A low-carbon green speed guidance method for intelligent connected vehicles

Abstract

The present invention relates to a low-carbon green speed guidance method for intelligent networked vehicles. In a mixed traffic flow scenario of intelligent networked vehicles and ordinary vehicles, the vehicle-road collaborative application data is collected and processed to obtain the current speed of the vehicle, the distance from the stop line, the entrance road and lane where the vehicle is located, the phase timing information, the current light color and the remaining time and other parameters. Under the premise of not adjusting the traffic lights, the speed of the intelligent networked vehicle is calculated, and the speed guidance information is released to the intelligent networked vehicle, so that the vehicle does not stop at the intersection, stops less, changes speed gradually, has low fuel consumption and low emissions, and controls the intelligent networked vehicle to adjust the operation status of the overall traffic flow. Compared with the prior art, the present invention is aimed at global guidance of intersections, considers multiple objectives, reduces emissions and optimizes traffic efficiency; and the present invention can solve the speed guidance in a mixed flow environment, and the algorithm calculation speed is fast; at the same time, the present invention is highly generalizable, the algorithm requires fewer parameters, and no parameter correction is required, and it can be generalized and applied to other signal-controlled intersections.

Description

Technical Field

[0001] The present invention relates to the field of intelligent network-connected vehicle control and low-carbon green driving, and in particular to a low-carbon green vehicle speed guidance method for an intelligent network-connected vehicle. Background Art

[0002] Improving the operating efficiency of fuel vehicles in road traffic and reducing emissions through vehicle intelligent networking technology will be an effective measure to improve environmental quality. With the support of communication technology and data acquisition technology, intelligent networked vehicles can publish their own trajectory information to edge computing devices through real-time communication between roadside communication modules and on-board communication modules. Ordinary vehicle trajectory information can be obtained through detectors, realizing holographic perception of road traffic information.

[0003] In the above context, a vehicle speed guidance method that can reduce the frequent idling and acceleration and deceleration changes of the vehicle has great exploration value. However, the existing speed guidance method still has the following defects:

[0004] 1) Only consider efficiency and safety while ignoring environmental benefits;

[0005] 2) A pure intelligent connected environment is required, which does not conform to the speed guidance in a mixed flow environment of intelligent connected vehicles and ordinary vehicles in reality;

[0006] 3) The speed guidance calculation is performed independently for each vehicle, without considering the relationship between the front and rear vehicles, and the calculation time is long;

[0007] 4) Only the speed guidance of a single vehicle is considered while ignoring the mutual influence between vehicles. Summary of the invention

[0008] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and provide a low-carbon and green vehicle speed guidance method for an intelligent networked vehicle.

[0009] The purpose of the present invention can be achieved by the following technical solutions:

[0010] A low-carbon green speed guidance method for an intelligent network-connected vehicle comprises the following steps:

[0011] Collect raw data of vehicle-road collaboration applications;

[0012] Processing the raw data to obtain vehicle operation related information;

[0013] Based on the vehicle operation related information, using a vehicle speed guidance algorithm to calculate vehicle speed guidance information;

[0014] The vehicle speed guidance information is released to the intelligent networked vehicle, and the intelligent networked vehicle drives according to the vehicle speed guidance information.

[0015] Furthermore, the original data of the vehicle-road cooperative application includes SPAT (Signal phase timing message), BSM (Basic Safety Message), and MAP (map message) data.

[0016] Furthermore, the vehicle operation related information includes signal timing, current light color, remaining light color duration, vehicle speed, vehicle position and lane information.

[0017] Furthermore, the vehicle speed guidance algorithm comprises the following steps:

[0018] Calculating the guidance decision behavior of the single vehicle based on the vehicle operation related information;

[0019] Calculating a vehicle ranking of multiple vehicles based on the guidance decision behavior of the single vehicle;

[0020] Based on the vehicle ranking, calculating the expected target speed of each vehicle;

[0021] Based on the desired target speed, calculating the guidance speed per second of each vehicle;

[0022] The vehicle speed guidance information from the current position to the stop line, the vehicle ID, the speed at the future time t and the time t are published to the intelligent connected vehicles.

[0023] Furthermore, the calculation of the bicycle guidance decision specifically includes:

[0024] According to the vehicle's current position and lane information, determine the entry lane and phase to which the vehicle belongs;

[0025] According to the current vehicle speed of the vehicle, determine whether the vehicle is a queue vehicle;

[0026] Provide speed guidance for non-queuing vehicles in each phase on each entrance lane;

[0027] A single intelligent connected vehicle determines the guidance decision behavior of the vehicle at the stop line of the intersection at the current moment based on the scenario, including accelerating through the intersection, passing through the intersection at a constant speed, decelerating through the intersection, and decelerating to stop.

[0028] Furthermore, the calculating of the vehicle order of the multiple vehicles specifically includes: based on the guidance decision behavior of the single vehicle, sequentially adding the corresponding vehicle to the acceleration vehicle set, the uniform speed vehicle set, and the deceleration vehicle set of the corresponding entrance lane and phase;

[0029] According to the position of the vehicle from the stop line, the vehicles in each set are sorted and numbered to obtain a sorted accelerating vehicle set, a sorted uniform speed vehicle set, a decelerating passing vehicle set, an accelerating passing vehicle set and a decelerating stopping vehicle set.

[0030] Further, the calculation of the expected target speed of each vehicle specifically includes:

[0031] The expected target speed of the vehicles in the deceleration and stopping vehicle set at the stop line is set to 0;

[0032] For vehicles in the decelerating vehicle set and the accelerating vehicle set, the vehicle serial number and traffic light information are obtained, the expected arrival time of the vehicle is calculated, and then the expected target speed of the vehicle is calculated based on the distance of the vehicle from the stop line and the expected arrival time.

[0033] Further, the calculation of the guidance speed per second of each vehicle specifically includes:

[0034] For a vehicle with a desired target speed of 0, the guidance speed per second from the vehicle to the stop line is calculated according to the vehicle's current speed, desired target speed, and distance to the stop line, in accordance with uniform deceleration.

[0035] For vehicles whose expected target speed is not 0, the guidance speed per second of the vehicle to the stop line is calculated based on trigonometric functions.

[0036] Furthermore, the expression for calculating the guiding speed per second of the vehicle to the stop line according to the trigonometric function is:

[0037] v d =v h -v c

[0038]

[0039] Where m, n, and jerk are the parameters of the trigonometric model, and v h is the expected target speed of the vehicle to reach the stop line, v c is the instantaneous speed of the vehicle at the current moment.

[0040] Furthermore, the vehicle speed guidance information from the current position to the stop line is sent to the vehicle in a second-by-second or interval sending manner.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] 1. The present invention collects the trajectory information of the intelligent networked vehicle, processes the original data to obtain the signal timing, current light color, remaining light color duration, vehicle speed, vehicle position, lane information, etc., and then calculates the speed guidance information according to the low-carbon green speed guidance algorithm, and then releases the speed guidance information to the intelligent networked vehicle. Under the premise of ensuring safety and efficiency, by reasonably guiding the speed of the intelligent networked vehicle, the vehicle can achieve no stopping, less stopping, gradual speed change, low fuel consumption and low emissions at intersections, taking into account efficiency, safety and environmental protection benefits.

[0043] 2. The present invention is highly practical and can be used to control mixed flows of manually driven vehicles and intelligent networked vehicles, breaking the limitation of the prior art that a pure intelligent networked environment is required.

[0044] 3. In view of the problems that the vehicle speed guidance algorithm is complex and takes a long time to calculate, the application value and practicality need to be improved. The vehicle speed guidance algorithm proposed in the present invention is based on rules, has fast calculation speed and strong practicality.

[0045] 4. In view of the problem of only considering the speed guidance of a single vehicle while ignoring the mutual influence between vehicles, the guidance effect has limitations and cannot make the intersection globally optimal. The method of the present invention controls the intelligent connected vehicles and then adjusts the operating conditions of the overall traffic flow to improve the overall efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 This is a flow chart of a low-carbon green vehicle speed guidance method for an intelligent network-connected vehicle according to an embodiment of the present invention;

[0047] Figure 2 This is a schematic diagram of a guiding decision according to an embodiment of the present invention;

[0048] Figure 3 This is a flow chart of the guide vehicle speed calculation for multiple vehicles according to an embodiment of the present invention;

[0049] Figure 4 The expected arrival time calculation for multiple vehicles according to the embodiment of the present invention;

[0050] Figure 5 It is a schematic diagram of a guiding speed curve according to an embodiment of the present invention. DETAILED DESCRIPTION

[0051] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0052] At present, the existing domestic driving guidance methods for intelligent connected vehicles have the problem that the guidance goals only consider improving efficiency and safety while ignoring environmental benefits, that is, they ignore the optimization of fuel consumption or emissions, which is not conducive to promoting the green development of transportation; secondly, the existing domestic low-carbon green driving guidance methods for intelligent connected vehicles require a pure intelligent connected vehicle environment, which is not in line with the current actual traffic environment.

[0053] In view of the problems existing in the prior art, the present invention proposes a set of rule-based multi-vehicle speed guidance methods for urban roads. The method proposed by the present invention can achieve speed guidance in a mixed flow environment of intelligent networked vehicles and ordinary vehicles that is more in line with the actual situation. Without adjusting traffic lights and ensuring safety and efficiency, by reasonably guiding the speed of intelligent networked vehicles and adjusting the operation status of the overall traffic flow, vehicles can achieve no stopping, less stopping, gradual speed change, low fuel consumption and low emissions at intersections.

[0054] Moreover, this method has a fast calculation speed and can maximize environmental benefits while ensuring safety and efficiency. Its applicable conditions are closer to the current situation of the transportation industry and will help promote the intelligent and green development of transportation.

[0055] like Figures 1 to 5 As shown, the method proposed by the present invention specifically comprises the following steps:

[0056] 1) Collect vehicle-road cooperative application data information. The original data includes SPAT (Signal phase timing message), BSM (Basic Safety Message), and MAP (map message) data. The data structure is input in accordance with T / CSAE 53-2017 "Cooperative Intelligent Transportation System Vehicle Communication System Application Layer and Application Data Interaction Standard". The original data is processed to obtain signal timing, current light color, remaining light color duration, vehicle speed, vehicle position, lane information, etc., and then the speed guidance information is calculated according to the low-carbon green speed guidance algorithm, and then the speed guidance information is released to the intelligent connected vehicle. The speed guidance algorithm implementation steps are as follows 2) to 6).

[0057] Parameter explanation: As shown in Table 1, the parameters involved in this embodiment are explained.

[0058] Table 1 Parameters of vehicle speed guidance calculation

[0059]

[0060]

[0061] 2) Guidance decision for a single vehicle: Determine the entrance lane and phase to which the vehicle belongs based on the vehicle's current position and lane, and determine whether the vehicle is a queued vehicle based on the vehicle's current speed. If the vehicle speed is 0, the vehicle is considered to be in a queue. After the green light comes on, these vehicles will accelerate through the intersection at the saturated headway without guidance. If the vehicle speed is greater than 0, the vehicle is considered not to be in a queue, and speed guidance is performed on non-queued vehicles in each phase of each entrance lane. A single intelligent connected vehicle determines the guidance decision behavior of the vehicle at the current moment before reaching the stop line of the intersection based on the scenario. The arriving vehicles are divided into decelerating through, accelerating through, and decelerating and stopping according to their driving characteristics. The current moment is recorded as 0. The specific steps are as follows:

[0062] (2.1) If the time t at which the vehicle reaches the stop line at a constant speed arrive <Remaining time of current phase light color t currentColor_End , then the light color when it reaches the stop line at a constant speed is l arriveColor Still the current light color currentColor The cycle to which the vehicle belongs is the current cycle, and the cycle mark is denoted by C current =1;

[0063] (2.2) If the time t at which the vehicle reaches the stop line at a constant speed arrive > Current phase light color remaining time t currentColor_End The light color changes when the vehicle reaches the stop line. According to the vehicle's current light color, remaining light color time, and signal timing, the vehicle is predicted to reach the light color l arriveColor , then mark the traffic light cycle C to which the vehicle belongs current , the specific steps are as follows:

[0064] (2.2.1) If the current light color is l currentColor It is green, and reaches the light color l at a constant speed arriveColor and cycle marker C current There are several situations:

[0065] (2.2.1.1) If t currentColor_End ≤t arrive <t currentColor_End +t Yellow , then l arriveColor It is yellow, the cycle to which the vehicle belongs is the current cycle, and the cycle mark is C current =1;

[0066] (2.2.1.2) If t currentColor_End +t Yellow ≤t arrive <t currentColor_End +t Yellow +t Red , then l arriveColoris red, the cycle to which the vehicle belongs is the current cycle, and the cycle mark is C current =1;

[0067] (2.2.1.3) If t currentColor_End +t Yellow +t Red ≤t arrive <t currentColor_End +t Yellow +t Red +t Green , then l arriveColor The next cycle is green, the cycle to which the vehicle belongs is the next cycle, and the cycle mark is C current =2;

[0068] (2.2.1.4) If

[0069] t currentColor_End +t Yellow +t Red +t Green ≤t arrive <t currentColor_End +t Yellow +t Red +t Green +t Yellow , then l arriveColor The next cycle is yellow, the cycle to which the vehicle belongs is the next cycle, and the cycle mark is C current =2;

[0070] (2.2.1.5) If t currentColor_End +t Yellow +t Red +t Green +t Yellow ≤t arrive <t currentColor_End +t Yellow +t Red +t Green +t Red , then l arriveColor The next cycle is red, the cycle to which the vehicle belongs is the next cycle, and the cycle mark is C current =2.

[0071] (2.2.2) If l currentColor It is yellow, the color of the arrival light arriveColor and cycle marker C current The following situations are discussed:

[0072] (2.2.2.1) If t currentColor_End ≤t arrive <t currentColor_End +t Red , then l arriveColoris red, the cycle to which the vehicle belongs is the current cycle, and the cycle mark is C current =1;

[0073] (2.2.2.2) If t currentColor_End +t Red ≤t arrive <t currentColor_End +t Red +t Green , then l arriveColor If it is green, the cycle to which the vehicle belongs is the current cycle, and the cycle mark is C. current =1;

[0074] (2.2.2.3) If

[0075] t currentColor_End +t Red +t Green ≤t arrive <t currentColor_End +t Red +t Green +t Yellow , then l arriveColor The next cycle is yellow, the cycle to which the vehicle belongs is the next cycle, and the cycle mark is C current =2;

[0076] (2.2.2.4) If t currentColor_End +t Red +t Green +t Yellow ≤t arrive <t currentColor_End +t Red +t Green +t Yellow +t Red , then l arriveColor The next cycle is red, the cycle to which the vehicle belongs is the next cycle, and the cycle mark is C current =2;

[0077] (2.2.2.5) If t currentColor_End +t Red +t Green +t Yellow +t Red ≤t arrive <t currentColor_End +t Red +t Green +t Yellow +t Red +t Green , then l arriveColor The next cycle is green, the cycle to which the vehicle belongs is the next cycle, and the cycle mark is C current =2.

[0078] (2.2.3) If l currentColor It is red, the arrival light color is l arriveColor and cycle marker C current The following situations are discussed:

[0079] (2.2.3.1) If t currentColor_Ed ≤t arrive <t currentColor_End +t Grenn , then l arriveColor If it is green, the cycle to which the vehicle belongs is the current cycle, and the cycle mark is C. current =1;

[0080] (2.2.3.2) If t currentColor_End +t Grenn ≤t arrive <t currentColor_End +t Green +t Yellow , then l arriveColor It is yellow, the cycle to which the vehicle belongs is the current cycle, and the cycle mark is C current =1;

[0081] (2.2.3.3) If

[0082] t currentColor_End +t Green +t Yellow ≤t arrive <t currentColor_End +t Green +t Yellow +t Red , then l arriveColor The next cycle is red, the cycle to which the vehicle belongs is the next cycle, and the cycle mark is C current =2;

[0083] (2.2.3.4) If t currentColor_End +t Green +t Yellow +t Red ≤t arrive <t currentColor_End +t Green +t Yellow +t Red +t Grenn , then l arriveColor The next cycle is green, the cycle to which the vehicle belongs is the next cycle, and the cycle mark is C current =2;

[0084] (2.2.3.5) If t currentColor_End +t Green +t Yellow +t Red +tGreen ≤t arrive <t currentColor_End +t Green +t Yellow +t Red +t Green +t Yellow , then l arr□□eColor The next cycle is yellow, the cycle to which the vehicle belongs is the next cycle, and the cycle mark is C current =2.

[0085] (2.3) Calculate the critical acceleration time t of the vehicle acc and critical deceleration time t dec :

[0086] (2.3.1) Calculate the critical deceleration time of the vehicle: the speed of the vehicle to the stop line is 0, then the critical deceleration dec max =v c 2 / (2*L stop ), critical deceleration time t dec =v c / dec max ;

[0087] (2.3.2) Calculate the critical acceleration time of the vehicle; if

[0088]

[0089] The vehicle first accelerates to the maximum speed, then drives at a constant speed to the stop line. The acceleration time Otherwise, the vehicle follows the maximum acceleration acc max Accelerate uniformly to the stop line

[0090] (2.4) According to the current light color l currentColor , Arrival light color arriveColor 、Cycle C current , critical deceleration time t dec , critical acceleration time t acc , the vehicle selection, acceleration guidance, and deceleration guidance are as follows:

[0091] (2.4.1) If the current light color is l currentColor It is green, arrival light color arriveColor is red, and C current =1, the guidance rules are discussed in the following cases:

[0092] (2.4.1.1) If you can arrive during the next green light period according to the deceleration guidance, that is, t currentColor_End +t Yellow +tRed ≤t dec <t currentColor_End +t Yellow +t Red +t Green , then execute deceleration guidance; otherwise execute (2.4.1.2);

[0093] (2.4.1.2) If the acceleration guidance allows the vehicle to arrive during the green light period, that is, t acc ≤t currentColor_End , then execute accelerated guidance; otherwise execute (2.4.1.3);

[0094] (2.4.1.3) Execute parking guidance by decelerating to the stop line.

[0095] (2.4.2) If the current light color is l currentColor It is green, arrival light color arriveColor If it is green and yellow, it will not guide.

[0096] (2.4.3) If the current light color is l currentColor It is red, the color of the arrival light arriveColor is red, and C current =1, the guidance rules are discussed in the following cases:

[0097] (2.4.3.1) If t currentColor_End ≤t dec <t currentColor_End +t Green , execute deceleration guidance; otherwise execute (2.4.3.2);

[0098] (2.4.3.2) Execute parking guidance;

[0099] (2.4.4) If the current light color is l currentColor It is red and reaches l arriveColor is red, and C current =2, the guidance rules are discussed in the following cases:

[0100] (2.4.3.1) If

[0101] t currentColor_End +t Green +t Yellow ≤t dec <t currentColor_End +t Green +t Yellow +t Red +t Green , then no guidance;

[0102] (2.4.3.2) If t acc <t currentColor_End +tGreen , perform accelerated guidance;

[0103] (2.4.3.3) In other cases, execute parking guidance.

[0104] (2.4.5) If the current light color is l currentColor It is yellow, arriveColor is red or green, and C current =1, execute deceleration guidance;

[0105] (2.4.6) If the current light color is l currentColor It is yellow, arriveColor is red, and C current =2, the guidance rules are discussed in the following cases:

[0106] (2.4.6.1) If t currentColor_End +t Red ≤t dec <t currentColor_End +t Red +t Green , then no guidance;

[0107] (2.4.6.2) If t acc ≤t currentColor_End +t Green , then no guidance;

[0108] (2.4.6.3) In other cases, parking guidance shall be implemented;

[0109] (2.4.7) In other cases, no guidance will be given.

[0110] 3) Sorting of multiple vehicles: According to the derived guidance decision behavior of the vehicle in step (2), the vehicle is added to the corresponding acceleration guidance vehicle set, uniform speed guidance vehicle set, and deceleration guidance vehicle set of the entrance lane and phase. Then, the vehicles in each set are sorted and numbered according to the position of the vehicle from the stop line to obtain the sorted uniform speed passing vehicle set, deceleration passing vehicle set, acceleration passing vehicle set, and deceleration stop vehicle set.

[0111] 4) Next is the calculation of the expected target speed of the vehicle reaching the stop line:

[0112] (4.1) The expected target speed of the vehicle slowing down to stop at the stop line is set to 0;

[0113] (4.2) For vehicles that accelerate or decelerate, first obtain the vehicle serial number and traffic light information according to step (3) and calculate the expected arrival time of the vehicle. The expected arrival time calculation is divided into deceleration guidance and acceleration guidance:

[0114] (4.2.1) Calculation of expected arrival time for deceleration guidance:

[0115] (4.2.1.1) Current light color currentColor The light is green, and the vehicle arrives during the red light. The expected arrival time is calculated as follows:

[0116] t n =t currentColor_End +t Yellow +t Red +t clear +nh0

[0117] (4.2.1.2) Current light color currentColor The light is yellow, and the vehicle arrives during the red light, i.e. arriveColor For a red light, the expected arrival time is calculated as follows:

[0118] t n =t currentColor_End +t Red +t clear +nh0

[0119] (4.2.1.3) Current light color l currentColor The traffic light is red, and the vehicle arrives during the green or yellow traffic light, i.e. arriveColor If the light is green or yellow, the expected arrival time is calculated as follows:

[0120] t n =t currentColor_End +t clear +nh0

[0121] (4.2.2) Acceleration guidance corresponds to two scenarios:

[0122] (4.2.2.1) Current light color currentColor The light is green, and the vehicle arrives during the red light, i.e. arriveColor For a red light, the expected arrival time is calculated as follows:

[0123] t n =t clear +nh0

[0124] (4.2.2.2) Current light color currentColor If the light is red or yellow, the vehicle arrives during the next red light period, i.e. arriveColor The light is red and Cc urrent =2, the expected arrival time is calculated as follows:

[0125] t n =t currentColor_End +t clear +t green +t Yellow +tRed +nh0

[0126] (4.3)Then calculate the expected target speed of the vehicle reaching the stop line.

[0127] (4.3.1) The expected target speed of a vehicle passing through the stop line is calculated based on the distance between the vehicle and the stop line and the expected arrival time. Assuming that the vehicle reaches the stop line at a constant speed at the expected arrival time according to the expected target speed, the expected target speed of the vehicle is as follows:

[0128]

[0129] (4.3.2) Expected target speed v for uniform deceleration parking guidance h =0.

[0130] (4.3.3) The expected target speed v of the vehicle passing at a constant speed h =v c

[0131] 5) If Figure 5 As shown, the last part is the calculation of the guidance speed per second when the vehicle reaches the stop line: for a vehicle that decelerates to stop with an expected speed of 0, the speed of the vehicle to the stop line is calculated according to the current speed of the vehicle, the expected target speed and the position from the stop line according to uniform deceleration; for a vehicle with an expected target speed not equal to 0, the speed of the vehicle to the stop line is calculated according to trigonometric functions.

[0132] (5.1) The second-by-second speed calculation of the acceleration and deceleration guidance is based on the vehicle speed calculation model based on trigonometric functions:

[0133] v d =v h -v c

[0134]

[0135] m, n, jerk are the parameters of the trigonometric model, jerk max =10, where energy consumption is minimum when m is maximum. Solve for m and n to calculate the second-by-second speed result under acceleration or deceleration guidance decision. Minimize energy consumption as the optimization goal and meet the following constraints: ① Maintain driving smoothness and reduce speed fluctuations; ② Because the expected target speed is the speed at which the vehicle arrives at the stop line at the expected arrival time at the expected target speed, there is an acceleration / acceleration process when the vehicle is accelerated / decelerated. The overall distance to the stop line must be the same as that of the uniform speed. The optimization problem is as follows. Solve the following optimization problem to calculate m and n.

[0136] max m

[0137] st

[0138]

[0139] v d mn=jerk max

[0140] m>0,n>0

[0141] (5.2) Deceleration and parking guidance: Considering the driving smoothness, a uniform deceleration guidance strategy is adopted until the car stops. First, calculate a n_adjust , the second-by-second speed v is calculated by the following formula second :

[0142]

[0143] v second =v h -a n_adjust ·t

[0144] (5.2) Uniform speed guidance: The second-by-second speed of the uniformly guided vehicle to the stop line is equal to the current speed:

[0145] v second =v c

[0146] 6) The vehicle speed guidance information, vehicle ID, speed at future time t, and time t are published to the intelligent networked vehicles. The speed guidance information can be published every second from the current time to the stop line. It is also possible to send the speed guidance information to the intelligent networked vehicles every 5 seconds or more seconds, which can solve the problem of too frequent publishing that drivers cannot accept.

[0147] like Figure 1 As shown in FIG. 1 , the main modules of the low-carbon green speed guidance method for intelligent connected vehicles include data processing module, guidance decision module, and speed calculation module. The specific steps are as follows:

[0148] 1) Data processing: The original data includes SPAT, BSM, and MAP data. The data structure is input in accordance with T / CSAE 53-2017 "Cooperative Intelligent Transportation System Vehicle Communication System Application Layer and Application Data Interaction Standard". The signal timing, current light color, remaining light color duration, vehicle speed, vehicle position, lane information, etc. are obtained based on the input SPAT, BSM, and MAP original data.

[0149] 2) Guidance decision: First, determine whether the target vehicle is a queue vehicle. If it is a queue vehicle, no guidance will be given. If it is a non-queue vehicle, it will be regarded as a guidance target vehicle. Figure 2 Make judgments based on the rules shown in (a)-(d). Figure 2(a) is a scenario where the vehicle originally passes through the green light time. At this time, the vehicle can be guided to a constant speed or a deceleration speed according to the queue situation. Figure 2 (b) is a scenario where the vehicle originally passed through the intersection at a red light, but could have passed through the intersection if it had been decelerated. In this case, deceleration guidance would have been implemented. Figure 2 (c) The vehicle originally passed through the intersection at a red light. If deceleration guidance is implemented, the vehicle cannot pass through the intersection, but if acceleration guidance is implemented, the vehicle can pass through the intersection at a green light. In this case, acceleration guidance is implemented. Figure 2 (d) is a scenario where the vehicle originally passed through the intersection at a red light and could not pass through the intersection regardless of acceleration or deceleration guidance. In this case, parking guidance is performed.

[0150] 3) Sorting of multiple vehicles: According to the derived guidance decision behavior of the vehicle in step 2), the vehicle is added to the corresponding acceleration guidance vehicle set, uniform speed guidance vehicle set, and deceleration guidance vehicle set of the entrance lane and phase. Then, the vehicles in each set are sorted and numbered according to the position of the vehicle from the stop line to obtain the sorted uniform speed passing vehicle set, deceleration passing vehicle set, acceleration passing vehicle set, and deceleration stop vehicle set.

[0151] 4) Next, calculate the expected target speed of the vehicle to reach the stop line;

[0152] 5) Finally, the guidance speed per second when the vehicle reaches the stop line is calculated: for vehicles that decelerate to stop with an expected speed of 0, the second-by-second speed of the vehicle to the stop line is calculated according to the vehicle's current speed, expected target speed and distance from the stop line according to uniform deceleration; for vehicles whose expected target speed is not 0, the speed per second of the vehicle to the stop line is calculated based on trigonometric functions.

[0153] 6) The vehicle speed guidance information, vehicle ID, speed at future time t, and time t are published to the intelligent networked vehicles. The speed guidance information per second from the current time to the stop line can be published.

[0154] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A low-carbon green speed guidance method for intelligent network-connected vehicles, characterized in that: The following steps are involved: Collect raw data of vehicle-road collaboration applications; Processing the raw data to obtain vehicle operation related information; Based on the vehicle operation related information, using a vehicle speed guidance algorithm to calculate vehicle speed guidance information; The vehicle speed guidance information is released to the intelligent networked vehicle, and the intelligent networked vehicle drives according to the vehicle speed guidance information; The vehicle operation related information includes signal timing, current light color, remaining light color duration, vehicle speed, vehicle position and lane information; The vehicle speed guidance algorithm comprises the following steps: Calculating a guidance decision behavior of the single vehicle based on the vehicle operation related information; Calculating a vehicle ranking of multiple vehicles based on the guidance decision behavior of the single vehicle; Based on the vehicle ranking, calculating the expected target speed of each vehicle; Based on the desired target speed, calculating the guidance speed per second of each vehicle; Publish the vehicle speed guidance information from the current position to the stop line, the vehicle ID, and the speed at the future time t and time t to the intelligent connected vehicle; The calculating of the vehicle order of the multiple vehicles specifically includes: based on the guidance decision behavior of the single vehicle, sequentially adding the corresponding vehicle to the acceleration vehicle set, the uniform speed vehicle set, and the deceleration vehicle set of the corresponding entrance lane and phase; According to the distance between the vehicle and the stop line, the vehicles in each set are sorted and numbered to obtain a sorted accelerating vehicle set, a sorted uniform speed vehicle set, a decelerating passing vehicle set, an accelerating passing vehicle set, and a decelerating stopping vehicle set; The calculation of the expected target speed of each vehicle specifically includes: The expected target speed of the vehicles in the deceleration and stopping vehicle set at the stop line is set to 0; For vehicles in the decelerated passing vehicle set and the accelerated passing vehicle set, the vehicle serial number and signal light information are obtained, the expected arrival time of the vehicle is calculated, and then the expected target speed of the vehicle is calculated according to the distance of the vehicle from the stop line and the expected arrival time; The calculation of the guidance speed per second of each vehicle specifically includes: For a vehicle with a desired target speed of 0, the guidance speed per second from the vehicle to the stop line is calculated according to the vehicle's current speed, desired target speed, and distance to the stop line, in accordance with uniform deceleration. For vehicles whose expected target speed is not 0, the guidance speed per second of the vehicle to the stop line is calculated based on trigonometric functions.

2. A low-carbon green speed guidance method for intelligent networked vehicles according to claim 1, characterized in that: The original data of the vehicle-road cooperative application includes SPAT, BSM, and MAP data.

3. A low-carbon green speed guidance method for intelligent networked vehicles according to claim 1, characterized in that: The guiding decision of the computing bicycle specifically includes: According to the vehicle's current position and lane information, determine the entry lane and phase to which the vehicle belongs; According to the current vehicle speed of the vehicle, determine whether the vehicle is a queue vehicle; Provide speed guidance to non-queuing vehicles in each phase on each entrance lane; A single intelligent connected vehicle determines the guidance decision behavior of the vehicle at the stop line of the intersection at the current moment based on the scenario, including accelerating through the intersection, passing through the intersection at a constant speed, decelerating through the intersection, and decelerating to stop.

4. A low-carbon green speed guidance method for intelligent networked vehicles according to claim 1, characterized in that: The expression for calculating the guiding speed per second of the vehicle to the stop line according to the trigonometric function is: Where m, n, and jerk are the parameters of the trigonometric model, and v h is the expected target speed of the vehicle to reach the stop line, v c is the instantaneous speed of the vehicle at the current moment, v d is the speed difference between the expected target speed and the current speed, v second The second-by-second speed of the vehicle at the stop line.

5. The low-carbon green speed guidance method for intelligent networked vehicles according to claim 1, characterized in that: Select the second-by-second or interval sending method to send the vehicle speed guidance information from the current position to the stop line to the vehicle.

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