CN111474934A - Personalized cruise control system based on high-precision map - Google Patents
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- G05D1/02—Control of position or course in two dimensions
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- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0221—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving a learning process
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
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Abstract
The invention discloses an individualized cruise control system based on a high-precision map. The system comprises a data receiving module, a driver habit analyzing module and a high-precision map data module, wherein the data receiving module is used for collecting vehicle network messages and extracting habit information of a current driver, the driver habit analyzing module is used for determining the habit type of the current driver, and the high-precision map data module is used for sending data information of a high-precision map to the data calculating module; the ESC system module is used for outputting corresponding torque through the power transmission according to the expected torque value. The invention can analyze the habit of the driver, classify the driver, control the following distance of the self-adaptive cruise system, and individually customize the self-adaptive cruise system according to the habit of the driver, thereby meeting the driving requirements of different crowds and improving the driving experience.
Description
Technical Field
The invention belongs to the technical field of automobile control, and particularly relates to a high-precision map-based personalized cruise control system.
Background
In recent years, with the increase of the degree of automobile intelligence, the requirements of automobile automatic driving technology on electronic maps are increasing. In order to realize accurate control of vehicles, the data accuracy of the common navigation map meter level obviously does not meet the requirement, and the concept of a high-accuracy map is developed. Compared with a common map, the high-precision map provides richer information, such as lane number, shape, lane gradient, lane width, lane curvature, lane roll, lane course and the like. Map data is mainly divided into vector data and raster data, the vector data is composed of discrete points, and geometric information (the geometric shape of the map data) and attribute information (area, length, number, name and the like) are stored; the grid data mainly comprises an aerial photograph image and a satellite image. Data information of a high-precision map is essential for precise control of the vehicle.
The self-adaptive cruise control system is derived from a cruise control technology, can acquire relative distance information with a front vehicle according to a radar sensor at the front part of the vehicle, and simultaneously acquires speed information of the vehicle by a wheel speed sensor. The system can be coordinated with a brake anti-lock system and an engine control system to brake the vehicle properly when the relative vehicle distance is smaller than a threshold value, and the following distance under different vehicle speeds is kept. The self-adaptive cruise system relieves the long-time car following pressure of a driver to a certain extent, and improves the driving comfort of the driver.
The driving habit refers to the relatively stable and inherent behavior tendency of the driver, and the driving habit difference among different individuals is obvious. The driving behavior of the driver comprises information fusion, comprehensive judgment, reasoning, decision making and the like, and finally the operations of direction control, driving control, braking control and the like required by the automobile are generated through the reaction of neuromuscular, so that the driver has randomness, self-adaptability, discreteness and time-varying property. The influence factors are complex and changeable, the driving behaviors can be obviously influenced by different ages, sexes, characters, driving ages, driving proficiency degrees, operation styles and the like, and even if the same driver drives the vehicle, the driving behaviors in different environments and different physiological and psychological states are different. At present, the vehicle cruise system does not consider the habit of a driver, so that the driving experience of the driver is greatly reduced.
Disclosure of Invention
The invention aims to solve the defects of the background technology and provide a personalized cruise control system based on a high-precision map.
The technical scheme adopted by the invention is as follows: a high-precision map-based personalized cruise control system comprises
The data receiving module is used for collecting vehicle network messages, extracting the habit information of the current driver and sending the habit information to the driver habit analysis module,
the driver habit analysis module is internally stored with a trained driver model and used for determining the habit type of the current driver according to the received driver habit information and the driver model and sending the habit type to the data calculation module;
the high-precision map data module is used for sending data information of the high-precision map to the data calculation module;
the data calculation module is used for calculating the following time interval and the acceleration value under the corresponding habit type according to the received habit type of the driver and the data information of the high-precision map, calculating an expected torque by combining the vehicle speed, and outputting the expected torque to the ESC system module;
and the ESC system module is used for outputting corresponding torque through power transmission according to the received expected torque value so as to realize vehicle control.
Further, the vehicle network message includes a steering wheel angle signal, a brake pedal signal, an accelerator pedal signal, and headway information.
Further, after receiving the driver habit information, the driver habit analysis module performs initialization identification according to the trained driver model, performs online identification through a hidden Markov algorithm to obtain a maximum likelihood value, and judges the habit type of the driver according to the maximum likelihood value.
Further, the habit type of the driver is any one of an aggressive type, a general type and a robust type, and each habit type has a corresponding maximum likelihood value.
Further, the data information of the high-precision map comprises road information and POI information, the road information comprises lane width, lane material, lane number, lane gradient, curvature and elevation information, and the POI information comprises traffic light information and speed limit information.
The data calculation module calculates the economically optimal vehicle following speed according to the economic mode, the habit type of the driver and the data information of the high-precision map; after the driver selects the time mode and outputs the time mode to the data calculation module, the data calculation module calculates the running speed with the shortest time according to the time mode, the habit type of the driver and the data information of the high-precision map;
the data calculation module determines expected torque in the cruising process according to the economically optimal following speed or the driving speed with the shortest time and sends the expected torque to the ESC system module.
Furthermore, the system also comprises a user data storage module, wherein the data storage module is used for storing the habit information of the drivers, and extracting the corresponding habit information of the drivers when different drivers drive and inputting the extracted habit information to the data calculation module.
The invention can analyze the habit of the driver, classify the driver, control the following distance of the self-adaptive cruise system, select a time mode and an economic mode, individually customize the self-adaptive cruise system according to the habit of the driver, meet the driving requirements of different crowds and improve the driving experience.
Drawings
FIG. 1 is a schematic diagram of the system of the present invention.
FIG. 2 is a schematic diagram of a vehicle behavior data acquisition platform according to the present invention.
FIG. 3 is a schematic diagram of a driver behavior training and analyzing module according to the present invention.
Fig. 4 is a flow chart of the personalized control based on the high-precision map.
Detailed Description
The following further describes embodiments of the present invention with reference to the drawings. It should be noted that the description of the embodiments is provided to help understanding of the present invention, but the present invention is not limited thereto. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
Fig. 1 is a schematic diagram of the personalized cruise control system of the present invention, which includes a data collection module 1, a driver behavior analysis module 2, a user data storage module 3, a mode selection module 4, a data calculation module 5, a high-precision map data module 7, and an ESC system module 6.
The data collection module 1 collects vehicle network messages from a finished vehicle CAN network, wherein the vehicle network messages comprise information such as steering wheel corner signals, brake pedal signals, accelerator pedal signals, vehicle head time interval and the like, and extracts driver habit information from the information and inputs the information into the driver habit analysis module.
The driver habit analysis module 2 stores the trained driver model, after receiving the driver habit information, the driver habit analysis module carries out initialization identification according to the trained driver model, then carries out online identification through a hidden Markov algorithm to obtain a maximum likelihood value, judges the habit type of the driver according to the maximum likelihood value and outputs the habit type to the user data storage module. The habit type of the driver is any one of an aggressive type, a general type and a robust type, and each habit type has a corresponding maximum likelihood value.
The user data storage module 3 is used for storing the habit information of the drivers, and extracting the corresponding habit information of the drivers when different drivers drive and inputting the habit information into the data calculation module.
The mode selection module 4 is internally provided with two modes for manual selection of a driver and outputs the two modes to the data calculation module, wherein the two modes are an economy mode and a time mode respectively.
The high-precision map data module 7 stores relevant road high-precision map elements including road information and POI information, wherein the road information includes information such as lane width, lane material, lane number, lane gradient, curvature and elevation, and the POI information includes information such as traffic lights and speed limit.
The data calculation module 5 receives mode information, user habit types and high-precision map data information selected by a user, calculates different following time distances and acceleration values under different user habit types, calculates following speeds under different modes by means of the information of the high-precision map and updates the following speeds in real time, calculates expected torque through the following time distances, the acceleration and the following speed values, and outputs the expected torque to the ESC system module. If the driver selects the economic mode and outputs the economic mode to the data calculation module, the data calculation module calculates the economically optimal vehicle following speed according to the economic mode, the habit type of the driver and the data information of the high-precision map; and when the driver selects the time mode and outputs the time mode to the data calculation module, the data calculation module calculates the running speed with the shortest time according to the time mode, the habit type of the driver and the data information of the high-precision map, wherein the shortest time is the shortest time from the departure place to the destination.
And the ESC system module 6 receives the expected torque value and outputs corresponding torque through the power transmission, and finally vehicle control is realized.
The preparation work before the cruise system is formed needs to build a real vehicle acquisition platform for driver habit data, as shown in figure 2, the real vehicle acquisition platform mainly comprises a front vehicle and a main vehicle, wherein the front vehicle and the main vehicle are positioned by a Mobile Radio antenna and a GPS antenna, an RT3002 system and an RT-Range system are used as sensors, RT-Range equipment comprises a Hunter and a Target, and data acquisition equipment adopts a CAN Vector for acquisition. The two acquisition modes read data through a CAN bus.
The built real vehicle data acquisition platform mainly comprises a driving scene, a driver and the configuration of acquisition equipment. The driving scene is mainly based on urban structured roads with high adhesion coefficients, driving data of a large number of drivers are collected by setting different working conditions, and three driving habit models are obtained by extracting habit information features and performing particle swarm clustering. The trained driver model is led into a driver habit analysis module, the driver habit can be classified by a hidden Markov algorithm in a very short time after the driver gets on the vehicle and drives, and the driving style of the driver, such as aggressive type, general type and steady type, is judged. After the self-adaptive cruise system is started, the following distance can be self-adaptively adjusted according to the habit of the driver, and the requirements of the driver with different driving habits can be met.
In fig. 3, the system preprocesses the acquired data. The acceleration root mean square of the main vehicle, the reaction time of a driver and the headway are used as the representation quantity of the driving habit, and the change rate of the speed of the main vehicle in a specific time interval reaches the sampling time corresponding to the threshold value for the first time and is used as the reaction time of the driver. The headway is defined as the ratio of the relative distance between two vehicles and the speed of the main vehicle in the stable running state of the front and the rear vehicles, and the statistic of the ratio represents the 'clinging' degree of the driver to the vehicle. The method is characterized in that a driver characteristic model is established through a machine learning method, and mainly comprises a particle swarm clustering process and a clustering center and habit type mapping process. And importing the trained driver model into a system, acquiring the driving habit characteristics of the current driver in real time, initializing an identification model, carrying out online identification through a hidden Markov algorithm, and outputting the maximum likelihood values of the three driver models.
In fig. 4, the parameters identified by the driver's habit in the system update the default parameters in the original adaptive cruise, and the driver is allowed to select whether to operate in the economy mode or the time mode. The vehicle acquires attribute information and POI information in a high-precision map, such as lane material, curvature, elevation, gradient, traffic light, speed limit board and the like. Under the economic mode, the automobile can obtain the information of traffic lights and speed limit in a high-precision map, and the information of road gradient, curvature and the like to automatically calculate the current oil consumption and automatically calculate the optimal economical following speed. Under the time mode, the automobile follows the automobile at the highest speed limit speed under the premise of safety according to the road attribute data extracted from the high-precision map. And introducing the calculated speed per hour signal into a vehicle power system to obtain the expected torque and the expected deceleration during cruising.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Those not described in detail in this specification are within the skill of the art.
Claims (7)
1. A personalized cruise control system based on a high-precision map is characterized in that: comprises that
The data receiving module is used for collecting vehicle network messages, extracting the habit information of the current driver and sending the habit information to the driver habit analysis module,
the driver habit analysis module is internally stored with a trained driver model and used for determining the habit type of the current driver according to the received driver habit information and the driver model and sending the habit type to the data calculation module;
the high-precision map data module is used for sending data information of the high-precision map to the data calculation module;
the data calculation module is used for calculating the following time interval and the acceleration value under the corresponding habit type according to the received habit type of the driver and the data information of the high-precision map, calculating an expected torque by combining the vehicle speed, and outputting the expected torque to the ESC system module;
and the ESC system module is used for outputting corresponding torque through power transmission according to the received expected torque value so as to realize vehicle control.
2. A high accuracy map based personalized cruise control system according to claim 1, characterized in that: the vehicle network message comprises a steering wheel corner signal, a brake pedal signal, an accelerator pedal signal and headway information.
3. A high accuracy map based personalized cruise control system according to claim 1, characterized in that: and after receiving the driver habit information, the driver habit analysis module carries out initialization identification according to the trained driver model, then carries out online identification through a hidden Markov algorithm to obtain a maximum likelihood value, and judges the habit type of the driver according to the maximum likelihood value.
4. A high accuracy map based personalized cruise control system according to claim 1, characterized in that: the habit type of the driver is any one of an aggressive type, a general type and a robust type, and each habit type has a corresponding maximum likelihood value.
5. A high accuracy map based personalized cruise control system according to claim 1, characterized in that: the data information of the high-precision map comprises road information and POI information, wherein the road information comprises lane width, lane material, lane number, lane gradient, curvature and elevation information, and the POI information comprises traffic light information and speed limit information.
6. A high accuracy map based personalized cruise control system according to claim 1, characterized in that: the vehicle-following driving speed calculation system is characterized by further comprising a mode selection module, wherein two modes are arranged in the mode selection module and are manually selected by a driver, the two modes are respectively an economic mode and a time mode, after the economic mode is selected by the driver and output to the data calculation module, the data calculation module calculates the economically optimal vehicle-following speed according to the economic mode, the habit type of the driver and data information of a high-precision map; after the driver selects the time mode and outputs the time mode to the data calculation module, the data calculation module calculates the running speed with the shortest time according to the time mode, the habit type of the driver and the data information of the high-precision map;
the data calculation module determines expected torque in the cruising process according to the economically optimal following speed or the driving speed with the shortest time and sends the expected torque to the ESC system module.
7. A high accuracy map based personalized cruise control system according to claim 1, characterized in that: the driver habit information acquisition system further comprises a user data storage module, wherein the data storage module is used for storing driver habit information, and extracting corresponding driver habit information to input the information to the data calculation module when different drivers drive.
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