluation metric for queue stability. The DSRTH strategy reduces the recovery time by 2% to 13% compared to the CTH strategy, demonstrating performance comparable to the VTH strategy. The advantage of the DSRTH strategy lies in the velocity change phase, through… The parameters respond positively to the state of the vehicle ahead. This allows for a rapid return to a stable, constant speed state after a speed change. The DSRTH strategy is particularly effective for vehicles behind in a convoy, such as... Figure 3 As shown.

[0102] The results show that during speed oscillations, the vehicle in front in the platoon can respond quickly, increasing the desired headway to ensure safety. Simultaneously, the vehicles behind in the platoon exhibit an earlier response to the acceleration of the vehicle in front, resulting in a smaller variation in the desired headway and improved fuel economy.

[0103] 2) Evaluation of the effectiveness of the improved CACC model

[0104] a. Validation of the effectiveness of CATP:

[0105] To verify the model's improvement effect on CATP, the IDM model was compared with the unmodified PATH laboratory CACC model. To avoid systematic errors caused by the headway strategy, all three models used the DSRTH strategy. The parameter values ​​for the three models were obtained through ACC data calibration to improve model accuracy; the parameter values ​​are as follows.

[0106]

[0107] Using the driving scenario of the lead car in ACC Data as the simulation condition, numerical simulation is used to analyze the changes in speed, acceleration, and position of the five following vehicles during acceleration and deceleration, as follows: Figure 4 As shown.

[0108] Comparison of evaluation metrics for different vehicle car-following models

[0109]

[0110] A comparative analysis of driving behavior at different stages:

[0111] Speed ​​maintenance period: During the 50-60 second period, the vehicle platoon travels at a constant speed. Since the acceleration of the vehicle in front is essentially zero, the vehicles are unaffected by the acceleration or speed difference of the vehicle in front. Therefore, during the speed maintenance phase, the driving behavior of the three strategies remains largely consistent.

[0112] Deceleration-acceleration period: 60-80 seconds is the rapid speed change phase, designed to verify the response capabilities of different car-following models to speed changes. This is achieved through observation of the above... Figure 4 The time difference between (d) and (f), where V1 and V5 reach the velocity trough, serves as an indicator of the propagation time of external disturbances within the truck queue. The time interval for the IDM model is 13.5 seconds, for the PATH-CACC model it is 11.2 seconds, and for the improved CACC model it is 6 seconds, showing a significant improvement in response speed.

[0113] It is worth noting that, in order to reduce energy consumption and improve queue stability, the queue aims to reduce the magnitude of speed changes. Therefore, the timing of the speed trough and the speed trough values ​​are compared to reflect the queue's effectiveness in suppressing the propagation of disturbances. The results show that the improved CACC model increases the minimum speed and arrives at the speed trough earlier. Specifically, when V5 reaches the speed trough, compared with the IDM and CACC models, the minimum speed is 2.1% higher than the IDM model and 3.6% higher than the CACC model; the time is 12.2% earlier than the IDM model and 8.6% earlier than the CACC model. Because the improved CACC model introduces sub-objectives of control quantity and trajectory tracking into its objective function, it exhibits a more balanced advantage in terms of speed response and stability.

[0114] Speed ​​recovery period: After 80 seconds, the queue enters the speed recovery phase. This is observed... Figure 4 In the IDM model, the acceleration of V5 approaches zero at 109.5 seconds; in the CACC model, it approaches zero at 91.2 seconds; and in the improved CACC model, it approaches zero at 88.3 seconds, 19.4% earlier than the IDM model and 3.2% earlier than the CACC model. Analysis of this index indicates that the improved CACC model significantly enhances its ability to recover stability when disturbed by the speed of the vehicle ahead.

[0115] The improvements in three metrics for vehicles at different positions in the queue in the improved CACC model are as follows: Figure 5 As shown.

[0116] The results show that as vehicles move to the rear of the platoon, the improvements in the timing of speed troughs, the speed trough itself, and the moment acceleration returns to zero are more significant. This highlights the impact of information from the leading vehicle on the driving behavior of the following vehicles. The implementation of the improved CACC strategy demonstrates good rapid response and rapid recovery.

[0117] b. Regarding the fuel economy improvement effect of CATP:

[0118] When multiple trucks travel in CACC mode, forming CATP (Combined Autopilot-Action Train), the lead truck acts as a windbreak, effectively reducing air resistance for vehicles behind, thus providing a protective barrier. Simultaneously, following vehicles alter the wake pattern of the truck in front, further reducing its air resistance. Therefore, the air resistance coefficient of vehicles at different positions within the platoon changes after formation, necessitating the design of air resistance correction coefficients based on platoon position. Hussein, considering different vehicle types and positions, proposed the following expression for vehicle platoon air resistance correction coefficients.

[0119]

[0120] In the formula, This represents the distance between the two vehicles. When vehicle k is the lead vehicle... It is the distance between the target vehicle and the following vehicle; when k is the following vehicle, The distance between the target vehicle and the vehicle in front.

[0121] This invention uses five trucks arranged longitudinally, with the lead vehicle driving under ACC Data conditions. Fuel consumption simulation experiments of the truck platoon are conducted using TruckSim. TruckSim is software for truck simulation, and this platform can be used to verify the effectiveness of the formulated vehicle control strategy. This invention selects the TS-2A-LCF-VAN 5.5T model as the platoon vehicle, and performs co-simulation with TruckSim. The vehicle speed is calculated using an improved CACC model and then transmitted to the target vehicle for control. Fuel consumption is calculated using the engine model corresponding to the TS-2A-LCF-VAN 5.5T. To verify the improvement in fuel economy by different headway strategies and car-following models, the headway strategies used are CTH, VTH, and DSRTH, and the car-following models used are IDM, CACC, and an improved CACC model. Five experimental scenarios are obtained through combinations, as shown in the table below.

[0122] Fuel consumption test scenario

[0123]

[0124] The fuel consumption test results for different combination strategies are shown in the table below.

[0125] Fuel consumption test results

[0126]

[0127] Regarding the headway strategy, Scenario 1 was used as a control group. In Scenario 2, the VTH strategy enabled timely response to the speed of the vehicle ahead, reducing speed changes and resulting in a 0.04% reduction in fuel consumption. In Scenario 3, the DSRTH strategy allowed for closer headway, reducing drag coefficient and lowering fuel consumption by 0.17% compared to Scenario 1.

[0128] Regarding the car-following model, scenario 3 was used as a control group. In scenario 4, the CACC model controlled the vehicle based on the difference between the expected and actual distance, reducing the magnitude of speed changes and lowering overall fuel consumption by 0.33%. In scenario 5, the improved CACC model used MPC to predict changes in the speed of the vehicle ahead and dynamically adjusted parameters, using PD to control the distance error, resulting in a significant reduction in fuel consumption of 3.89%. This effectively saved fuel consumption.

[0129] c. The effect of traffic flow improvement:

[0130] To evaluate the effectiveness of CATP in traffic flow, a simulation experiment was conducted using SUMO to simulate traffic flow scenarios on a basic highway segment. The simulation scenario was a 5km long, two-lane highway segment. The speed limit for cars was 120km / h, and the speed limit for trucks was 80km / h. The simulation time was set to 500 seconds, and the vehicle arrival probability conforms to the specified parameters. The distribution follows a negative exponential distribution of 0.65. Three vehicle types were included on the road: manually driven cars, manually driven trucks, and trucks equipped with a CACC system. The manually driven vehicles used the IDM model, while the CAT (Car Accelerator-Assisted Traffic Flow) system used a modified CACC model. To avoid the influence of systematic errors and random factors, each experiment was repeated 5 times and the average value was taken. Results observed between 200 and 500 seconds during the simulation were used to calculate the indicators to ensure that the traffic flow was stable and to ensure the consistency of the evaluation process.

[0131] Vehicle model ratio analysis:

[0132] It is known that trucks typically travel in the outermost lane on highways, and their proportion is generally lower than that of cars. However, the proportion of trucks varies at different times of day, leading to differences in vehicle type ratios. Furthermore, with the continuous advancement of intelligent connected vehicle (ICV) technology, the penetration rate of CAT (Car-to-Everything) technology will continue to increase, exhibiting variations in penetration rates during its development. Therefore, it is necessary to analyze traffic flow under different vehicle type ratios and penetration rates. In the experiment, the proportion of cars and the CATP (Car-to-Everything) ratio were varied at 0%, 20%, 40%, 60%, 80%, and 100%. Using traffic flow rate as the evaluation index, the results are as follows.

[0133] Comparison of traffic flow rates under different vehicle ratios

[0134]

[0135] To better illustrate the impact of different vehicle type ratios on traffic flow, a visualization is provided to show the changing trend of traffic flow rate as the vehicle type ratio changes, as follows: Figure 6 As shown.

[0136] Regarding CATP penetration rate, increasing penetration rate can increase traffic flow rate. Except when the proportion of private cars is 100%, there are no trucks on the road, resulting in no CAT (Car-to-Truck) traffic. Figure 6Traffic flow rate shows an upward trend from right to left. This is because CATP (Continuous Auxiliary Travel Programme) improves the utilization rate of road resources. Therefore, when the vehicle type ratio remains constant, the higher the CATP penetration rate and the more vehicles equipped with CACC (Continuous Acceleration and Adaptive Cruise Control) systems, the more significant the positive impact on traffic efficiency. Observing the trend of traffic flow rate changes, the improvement effect on traffic flow rate is more significant when the penetration rate is from 0% to 60%. When the penetration rate exceeds 60%, the marginal benefit of improving traffic flow rate diminishes. Therefore, in the early stages of CATP development, the increase in penetration rate has a more obvious effect on improving traffic efficiency.

[0137] Regarding the proportion of cars, when the CATP ratio is between 0% and 60%, increasing the proportion of cars will improve traffic flow. However, when the CATP ratio is between 60% and 100%, increasing the proportion of cars will actually reduce traffic flow. This means that when the CATP ratio is high, the traffic efficiency of trucks may be slightly higher than that of cars.

[0138] Queue size analysis:

[0139] In CATP configuration, maximum queue size is a key parameter. However, the impact of queue size on traffic flow has not been fully studied. Therefore, to investigate the impact of different queue sizes on traffic flow, the experimental scenario was set up as follows: maintaining a constant vehicle type ratio (CATP penetration rate of 100%, car ratio of 60%, basically consistent with the daily environment), controlling queue sizes from 1 to 10, and considering the extreme case of CATP penetration rate of 0. Traffic flow rate, average vehicle speed, and speed variance were used as evaluation indicators. Traffic flow rate was used to evaluate traffic efficiency, average speed was used to evaluate vehicle efficiency, and speed variance was used to evaluate traffic flow stability. The experimental results are as follows. Figure 7 As shown.

[0140] The results show that traffic flow rate increases with queue size, especially when the queue size ranges from 1 to 5. This is because when a following vehicle in the queue is using the CACC model, it degenerates into the ACC model when the lead vehicle is driving a manually operated vehicle. Therefore, although the number of CATs remains constant in the simulation, increasing the queue size results in more CACC vehicles in the CATs, thus increasing the traffic flow rate.

[0141] However, when the queue size is between 6 and 10, the improvement in traffic efficiency is not significant, as shown above. Figure 7 As shown in (b), apart from the increase in average velocity due to an increase in CATP permeability from 0% to 100%, the effect of increasing cohort size on average velocity is negligible. Figure 7In (c), the speed variance gradually increases with the increase of queue size, indicating increased speed fluctuations and decreased stability within the traffic flow. This leads to the conclusion that the stability of the queue decreases with increasing queue size, offsetting the improvement brought about by the reduction in headway. Therefore, when the queue size is 6 to 10, the improvement in traffic flow rate is not significant, and instability increases rapidly. CATP does not recommend using excessively large queue sizes. For queues with sizes of 3 to 5, the speed change is not significant, but there is a significant improvement in traffic flow rate.

[0142] To analyze the correlation between CATP and internal traffic flow bottlenecks, vehicle trajectory maps under different queue sizes were analyzed, as follows: Figure 8 As shown in the figure. The colors in the figure correspond to the velocity values, and the dashed line represents the trajectory of CATP.

[0143] From the top Figure 8 An observation was made of congestion propagating downstream due to increased queue size. In a simulated two-lane road scenario, manually driven vehicles can gain speed advantages by changing lanes due to varying speed limits. When the queue size is small, there is no significant negative impact on lane changing, and lane changing flexibility is high, resulting in a higher number of lane changes. However, when truck queues are spaced closely together and are long, vehicles in the inner lanes face difficulties trying to change lanes to the lane where the CATP (Congestion-Aided Train) system is located. They can only overtake or wait for the CATP to pass. Therefore, when vehicle density is high, lane changing becomes more difficult, leading to a bottleneck in the CATP system. Theoretically, the CATP queue formation can improve traffic efficiency by maintaining a smaller following distance. However, as the queue size increases, the likelihood of traffic bottlenecks increases, leading to the propagation of disturbances and ultimately reducing the stability of the overall traffic flow.

[0144] 3) Evaluation of the effectiveness of coordinated lane changing control

[0145] To evaluate the effectiveness of CATP (Cooperative Lane Changing Control) in traffic flow, in the following... Figure 9 Traffic flow is incorporated into the road shown, with trucks accounting for 40% and cars for 60%, which is relatively consistent with the vehicle type ratio in daily life. Among the cars, 40% need to exit at the off-ramp, therefore, forced lane changing is required in the diversion area to leave the main road. The effectiveness is validated under different traffic flow and queue size scenarios.

[0146] a. Traffic density analysis:

[0147] To verify the effectiveness of the coordinated control strategy under different traffic densities, single-lane traffic flows were set to [600, 1000, 1400, 1800], and the two-lane conversion factor was 0.8. The effectiveness of platooning control and adaptive platooning strategies was verified in the diversion zone. When the control model was the improved CACC model (i.e., the car-following model used a CACC model improved based on dynamic safety demand distance without coordinated lane-changing control), the car-following model used a CACC model improved based on dynamic safety demand distance, and coordinated lane-changing control was also employed. Average travel time and average speed were calculated as evaluation indicators. The main purpose was to analyze the impact of different control methods on vehicle driving efficiency under different traffic densities. The calculation results are as follows.

[0148] Average travel time of traffic flow

[0149]

[0150] Analysis of average travel time in traffic flow shows that the improved CACC model reduces the average travel time compared to the uncontrolled platooning model. (From the following...) Figure 10 The results show that, using the uncontrolled experiment results as a control group, the improvement of the other two strategies was calculated. When the traffic flow was low (600veh / h / lane and 1000veh / h / lane), the improved CACC model and the cooperative lane-changing control strategy had a small improvement in average travel time. This is because when the traffic density is low, the distance between vehicles is large, and they travel more in a free-flow mode. Therefore, the strategy of improving vehicle spacing has little impact on traffic flow.

[0151] As traffic flow increases (1400veh / h / lane and 1800veh / h / lane), the improvement in average travel time by the improved CACC model gradually decreases. This is because the improved CACC car-following model does not consider the impact of forced lane changes. Therefore, CATP travels at close intervals, and off-ramp vehicles that need to change lanes can only complete the lane change by waiting for CATP to pass, which has a certain negative impact on traffic efficiency.

[0152] Meanwhile, the introduction of the Cooperative Lane Change Control Strategy has significantly improved the average travel time. When vehicles on off-ramp need to change lanes, CATP can proactively create gaps and restore the queue, resulting in a significant improvement in traffic efficiency for both off-ramp vehicles and mainline vehicles. At a traffic flow of 1800veh / h, the average travel time on off-ramp is reduced by 19.6%, and the average travel time on the mainline is reduced by 1.5%.

[0153] Average driving speed reflects the vehicle's own benefits to some extent. As the background traffic volume increases, the average driving speed of vehicles traveling on the main line and those exiting the ramp decreases. However, the improvement of the cooperative lane-changing control strategy compared to the uncontrolled scenario continues to increase, and the greater the traffic flow, the more significant the improvement effect.

[0154] Average speed of traffic flow

[0155]

[0156] Analysis of average travel speed in traffic flow reveals that traffic flow has a significant impact on average travel speed. As traffic flow increases, the average travel speed gradually decreases, both on the main road and on off-ramp. This is because increased traffic flow leads to smaller distances between vehicles, making vehicles more susceptible to the influence of other vehicles on their speed, thus causing a decrease in speed.

[0157] Under the same traffic flow, both the improved CACC model and the cooperative lane-changing control strategy can increase the average speed on the main road and off-ramp compared to the uncontrolled scenario. This indicates that these two control strategies can alleviate traffic congestion and improve traffic efficiency to some extent.

[0158] When traffic flow is low (600 veh / h / lane and 1000 veh / h / lane), the improved CACC model and cooperative lane-changing control strategy have relatively small effects on improving the average speed of off-ramp. When traffic flow is high (1400 veh / h / lane and 1800 veh / h / lane), the cooperative lane-changing control strategy significantly improves the average speed. At a traffic flow of 1800 veh / h, the average speed of off-ramp increases by 10.3%, and the average speed on the main line increases by 2.3%, consistent with the improvement trend in average travel time. Figure 11 As shown.

[0159] b. Queue size analysis:

[0160] To verify the effectiveness of the cooperative control strategy under different queue sizes, the control queue size was set from 1 to 10 while maintaining a consistent CACC penetration rate. The experiment was conducted under the traffic flow state (1800 veh / h / lane) where the cooperative lane-changing control effect was most significant. Since the queue size was 0 in the uncontrolled state, there was no difference under different queue sizes. Therefore, only the application of the cooperative lane-changing control strategy was compared. The results are as follows.

[0161] Average travel time for different queue sizes

[0162]

[0163] Analysis of the average travel time under different queue sizes shows that, in highway diversion zones, the average travel time first decreases and then increases with the increase of queue size, as follows: Figure 12 As shown. This differs from the downward trend in the basic road section. In the diversion zone, due to the presence of lane-changing vehicles, when the queue size is too large, the positive impact of close-spaced driving on the road is offset by the negative impact of the lane-changing barriers created by the queue. In fact, when the queue length exceeds 7, traffic efficiency deteriorates further.

[0164] By comparing the average travel time of the improved CACC model and the cooperative lane-changing control strategy under different queue sizes, the results show that when the queue size is small, the improvement effect of adding the cooperative lane-changing control strategy on the average travel time is not significant, and the delay time for lane-changing vehicles waiting for CATP to pass is relatively short. As the queue size gradually increases (queue size 1-7), the improvement effect on the mainline and off-ramp becomes more significant. However, with the queue size further increasing (queue size 8-10), due to the excessive queue length, 2-3 gaps need to be created within the queue when handling cooperative lane changes, resulting in poor queue stability. The process of creating gaps and restoring queue stability has a significant impact on traffic flow. Therefore, in highway diversion zones, CATP is not suitable for using long queue sizes. It can be improved to some extent by splitting into multiple queues with smaller queue sizes in advance.

[0165] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method of adaptive cruise control for a vehicle platoon, the method comprising: Comprise: Step A: Establish a dynamic safety demand distance model for describing the demand of intelligent connected driving platoon for safety distance in a connected environment; combine the speed and acceleration information of the front vehicle in the platoon and the influence of different positions in the platoon, introduce information perception coefficient and position influence coefficient, and establish a dynamic safety demand distance strategy; Step B: Based on the dynamic safety demand distance model, improve the cooperative adaptive cruise control model CACC, update the headway strategy to the dynamic safety demand distance strategy, and introduce the speed and spacing delay to simulate the time delay relationship between the upper layer planning and the bottom layer control; Step C: Establish a cooperative lane changing control strategy based on the adaptive spacing strategy, set the relaxation spacing, recovery spacing and equilibrium spacing for different stages of vehicle forced lane changing, and adaptively change according to the lane changing stage indicator variable; Based on the demand in different stages of lane changing process, analyze the mutual relationship between each sub-target, determine the weight distribution of each stage target function, combine the constraint conditions of vehicles in the platoon, and construct a cooperative lane changing optimization model to realize centralized control of the platoon; The step A comprises: Step A1: Set the VTH spacing strategy as follows: ； in, The desired safe distance; For the following car speed; This refers to the headway of the train. These are the coefficients to be calibrated; The speed difference between the vehicle in front and the vehicle behind; Step A2: Introduce information perception coefficient and position influence coefficient: Establish a dynamic safety demand distance strategy as follows: ； wherein represents the speed difference between the target vehicle and the preceding vehicle; is the information perception coefficient of vehicle j; is the position influence coefficient of vehicle j; is the acceleration of vehicle j; , and are parameters to be calibrated, represents the influence of the speed difference from the preceding vehicle, represents the influence of the acceleration of the preceding vehicle; is the number of the last vehicle in the platoon. Step A3: Parameter calibration: Calibrate the model parameters of the dynamic safety demand distance strategy, use particle swarm optimization algorithm as the calibration method, and select root mean square error to measure the error between the actual spacing and the dynamic safety demand distance strategy; The step B comprises: Step B1: Considering time delay and dynamic safety demand distance, the improved CACC model is: ； wherein is the gap error; and are the longitudinal positions of the vehicle and respectively; is the speed of the target vehicle; t is the time headway; is the update step; and are control coefficients; is the vehicle length; and denote the distance delay and the speed delay respectively; Step B2: Dynamic parameter adjustment based on MPC: Combine the constraint conditions and the objective function to establish an optimization model for dynamic parameter adjustment of PD control parameters under the MPC framework.

2. The method of claim 1, wherein The step C comprises: Step C1: Adaptive spacing strategy: Split the lane changing process into three stages of relaxation, equilibrium and recovery, and construct the corresponding spacing; Introduce an indicator variable to describe the progress of the vehicle in cooperative lane changing; Establish an adaptive adjustment mechanism based on the relationship between the current spacing and the required spacing; Step C2: Establish a cooperative lane changing optimization model.

3. The method of claim 2, wherein, The step C further comprises: Select a multi-criteria decision analysis considering prior knowledge for multi-objective optimization solution of the cooperative lane changing stage.

4. The method of claim 3, wherein, The multi-criteria decision analysis comprises: In the following stage, use a smaller safety weight coefficient, increase the weight coefficient of fuel, and adjust the free flow speed of tracking to improve the overall traffic efficiency.

5. The method of claim 3, wherein, The multi-criteria decision analysis comprises: In the creation spacing stage, the platoon accelerates and decelerates to create a larger spacing for the lane changing vehicle to merge, while reducing the tracking of the efficiency target.

6. The method of claim 3, wherein, The multi-criteria decision analysis comprises: In the recovery spacing stage, there is a larger spacing in the platoon, the vehicle speeds up to shorten the spacing and reach the equilibrium spacing, and the equilibrium target weight is improved.

7. The method of claim 3, wherein, The multi-criteria decision analysis comprises: In the equilibrium stage, increase the value of the equilibrium weight.

8. The method of claim 3, wherein, The multi-criteria decision analysis comprises: By means of the lane-changing stage indication variable, the weight coefficients in different stages are switched to realize adaptive variable weight.

Images