Rainy day emergency braking self-adaptive control system and method based on Internet of Vehicles

Abstract

The invention discloses a rainy day emergency braking self-adaptive control system and method based on the Internet of Vehicles, and belongs to the technical field of safe driving, and the system comprises a data collection module which is used for obtaining the relative distance, the relative speed and the raining grade of a target vehicle and a vehicle in front of the target vehicle; the data analysis module is used for calculating a collision risk value according to the relative distance and the relative speed; the self-adaptive emergency braking module is used for carrying out different levels of emergency braking on the target vehicle according to the collision risk value; the data acquisition module performs data interaction with the data analysis module through the Internet of Vehicles, the data analysis module performs data interaction with the adaptive emergency braking module in the cloud platform through the Internet of Vehicles, and the cloud platform sends an emergency braking instruction to a target vehicle through the Internet of Vehicles. According to the relative distance, the relative speed and the raining level of the target vehicle and the vehicle in front of the target vehicle, self-adaptive emergency braking can be carried out on the target vehicle.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of safe driving, and particularly relates to a rain emergency braking adaptive control system and method based on vehicle networking. BACKGROUND

[0002] In recent years, with the gradually deepening of the awareness of vehicle driving safety and its wide popularity, motor vehicle speed limit signs have been added on highways in various places. The traditional method is to set the speed limit sign for vehicles on the highway as a fixed value, which is generally set as 120 kilometers per hour. However, in the actual road driving process, different road environment factors will have a significant impact on the speed safety of high-speed vehicles. These factors include but are not limited to the size of the road curve radius, the steepness of the road slope, the current road slip degree, i.e. the high and low of the road surface friction coefficient, and the visibility of the road, and many other parameters. If only relying on the traditional, fixed speed limit sign, it will not be able to flexibly adjust the speed limit according to the actual situation when facing complex road conditions, so as to more effectively avoid possible collision accidents in the process of road vehicle driving and ensure the safety of highway driving. Therefore, it is particularly important to take more scientific and reasonable dynamic speed limit measures for different road conditions. SUMMARY

[0003] The application aims to provide a rain emergency braking adaptive control system and method based on vehicle networking, which can adaptively brake the target vehicle according to the relative distance L, relative speed△V and rain grade of the target vehicle and the vehicle in front.

[0004] To achieve the above-mentioned purpose, the application provides the following technical scheme:

[0005] The first purpose of the application is to provide a rain emergency braking adaptive control system based on vehicle networking, which comprises:

[0006] A data acquisition module is configured to obtain the relative distance L, relative speed△V and rain grade of the target vehicle and the vehicle in front.

[0007] A data analysis module is configured to calculate a collision risk value TTC according to the relative distance L and relative speed△V; TTC=L / △V.

[0008] An adaptive emergency braking module is configured to brake the target vehicle at different levels according to the collision risk value TTC.

[0009] The data acquisition module interacts with the data analysis module through the Internet of Vehicles, the data analysis module interacts with the adaptive emergency braking module in the cloud platform through the Internet of Vehicles, and the cloud platform sends an emergency braking instruction to the target vehicle through the Internet of Vehicles.

[0010] The second object of the application is to provide a rain emergency braking adaptive control method based on the Internet of Vehicles, comprising:

[0011] S1, obtaining the relative distance L, relative speed△V and rain level of the target vehicle and the vehicle in front of it;

[0012] S2, calculating the collision risk value TTC according to the relative distance L and the relative speed△V; TTC=L / △V;

[0013] S3, performing different levels of emergency braking on the target vehicle according to the collision risk value TTC.

[0014] Compared with the prior art, the application has the following beneficial effects:

[0015] According to the relative distance L, relative speed△V and rain level in the current environment between the target vehicle and the vehicle in front of it, the application can comprehensively consider these key parameters to perform adaptive emergency braking on the target vehicle, thereby effectively ensuring the safe driving of the vehicle in rainy weather conditions and avoiding traffic accidents caused by wet road surface. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 The system block diagram of the preferred embodiment of the application is shown in the figure;

[0017] Figure 2 The communication principle diagram of the preferred embodiment of the application is shown in the figure;

[0018] Figure 3 The structure schematic diagram of the preferred embodiment of the application is shown in the figure;

[0019] Figure 4 The structure schematic diagram of the auxiliary braking device in the preferred embodiment of the application is shown in the figure;

[0020] Figure 5 The structure schematic diagram of the preferred embodiment of the application under the condition of no rain is shown in the figure;

[0021] Figure 6 Structure diagram in light rain condition in the preferred embodiment of the present application;

[0022] Figure 7 Structure diagram in light rain condition in the preferred embodiment of the present application;

[0023] Figure 8 Structure diagram in heavy rain condition in the preferred embodiment of the present application. DETAILED DESCRIPTION

[0024] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the present application.

[0025] Please refer to Figure 1 A rain day emergency braking adaptive control system based on Internet of Vehicles, comprising:

[0026] A data acquisition module is configured to acquire a relative distance L, a relative speed ΔV and a rain level of a target vehicle and a vehicle in front of the target vehicle.

[0027] A data analysis module is configured to calculate a collision risk value TTC according to the relative distance L and the relative speed ΔV; TTC=L / ΔV.

[0028] An adaptive emergency braking module is configured to perform different levels of emergency braking on the target vehicle according to the collision risk value TTC.

[0029] The data acquisition module is configured to perform data interaction with the data analysis module through Internet of Vehicles, and the data analysis module is configured to perform data interaction with the adaptive emergency braking module in a cloud platform through Internet of Vehicles, and the cloud platform is configured to send an emergency braking instruction to the target vehicle through Internet of Vehicles.

[0030] In the above preferred embodiment, the data acquisition module is one of the core parts of the system, please refer to Figure 2 and Figure 3 Specifically, the data acquisition module mainly comprises:

[0031] A roadside camera 1 is configured to acquire a two-dimensional image of a target vehicle and a vehicle in front of the target vehicle on a road. The roadside camera is arranged above a road pole, adopts a visual perception device combined with a high-definition sensor and an infrared sensor, can acquire a relative speed and a relative distance of the target vehicle (for example, an automatic driving vehicle) and the vehicle in front of the target vehicle on the road in real time in day and night, and transmit the acquired 2D image to an edge computing unit for calculation of TTC of the automatic driving vehicle by the edge computing unit.

[0032] Roadside laser radar 2, for acquiring three-dimensional point cloud images of target vehicles and vehicles in front of them on the road; the roadside laser radar is arranged above a road pole, adopts a 256-line semi-solid laser radar, collects relative speeds and relative distances of autonomous vehicles on the road and vehicles in front of them in real time, and transmits 3D images to an edge computing unit for calculation of TTC of the autonomous vehicles by the edge computing unit;

[0033] The edge computing unit 3 receives two-dimensional images and three-dimensional point cloud images, and fuses the two, to calculate relative distances L and relative speeds AV of target vehicles and vehicles in front of them; the main function of the edge computing unit is to receive perception information transmitted by a roadside camera and a roadside laser radar, fuse 2D image information collected by the roadside camera and 3D information collected by the roadside laser radar, construct relative distances L and relative speeds AV of autonomous vehicles on the road and vehicles in front of them, calculate a collision risk value TTC, TTC=L / AV, and send the TTC value to an adaptive emergency braking module in a cloud platform 4 for instruction judgment.

[0034] A rain amount monitor 5 acquires rainfall information of the current period on the road. The rain amount monitor is arranged above a roadside pole, adopts an optical rain amount sensor, monitors rainfall amount by optical changes caused by raindrops passing through a measurement area, divides the rainfall amount into four levels of no rain, light rain, moderate rain, and heavy rain, and transmits the monitored rainfall level information to the adaptive emergency braking module in the cloud platform through wireless communication LTEV for instruction judgment of the cloud platform.

[0035] In order to achieve better emergency braking purposes, an auxiliary braking device is installed on the target vehicle, please refer to Figure 4 , the auxiliary braking device comprises:

[0036] A friction wheel support plate is installed on the target vehicle, and the friction wheel support plate can be fixed on the bottom of the target vehicle by bolts or welding;

[0037] A lifting mechanism is installed on the lower surface of the friction wheel support plate;

[0038] A friction wheel is installed at the lower end of the lifting mechanism.

[0039] There are many implementation modes of the lifting mechanism, in order to better understand the concept of the present application, this example is illustrated:

[0040] The lifting mechanism comprises:

[0041] An upper connecting rod connected with the friction wheel support plate;

[0042] A lower connecting rod connected with the friction wheel;

[0043] The lower end of the upper connecting rod and the upper end of the lower connecting rod are connected by a rotating mechanism;

[0044] A controller controls the operation of the rotating mechanism, and the controller includes a driving motor and a transmission shaft.

[0045] The rain level in the embodiment includes four levels: no rain, light rain, moderate rain, and heavy rain. Therefore, the auxiliary braking device includes a first braking device for light rain, a second braking device for moderate rain, and a third braking device for heavy rain.

[0046] Referring to Figures 5 to 8 , the different levels of emergency braking of the target vehicle according to the collision risk value TTC include:

[0047] When the rain level is light rain, the first braking device is started, so that the friction wheel of the first braking device is in contact with the ground;

[0048] When the rain level is moderate rain, the second braking device is started, so that the friction wheel of the first braking device and the friction wheel of the second braking device are in contact with the ground;

[0049] When the rain level is heavy rain, the third braking device is started, so that the friction wheel of the first braking device, the friction wheel of the second braking device, and the friction wheel of the third braking device are in contact with the ground.

[0050] The friction coefficient between the friction wheel (first friction wheel) of the first braking device and the ground is less than the friction coefficient between the friction wheel (second friction wheel) of the second braking device and the ground, and the friction coefficient between the friction wheel of the second braking device and the ground is less than the friction coefficient between the friction wheel (third friction wheel) of the third braking device and the ground.

[0051] The friction coefficient between the friction wheel of the first braking device and the ground is not less than 0.8, the friction coefficient between the friction wheel of the second braking device and the ground is not less than 1.0, and the friction coefficient between the friction wheel of the first braking device and the ground is not less than 1.2.

[0052] The cloud platform receives the collision risk value TTC information of the autonomous vehicle and the vehicle in front of it input by the edge computing unit and the rain level information transmitted by the rain monitor, and performs logical judgment on the vehicle emergency braking control system instruction, which specifically includes:

[0053] The cloud platform receives the TTC value and the rain level information fed back by the edge computing unit. When the rain monitor feedback is no rain, 0≤TTC≤1.4, the cloud platform will issue instruction 1 to the autonomous driving controller, and the controller will send a signal to the braking system that the XBR braking deceleration is-6m / s 2 for emergency braking;

[0054] The cloud platform receives the TTC value and rainfall level information fed back by the edge computing unit. When the rain monitor feedback indicates light rain, 0≤TTC≤2.4, the cloud platform will send instruction 2 to the autonomous driving controller, and the controller will issue an XBR braking deceleration of -6m / s. 2 The signal is sent to the braking system for emergency braking. At the same time, the drive motor is started to control the first-level friction wheel to rotate 90 degrees around the first-level friction wheel connecting rod, making contact with the ground, increasing the first-level friction force, thereby facilitating effective braking and increasing braking stability.

[0055] The cloud platform receives the TTC value and rainfall level information fed back by the edge computing unit. When the rain gauge feedback indicates moderate rain, 0≤TTC≤3.4, the cloud platform will send instruction 3 to the autonomous driving controller, and the controller will issue an XBR braking deceleration of -6m / s. 2 The signal is sent to the braking system for emergency braking. At the same time, the drive motor is started to control the primary and secondary friction wheels to rotate 90 degrees around the primary and secondary friction wheel connecting rods, making contact with the ground, increasing the secondary friction force, thereby facilitating effective braking and increasing braking stability.

[0056] The cloud platform receives the TTC value and rainfall level information fed back by the edge computing unit. When the rain gauge feedback indicates heavy rain, 0≤TTC≤4.4, the cloud platform will send instruction 4 to the autonomous driving controller, and the controller will send an XBR braking deceleration of -6m / s. 2 The signal is given to the braking system for emergency braking. At the same time, the drive motor is started to control the primary and secondary friction wheels to rotate 90 degrees around the primary and secondary friction wheel connecting rods and make contact with the ground. At the same time, the tertiary friction wheel connecting rod extends downward, and the tertiary friction wheel makes contact with the ground, increasing the tertiary friction force, thereby facilitating effective braking and increasing braking stability.

[0057] The implementation process of the cloud platform sending an emergency braking command to the target vehicle through the Internet of Vehicles includes:

[0058] The roadside RSU6 is placed above the roadside pole, receives commands from the cloud platform, and sends the commands to the onboard OBU to pass them on to the autonomous vehicle controller for command control;

[0059] The on-board OBU7 is placed on the floor of the autonomous vehicle's cockpit, receives control commands from the cloud platform transmitted by the roadside RSU, and transmits them to the autonomous driving controller for operational control;

[0060] The autonomous driving vehicle controller 8 is placed on the floor of the autonomous driving vehicle cabin, receives the instructions transmitted by the vehicle OBU to the cloud platform, and sends a -6m / s signal to the braking system according to the instructions. 2The brake deceleration XBR request is requested, and the driving motor is controlled to extend the first, second and third friction wheels to the ground to increase the friction force between the vehicle and the ground, thereby facilitating effective braking and increasing braking stability.

[0061] The brake system 9 is arranged at the bottom of the autonomous vehicle and connected with the wheels of the autonomous vehicle, receives the brake request signal of the autonomous controller, and controls the braking of the vehicle.

[0062] The driving motor 10 is arranged at the bottom of the autonomous vehicle and connected with the first, second and third friction wheels through the transmission shaft, and under the action of the driving motor, the first and second friction wheels can be controlled to rotate 90° around the first and second friction wheel links to extend out of the vehicle and contact the ground, and the third friction wheel link is controlled to extend to drive the third friction wheel to extend to the ground, thereby increasing the friction force between the vehicle and the ground, thereby facilitating effective braking and increasing braking stability.

[0063] The friction wheel support plate 11 is arranged at the bottom of the autonomous vehicle, one end is connected with the bottom of the vehicle, and the other end is fixedly connected with the first, second and third friction wheel links, and is connected with the friction wheels through the links, thereby fixedly connecting the friction wheel links.

[0064] The first friction wheel link 12 is a cylindrical link made of stainless steel, one end is fixedly connected with the friction wheel support plate, and the other end is connected with the first friction wheel through a rotating mechanism, and the first friction wheel can rotate 90° around the rotating mechanism along the first friction link under the action of the driving motor, so that the first friction wheel extends to the ground to increase the friction force between the autonomous vehicle and the ground.

[0065] The first friction wheel 13 is made of natural rubber material of ordinary friction system, one end is fixedly connected with the L-shaped link, and one end of the L-shaped link can rotate under the action of the driving motor, and the friction wheel and the ground friction system can reach 0.8.

[0066] The second friction wheel link 14 is a cylindrical link made of stainless steel, one end is fixedly connected with the friction wheel support plate, and the other end is connected with the first friction wheel through a rotating mechanism, and the second friction wheel can rotate 90° around the rotating mechanism along the first friction link under the action of the driving motor, so that the second friction wheel extends to the ground to increase the friction force between the autonomous vehicle and the ground.

[0067] The second friction wheel 15 is made of natural rubber material of medium friction system, one end is fixedly connected with the L-shaped link, and one end of the L-shaped link can rotate under the action of the driving motor, and the friction wheel and the ground friction system can reach 1.0.

[0068] Three-stage friction wheel connecting rod 16, cylindrical structure, the handrail can be telescoped up and down under the action of the drive motor, connected with the three-stage friction wheel, can be extended under the action of the drive motor to drive the three-stage friction wheel to contact with the ground, increase the friction force between the automatic driving vehicle and the ground.

[0069] Three-stage friction wheel 17, the tire width is twice the width of the first-stage friction wheel, fixedly connected with the three-stage friction wheel connecting rod, made of natural rubber material with high friction system, the friction wheel and the ground friction system can reach 1.2, can be extended under the action of the drive motor to drive the three-stage friction wheel to contact with the ground, increase the friction force between the automatic driving vehicle and the ground.

[0070] Please refer to Figure 6 A rain emergency braking adaptive control method based on vehicle networking, using the rain emergency braking adaptive control system based on vehicle networking in the above embodiment, the following steps are executed:

[0071] S1, obtaining the relative distance L, relative speed△V and rain level of the target vehicle and the vehicle in front of it;

[0072] S2, calculating the collision risk value TTC according to the relative distance L and the relative speed△V; TTC=L / △V;

[0073] S3, performing different levels of emergency braking on the target vehicle according to the collision risk value TTC.

[0074] Specifically:

[0075] The data acquisition module is used to obtain the relative distance L, relative speed△V and rain level of the target vehicle and the vehicle in front of it;

[0076] The data analysis module calculates the collision risk value TTC according to the relative distance L and the relative speed△V; TTC=L / △V;

[0077] The adaptive emergency braking module in the cloud platform performs different levels of emergency braking on the target vehicle according to the collision risk value TTC.

[0078] The data acquisition module interacts with the data analysis module through vehicle networking, the data analysis module interacts with the adaptive emergency braking module in the cloud platform through vehicle networking, and the cloud platform sends emergency braking instructions to the target vehicle through vehicle networking.

[0079] Although embodiments of the present application have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A rainy day emergency braking adaptive control system based on the Internet of Vehicles, characterized by: include: A data acquisition module is used to obtain the relative distance L between the target vehicle and the vehicle in front of it, the relative speed △V and the rain level; The data analysis module calculates the collision risk value TTC based on the relative distance L and relative speed ΔV; TTC = L / ΔV; An adaptive emergency braking module performs different levels of emergency braking on the target vehicle according to the collision risk value TTC; The data acquisition module exchanges data with the data analysis module through the Internet of Vehicles, the data analysis module exchanges data with the adaptive emergency braking module in the cloud platform through the Internet of Vehicles, and the cloud platform sends an emergency braking instruction to the target vehicle through the Internet of Vehicles.

2. The adaptive control system for emergency braking in rainy weather based on the Internet of Vehicles according to claim 1, characterized in that: The data acquisition module includes: Roadside cameras, used to obtain two-dimensional images of the target vehicle and the vehicle in front of it on the road; Roadside LiDAR, used to obtain a three-dimensional point cloud image of the target vehicle and the vehicle in front of it on the road; The edge computing unit receives the 2D image and the 3D point cloud image, fuses the two, and calculates the relative distance L and relative speed ΔV between the target vehicle and the vehicle in front of it through the fused data; Rain monitor, obtains rainfall information on the road during the current period.

3. The adaptive control system for emergency braking in rainy weather based on the Internet of Vehicles according to claim 1, characterized in that: An auxiliary braking device is installed on the target vehicle, and the auxiliary braking device includes: a friction wheel support plate mounted on a target vehicle; a lifting mechanism installed on the lower surface of the friction wheel support plate; A friction wheel is installed at the lower end of the lifting mechanism.

4. The adaptive control system for emergency braking in rainy weather based on the Internet of Vehicles according to claim 3 is characterized in that: The lifting mechanism comprises: an upper connecting rod connected to the friction wheel support plate; a lower connecting rod connected to the friction wheel; The lower end of the upper connecting rod and the upper end of the lower connecting rod are connected by a rotating mechanism; A controller that controls the movement of a rotating mechanism.

5. The adaptive control system for emergency braking in rainy weather based on the Internet of Vehicles according to claim 3 is characterized in that: The rain levels include four levels: no rain, light rain, moderate rain, and heavy rain; the auxiliary braking device includes a first-level braking device for light rain, a second-level braking device for moderate rain, and a third-level braking device for heavy rain.

6. The adaptive control system for emergency braking in rainy weather based on the Internet of Vehicles according to claim 5, characterized in that: The performing of different levels of emergency braking on the target vehicle according to the collision risk value TTC includes: When the rain level is light rain, the first-stage brake device is activated so that the friction wheel of the first-stage brake device contacts the ground; When the rain level is moderate, the secondary braking device is activated so that the friction wheels of the primary braking device and the secondary braking device are both in contact with the ground; When the rain level is heavy rain, the three-stage braking device is activated so that the friction wheels of the first-stage braking device, the second-stage braking device and the third-stage braking device are all in contact with the ground.

7. The adaptive control system for emergency braking in rainy weather based on the Internet of Vehicles according to claim 5, characterized in that: The friction coefficient between the friction wheel of the first-stage braking device and the ground is smaller than that between the friction wheel of the second-stage braking device and the ground, and the friction coefficient between the friction wheel of the second-stage braking device and the ground is smaller than that between the friction wheel of the third-stage braking device and the ground.

8. The rainy day emergency braking adaptive control system based on the Internet of Vehicles according to claim 5 is characterized in that: The friction coefficient between the friction wheel of the primary braking device and the ground is not less than 0.8, the friction coefficient between the friction wheel of the secondary braking device and the ground is not less than 1.0, and the friction coefficient between the friction wheel of the primary braking device and the ground is not less than 1.

2.

9. A rainy day emergency braking adaptive control method based on the Internet of Vehicles, characterized in that: include: S1. Obtain the relative distance L, relative speed ΔV, and rain level between the target vehicle and the vehicle in front of it; S2. Calculate the collision risk value TTC based on the relative distance L and the relative speed ΔV; TTC = L / ΔV; S3. Perform different levels of emergency braking on the target vehicle according to the collision risk value TTC.

10. The adaptive control method for emergency braking in rainy weather based on the Internet of Vehicles according to claim 9, characterized in that: The data acquisition module is used to obtain the relative distance L, relative speed △V and rain level between the target vehicle and the vehicle in front of it; The data analysis module calculates the collision risk value TTC based on the relative distance L and relative speed ΔV; TTC = L / ΔV; The adaptive emergency braking module in the cloud platform performs different levels of emergency braking on the target vehicle according to the collision risk value TTC; The data acquisition module exchanges data with the data analysis module through the Internet of Vehicles, the data analysis module exchanges data with the adaptive emergency braking module in the cloud platform through the Internet of Vehicles, and the cloud platform sends an emergency braking instruction to the target vehicle through the Internet of Vehicles.

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