Intelligent networked bus vehicle green driving control method

Abstract

The application relates to a kind of intelligent network connection public transport vehicle green driving control methods, comprising: based on V2I communication, road information, vehicle information, traffic information are sent to roadside computing unit, the data received by roadside computing unit is preprocessed;Roadside computing unit utilizes intelligent network connection public transport vehicle green driving control model, plans and decides intelligent network connection public transport vehicle green driving scheme, obtains including expected speed, expected lane green driving decision information;Based on V2I communication, green driving decision information is sent to intelligent network connection public transport vehicle by roadside computing unit, and the driving state of bus is guided or controlled.Compared with prior art, the application realizes the green driving control of intelligent network connection public transport vehicle horizontal and longitudinal coupling in vehicle-road cooperation environment, and can guide and control intelligent network connection public transport vehicle to pass through urban road section and intersection area safely, greenly and efficiently.

Description

Technical Field

[0001] This invention relates to the field of intelligent connected bus decision-making and control technology, and in particular to a green driving control method for intelligent connected buses. Background Technology

[0002] Motor vehicle exhaust pollution has become a significant source of air pollution. Factors influencing vehicle emissions and energy consumption mainly fall into three categories: vehicle technology, road conditions, and vehicle operation. Among these, vehicle operation, specifically the driver's control over the vehicle, is a crucial factor affecting energy consumption. Green driving is a method of vehicle operation that achieves energy conservation and emission reduction by improving driving behavior without altering the vehicle's structure. Studies show that green driving can reduce fuel consumption by 5-10%, and for skilled drivers, this can even reach 20-50%.

[0003] Current research on green driving mainly focuses on adjusting driving trajectories based on surrounding road traffic environment information to reduce fuel consumption, but it does not consider the impact of random factors throughout the entire process or the human-vehicle interaction for practical applications. Green driving systems still have room for improvement in terms of effectiveness, stability, and reliability. In addition, as higher requirements are placed on the source, content, and accuracy of transmitted information, the mismatch between the computing power of onboard computing systems and the massive heterogeneous data volume may further limit the further development of green driving.

[0004] With the development of a new round of technological revolution represented by big data integration, computer perception and recognition, mobile Internet, cloud computing and other technologies, vehicle-road cooperative technology is constantly maturing. Roadside basic units can realize functions such as perception, processing, calculation and transmission, and complete active response interaction for connected human-driven vehicles and connected autonomous vehicles, bringing huge opportunities for the upgrade of green driving technology.

[0005] However, existing vehicle-road cooperative green driving solutions still have certain shortcomings: in a vehicle-road cooperative environment, the information input and control responses of driving entities with different vehicle control forms, such as connected human-driven vehicles and connected autonomous vehicles, are heterogeneous. Therefore, it is necessary to design multi-dimensional green driving solutions that are compatible with both human-driven and autonomous driving. In addition, how to ensure traffic efficiency and driving experience while reducing fuel consumption is also an urgent issue to consider.

[0006] Existing intelligent connected buses still face challenges such as low intelligence levels, high computing power requirements, and difficulties in application implementation, making it difficult to ensure that buses can pass through urban road sections and intersections safely, greenly, and efficiently. Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a green driving control method for intelligent connected buses. By designing a multi-dimensional green driving solution that is compatible with both human driving and autonomous driving, the invention determines control strategies for driving subjects with various vehicle control modes, thereby guiding and controlling buses to pass through urban road sections and intersections safely, greenly, and efficiently.

[0008] The objective of this invention can be achieved through the following technical solution: a green driving control method for intelligent connected buses, comprising the following steps:

[0009] S1. Based on V2I (Vehicle to Infrastructure) communication, road information, vehicle information, and traffic information are sent to the roadside computing unit, which then preprocesses the received data.

[0010] S2. The roadside computing unit uses the intelligent connected bus green driving control model to plan and make decisions on the green driving scheme of the intelligent connected bus, and obtains green driving decision information including the desired vehicle speed and desired lane.

[0011] S3. Based on V2I communication, green driving decision information is sent from the roadside computing unit to the intelligent connected bus to guide or control the bus's driving status.

[0012] Furthermore, in step S1, the roadside computing units are evenly distributed along the longitudinal direction of the road to synchronously receive information from traffic signal controllers, store characteristic data of the corresponding road sections, and receive data information sent by intelligent connected buses in the corresponding road sections through V2I communication.

[0013] Furthermore, in step S1, the roadside calculation unit specifically performs coordinate unification processing on the received data, that is, processes the road information, vehicle information, and traffic information into data under the same geodetic coordinate system.

[0014] Furthermore, step S2 specifically includes the following steps:

[0015] S21. Based on the vehicle information of the intelligent connected bus at the current moment, determine the changes in the bus's driving status on the road section ahead of the intersection; combine with traffic information to determine the expected arrival time of the intelligent connected bus at the intersection.

[0016] S22. Taking the current state of the intelligent connected bus as the initial state, the lateral and longitudinal control of the bus in the road unit section as the control variables, the goal of the bus passing through the intersection at the maximum speed at the desired time and with low fuel consumption throughout the journey, and considering road alignment constraints and vehicle performance constraints, a spatial domain model predictive control model is established to obtain the optimal control sequence for bus driving. The spatial domain model predictive control model is the green driving control model.

[0017] S23. Based on the optimal control sequence, the next evolution state of the bus is taken as the desired green driving state, including the desired speed and the desired lateral position. The desired lateral position is adjusted according to the environmental vehicle information, thereby determining the desired lane of the bus.

[0018] Furthermore, the vehicle information of the intelligent connected bus in step S21 specifically includes location and speed information, and the traffic information in step S21 specifically includes traffic signal controller information, including the current signal light color, the remaining duration of the current signal light color, the signal cycle, and the phase sequence.

[0019] Furthermore, the system dynamic equations of the spatial domain model predictive control model in step S22 are specifically as follows:

[0020] X(s p +Δs p ) = AX(s p )+BU(s p )+C

[0021]

[0022]

[0023] U(s p )=[b(s p ), δ f (s p )] T

[0024] Where X(s) p ) represents the model state variable, s p t(s) represents the longitudinal distance of the vehicle's current cross-section from the origin. p ) indicates that the vehicle has arrived at s p At time y(s) p ) indicates that the vehicle has arrived at s p At the horizontal position, w(s) p ) indicates that the vehicle has arrived at s p Slowness refers to the time it takes for a vehicle to travel a unit distance. Indicates that the vehicle has arrived at s p At the vehicle's front wheel deflection angle, U(s) pb(s) is the model control variable. p ) indicates that the vehicle has arrived at s p Slowness, i.e., the change in slowness per unit distance traveled by a vehicle, δ f (s p ) indicates that the vehicle has arrived at s p Steering wheel angle;

[0025] Δs p Indicates the unit distance traveled by a vehicle, l fr This represents the distance between the front and rear axles of the vehicle, and k represents the road curvature.

[0026] Furthermore, the objective function of the spatial domain model predictive control model in step S22 is specifically:

[0027]

[0028]

[0029] X des =[0, y des w min ,0] T ,

[0030] Where s0 represents the vehicle's current position, s end This indicates the position of the parking line, where q1, q2, q3, q4, r1, and r2 are weighting adjustment parameters, and w min For the minimum slowness, t des Indicates the expected time when the vehicle will arrive at the stop line, y des This indicates the vehicle's desired lateral position relative to the lane centerline.

[0031] Furthermore, step S3 specifically involves sending the green driving decision information to the onboard controller of the fully automated driving bus or to the human-machine interaction module of the human-driven connected bus.

[0032] Furthermore, in step S3, if the green driving decision information is sent to the onboard controller of the fully automated driving bus, the onboard controller will use the green driving decision information as the tracking target, and combine it with the actual driving characteristics of the vehicle, using the longitudinal acceleration of the bus and the steering wheel angle as the control objects, to control the vehicle to drive along the green driving trajectory; when the surrounding vehicles are too close to the bus, the controller will switch to car-following control to control the bus to follow the vehicle in front.

[0033] Furthermore, in step S3, if the green driving decision information is sent to the human-machine interaction module of the connected bus driven by humans, the human-machine interaction module will display the green driving decision information to the human driver, thereby guiding and controlling the driving status of the bus.

[0034] Compared with the prior art, the present invention has the following advantages:

[0035] I. This invention is based on V2I communication. On one hand, road information, vehicle information, and traffic information are sent to a roadside computing unit. The roadside computing unit preprocesses the received data and uses an intelligent connected bus green driving control model to plan and decide on a green driving scheme for the intelligent connected bus, obtaining green driving decision information including desired speed and desired lane. On the other hand, the roadside computing unit sends the green driving decision information to the intelligent connected bus to guide or control the bus's driving state. This realizes a green driving control method for intelligent connected buses in a vehicle-road cooperative environment with lateral and longitudinal coupling, capable of guiding and controlling intelligent connected buses to safely, greenly, and efficiently pass through urban road sections and intersections.

[0036] Second, this invention fully considers background traffic flow and traffic signal control. When constructing the green driving control model, the current state of the intelligent connected bus is taken as the initial state, the lateral and longitudinal control of the bus in the road unit section is taken as the control quantity, and the goal is for the bus to pass through the intersection at the maximum speed at the expected time and with low fuel consumption throughout the journey. Considering road alignment constraints and vehicle performance constraints, the green driving control model obtains green driving decision information including the expected speed and the expected lane. It can effectively optimize the expected lateral and longitudinal behavior of the bus, catch up with the green light time window, and update the control instructions in real time along the road space section, thereby reducing fuel consumption and improving traffic efficiency.

[0037] Third, in this invention, corresponding decision-making content is designed according to the autonomous driving level of the application object: for fully autonomous buses, lateral and longitudinal coupled decision control instructions are designed and executed by the on-board terminal controller; for human-driven connected buses, desired speed and desired lane driving guidance instructions are designed. The decision information is transmitted to the human-machine interface in the driver's cab to guide the human driver to complete the final control action. This avoids the problems of traditional green driving functions having a single application object and being limited by the level of vehicle control. It can be adapted to both autonomous buses and human-driven buses, realizing a multi-dimensional green driving solution that is compatible with both human driving and autonomous driving. It can determine the control strategy for driving subjects with multiple vehicle control forms, providing technical support for the green and efficient passage of intelligent connected vehicles in urban road scenarios.

[0038] Fourth, this invention distributes roadside computing units evenly along the longitudinal direction of the road, so that each roadside computing unit can correspond to a road section of a set length. Each roadside computing unit independently completes the green driving decision planning for its respective road section. Thus, the roadside deployment of the decision module is realized in the vehicle-road cooperative environment, which can overcome the limitation of insufficient computing power of the vehicle computing unit and help to implement more complex autonomous driving decision control models.

[0039] Fifth, in this invention, the green driving decision information is designed to include the desired vehicle speed and desired lane corresponding to each section of road. This allows the desired trajectory to be sampled into coarse-grained guidance information. When facing a human-driven connected bus, the actual response is still controlled by the human driver, ensuring the safety of the autonomous bus and avoiding obstacles to application and promotion due to driver concerns. This helps to realize the green driving function of autonomous vehicles. Attached Figure Description

[0040] Figure 1 This is a schematic diagram of the method flow of the present invention. Detailed Implementation

[0041] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0042] Example

[0043] like Figure 1 As shown, a green driving control method for intelligent connected buses includes the following steps:

[0044] S1. Based on V2I communication, road information, vehicle information, and traffic information are sent to the roadside computing unit, which then preprocesses the received data.

[0045] S2. The roadside computing unit uses the intelligent connected bus green driving control model to plan and make decisions on the green driving scheme of the intelligent connected bus, and obtains green driving decision information including the desired vehicle speed and desired lane.

[0046] S3. Based on V2I communication, green driving decision information is sent from the roadside computing unit to the intelligent connected bus to guide or control the bus's driving status.

[0047] This embodiment applies the above technical solution, and the specific content is as follows:

[0048] Step 1: Use V2X communication to send road information, vehicle information, and traffic information to the roadside computing unit, which will then preprocess the received data.

[0049] In step one, the roadside computing unit receives real-time road, vehicle, and traffic information to provide data input for subsequent optimization control algorithms. This includes the following steps:

[0050] Step 1.1: Deploy a series of roadside computing units along the longitudinal direction of the road. Each roadside computing unit corresponds to a fixed-length road section, serving as the basic spatial unit for perception, communication, and control in urban road scenarios. The roadside computing units synchronize traffic signal controller information in real time and store corresponding road segment feature data, including road topology, lane alignment, and speed limits. Specifically, with the lane centerline as the vertical axis, the approach position as the origin of the vertical axis, and the vertical direction as the horizontal axis, the lane boundary of lane i at a distance of s meters from the origin is: The speed limit on the road is v max ;

[0051] Step 1.2: The intelligent connected bus is equipped with sensors to perceive the road environment and identify the status of vehicles ahead in its lane and the status of obstacles ahead and behind in adjacent lanes. Specifically, this includes: the longitudinal position of obstacles behind the left lane, the longitudinal position of obstacles behind the right lane, the longitudinal position of obstacles ahead in the left lane, the longitudinal position of obstacles ahead in the right lane, the speed of obstacles ahead in its lane, and the length of obstacles ahead in its lane.

[0052] Step 1.3: Utilizing V2I communication, the intelligent connected bus sends the perceived information and its own vehicle status information to the roadside calculation sheet for the relevant section. The vehicle status information includes: vehicle lateral position, vehicle longitudinal position, vehicle speed, vehicle acceleration, vehicle front wheel deflection angle, vehicle steering wheel angle, and vehicle length;

[0053] Step 1.4: In the roadside computing unit, the collected vehicle, road, and traffic information is processed into data in the vehicle lane centerline coordinate system, thereby interfacing with the input of the optimized control model (i.e., the green driving control model).

[0054] Step 2: Using roadside computing units and based on optimization control model methods, a green driving scheme is determined for the intelligent connected bus, including the desired vehicle speed and desired lane.

[0055] In step two, addressing the needs of reducing delays and lowering fuel consumption for buses, a spatial domain intelligent connected bus behavior decision-making model is established. This model determines the desired speed and lane for the intelligent connected bus, guiding the vehicle to reduce stops and avoid sudden acceleration and deceleration, thereby achieving green driving, energy conservation, and emission reduction. The calculation process includes the following steps:

[0056] Step 2.1: Based on the current time, the current location and speed of the intelligent connected bus, estimate the changes in the bus's driving status on the road segment ahead of the intersection; combine the signal control scheme information, including the current signal light color, the remaining duration of the current signal light color, the signal cycle, and the phase sequence, to design the expected arrival time of the intelligent connected bus at the intersection;

[0057] Step 2.2: Taking the current state of the intelligent connected bus as the initial state, using the lateral and longitudinal control of the bus within the road unit section as the control variables, and aiming for the bus to pass through the intersection at the maximum speed at the desired time and achieve low fuel consumption throughout the journey, a spatial domain model predictive control model is established, considering road alignment constraints and vehicle performance constraints, to obtain the optimal control sequence for bus operation. The spatial domain model predictive control model is as follows:

[0058] The model state variables are s p t(s) represents the longitudinal distance of the vehicle's current cross-section from the origin. p ) indicates that the vehicle has arrived at s p At time y(s) p ) indicates that the vehicle has arrived at s p At the horizontal position, w(s) p ) indicates that the vehicle has arrived at s p Slow speed (time taken for a vehicle to travel a unit distance), Indicates that the vehicle has arrived at s p The vehicle's front wheel deflection angle;

[0059] The model control variable is U(s) p )=[b(s p ), δ f (s p )] T b(s) p ) indicates that the vehicle has arrived at s p Added deceleration (change in deceleration per unit distance traveled by the vehicle), δ f (s p ) indicates that the vehicle has arrived at s p Steering wheel angle;

[0060] The dynamics of the model system are X(s) p +Δs p ) = AX(s p )+BU(s p )+C, where: Δs p Indicates the unit distance traveled by a vehicle, l fr This represents the distance between the front and rear axles of the vehicle, and k represents the road curvature.

[0061] The objective function of the model is:

[0062]

[0063] Where s0 represents the vehicle's current position, s end Indicates the location of the parking line. q1, q2, q3, q4, r1, and r2 are weight adjustment parameters;

[0064] X des =[0, y des w min ,0] T , w min For the minimum slowness, t des Indicates the expected time when the vehicle will arrive at the stop line, y des Indicates the vehicle's desired lateral position relative to the lane centerline;

[0065] Step 2.3: Based on the optimal control sequence, the next evolutionary state of the bus is taken as the desired green driving state, including the desired speed v. des Expected horizontal position y des The desired lateral position will be adjusted based on environmental vehicle information to determine the desired bus lane. des .

[0066] Step 3: Using V2I communication, green driving decision information is sent from the roadside computing unit to the intelligent connected bus. Based on the different levels of vehicle intelligence, the bus is guided or controlled to achieve green driving.

[0067] In step three, the onboard planning and control system tracks desired decisions and controls connected buses with different levels of intelligence to achieve green driving. The calculation process includes the following steps:

[0068] Step 3.1: The desired speed and desired lane decision information calculated by the roadside computing unit are transmitted to the intelligent connected bus via I2V communication: for fully automated driving buses, the decision information is transmitted to the on-board controller interface; for human-driven connected buses, the decision information is transmitted to the human-machine interface in the driver's cab.

[0069] Step 3.2: For fully automated buses, design the onboard controller as an optimized controller based on the vehicle's desired decision information (desired speed v). des Expected lane des To track the target, the controller combines the actual driving characteristics of the vehicle with the longitudinal acceleration and steering wheel angle of the bus as the control objects to control the vehicle to drive along the green driving trajectory; when the surrounding vehicles are too close to the bus, the controller switches to car-following control, and the bus follows the vehicle in front.

[0070] Step 3.3: For human-driven connected buses, the human driver will control the bus independently based on the green driving goals such as desired speed and desired lane shown on the interactive interface.

[0071] In summary, this technical solution addresses the pain points and challenges of low intelligence levels, high computing power requirements, and difficulties in application implementation of green driving functions for buses. It designs a green driving method for intelligent connected buses with lateral and longitudinal coupling in urban road vehicle-road cooperative environments, guiding buses to pass safely, greenly, and efficiently through urban road sections and intersections. At the same time, by designing a multi-dimensional green driving solution compatible with both human and autonomous driving, it determines control strategies for driving subjects with various vehicle control methods, providing technical support for the green and efficient passage of intelligent connected vehicles in urban road scenarios.

Claims

1. A green driving control method for intelligent connected buses, characterized in that, Includes the following steps: S1. Based on V2I communication, road information, vehicle information, and traffic information are sent to the roadside computing unit, which then preprocesses the received data. S2. The roadside computing unit uses the intelligent connected bus green driving control model to plan and make decisions on the green driving scheme of the intelligent connected bus, and obtains green driving decision information including the desired vehicle speed and desired lane. S2 specifically includes the following steps: S21. Based on the vehicle information of the intelligent connected bus at the current moment, determine the changes in the bus's driving status on the road section ahead of the intersection; combine with traffic information to determine the expected arrival time of the intelligent connected bus at the intersection. S22. Taking the current state of the intelligent connected bus as the initial state, the lateral and longitudinal control of the bus in the road unit section as the control variables, the goal of the bus passing through the intersection at the maximum speed at the desired time and with low fuel consumption throughout the journey, and considering road alignment constraints and vehicle performance constraints, a spatial domain model predictive control model is established to obtain the optimal control sequence for bus driving. The spatial domain model predictive control model is the green driving control model. S23. Based on the optimal control sequence, the next evolution state of the bus is taken as the expected green driving state, including the expected speed and the expected lateral position. The expected lateral position is adjusted according to the environmental vehicle information, thereby determining the expected lane of the bus. The specific system dynamic equations of the spatial domain model predictive control model in step S22 are as follows: ， ， in, X ( s p ) represents the model state variables. This indicates the longitudinal distance of the vehicle's current position cross-section from the origin. Indicates vehicle arrival At any time, Indicates vehicle arrival In the horizontal position, Indicates vehicle arrival Slowness refers to the time it takes for a vehicle to travel a unit distance. Indicates vehicle arrival The vehicle's front wheel deflection angle, U ( s p ) represents the model control variable. Indicates vehicle arrival The rate of decrease in deceleration refers to the change in deceleration per unit distance traveled by a vehicle. Indicates vehicle arrival Steering wheel angle; Indicates the unit distance traveled by the vehicle. Indicates the distance between the front and rear axles of a vehicle. Indicates the curvature of the road; S3. Based on V2I communication, green driving decision information is sent from the roadside computing unit to the intelligent connected bus to guide or control the bus's driving status.

2. The method for green driving control of intelligent connected buses according to claim 1, characterized in that, In step S1, the roadside computing units are evenly distributed along the longitudinal direction of the road. They are used to synchronously receive information from traffic signal controllers, store characteristic data of the corresponding road sections, and receive data information sent by intelligent connected buses in the corresponding road sections through V2I communication.

3. The method for green driving control of intelligent connected buses according to claim 1, characterized in that, In step S1, the roadside calculation unit specifically performs coordinate unification processing on the received data, that is, it processes road information, vehicle information, and traffic information into data under the same geodetic coordinate system.

4. The method for green driving control of intelligent connected buses according to claim 1, characterized in that, The vehicle information of the intelligent connected bus in step S21 specifically includes location and speed information, and the traffic information in step S21 specifically includes traffic signal controller information, including the current signal light color, the remaining duration of the current signal light color, the signal cycle, and the phase sequence.

5. The method for green driving control of intelligent connected buses according to claim 1, characterized in that, The objective function of the spatial domain model prediction control model in step S22 is specifically: ， in, Indicates the vehicle's current location. Indicates the location of the parking line. For minimum slowness, The expected time when the vehicle will arrive at the stop line. This indicates the vehicle's desired lateral position relative to the lane centerline.

6. The method for green driving control of intelligent connected buses according to claim 1, characterized in that, Specifically, step S3 involves sending the green driving decision information to the onboard controller of a fully automated driving bus or to the human-machine interaction module of a human-driven connected bus.

7. The green driving control method for intelligent connected buses according to claim 6, characterized in that, In step S3, if the green driving decision information is sent to the onboard controller of the fully automated driving bus, the onboard controller will use the green driving decision information as the tracking target, and combine it with the actual driving characteristics of the vehicle, using the longitudinal acceleration of the bus and the steering wheel angle as the control objects, to control the vehicle to drive along the green driving trajectory; when the surrounding vehicles are too close to the bus, the controller will switch to car-following control to control the bus to follow the vehicle in front.

8. A green driving control method for intelligent connected buses according to claim 6, characterized in that, In step S3, if the green driving decision information is sent to the human-machine interaction module of the connected bus, the human-machine interaction module will display the green driving decision information to the human driver, thereby guiding and controlling the driving status of the bus.

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