Emergency vehicle road section priority passing control method considering networked automatic driving exclusive lane

Abstract

The invention discloses an emergency vehicle road section priority passing control method considering a network-connected automatic driving special road, and the method comprises the steps: collecting the vehicle state information of an emergency vehicle and all vehicles of a target road section, and guiding the emergency vehicle to enter the network-connected automatic driving special road of the target road section if the emergency vehicle enters the target road section, calculating a dynamic emptying area in real time; a networked automatic driving vehicle in a dynamic emptying area is used as an optimized vehicle, and a multi-target optimization model which is established by taking minimum emergency vehicle delay and minimum interference to surrounding vehicles as targets is used for optimizing a speed change value and a lane changing decision of the optimized vehicle so as to control the optimized vehicle. Therefore, the networked automatic driving vehicle which affects passing in front of the emergency vehicle is emptied. According to the method, the longitudinal following and transverse lane changing decisions of the networked automatic driving vehicles in the social vehicles in the mixed traffic flow environment are optimized, and the influence on the social vehicles is reduced while the emergency vehicles are guaranteed to pass preferentially.

Description

Technical Field

[0001] The present invention relates to the field of traffic control, and in particular to a method for controlling the priority passage of emergency vehicles on a road section taking into account a networked autonomous driving dedicated lane. Background Art

[0002] With the deepening of aging and urbanization, various types of emergency rescue incidents occur frequently, and emergency vehicle travel delays have increased seriously. Although the law grants emergency vehicles higher road access rights, this passive priority method is still affected by surrounding vehicles in the real urban road environment, and the short-term lane-changing behavior to reduce obstacles to emergency vehicles is more likely to cause traffic congestion, making it difficult to achieve emergency vehicle priority. Therefore, formulating a scientific emergency vehicle priority control method to improve the driving efficiency of emergency vehicles while reducing the negative impact on surrounding traffic is of great significance to the development of urban emergency rescue.

[0003] At present, the priority control of emergency vehicles is mainly focused on three aspects: path optimization, priority control at signalized intersections, and priority control at road sections. For road section priority control, the existing strategies can be divided into the following three types: giving way strategies based on technologies such as emergency vehicle sound and light alarms or vehicle-mounted road networks; establishing temporary or permanent emergency vehicle dedicated lanes; and strategies using special methods such as shoulder lanes or reverse driving. Compared with the research on path optimization and priority control at signalized intersections, there are fewer studies on road section priority control for emergency vehicles. However, in normal sections such as highway ramps or extended sections between intersections on urban roads, unlike signalized intersections with clear timing plans, they are more dependent on current road traffic flow conditions that are difficult to predict or estimate in advance. Emergency vehicles will be hindered by surrounding vehicles, resulting in deceleration and parking, and the travel time uncertainty is relatively large. Therefore, it is very necessary to study the road section priority strategy for emergency vehicles.

[0004] With the rapid development of vehicle networking technology in recent years, many scholars have proposed that the establishment of connected autonomous driving lanes can be used to spatially divide the right of way in mixed traffic flow scenarios that will exist for a long time in the future. How to use connected autonomous driving lanes to give emergency vehicles in mixed traffic flow scenarios priority control is also a question worth considering.

[0005] The Chinese patent application with publication number CN116597674A proposed a dynamic emergency lane clearing method based on the Internet of Vehicles. In a fully connected environment, the upstream section of the urban road intersection is taken as the research object, the queuing effect of the intersection is considered, and a discretization method is adopted. With the minimum clearing cost as the principle, the optimal clearing plan and the optimal start clearing time are obtained. Finally, the vehicle moves according to the clearing plan to complete the emergency lane clearing. However, this scheme only considers the fully connected environment, lacks research on the mixed traffic flow environment, and the scheme adopts a discretization method, which greatly simplifies the complexity of the problem and has a large gap with the actual traffic flow environment. Summary of the invention

[0006] The technical problem to be solved by the present invention is as follows: In view of the above-mentioned problems in the prior art, a method for controlling the priority passage of emergency vehicles on road sections taking into account a connected autonomous driving dedicated lane is provided. By reusing the connected autonomous driving dedicated lane as an emergency lane and optimizing the longitudinal following and lateral lane changing decisions of the connected autonomous driving vehicle, the traffic efficiency of social vehicles is guaranteed under the premise of giving priority to emergency vehicles.

[0007] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0008] A method for controlling the priority passage of emergency vehicles on a road section considering a dedicated networked autonomous driving lane comprises the following steps:

[0009] Collect the vehicle status information of the emergency vehicle and all vehicles on the target road section, and determine whether the emergency vehicle has entered the target road section based on the vehicle status information of the emergency vehicle. If so, guide the emergency vehicle to enter the networked automatic driving lane of the target road section, and calculate the dynamic clearing area in front of the emergency vehicle in real time;

[0010] A multi-objective optimization model is established with the goal of minimizing the delay of emergency vehicles and minimizing the interference to surrounding vehicles. The connected autonomous driving vehicles in the dynamic clearing area are used as optimized vehicles. The multi-objective optimization model is used to calculate the speed change value and lane change decision of each optimized vehicle.

[0011] The speed change value and lane change decision control are used to optimize the vehicle, thereby clearing the connected autonomous driving vehicles in front of the emergency vehicle that are affecting traffic.

[0012] Furthermore, the real-time calculation of the dynamic clearing area in front of the emergency vehicle includes:

[0013] Calculate the dynamic clearance distance in front of the emergency vehicle in real time. If the value of the dynamic clearance distance is less than or equal to the preset specified value, set the value of the dynamic clearance distance to the specified value. The dynamic clearance distance expression is as follows:

[0014]

[0015] Among them, D c is the dynamic clearance distance; e represents the emergency vehicle; v e (t) is the current speed of the emergency vehicle; T e The time to ensure that the emergency vehicle continues to travel at the current speed; dec e is the maximum deceleration of the emergency vehicle; Gap min is the minimum parking distance;

[0016] The rectangular area formed by the dynamic clearance distance in front of the emergency vehicle on the connected autonomous driving lane and its adjacent lanes is used as the dynamic clearance area.

[0017] Furthermore, the objective function expression of the multi-objective optimization model is as follows:

[0018] minO=w1*O1+w2*O2+w3*(O3+O4)

[0019] Among them, w1, w2, and w3 are the weights of each part in the objective function;

[0020] O1 is the distance between the emergency vehicle and the road section exit, expressed as follows:

[0021]

[0022] Where L is the length of the road section; t is the current time; T0 is the optimization step length; x e (t) is the position of the emergency vehicle at time t; v e (t) is the speed of the emergency vehicle at time t; The optimized speed change value of the emergency vehicle at time t;

[0023] O2 is the number of optimized vehicles among all optimized vehicles that are still in the connected autonomous driving lane and have not left the target section after this optimization. The expression is as follows:

[0024]

[0025] Where ω represents the optimized vehicle; k is the lane index; Indicates whether the optimized vehicle is still in lane k after optimization at time t. If it is in lane k, it is 1, otherwise it is 0; ρ ω (t+T0) is a 0-1 variable;

[0026] O3 is the inverse of the sum of the negative vehicle speed changes of all optimized vehicles after this optimization, and the expression is as follows:

[0027]

[0028] in, To optimize the speed change value of the vehicle in this optimization; c ω (t+T0) is an integer variable;

[0029] O4 is the opposite of the sum of the positive vehicle speed change values ​​of all optimized vehicles after this optimization, and the expression is as follows:

[0030]

[0031] in, is the speed change value of the emergency vehicle in this optimization; c ω (t+T0) is an integer variable.

[0032] Furthermore, the constraints of the multi-objective optimization model include vehicle dynamics constraints, which are expressed as follows:

[0033]

[0034] 0≤v ω (t+T0)≤vmax ω

[0035] dec ω ≤a ω (t+T0)≤acc ω

[0036] Where ω represents the optimized vehicle; t is the current time; T0 is the optimization step length; x ω (t+T0) is the optimized position of the vehicle at time t; x ω (t) is the optimized position of the vehicle at time t; v ω (t) is the optimized vehicle speed at time t; To optimize the speed change value of the vehicle at time t; v ω (t+T0) is the optimized speed of the optimized vehicle at time t; a ω (t+T0) is the optimized acceleration of the optimized vehicle at time t; vmax ω To optimize the maximum speed of the vehicle; dec ω To optimize the maximum deceleration of the vehicle; acc ω To optimize the vehicle's maximum acceleration.

[0037] Furthermore, the constraints of the multi-objective optimization model include lane constraints, which are expressed as follows:

[0038]

[0039] Where ω represents the optimized vehicle; k is the lane index; Indicates whether the optimized vehicle is still in lane k after optimization at time t. If it is in lane k, it is 1, otherwise it is 0.

[0040] Furthermore, the constraints of the multi-objective optimization model include a following safety distance constraint, which is expressed as follows:

[0041]

[0042] d1 ω (t+T0)≥max{ds1 ω (t+T0),0}

[0043]

[0044] d2 ω (t+T0)≥max{ds2 ω (t+T0),0}

[0045] Among them, ω represents the optimized vehicle; ds1 ω (t+T0) is the safe distance between the optimized vehicle and the vehicle ahead in the lane after optimization at time t; TTC min As an alternative safety indicator, the collision time threshold limit; v ω (t+T0) is the optimized speed of the optimized vehicle at time t; To optimize the vehicle's lane, the speed of the vehicle ahead at time t after optimization; d1 ω (t+T0) is the actual distance between the optimized vehicle and the vehicle ahead in the lane after optimization at time t; To optimize the position of the vehicle in front of the vehicle in the lane at time t; x ω (t+T0) is the optimized position of the vehicle at time t; To optimize the length of the vehicle in front of the vehicle in the lane; ds2 ω (t+T0) is the safe distance between the optimized vehicle and the vehicle behind it in the lane after optimization at time t; To optimize the vehicle's lane, the vehicle behind it is optimized at time t; d2 ω (t+T0) is the actual distance between the optimized vehicle and the vehicle behind it in the lane after optimization at time t; To optimize the position of the vehicle behind the vehicle in the lane at time t; l ω To optimize the vehicle's length.

[0046] Furthermore, the constraints of the multi-objective optimization model include a lane-changing safety distance constraint, which is expressed as follows:

[0047]

[0048] d3 ω (t+T0)≥max{ds3 ω (t+T0),0}

[0049]

[0050] d4 ω (t+T0)≥max{ds4 ω (t+T0),0}

[0051] Among them, ω represents the optimized vehicle; ds3 ω (t+T0) is the safe distance between the optimized vehicle and the vehicle in front of the target lane at time t; TTC min As an alternative safety indicator, the collision time threshold limit; v ω (t+T0) is the optimized speed of the optimized vehicle at time t; The optimized speed of the vehicle in the target lane at time t; d3 ω (t+T0) is the actual distance between the optimized vehicle and the vehicle in front of the target lane after optimization at time t; The optimized position of the vehicle in front of the target lane at time t; x ω (t+T0) is the optimized position of the vehicle at time t; To optimize the vehicle's target lane front vehicle length; ds4 ω (t+T0) is the safe distance between the optimized vehicle and the vehicle behind it in the target lane at time t; The optimized speed of the vehicle behind the target lane at time t; d4 ω (t+T0) is the actual distance between the optimized vehicle and the vehicle behind it in the target lane after optimization at time t; To optimize the position of the vehicle behind the vehicle in the lane at time t; l ω To optimize the vehicle's length.

[0052] Furthermore, the constraints of the multi-objective optimization model include auxiliary variable constraints, which are expressed as follows:

[0053]

[0054] Among them, ω represents the optimized vehicle; v ω (t+T0) is the optimized speed of the optimized vehicle at time t; x ω (t+T0) is the optimized position of the optimized vehicle at time t; auxiliary variable c ω (t+T0) is an integer variable. -1 indicates that the optimized vehicle accelerates after this optimization; 0 indicates that the optimized vehicle does not change speed after this optimization; 1 indicates that the optimized vehicle decelerates after this optimization; auxiliary variable ρ ω (t+T0) is a binary variable, where 1 indicates that the optimized vehicle has not left the target section after this optimization; and 0 indicates that the optimized vehicle has left the target section.

[0055] Furthermore, before using the multi-objective optimization model to calculate the speed change value and lane-changing decision of each optimized vehicle, the following steps are performed: corresponding following models and lane-changing models are configured for all vehicles on the target road section; when using the multi-objective optimization model to calculate the speed change value and lane-changing decision of each optimized vehicle, the following steps are performed:

[0056] The manually driven vehicles on the target road section and the connected autonomous driving vehicles outside the dynamic clearing area are regarded as non-optimized vehicles. The vehicle state information of the non-optimized vehicles at the current time step is input into the corresponding following model and lane changing model respectively, and the vehicle state information of the non-optimized vehicles after one optimization step is calculated.

[0057] The vehicle state information of the optimized vehicle at the current time step and the vehicle state information of the non-optimized vehicle after one optimization step are input into the multi-objective optimization model and solved to obtain the speed change value and lane changing decision of each optimized vehicle.

[0058] Furthermore, when each optimized vehicle is controlled respectively using the speed change value and the optimized lane change decision, it includes:

[0059] If the optimization step of the multi-objective optimization model is greater than the control step, the speed change value is decomposed into each control step through linear interpolation to control the speed change of the corresponding optimized vehicle, and the lane changing decision is made at the last control step of the corresponding optimization step to control the position change of the corresponding optimized vehicle.

[0060] Compared with the prior art, the advantages of the present invention are:

[0061] The present invention temporarily opens up a networked autonomous driving lane as an emergency lane for mixed traffic flow environments, and dynamically divides the right of way in space by reusing the dedicated lane as a dynamic emergency lane. When an emergency vehicle enters a road section, it can use the networked autonomous driving lane for priority passage, while calculating the dynamic clearance distance in front of the emergency vehicle and optimizing the longitudinal following and lateral lane changing decisions of the networked vehicles within the dynamic clearance distance, realizing an avoidance strategy based on coordinated lane changing, clearing the area in front of the emergency vehicle in time, and realizing priority passage for the emergency vehicle.

[0062] The present invention aims to minimize the delay of emergency vehicles and the interference to surrounding vehicles, takes the longitudinal speed and lane-changing behavior of networked vehicles as decision variables, considers constraints such as vehicle dynamics, following and lane-changing safety distance, establishes a multi-objective optimization model to obtain the speed of all networked autonomous driving vehicles (CAVs) and the lane-changing decision of the networked autonomous driving vehicles (CAVs) in front of the emergency vehicles on the dedicated lanes, and decomposes the optimization results into each control step for control. While ensuring that emergency vehicles have priority, the impact on social vehicles is reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1is a flow chart of an embodiment of the present invention.

[0064] Figure 2 This is a schematic diagram of the road scene.

[0065] Figure 3 This is a statistical chart of the average speed of emergency vehicles.

[0066] Figure 4 Statistics of CAV lane changes to assist emergency vehicles. DETAILED DESCRIPTION

[0067] The present invention is further described below in conjunction with the accompanying drawings and specific preferred embodiments, but the protection scope of the present invention is not limited thereby.

[0068] This embodiment proposes a method for controlling the priority passage of emergency vehicles on a road section taking into account a networked autonomous driving dedicated lane. For a mixed traffic flow environment, the method realizes the priority passage of emergency vehicles on a target road section. The overall idea is that when an emergency vehicle enters the target road section, the networked autonomous driving dedicated lane is reused as an emergency lane, and vehicles within the dynamic clearance distance in front of the emergency vehicle are pre-cleared by optimizing their following and lane-changing decisions, thereby ensuring the priority passage of emergency vehicles while reducing the impact on social vehicles.

[0069] like Figure 1 As shown, the method of this embodiment includes the following steps:

[0070] S1) collecting vehicle status information of all social vehicles and emergency vehicles on the target road section. In this embodiment, social vehicles include manually driven vehicles HDV and networked automatic driving vehicles CAV. The vehicle status information includes vehicle speed, acceleration, position and other information;

[0071] According to the position information in the vehicle status information of the emergency vehicle, it is judged whether the emergency vehicle has entered the target section. If the emergency vehicle has entered the target section, the networked autonomous driving dedicated lane of the target section is reused as the emergency lane, and the emergency vehicle is guided to enter the emergency lane. At the same time, the dynamic clearance distance in front of the emergency vehicle is calculated to determine the dynamic clearance area of ​​this time, also known as the optimization area;

[0072] S2) With the goal of minimizing the delay of emergency vehicles and minimizing the interference to surrounding vehicles, a multi-objective optimization model is established, taking into account constraints such as vehicle dynamics, following and lane-changing safety distances, and taking the connected autonomous driving vehicles in the dynamic clearance area as the optimized vehicles. The multi-objective optimization model is used to calculate the speed change value and lane-changing decision of the optimized vehicles as the optimization results corresponding to each optimized vehicle;

[0073] S3) Optimize vehicle control according to the optimization result, so as to clear the connected autonomous driving vehicles in front of the emergency vehicle that affect the passage.

[0074] Through the above steps, when an emergency vehicle enters the road section, first, the networked automatic driving lane is reused as the emergency lane, and its dynamic clearance distance is calculated according to the current speed of the emergency vehicle, and the networked automatic driving vehicle that needs to be optimized in the optimization area is determined. Secondly, with the goal of minimizing the delay of the emergency vehicle and the least interference to the surrounding vehicles, considering the constraints such as vehicle dynamics, following and lane changing safety distance, a multi-objective optimization model is established to calculate the speed change value and lane changing decision of the optimized vehicle. Finally, control is performed according to the optimization results, and the optimization results are decomposed into each control step and reverse control. Only by controlling the following and lane changing decisions of vehicles within a certain dynamic distance in front of the emergency vehicle, the networked automatic driving vehicles that affect the passage of the emergency vehicle are pre-cleared, reducing the delay of the emergency vehicle. It is of great help to the problem of priority passage of emergency vehicles in mixed traffic flow, and at the same time, it also achieves the least interference to social vehicles.

[0075] Each step is described in detail below.

[0076] In step S1 of this embodiment, all vehicles on the target road section are configured with corresponding following models and lane changing models. After the vehicle status information is collected, the following steps are performed:

[0077] S11) judging whether the emergency vehicle has entered the road section according to the position information; if the emergency vehicle has not entered, all vehicles drive according to their following model and lane changing model; if the emergency vehicle has entered, reusing the networked autonomous driving dedicated lane as the emergency lane and guiding the emergency vehicle to enter the emergency lane;

[0078] S12) Calculate the dynamic clearance distance of the emergency vehicle according to its current speed. In this embodiment, three parts are considered when calculating the dynamic clearance distance: the emergency vehicle travels at the current speed T e The distance required for each second, the distance required to decelerate from the current speed to 0, and the minimum parking distance. The dynamic clearing distance expression is as follows:

[0079]

[0080] Where D2 is the calculated value of the dynamic clearance distance of the emergency vehicle; the emergency vehicle is defined as e, and the optimization vehicle is defined as ω; v e (t) is the current speed of the emergency vehicle; T e In order to ensure that the emergency vehicle continues to travel at the current speed, the time is set to 2s in this embodiment; e is the maximum deceleration of the emergency vehicle; Gap min is the minimum parking distance, which is set to 2m in this embodiment;

[0081] If the clearance distance is too short, the vehicles may not be cleared in time and the priority passage of emergency vehicles cannot be guaranteed. In this case, when the dynamic clearance distance D of the emergency vehicle is c The value of is too short, so the specified 50 meters is taken as the dynamic clearance distance of the emergency vehicle. The expression is as follows:

[0082]

[0083] Where D e The final value of the dynamic clearance distance for emergency vehicles;

[0084] S13) Based on the calculated dynamic clearance distance of the emergency vehicle, determine the dynamic clearance area as a rectangular area formed by the dynamic clearance distance in front of the emergency vehicle on the networked autonomous driving lane and its adjacent lanes, and the networked autonomous driving vehicles in this area are the next optimization vehicles.

[0085] Step S2 of this embodiment uses a multi-objective optimization model to solve and optimize the longitudinal following and lateral lane changing decisions of the vehicle based on the collected data, so as to achieve the goal of minimizing the delay of the emergency vehicle and minimizing the interference of surrounding vehicles, including the following steps:

[0086] S21) taking the minimum delay of the emergency vehicle and the minimum interference to surrounding vehicles as the goal, considering constraints such as vehicle dynamics, following and lane changing safety distance, a multi-objective optimization model is established to calculate the speed change value and lane changing decision of the optimized vehicle;

[0087] S22) The manually driven vehicles on the target road section and the networked autonomous driving vehicles outside the dynamic clearing area are regarded as non-optimized vehicles. The non-optimized vehicles are used as the front and rear vehicles of the optimized vehicle in the multi-objective optimization model. According to the vehicle position, speed, acceleration and other information collected at the current time step, the vehicle state of the non-optimized vehicle after one optimization step is obtained through the corresponding following and lane changing models of the vehicle.

[0088] S23) For each optimized vehicle, the vehicle state information of the optimized vehicle at the current time step and the vehicle state information of the corresponding non-optimized vehicle after one optimization step are respectively input into the multi-objective optimization model and solved to obtain the speed change value and the optimal solution of the lane changing decision of each optimized vehicle.

[0089] In this embodiment, the objective function of the multi-objective optimization model includes four parts; the constraints of the multi-objective optimization model include vehicle dynamics constraints, lane constraints, following safety distance constraints, lane changing safety distance constraints, and auxiliary variable constraints, which are described in detail below.

[0090] The objective function of the multi-objective optimization model consists of four parts, expressed as follows:

[0091] minO=w1*O1+w2*O2+w3*(O3+O4)

[0092] Among them, w1, w2, w3 are the weights of each part in the objective function respectively. In this embodiment, w1=400, w2=200, w3=1.

[0093] The first part of the objective function O1 is the distance between the emergency vehicle and the road section exit, and the expression is as follows:

[0094]

[0095] Where L is the length of the road section; t is the current time; T0 is the optimization step length; x e (t) is the position of the emergency vehicle at time t; v e (t) is the speed of the emergency vehicle at time t; The optimized speed change value of the emergency vehicle at time t.

[0096] The second part of the objective function O2 is the number of optimized vehicles among all optimized vehicles that are still in the connected autonomous driving lane and have not left the target section after this optimization. The expression is as follows:

[0097]

[0098] Among them, ω represents the optimized vehicle; k is the lane index, which is 0, 1, and 2 respectively; k = 0 means it is in lane 0, that is, the dedicated lane for connected autonomous driving; Indicates whether the optimized vehicle is still in lane k after optimization at time t. If it is in lane k, it is 1, otherwise it is 0; ρ ω (t+T0) is a 0-1 variable.

[0099] The third part of the objective function O3 is the inverse of the sum of the negative vehicle speed changes of all optimized vehicles after this optimization, and the expression is as follows:

[0100]

[0101] in, To optimize the speed change value of the vehicle in this optimization; c ω (t+T0) is an integer variable with values ​​of -1, 0, 1.

[0102] The fourth part O4 of the objective function is the inverse of the sum of the positive vehicle speed change values ​​of all optimized vehicles after this optimization, and the expression is as follows:

[0103]

[0104] in, is the speed change value of the emergency vehicle in this optimization; c ω(t+T0) is an integer variable with values ​​of -1, 0, 1.

[0105] Among the constraints of the multi-objective optimization model, the vehicle dynamics constraints of this embodiment ensure that the generated control strategy is physically reasonable, the system is stable, driving is safe, and can optimize the overall performance of the vehicle. The expression is as follows:

[0106]

[0107] 0≤v ω (t+T0)≤vmax ω

[0108] dec ω ≤a ω (t+T0)≤acc ω

[0109] Where ω represents the optimized vehicle; t is the current time; T0 is the optimization step length; x ω (t+T0) is the optimized position of the vehicle at time t; x ω (t) is the optimized position of the vehicle at time t; v ω (t) is the optimized vehicle speed at time t; To optimize the speed change value of the vehicle at time t; v ω (t+T0) is the optimized speed of the optimized vehicle at time t; a ω (t+T0) is the optimized acceleration of the optimized vehicle at time t; vmax ω To optimize the maximum speed of the vehicle; dec ω To optimize the maximum deceleration of the vehicle; acc ω To optimize the vehicle's maximum acceleration.

[0110] Among the constraints of the multi-objective optimization model, the lane constraint of this embodiment ensures that each vehicle only appears in one lane at each time, and when the optimized vehicle ω is in the k lane at time t, the optimized vehicle ω only changes lanes to the left and ensures that there will be no continuous lane changes. The expression is as follows:

[0111]

[0112] Where ω represents the optimized vehicle; k is the lane index; Indicates whether the optimized vehicle is still in lane k after optimization at time t. If it is in lane k, it is 1, otherwise it is 0.

[0113] Among the constraints of the multi-objective optimization model, the following safety distance constraint of this embodiment uses the time-to-collision (TTC) threshold limit of the alternative safety indicator that needs to be met between the vehicle and the front and rear vehicles in the lane, and the expression is as follows:

[0114]

[0115] d1 ω (t+T0)≥max{ds1 ω (t+T0),0}

[0116]

[0117] d2 ω (t+T0)≥max{ds2 ω (t+T0),0}

[0118] Among them, ω represents the optimized vehicle; ds1 ω (t+T0) is the safe distance between the optimized vehicle and the vehicle ahead in the lane after optimization at time t; TTC min To replace the safety indicator collision time threshold limit, it is set to 2s in this embodiment; v ω (t+T0) is the optimized speed of the optimized vehicle at time t; v ω (t+T0) is the optimized speed of the optimized vehicle at time t; To optimize the vehicle's lane, the speed of the vehicle ahead at time t after optimization; d1 ω (t+T0) is the actual distance between the optimized vehicle and the vehicle ahead in the lane after optimization at time t; To optimize the position of the vehicle in front of the vehicle in the lane at time t; x ω (t+T0) is the optimized position of the vehicle at time t; To optimize the length of the vehicle in front of the vehicle in the lane; ds2 ω (t+T0) is the safe distance between the optimized vehicle and the vehicle behind it in the lane after optimization at time t; To optimize the vehicle's lane, the vehicle behind it is optimized at time t; d2 ω (t+T0) is the actual distance between the optimized vehicle and the vehicle behind it in the lane after optimization at time t; To optimize the position of the vehicle behind the vehicle in the lane at time t; l ω To optimize the vehicle's length.

[0119] Among the constraints of the multi-objective optimization model, the lane change safety distance constraint of this embodiment uses the time-to-collision (TTC) threshold limit of the alternative safety indicator that needs to be met between the vehicle and the front and rear vehicles in the target lane. The expression is as follows:

[0120]

[0121] d3 ω (t+T0)≥max{ds3ω (t+T0),0}

[0122]

[0123] d4 ω (t+T0)≥max{ds4 ω (t+T0),0}

[0124] Among them, ω represents the optimized vehicle; ds3 ω (t+T0) is the safe distance between the optimized vehicle and the vehicle in front of the target lane at time t; TTC min To replace the safety indicator collision time threshold limit, it is set to 2s in this embodiment; v ω (t+T0) is the optimized speed of the optimized vehicle at time t; The optimized speed of the vehicle in the target lane at time t; d3 ω (t+T0) is the actual distance between the optimized vehicle and the vehicle in front of the target lane after optimization at time t; The optimized position of the vehicle in front of the target lane at time t; x ω (t+T0) is the optimized position of the vehicle at time t; To optimize the vehicle's target lane front vehicle length; ds4 ω (t+T0) is the safe distance between the optimized vehicle and the vehicle behind it in the target lane at time t; The optimized speed of the vehicle behind the target lane at time t; d4 ω (t+T0) is the actual distance between the optimized vehicle and the vehicle behind it in the target lane after optimization at time t; To optimize the position of the vehicle behind the vehicle in the lane at time t; l ω To optimize the vehicle's length.

[0125] Among the constraints of the multi-objective optimization model, the auxiliary variable constraints of this embodiment can improve the accuracy and control effect of the model, ensure physical feasibility, and simplify the solution of the optimization problem. The expression is as follows:

[0126]

[0127] Among them, ω represents the optimized vehicle; auxiliary variable c ω (t+T0) is an integer variable. -1 indicates that the optimized vehicle accelerates after this optimization; 0 indicates that the optimized vehicle remains at the same speed after this optimization; and 1 indicates that the optimized vehicle decelerates after this optimization.

[0128]

[0129] Among them, the auxiliary variable ρω (t+T0) is a binary variable, where 0 indicates that the optimized vehicle leaves the road section after this optimization; and 1 indicates that the optimized vehicle does not leave the road section.

[0130] From the above content, we can see that non-optimized vehicles can be regarded as obstacles in the optimization process of optimized vehicles. They are mainly used as the leading and following vehicles of the optimized vehicles in the following safety distance constraints and lane change safety distance constraints in the optimization model. For example: At the same time, because the optimized vehicle needs to meet the safety distance with surrounding vehicles in the next optimization step during the optimization process, the vehicle state of the non-optimized vehicle after one optimization step is used as the input of the multi-objective optimization model.

[0131] In step S3 of this embodiment, the vehicle control is optimized according to the optimization result. Specifically, when the model optimization step is larger than the control step, the speed change value of the optimized vehicle is decomposed into each control step by linear interpolation for control, and the lane change decision of the optimized vehicle is controlled at the last control step within the optimization step. In this embodiment, the control step is set to 0.1s. Since the model optimization step is 1s, the control step is 0.1s. Specifically, the following steps are included:

[0132] S31) performing linear interpolation processing on the solved speed change value of the optimized vehicle, and using the speed change value corresponding to each control step to perform speed control on the optimized vehicle, thereby achieving speed adjustment of the optimized vehicle;

[0133] S32) The lane-changing decision of the optimized vehicle is solved and the lane-changing control of the optimized vehicle is performed in the last control step within the corresponding optimization step, so as to achieve the position adjustment of the optimized vehicle.

[0134] The effect of the method of this embodiment is verified by specific experiments below.

[0135] Select a 1.2 km long three-lane urban expressway section, such as Figure 2 shown.

[0136] This embodiment combines SUMO and PYTHON joint simulation to set vehicle parameters for different vehicle types, as shown in Table 1.

[0137] Table 1 Vehicle parameter settings for different vehicle types

[0138]

[0139] This embodiment sets up two schemes for comparison to verify the effectiveness of the proposed model, where scheme 1 is the basic scheme and scheme 2 is the scheme proposed in this paper. The scheme setting comparison is shown in Table 2.

[0140] Table 2 Comparison of solution settings

[0141]

[0142] At the same time, this embodiment considers various scenarios such as different flow rates, permeability, and emergency vehicle entry time for simulation evaluation. The parameter settings are as follows: the flow value range is 900veh·h -1 -1500veh·h -1 , interval 200veh·h -1 The penetration rate ranges from 40% to 80% with an interval of 10%; the emergency vehicle entry time ranges from 80s to 170s with an interval of 10s. 200 (4*5*10) groups of simulation experiments are performed for each of the two schemes.

[0143] The method of executing this embodiment obtains the vehicle states of the current step lengths of the optimized vehicle and the non-optimized vehicle from SUMO, and then uses the corresponding following and lane changing models to calculate the vehicle state of the non-optimized vehicle after an optimization step length based on the vehicle state of the current step length of the non-optimized vehicle. The vehicle state of the non-optimized vehicle after an optimization step length and the vehicle state of the current step length of the optimized vehicle are used as inputs of the optimization model for subsequent solution and optimization of vehicle control.

[0144] Compared with the scheme without optimization model, the method proposed in this embodiment can reduce the average blocking time of emergency vehicles from 63.35 seconds to 2.50 seconds, as shown in Table 3; the average travel delay of emergency vehicles is reduced from 12.46 seconds to 0.87 seconds, as shown in Table 4; since the maximum speeds of CAV vehicles and emergency vehicles are 16m / s and 20m / s respectively, the average speed of emergency vehicles in scheme 1 is almost 16m / s, which means that the emergency vehicles are blocked by the CAV vehicles in front most of the time. The average speed of emergency vehicles in scheme 2 can almost reach 20m / s, which means that the emergency vehicles are basically not blocked by CAV vehicles and can maintain the maximum speed throughout the journey, as shown in Table 4. Figure 3 As shown in the figure, the proposed scheme in this paper can optimize the lane change and yielding of CAV vehicles for an average of 6 times for the emergency vehicle to have priority in the road section under the above simulation scenario, and the effectiveness of the model application has been verified, as shown in the figure. Figure 4 As shown in Table 5, the average speed of traffic except emergency vehicles did not change much, which means that the implementation of the scheme did not significantly reduce the traffic efficiency of surrounding vehicles and had little impact on surrounding vehicles, as shown in Table 5.

[0145] Table 3 Average blocking time of emergency vehicles (s)

[0146]

[0147] Table 4 Average travel delay of emergency vehicles (s)

[0148]

[0149] Table 5 Average speed of traffic except emergency vehicles (m·s -1 )

[0150]

[0151]

[0152] In summary, the present invention takes into account the priority problem of emergency vehicles on a road section, and proposes a method for controlling the priority passage of emergency vehicles on a road section that takes into account a networked automatic driving lane. The networked automatic driving lane is reused as a dynamic emergency lane to achieve dynamic division of road rights in space; secondly, with the goal of minimizing delays for emergency vehicles and minimizing interference from surrounding vehicles, a multi-objective optimization model is established to calculate the speed change value and lane changing decision of the optimized vehicle by considering constraints such as vehicle dynamics, following and lane changing safety distances; finally, control is performed according to the optimization results, and the speed change value of the optimized vehicle is decomposed into each control step by linear interpolation for control, and the lane changing decision of the optimized vehicle is controlled at the last control step within the optimization step. While ensuring the priority passage of emergency vehicles, the impact on social vehicles is reduced, which provides a new optimization idea for the study of the control method for the priority passage of emergency vehicles on a road section in mixed driving scenarios that will appear for a long time in the future.

[0153] The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A method for controlling the priority passage of emergency vehicles on a road section considering a networked autonomous driving dedicated lane, characterized in that: The following steps are involved: Collect the vehicle status information of the emergency vehicle and all vehicles on the target road section, and determine whether the emergency vehicle has entered the target road section based on the vehicle status information of the emergency vehicle. If so, guide the emergency vehicle to enter the networked automatic driving lane of the target road section, and calculate the dynamic clearing area in front of the emergency vehicle in real time; A multi-objective optimization model is established with the goal of minimizing the delay of emergency vehicles and minimizing the interference to surrounding vehicles. The connected autonomous driving vehicles in the dynamic clearing area are used as optimized vehicles. The multi-objective optimization model is used to calculate the speed change value and lane change decision of each optimized vehicle. The speed change value and lane change decision control are used to optimize the vehicle, thereby clearing the connected autonomous driving vehicles in front of the emergency vehicle that are affecting traffic.

2. The method for controlling the priority passage of emergency vehicles on a road section considering a networked automatic driving dedicated lane according to claim 1, characterized in that: Real-time calculation of the dynamic clearance area in front of the emergency vehicle includes: Calculate the dynamic clearance distance in front of the emergency vehicle in real time. If the value of the dynamic clearance distance is less than or equal to the preset specified value, set the value of the dynamic clearance distance to the specified value. The dynamic clearance distance expression is as follows: Among them, D c is the dynamic clearance distance; e represents the emergency vehicle; v e (t) is the current speed of the emergency vehicle; T e The time to ensure that the emergency vehicle continues to travel at the current speed; dec e is the maximum deceleration of the emergency vehicle; Gap min is the minimum parking distance; The rectangular area formed by the dynamic clearance distance in front of the emergency vehicle on the connected autonomous driving lane and its adjacent lanes is used as the dynamic clearance area.

3. The method for controlling the priority passage of emergency vehicles on a road section considering a networked automatic driving dedicated lane according to claim 1, characterized in that: The objective function expression of the multi-objective optimization model is as follows: min O=w1*O1+w2*O2+w3*(O3+O4) Among them, w1, w2, and w3 are the weights of each part in the objective function; O1 is the distance between the emergency vehicle and the road section exit, expressed as follows: Wherein, L is the length of the road section; t is the current time; T0 is the optimization step length, which is set to 1s in this embodiment; x e (t) is the position of the emergency vehicle at time t; v e (t) is the speed of the emergency vehicle at time t; The optimized speed change value of the emergency vehicle at time t; O2 is the number of optimized vehicles among all optimized vehicles that are still in the connected autonomous driving lane and have not left the target section after this optimization. The expression is as follows: Where ω represents the optimized vehicle; k is the lane index; Indicates whether the optimized vehicle is still in lane k after optimization at time t. If it is in lane k, it is 1, otherwise it is 0; ρ ω (t+T0) is a 0-1 variable; O3 is the inverse of the sum of the negative vehicle speed changes of all optimized vehicles after this optimization, and the expression is as follows: in, To optimize the speed change value of the vehicle in this optimization; c ω (t+T0) is an integer variable; O4 is the opposite of the sum of the positive vehicle speed change values ​​of all optimized vehicles after this optimization, and the expression is as follows: in, is the speed change value of the emergency vehicle in this optimization; c ω (t+T0) is an integer variable.

4. The method for controlling the priority passage of emergency vehicles on a road section considering a networked automatic driving dedicated lane according to claim 1, characterized in that: The constraints of the multi-objective optimization model include vehicle dynamics constraints, which are expressed as follows: 0≤v ω (t+T0)≤vmax ω dec ω ≤a ω (t+T0)≤acc ω Wherein, ω represents the optimized vehicle; t is the current time; T0 is the optimization step length, which is set to 1s in this embodiment; x ω (t+T0) is the optimized position of the vehicle at time t; x ω (t) is the optimized position of the vehicle at time t; v ω (t) is the optimized vehicle speed at time t; To optimize the speed change value of the vehicle at time t; v ω (t+T0) is the optimized speed of the optimized vehicle at time t; a ω (t+T0) is the optimized acceleration of the optimized vehicle at time t; vmax ω To optimize the maximum speed of the vehicle; dec ω To optimize the maximum deceleration of the vehicle; acc ω To optimize the vehicle's maximum acceleration.

5. The method for controlling the priority passage of emergency vehicles on a road section considering a networked automatic driving dedicated lane according to claim 1, characterized in that: The constraints of the multi-objective optimization model include lane constraints, which are expressed as follows: Where ω represents the optimized vehicle; k is the lane index; Indicates whether the optimized vehicle is still in lane k after optimization at time t. If it is in lane k, it is 1, otherwise it is 0.

6. The method for controlling the priority passage of emergency vehicles on a road section considering a networked automatic driving dedicated lane according to claim 1, characterized in that: The constraints of the multi-objective optimization model include the following safety distance constraint, which is expressed as follows: d1 ω (t+T0)≥max{ds1 ω (t+T0),0} d2 ω (t+T0)≥max{ds2 ω (t+T0),0} Among them, ω represents the optimized vehicle; ds1 ω (t+T0) is the safe distance between the optimized vehicle and the vehicle ahead in the lane after optimization at time t; TTC min As an alternative safety indicator, the collision time threshold limit; v ω (t+T0) is the optimized speed of the optimized vehicle at time t; To optimize the vehicle's lane, the speed of the vehicle ahead at time t after optimization; d1 ω (t+T0) is the actual distance between the optimized vehicle and the vehicle ahead in the lane after optimization at time t; To optimize the position of the vehicle in front of the vehicle in the lane at time t; x ω (t+T0) is the optimized position of the vehicle at time t; To optimize the length of the vehicle in front of the vehicle in the lane; ds2 ω (t+T0) is the safe distance between the optimized vehicle and the vehicle behind it in the lane after optimization at time t; To optimize the vehicle's lane, the vehicle behind it is optimized at time t; d2 ω (t+T0) is the actual distance between the optimized vehicle and the vehicle behind it in the lane after optimization at time t; To optimize the position of the vehicle behind the vehicle in the lane at time t; l ω To optimize the vehicle's length.

7. The method for controlling the priority passage of emergency vehicles on a road section considering a networked automatic driving dedicated lane according to claim 1, characterized in that: The constraints of the multi-objective optimization model include the lane change safety distance constraint, which is expressed as follows: d3 ω (t+T0)≥max{ds3 ω (t+T0),0} d4 ω (t+T0)≥max{ds4 ω (t+T0),0} Among them, ω represents the optimized vehicle; ds3 ω (t+T0) is the safe distance between the optimized vehicle and the vehicle in front of the target lane at time t; TTC min As an alternative safety indicator, the collision time threshold limit; v ω (t+T0) is the optimized speed of the optimized vehicle at time t; The optimized speed of the vehicle in the target lane at time t; d3 ω (t+T0) is the actual distance between the optimized vehicle and the vehicle in front of the target lane after optimization at time t; The optimized position of the vehicle in front of the target lane at time t; x ω (t+T0) is the optimized position of the vehicle at time t; To optimize the vehicle's target lane front vehicle length; ds4 ω (t+T0) is the safe distance between the optimized vehicle and the vehicle behind it in the target lane at time t; The optimized speed of the vehicle behind the target lane at time t; d4 ω (t+T0) is the actual distance between the optimized vehicle and the vehicle behind it in the target lane after optimization at time t; To optimize the position of the vehicle behind the vehicle in the lane at time t; l ω To optimize the vehicle's length.

8. The method for controlling the priority passage of emergency vehicles on a road section considering a networked automatic driving dedicated lane according to claim 1, characterized in that: The constraints of the multi-objective optimization model include auxiliary variable constraints, which are expressed as follows: Among them, ω represents the optimized vehicle; v ω (t+T0) is the optimized speed of the optimized vehicle at time t; x ω (t+T0) is the optimized position of the optimized vehicle at time t; auxiliary variable c ω (t+T0) is an integer variable. -1 indicates that the optimized vehicle accelerates after this optimization; 0 indicates that the optimized vehicle does not change speed after this optimization; 1 indicates that the optimized vehicle decelerates after this optimization; auxiliary variable ρ ω (t+T0) is a binary variable, where 1 indicates that the optimized vehicle has not left the target section after this optimization; and 0 indicates that the optimized vehicle has left the target section.

9. The method for controlling the priority passage of emergency vehicles on a road section considering a networked automatic driving dedicated lane according to claim 1, characterized in that: Before using the multi-objective optimization model to calculate the speed change value and lane-changing decision of each optimized vehicle, including: configuring corresponding following models and lane-changing models for all vehicles on the target road section; when using the multi-objective optimization model to calculate the speed change value and lane-changing decision of each optimized vehicle, including: The manually driven vehicles on the target road section and the connected autonomous driving vehicles outside the dynamic clearing area are regarded as non-optimized vehicles. The vehicle state information of the non-optimized vehicles at the current time step is input into the corresponding following model and lane changing model respectively, and the vehicle state information of the non-optimized vehicles after one optimization step is calculated. The vehicle state information of the optimized vehicle at the current time step and the vehicle state information of the non-optimized vehicle after one optimization step are input into the multi-objective optimization model and solved to obtain the speed change value and lane changing decision of each optimized vehicle.

10. The method for controlling the priority passage of emergency vehicles on a road section considering a networked automatic driving dedicated lane according to claim 1, characterized in that: When each optimized vehicle is controlled respectively using the speed change value and the optimized lane change decision, it includes: If the optimization step of the multi-objective optimization model is greater than the control step, the speed change value is decomposed into each control step through linear interpolation to control the speed change of the corresponding optimized vehicle, and the lane changing decision is made at the last control step of the corresponding optimization step to control the position change of the corresponding optimized vehicle.

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