Priority control method for automatic driving of bus in network connection environment

Abstract

The invention discloses a priority control method for an automatic driving bus in a network connection environment. The operation of the automatic driving bus is intelligently regulated and controlled in real time according to a bus priority passing demand coefficient F; if F is less than 0, intelligently regulating and controlling the running of the automatic driving bus in real time by adopting a reverse regulation stop control mode and / or a reverse speed guide mode; if F is more than or equal to 0 and less than 1, intelligently regulating and controlling the running of the automatic driving bus in real time by adopting a reverse regulation parking station control mode; when F is greater than or equal to 1 and less than 2 and the saturation is greater than a limit value, intelligently regulating and controlling the running of the automatic driving bus in real time by adopting a vehicle speed guiding mode; when F is larger than or equal to 2 and smaller than infinity, F is larger than or equal to 1 and smaller than a limit value, or F is larger than or equal to 2 and smaller than infinity, a vehicle speed guiding mode is matched with an active priority control mode to intelligently regulate and control running of the automatic driving bus in real time; and intelligently regulating and controlling the running of the automatic driving bus in real time by adopting a vehicle speed guiding mode and a parking station control mode matched with an active priority control mode. According to the invention, automatic driving bus operation and intersection signal timing are optimized and controlled.

Description

Technical Field

[0001] The present invention relates to the technical field of bus priority control, and in particular to a method for controlling bus priority in an autonomous driving environment. Background Art

[0002] Public transportation has the advantages of intensiveness and greenness, which can effectively alleviate the shortage of road traffic resources and become the preferred measure to solve the current traffic problems in large and medium-sized cities. The in-depth implementation of the public transportation priority development strategy has become an inevitable choice for urban development, which can effectively alleviate urban traffic pressure and improve the level of public transportation services.

[0003] However, in recent years, the proportion of urban ground public transportation has gradually decreased, and there are problems such as low punctuality, long waiting time, vehicle jams and large intervals. At the same time, studies have shown that delays caused by buses stopping at red lights at intersections account for more than 50% of the total delays in bus operations, resulting in a poor experience in bus travel and difficulty in meeting efficient and convenient travel needs. Therefore, how to provide buses with priority rights of way when road resources are limited is an issue that needs to be urgently addressed.

[0004] To address the above issues, bus priority strategies can usually be divided into time and space priorities. The specific technical level is mainly achieved through the construction of dedicated lanes and signal priority control. In particular, the priority control strategy allows buses to have priority at intersections, reducing delays and the number of stops at intersections. This can significantly improve the operating efficiency and reliability of buses, and is an important technical means to improve the level and attractiveness of bus services.

[0005] Bus signal priority control is generally divided into three strategies: passive priority, active priority and real-time priority.

[0006] Among them, passive priority is mostly optimized for offline solutions, without considering whether there are buses arriving at the intersection; active priority provides priority for vehicles through real-time detection data, such as extending green lights, turning off red lights early, and inserting phases.

[0007] Real-time priority provides real-time signal priority for public transportation vehicles based on real-time detection data, while optimizing the signal scheme based on a certain indicator.

[0008] In summary, we can see that the three strategic methods are all optimization adjustments to the intersection signal control plan. Buses can only adapt passively. The two lack effective interaction and connection. The benefits of implementing priority are ineffective or discounted. At the same time, simply adjusting the signal timing plan will also affect the system efficiency of the intersection operation.

[0009] Therefore, how to accurately obtain various dynamic traffic data and bus operation status data at intersections, optimize and control bus operation and intersection signal timing, improve bus operation efficiency while reducing the impact on social traffic operation, and achieve better comprehensive benefits of bus priority and social traffic operation, so as to improve the operation efficiency of the transportation system has become a technical problem that technical personnel in this field urgently need to solve. Summary of the invention

[0010] In view of the above-mentioned defects of the prior art, the present invention provides an automatic driving bus priority control method in a networked environment, and its purpose is to: based on intelligent network connection and automatic driving technology as a new path to solve the above-mentioned problems, using the advantages of vehicle-road cooperative interaction and multi-source holographic perception in an intelligent networked environment, accurately obtain various dynamic traffic data and bus operation status data at intersections, optimize and control bus operation and intersection signal timing, improve bus operation efficiency, reduce the impact on social traffic operation, and achieve better comprehensive benefits of bus priority and social traffic operation, thereby improving the operation efficiency of the transportation system.

[0011] To achieve the above-mentioned purpose, the present invention discloses a method for controlling priority of an autonomous driving bus in a networked environment. The operation of the autonomous driving bus is intelligently regulated in real time according to the priority passage demand coefficient F of the bus, and the autonomous driving bus updates the priority passage demand coefficient F of the bus every time it reaches a station:

[0012] When F<0, the operation of the autonomous driving bus is intelligently regulated in real time by adopting a reverse adjustment station control method and / or a reverse vehicle speed guidance method;

[0013] When 0≤F<1, the reverse regulation station control method is adopted to intelligently control the operation of the autonomous driving bus in real time;

[0014] When 1≤F<2, and the saturation is above the limit at the signal-controlled intersection, the speed guidance method is used to intelligently control the operation of the autonomous driving bus in real time;

[0015] When 1≤F<2, and the saturation is lower than the limit value of the signal-controlled intersection, or when 2≤F<∞, the speed guidance method is combined with the active priority control method to intelligently control the operation of the autonomous driving bus in real time;

[0016] When F=∞, the speed guidance mode, the station control mode and the active priority control mode are used to intelligently regulate the operation of the autonomous driving bus in real time, and the station control mode adopts positive regulation;

[0017] The calculation formula of the public transport priority demand coefficient F is as follows:

[0018]

[0019] Among them, α and β are weight coefficients, and α+β=1;

[0020] f s is the bus punctuality factor; f p is the passenger flow coefficient; f d is the dynamic scheduling coefficient.

[0021] Preferably, the bus punctuality coefficient f s The calculation method is as follows:

[0022]

[0023] Δt=T i -T s

[0024] Among them, T i The time when the autonomous bus actually arrives at a certain station;

[0025] T s The time when the autonomous bus should arrive at a certain station (in the schedule);

[0026] Δt is the difference between the actual arrival time and the time when the autonomous bus should arrive at the station, with early arrival being positive and late arrival being negative;

[0027] Δt accept is an acceptable early arrival time of the autonomous bus;

[0028] Δt' accept is an acceptable late arrival time of the autonomous bus;

[0029] Δt min is the minimum time for the autonomous bus to arrive seriously early;

[0030] Δt' min is the minimum time during which the autonomous bus is seriously delayed.

[0031] Preferably, the passenger flow coefficient f p The calculation method is as follows:

[0032]

[0033] Among them, P i is the current passenger capacity of the autonomous bus on the way; P max is the maximum passenger capacity of the autonomous driving bus; p0 is the passenger capacity ratio parameter of the autonomous driving bus, 0≤p0<1.

[0034] More preferably, p0<0.5.

[0035] Preferably, the dynamic scheduling coefficient f d The calculation method is as follows:

[0036]

[0037] Preferably, the guidance speed v allowed by the autonomous driving bus in the reverse adjustment station control mode, the reverse speed guidance mode, the reverse adjustment station control mode, the speed guidance mode, the station control mode and / or the active priority control mode is d The following requirements must be met:

[0038] v min ≤v d ≤v max ;

[0039] Among them, v min is the minimum speed limit; v max To guide the maximum speed limit;

[0040]

[0041] is the maximum safe speed of the autonomous bus at the intersection; is the speed limit of the lane where the autonomous bus is located.

[0042] Preferably, the reverse adjustment station control mode and the time of the autonomous driving bus stopping at station i in the station control mode are The time required for passengers to get on and off at stop i The following requirements must be met:

[0043]

[0044] in,

[0045]

[0046] is the number of passengers on the autonomous bus at station i and passenger volume The maximum value in ;

[0047] t p is the time it takes for each passenger to board or alight;

[0048] Δt0 is the fixed delay time of the station stop.

[0049] Preferably, the average saturation of the intersection is used as the basis for judging the active priority control strategy, that is, the average saturation of the intersection

[0050]

[0051] Among them, x j is the j-hour saturation of the import lane; q j is the traffic volume of the import lane in j hours, in pcu / h.

[0052] Preferably, the autonomous driving bus is equipped with advanced on-board computing units, sensing devices, monitoring devices, networked communications and positioning devices, and integrates communication and network technologies. It has complex environment perception functions, decision-making control planning functions, information data interaction functions, intelligent scheduling functions and passenger flow statistics functions, and can provide data support for obtaining the priority passage demand coefficient F of the bus.

[0053] Beneficial effects of the present invention:

[0054] The present invention adopts intelligent networking and automatic driving technology as a new path to solve the above problems. It takes advantage of vehicle-road collaborative interaction and multi-source holographic perception in an intelligent networking environment to accurately obtain various dynamic traffic data and bus operation status data at intersections, optimize and control bus operation and intersection signal timing, improve bus operation efficiency, reduce the impact on social traffic operation, and achieve better comprehensive benefits of bus priority and social traffic operation, thereby improving the operation efficiency of the transportation system.

[0055] The concept, specific structure and technical effects of the present invention will be further described below to fully understand the purpose, characteristics and effects of the present invention. DETAILED DESCRIPTION

[0056] Example

[0057] The autonomous driving bus priority control method in the networked environment uses the following method to intelligently control the operation of the autonomous driving bus in real time according to the bus priority demand coefficient F, and the autonomous driving bus updates the bus priority demand coefficient F every time it reaches a station:

[0058] When F < 0, the reverse adjustment station control method and / or reverse speed guidance method are used to intelligently control the operation of the autonomous driving bus in real time;

[0059] When 0≤F<1, the reverse adjustment station control method is used to intelligently control the operation of the autonomous driving bus in real time;

[0060] When 1≤F<2 and the saturation is above the limit at the signal-controlled intersection, the speed guidance method is used to intelligently control the operation of the autonomous bus in real time;

[0061] When 1≤F<2 and the saturation is below the limit at the signal-controlled intersection, or when 2≤F<∞, the vehicle speed guidance method is combined with the active priority control method to intelligently control the operation of the autonomous driving bus in real time;

[0062] When F=∞, the vehicle speed guidance mode, the station control mode and the active priority control mode are used to intelligently control the operation of the autonomous bus in real time, and the station control mode adopts positive regulation;

[0063] The calculation formula of the bus priority demand coefficient F is as follows:

[0064]

[0065] Among them, α and β are weight coefficients, and α+β=1;

[0066] f s is the bus punctuality factor; f p is the passenger flow coefficient; f d is the dynamic scheduling coefficient.

[0067] The present invention determines the bus priority passage demand coefficient F of the section according to various vehicle operation status data, judges the current state of bus operation compared with the real-time demand of the operation organization according to the bus priority passage demand coefficient F, calculates the driving speed, station duration or active priority phase duration according to the control mode corresponding to different degrees of lag or advance, and combines the real-time operation data of the vehicle and the control mode constraint conditions, thereby realizing dynamic collaborative optimization and regulation of bus operation scheduling, and improving the punctuality rate and service level of bus operation.

[0068] In some embodiments, the bus punctuality factor f s The calculation method is as follows:

[0069]

[0070] Δt=T i -T s

[0071] Among them, T i It is the moment when the autonomous bus actually arrives at a certain station;

[0072] T s It is the time when the autonomous bus should arrive at a certain station (in the schedule);

[0073] Δt is the difference between the actual arrival time of the autonomous bus and the time it should arrive at the stop. Early arrival is positive and late arrival is negative.

[0074] Δt accept is an acceptable early arrival time for the autonomous bus;

[0075] Δt' accept is the acceptable time for the autonomous bus to arrive late;

[0076] Δt min is the minimum time for the autonomous bus to arrive seriously early;

[0077] Δt' min This is the minimum time at which an autonomous bus will be seriously delayed.

[0078] In practical applications, the punctuality rate of autonomous buses refers to the proportion of buses that run according to the prescribed schedule and arrive at the destination on time. The bus arrival time is compared with the timetable to determine whether it is on time, that is, the bus punctuality coefficient f between each two stops. s .

[0079] In some embodiments, the passenger flow coefficient f p The calculation method is as follows:

[0080]

[0081] Among them, P i is the current passenger capacity of the autonomous bus on the way; P max is the maximum passenger capacity of the autonomous driving bus; p0 is the passenger capacity ratio parameter of the autonomous driving bus, 0≤p0<1.

[0082] In actual applications, the demand for intelligent scheduling mainly considers the passenger flow on the vehicle and the real-time dynamic scheduling requirements. The vehicle priority demand is adjusted according to the passenger flow size and the operational scheduling requirements under different conditions. This can improve the intelligent dynamic scheduling capabilities of public transportation and adapt to various operational scenarios such as large passenger flow and emergency evacuation.

[0083] In certain embodiments, p0<0.5.

[0084] In some embodiments, the dynamic scheduling coefficient f d The calculation method is as follows:

[0085]

[0086] In actual applications, various situations of public transportation vehicles in operation are taken into consideration. When abnormal situations such as vehicle failure and sudden return occur, the demand for vehicle priority passage is reduced to a minimum and recorded as offline operation.

[0087] In some embodiments, the guided speed v allowed for the autonomous bus in the reverse adjustment station control mode, the reverse speed guidance mode, the reverse adjustment station control mode, the speed guidance mode, the station control mode and / or the active priority control mode is d The following requirements must be met:

[0088] v min ≤v d ≤v max ;

[0089] Among them, v min is the minimum speed limit; v max To guide the maximum speed limit;

[0090]

[0091] is the maximum safe speed for the autonomous bus to reach the intersection; It is the speed limit of the lane where the autonomous bus is located.

[0092] In actual applications, self-driving buses in a connected environment need to adopt reasonable vehicle speed guidance according to different situations to be able to pass the signal-controlled intersection ahead. Specifically, it gathers information such as vehicle speed, signal cycle, current phase, surrounding traffic conditions, etc. to calculate, and guide the vehicle to move at a constant speed, accelerate, or decelerate within the maximum and minimum speed limits to stay or pass through the intersection without stopping according to scheduling needs, which corresponds to reverse and forward guidance.

[0093] In some embodiments, the station control mode and the time for the autonomous driving bus to stop at station i in the station control mode are reversely adjusted. The time required for passengers to get on and off at stop i The following requirements must be met:

[0094]

[0095] in,

[0096]

[0097] is the number of passengers on the autonomous bus at station i and passenger volume The maximum value in ;

[0098] t p is the time it takes for each passenger to board or alight;

[0099] Δt0 is the fixed delay time of the station stop.

[0100] In practical applications, station control integrates the operating conditions of public transportation vehicles during operation and imposes positive or negative adjustments on the operation process, thereby ensuring the stability and comprehensive benefits of the overall operation process of the public transportation system.

[0101] In some embodiments, the average saturation of the intersection is used as the basis for judging the active priority control strategy, that is, the average saturation of the intersection

[0102]

[0103] Among them, x j is the j-hour saturation of the import lane; q j is the traffic volume of the import lane in j hours, in pcu / h.

[0104] In actual applications, active priority control responds differently through real-time bus operation information, phase information and signal control logic, and adjusts the intersection signal timing plan by extending the green light, turning off the red light early, inserting the phase, etc., to provide vehicles with priority passage. However, there are certain limitations on the use of active priority control strategies at oversaturated intersections, which may lead to increased average vehicle delays at intersections, reduced vehicle traffic efficiency, and congestion and overflow.

[0105] The present invention adopts the average saturation of intersections as the basis for judging the active priority control strategy. According to the multi-source holographic real-time perception of traffic flow and status data in a networked environment, it is not suitable to use the active priority strategy when the current average saturation of the intersection exceeds a certain limit.

[0106] In certain embodiments, the autonomous bus is equipped with advanced on-board computing units, sensing devices, monitoring devices, networked communications and positioning devices, and integrates communication and network technologies. It has complex environment perception functions, decision-making control planning functions, information data interaction functions, intelligent scheduling functions and passenger flow statistics functions, and can provide data support for obtaining the priority passage demand coefficient F of the bus.

[0107] In practical applications, the above-mentioned technical means can provide more reliable technical support for collaborative optimization and intelligent scheduling control.

[0108] 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 method for controlling bus priority in an autonomous driving environment; characterized in that: According to the bus priority demand coefficient F, the following method is used to intelligently control the operation of the autonomous driving bus in real time, and the autonomous driving bus updates the bus priority demand coefficient F every time it reaches a station: When F<0, the operation of the autonomous driving bus is intelligently regulated in real time by adopting a reverse adjustment station control method and / or a reverse vehicle speed guidance method; When 0≤F<1, the reverse regulation station control method is adopted to intelligently control the operation of the autonomous driving bus in real time; When 1≤F<2, and the saturation is above the limit at the signal-controlled intersection, the speed guidance method is used to intelligently control the operation of the autonomous driving bus in real time; When 1≤F<2, and the saturation is lower than the limit value of the signal-controlled intersection, or when 2≤F<∞, the speed guidance method is combined with the active priority control method to intelligently control the operation of the autonomous driving bus in real time; When F=∞, the speed guidance mode, the station control mode and the active priority control mode are used to intelligently regulate the operation of the autonomous driving bus in real time, and the station control mode adopts positive regulation; The calculation formula of the public transport priority demand coefficient F is as follows: Among them, α and β are weight coefficients, and α+β=1; f s is the bus punctuality factor; f p is the passenger flow coefficient; f d is the dynamic scheduling coefficient.

2. The method for controlling bus priority in an autonomous driving environment under a networked environment according to claim 1, characterized in that: The bus punctuality coefficient f s The calculation method is as follows: Δt=T i -T s Among them, T i The time when the autonomous bus actually arrives at a certain station; T s The time when the autonomous bus should arrive at a certain station (in the schedule); Δt is the difference between the actual arrival time and the time when the autonomous bus should arrive at the station, with early arrival being positive and late arrival being negative; Δt accept is an acceptable early arrival time of the autonomous bus; Δt' accept is an acceptable late arrival time of the autonomous bus; Δt min is the minimum time for the autonomous bus to arrive seriously early; Δt' min is the minimum time during which the autonomous bus is seriously delayed.

3. The method for controlling bus priority in an autonomous driving environment under a networked environment according to claim 1, characterized in that: The vehicle passenger flow coefficient f p The calculation method is as follows: Among them, P i is the current passenger capacity of the autonomous bus on the way; P max is the maximum passenger capacity of the autonomous driving bus; p0 is the passenger capacity ratio parameter of the autonomous driving bus, 0≤p0<1.

4. The method for controlling bus priority in an automatic driving system under a networked environment according to claim 3, characterized in that: p0＜0.5。 5. The method for controlling bus priority in an autonomous driving environment under a networked environment according to claim 1, characterized in that: The dynamic scheduling coefficient f d The calculation method is as follows:

6. The method for controlling bus priority in an autonomous driving environment under a networked environment according to claim 1, characterized in that: The guidance speed v allowed for the autonomous driving bus in the reverse adjustment station control mode, the reverse vehicle speed guidance mode, the reverse adjustment station control mode, the vehicle speed guidance mode, the station control mode and / or the active priority control mode is d The following requirements must be met: in min ≤in d ≤in max ; Among them, v min is the minimum speed limit; v max To guide the maximum speed limit; is the maximum safe speed of the autonomous bus at the intersection; is the speed limit of the lane where the autonomous bus is located.

7. The method for controlling bus priority in an autonomous driving environment under a networked environment according to claim 1, characterized in that: The reverse adjustment station control mode and the time of the autonomous driving bus stopping at station i in the station control mode The time required for passengers to get on and off at stop i The following requirements must be met: in, is the number of passengers on the autonomous bus at station i and passenger volume The maximum value in ; t p is the time it takes for each passenger to board or alight; Δt0 is the fixed delay time of the station stop.

8. The method for controlling bus priority in an autonomous driving environment under a networked environment according to claim 1, characterized in that: The average saturation of the intersection is used as the basis for judging the active priority control strategy, that is, the average saturation of the intersection ≥Intersection average saturation limit Among them, x j is the j-hour saturation of the import lane; q j is the traffic volume of the import lane in hour j, in pcu / h.

9. The method for controlling bus priority in an autonomous driving environment under a networked environment according to claim 1, characterized in that: The autonomous driving bus is equipped with advanced on-board computing units, sensing equipment, monitoring equipment, networked communication and positioning equipment, and integrates communication and network technologies. It has complex environment perception functions, decision-making control planning functions, information data interaction functions, intelligent scheduling functions and passenger flow statistics functions, and can provide data support for the acquisition of the priority passage demand coefficient F of the bus.

Images