CN110749457A - Early warning method and system for depression congestion of road surface by intelligent driving automobile and intelligent driving automobile - Google Patents
Early warning method and system for depression congestion of road surface by intelligent driving automobile and intelligent driving automobile Download PDFInfo
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- CN110749457A CN110749457A CN201911062854.1A CN201911062854A CN110749457A CN 110749457 A CN110749457 A CN 110749457A CN 201911062854 A CN201911062854 A CN 201911062854A CN 110749457 A CN110749457 A CN 110749457A
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- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
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- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
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Abstract
The invention discloses a method and a system for early warning of depression congestion on a road surface by an intelligent driving automobile and the intelligent driving automobile, wherein the method comprises the following steps: step 1, scanning road conditions in front of a vehicle in real time by using a vehicle-mounted laser radar to obtain a point cloud data set of a road surface; the method comprises the following steps of utilizing a vehicle differential GPS to realize lane-level positioning, utilizing a wheel encoder and a steering wheel corner sensor to read wheel pulse signals and yaw rate signals respectively, and calculating a vehicle track; step 2, calculating the geometric center position and the maximum radius of the hollow or the hug; predicting whether a vehicle wheel presses a pothole or a hug or not by combining the vehicle position, converting the geometric center position of the pothole or the hug into a global coordinate position and sending the global coordinate position to a road maintenance system; step 3, judging whether the vehicle is in a condition that a human driver takes over, if so, sending out an early warning prompt by the system to prompt that a pothole or a hug exists in the front; and if the vehicle is under the ACC or IACC auxiliary driving condition, the vehicle carries out path planning so as to realize the obstacle avoidance function.
Description
Technical Field
The invention belongs to the technical field of road detection of automobiles, and particularly relates to a method and a system for early warning of pothole congestion on a road surface by an intelligent driving automobile and the intelligent driving automobile.
Background
With the rapid development of intelligent driving assistance technology for automobiles, passenger cars are becoming popular with the assistance driving technology of L2 level or higher, and at the same time, the demand of people for urbanized roads is becoming higher and higher. People can not only consider safety and convenience when going out, but also pursue the comfort of going out. And in the urban structured road, due to the fact that potholes or bumps appear on the road surface, if the urban structured road cannot be maintained in time, various land mines are distributed for passing vehicles, and some road killers even become road killers. Especially, under the condition of high speed, even traffic accidents can be caused by tire burst, and the life safety of drivers and passengers is threatened.
In the prior art, the early warning system for road surface potholes and bags is mainly realized through contact at home and abroad, namely when a vehicle presses the potholes or bags, the existence of the potholes or bags is found through a vehicle body sensor, and the suspension parameters of the potholes or bags are adjusted in time within a few millimeters, so that the impact of the potholes or bags on tires is reduced to the minimum, and comfortable driving feeling is brought to passengers. The adoption of the contact type early warning mode is easy to miss detection of hollow congestion due to limited wheel coverage, and the mode has high precision, but does not give much reaction time to a driver, thereby easily causing traffic accidents.
Therefore, there is a need to develop a new method and system for warning the road pothole congestion by using an intelligent driving automobile, and an intelligent driving automobile.
Disclosure of Invention
The invention aims to provide an early warning method and system for pothole congestion on a road surface by an intelligent driving automobile and the intelligent driving automobile, which can scan and determine the accurate positions of potholes and congestion in real time and carry out early warning prompt.
The invention relates to a method for early warning of hollow congestion on a road surface by an intelligent driving automobile, which comprises the following steps:
step 1, scanning road conditions in front of a vehicle in real time by using a vehicle-mounted laser radar to obtain a point cloud data set of a road surface; the method comprises the following steps of utilizing a vehicle differential GPS to realize lane-level positioning, utilizing a wheel encoder and a steering wheel corner sensor to read wheel pulse signals and yaw rate signals respectively, and calculating a vehicle track;
step 2, reconstructing a hollow or hug model based on the point cloud data set, and calculating the geometric center position and the maximum radius of the hollow or the hug; predicting whether a vehicle wheel presses a pothole or a hug or not by combining the vehicle position, converting the geometric center position of the pothole or the hug into a global coordinate position and sending the global coordinate position to a road maintenance system;
step 3, judging whether the vehicle is in a condition that a human driver takes over, if so, sending out an early warning prompt by the system to prompt that a pothole or a hug exists in the front; and if the vehicle is under the ACC or IACC auxiliary driving condition, the vehicle carries out path planning so as to realize the obstacle avoidance function.
Further, in the step 2, the geometric center position and the maximum radius of the hollow or the hug are calculated; predicting whether the vehicle wheel will press a pothole or a hug or not by combining the vehicle position, specifically:
calculating the geometric central position (x1, y1) of the pot or the bag, calculating the distance between the geometric central position of the pot or the bag and the farthest point of the pot or the bag, namely the maximum radius d of the bag or the bag, wherein the maximum transverse range covered by the actual pot or the bag is y1 +/-d, extracting the geometric central position (x2, y2) of the vehicle determined by the differential GPS, and obtaining the position of the wheel as
If the left/right wheel is in the range of [ y1-d, y1+ d ], then it is determined that the wheel is over pothole or hug.
The invention relates to an early warning system for depression congestion on a road surface by an intelligent driving automobile, which comprises a data acquisition module, a data processing module and an early warning transmission module;
the data acquisition module comprises a vehicle-mounted laser radar, a vehicle differential GPS, a wheel encoder and a steering wheel corner sensor, and the vehicle-mounted laser radar is used for scanning the road condition in front of the vehicle in real time to acquire a point cloud data set of the road surface; the method comprises the following steps of utilizing a vehicle differential GPS to realize lane-level positioning, utilizing a wheel encoder and a steering wheel corner sensor to read wheel pulse signals and yaw rate signals respectively, and calculating a vehicle track;
the data processing module reconstructs a hollow or hugging model based on the point cloud data set, and calculates the geometric center position and the maximum radius of the hollow or hugging; predicting whether a vehicle wheel presses a pothole or a hug or not by combining the vehicle position, converting the geometric center position of the pothole or the hug into a global coordinate position and sending the global coordinate position to a road maintenance system;
the early warning transmission module is used for judging whether the vehicle is in a condition that a human driver takes over, and if so, the system sends out an early warning prompt to prompt that a pothole or a hug exists in the front; and if the vehicle is under the ACC or IACC auxiliary driving condition, the vehicle carries out path planning so as to realize the obstacle avoidance function.
Further, calculating the geometric center position and the maximum radius of the hollow or the hug; predicting whether the vehicle wheel will press a pothole or a hug or not by combining the vehicle position, specifically:
calculating the geometric central position (x1, y1) of the pot or the bag, calculating the distance between the geometric central position of the pot or the bag and the farthest point of the pot or the bag, namely the maximum radius d of the bag or the bag, wherein the maximum transverse range covered by the actual pot or the bag is y1 +/-d, extracting the geometric central position (x2, y2) of the vehicle determined by the differential GPS, and obtaining the position of the wheel as
If the left/right wheel is in the range of [ y1-d, y1+ d ], then it is determined that the wheel is over pothole or hug.
Further, the vehicle-mounted laser radar is installed right above a windshield of the vehicle.
The invention relates to an intelligent driving automobile, which adopts an early warning system for depression congestion on a road surface.
The invention has the following advantages: the accurate positions of the hollow and the hug can be scanned and determined in real time; on one hand, the information of the position of the hollow or the hug is stored and transmitted to a road maintenance department so as to be maintained in time, and on the other hand, the obstacle avoidance function of the hollow or the hug is completed by calculating the relative position of the hollow hug and the vehicle under the condition that the intelligent driving assistance function is started and the path planning is carried out by the vehicle. The invention adopts a non-contact early warning mode, senses the road condition ahead in real time on the premise of ensuring the high precision of the system, reduces the missing rate of hollow congestion, reserves more reaction time for a driver through early warning, and improves the safety and comfort of drivers and passengers.
Drawings
FIG. 1 is a flow chart of the present invention;
fig. 2 is a schematic diagram of the system determining through pothole or hug conditions.
Detailed Description
The invention will be further explained with reference to the drawings.
In this embodiment, an early warning method for depression congestion on a road surface by an intelligent driving automobile includes the following steps:
step 1, scanning road conditions in front of a vehicle in real time by using a vehicle-mounted laser radar to obtain a point cloud data set of a road surface; the vehicle differential GPS is used for realizing lane-level positioning, a wheel encoder and a steering wheel corner sensor are used for respectively reading a wheel pulse signal and a yaw rate signal, and a vehicle track is calculated.
Step 2, reconstructing a hollow or hug model based on the point cloud data set, and calculating the geometric center position and the maximum radius of the hollow or the hug; and predicting whether the vehicle wheel presses the pothole or the hug or not by combining the vehicle position, converting the geometric center position of the pothole or the hug into a global coordinate position and sending the global coordinate position to a road maintenance system.
Step 3, judging whether the vehicle is in a condition that a human driver takes over, if so, sending out an early warning prompt by the system to prompt that a pothole or a hug exists in the front; and if the vehicle is under the ACC or IACC auxiliary driving condition, the vehicle carries out path planning so as to realize the obstacle avoidance function.
As shown in fig. 1, the concrete flow of the early warning method for road depression congestion by an intelligent driving automobile is as follows:
1) and real-time scanning is carried out on the road condition in front of the vehicle through the vehicle-mounted laser radar.
2) The cloud data of the measured point is measured based on spherical coordinates, the X0Y surface is transversely scanned, the Z axis is vertical to the transverse scanning surface, narrow laser pulses emitted by a laser pulse emitter sequentially scan a front scanning area, the phase difference of each laser pulse passing through the surface of the measured object and returning to a scanning head is measured to calculate the distance S, and meanwhile, a precise clock control encoder is arranged in the laser pulse scanning device to synchronously measure the transverse scanning angle observed value α and the longitudinal scanning angle observed value theta of each laser pulse, so that the coordinates of any point of the pit or the dug surface can be obtained as (Scos theta cos α, Scos theta sin α and Ssin α).
3) On the other hand, while the lane-level positioning of the vehicle is realized through the differential GPS, the vehicle information such as wheel pulse, yaw rate and the like is recorded by utilizing a wheel speed encoder, a steering wheel angle sensor and the like, so that the vehicle track is calculated.
4) Data extraction: after a point cloud data set of the surrounding environment is obtained through scanning, a ground parameter model is calculated after preprocessing such as filtering and denoising, and then abnormal points such as potholes or hugs are extracted.
5) Model reconstruction: and performing interpolation, stitching and smoothing treatment on the extracted abnormal points of a series of hollow congestion packets, and reconstructing data points by adopting a local projection triangulation algorithm to obtain a final hollow or congestion packet model.
6) Parameter comparison: the reconstructed pothole or hug model is compared with potholes or hugs having a greater impact on vehicle travel as defined in the section of traffic "road asphalt pavement maintenance technical Specification" (JTJ073.2-2001) from average depth (height), maximum radius. If the average depth (height) of the pothole or hug model is greater than a set threshold (e.g., 2cm) and the maximum radius is greater than 3cm, then it is determined that a pothole or hug exists ahead. In this embodiment, the average depth is calculated if the hole is hollow, and the average height is calculated if the hole is filled.
7) As shown in fig. 2, the position calculation: calculating the geometric centre position (x1, y1) of the hole or congestion, finding the hole or congestionThe distance between the geometric center position of the bag and the farthest point of the hollow or the hollow is the maximum radius d of the hollow or the hollow, then the maximum transverse range covered by the actual hollow or the hollow is y1 +/-d, the geometric center position (x2, y2) of the vehicle determined by the differential GPS is extracted, and the position of the wheel is obtained
8) And (3) system judgment: through dead reckoning, if the left/right wheel is in the range of [ y1-d, y1+ d ], the wheel is judged to be pressed by a pothole or a hug, and a schematic diagram can be judged through pothole or hug conditions by referring to the system in FIG. 2.
9) When the presence of a pothole or hug is identified, the system will transmit its location information to the road maintenance system for timely maintenance of the road surface.
10) After the relative position of the pothole or the hug and the vehicle is confirmed, whether the system is taken over by a human driver is judged, and if yes, the system sends out early warning to prompt the pothole or the hug in front; and if the vehicle is under the ACC or IACC auxiliary driving condition, the vehicle carries out path planning, and the system actively avoids the obstacle.
The invention relates to an early warning system for depression congestion on a road surface by an intelligent driving automobile, which comprises a data acquisition module, a data processing module and an early warning transmission module; the data acquisition module is electrically connected with the data processing module, and the data processing module is electrically connected with the early warning transmission module.
The data acquisition module comprises a vehicle-mounted laser radar, a vehicle differential GPS, a wheel encoder and a steering wheel corner sensor, and the vehicle-mounted laser radar is used for scanning the road condition in front of the vehicle in real time to acquire a point cloud data set of the road surface; the vehicle differential GPS is used for realizing lane-level positioning, a wheel encoder and a steering wheel corner sensor are used for respectively reading a wheel pulse signal and a yaw rate signal, and a vehicle track is calculated.
The data processing module reconstructs a hollow or hugging model based on the point cloud data set, and calculates the geometric center position and the maximum radius of the hollow or hugging; and predicting whether the vehicle wheel presses the pothole or the hug or not by combining the vehicle position, converting the geometric center position of the pothole or the hug into a global coordinate position and sending the global coordinate position to a road maintenance system.
The early warning transmission module is used for judging whether the vehicle is in a condition that a human driver takes over, and if so, the system sends out an early warning prompt to prompt that a pothole or a hug exists in the front; and if the vehicle is under the ACC or IACC auxiliary driving condition, the vehicle carries out path planning so as to realize the obstacle avoidance function.
In this embodiment, the vehicle-mounted laser radar is installed right above a windshield of a vehicle.
In this embodiment, an intelligent driving automobile adopts the early warning system for road surface depression congestion as described in this embodiment.
Claims (6)
1. The method for early warning of the pothole congestion on the road surface by intelligently driving the automobile is characterized by comprising the following steps of:
step 1, scanning road conditions in front of a vehicle in real time by using a vehicle-mounted laser radar to obtain a point cloud data set of a road surface; the method comprises the following steps of utilizing a vehicle differential GPS to realize lane-level positioning, utilizing a wheel encoder and a steering wheel corner sensor to read wheel pulse signals and yaw rate signals respectively, and calculating a vehicle track;
step 2, reconstructing a hollow or hug model based on the point cloud data set, and calculating the geometric center position and the maximum radius of the hollow or the hug; predicting whether a vehicle wheel presses a pothole or a hug or not by combining the vehicle position, converting the geometric center position of the pothole or the hug into a global coordinate position and sending the global coordinate position to a road maintenance system;
step 3, judging whether the vehicle is in a condition that a human driver takes over, if so, sending out an early warning prompt by the system to prompt that a pothole or a hug exists in the front; and if the vehicle is under the ACC or IACC auxiliary driving condition, the vehicle carries out path planning so as to realize the obstacle avoidance function.
2. The method for early warning of road surface depression congestion by using intelligent driving automobile as claimed in claim 1, wherein the method comprises the following steps: in the step 2, calculating the geometric center position and the maximum radius of the hollow or the hug; predicting whether the vehicle wheel will press a pothole or a hug or not by combining the vehicle position, specifically:
calculating the geometric central position (x1, y1) of the pot or the bag, calculating the distance between the geometric central position of the pot or the bag and the farthest point of the pot or the bag, namely the maximum radius d of the bag or the bag, wherein the maximum transverse range covered by the actual pot or the bag is y1 +/-d, extracting the geometric central position (x2, y2) of the vehicle determined by the differential GPS, and obtaining the position of the wheel as
If the left/right wheel is in the range of [ y1-d, y1+ d ], then it is determined that the wheel is over pothole or hug.
3. The utility model provides an intelligence driving car is to road surface depression embrace a packet early warning system which characterized in that: the system comprises a data acquisition module, a data processing module and an early warning transmission module;
the data acquisition module comprises a vehicle-mounted laser radar, a vehicle differential GPS, a wheel encoder and a steering wheel corner sensor, and the vehicle-mounted laser radar is used for scanning the road condition in front of the vehicle in real time to acquire a point cloud data set of the road surface; the method comprises the following steps of utilizing a vehicle differential GPS to realize lane-level positioning, utilizing a wheel encoder and a steering wheel corner sensor to read wheel pulse signals and yaw rate signals respectively, and calculating a vehicle track;
the data processing module reconstructs a hollow or hugging model based on the point cloud data set, and calculates the geometric center position and the maximum radius of the hollow or hugging; predicting whether a vehicle wheel presses a pothole or a hug or not by combining the vehicle position, converting the geometric center position of the pothole or the hug into a global coordinate position and sending the global coordinate position to a road maintenance system;
the early warning transmission module is used for judging whether the vehicle is in a condition that a human driver takes over, and if so, the system sends out an early warning prompt to prompt that a pothole or a hug exists in the front; and if the vehicle is under the ACC or IACC auxiliary driving condition, the vehicle carries out path planning so as to realize the obstacle avoidance function.
4. The system of claim 3, wherein the intelligent driving vehicle is used for early warning of road depression congestion, and comprises: calculating the geometric center position and the maximum radius of the hollow or the hug; predicting whether the vehicle wheel will press a pothole or a hug or not by combining the vehicle position, specifically:
calculating the geometric central position (x1, y1) of the pot or the bag, calculating the distance between the geometric central position of the pot or the bag and the farthest point of the pot or the bag, namely the maximum radius d of the bag or the bag, wherein the maximum transverse range covered by the actual pot or the bag is y1 +/-d, extracting the geometric central position (x2, y2) of the vehicle determined by the differential GPS, and obtaining the position of the wheel as
If the left/right wheel is in the range of [ y1-d, y1+ d ], then it is determined that the wheel is over pothole or hug.
5. The system of claim 3 or 4, wherein the system comprises: the vehicle-mounted laser radar is arranged right above a vehicle windshield.
6. The utility model provides an intelligent driving car which characterized in that: early warning system for road pothole congestion using the intelligent driving automobile as claimed in any one of claims 3 to 5.
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Cited By (12)
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| CN111516692A (en) * | 2020-04-20 | 2020-08-11 | 重庆长安汽车股份有限公司 | Control system and method for vehicle running on hollow road surface |
| CN112356851A (en) * | 2020-04-22 | 2021-02-12 | 青岛慧拓智能机器有限公司 | Planning method for riding pit of unmanned mine car |
| CN112455467A (en) * | 2021-01-11 | 2021-03-09 | 湖南汽车工程职业学院 | Early warning method for depression congestion of road surface by intelligent driving automobile |
| CN112991737A (en) * | 2021-03-10 | 2021-06-18 | 英博超算(南京)科技有限公司 | Method for collecting road condition information by automobile |
| CN113029597A (en) * | 2021-04-06 | 2021-06-25 | 北京理工大学 | Unmanned vehicle full-line control chassis test system |
| CN113370982A (en) * | 2021-06-17 | 2021-09-10 | 北京百度网讯科技有限公司 | Method and device for detecting bumpy area of road surface, electronic equipment and storage medium |
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| CN115503817A (en) * | 2022-08-30 | 2022-12-23 | 大运汽车股份有限公司 | Steering power-assisted mode switching control system and method through road surface conditions |
| CN115546749A (en) * | 2022-09-14 | 2022-12-30 | 武汉理工大学 | Road surface depression detection, cleaning and avoidance method based on camera and laser radar |
| CN115871622A (en) * | 2023-01-19 | 2023-03-31 | 重庆赛力斯新能源汽车设计院有限公司 | Driving assistance method based on drop road surface, electronic device and storage medium |
| CN116620296A (en) * | 2023-05-18 | 2023-08-22 | 中公高远(北京)汽车检测技术有限公司 | Danger avoiding method and system for intelligent automobile |
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| CN111516692A (en) * | 2020-04-20 | 2020-08-11 | 重庆长安汽车股份有限公司 | Control system and method for vehicle running on hollow road surface |
| CN112356851B (en) * | 2020-04-22 | 2021-11-05 | 青岛慧拓智能机器有限公司 | Planning method for riding pit of unmanned mine car |
| CN112356851A (en) * | 2020-04-22 | 2021-02-12 | 青岛慧拓智能机器有限公司 | Planning method for riding pit of unmanned mine car |
| CN112455467A (en) * | 2021-01-11 | 2021-03-09 | 湖南汽车工程职业学院 | Early warning method for depression congestion of road surface by intelligent driving automobile |
| CN112991737A (en) * | 2021-03-10 | 2021-06-18 | 英博超算(南京)科技有限公司 | Method for collecting road condition information by automobile |
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| CN113370982A (en) * | 2021-06-17 | 2021-09-10 | 北京百度网讯科技有限公司 | Method and device for detecting bumpy area of road surface, electronic equipment and storage medium |
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| CN115503817A (en) * | 2022-08-30 | 2022-12-23 | 大运汽车股份有限公司 | Steering power-assisted mode switching control system and method through road surface conditions |
| CN115546749A (en) * | 2022-09-14 | 2022-12-30 | 武汉理工大学 | Road surface depression detection, cleaning and avoidance method based on camera and laser radar |
| CN115871622A (en) * | 2023-01-19 | 2023-03-31 | 重庆赛力斯新能源汽车设计院有限公司 | Driving assistance method based on drop road surface, electronic device and storage medium |
| WO2024159803A1 (en) * | 2023-01-31 | 2024-08-08 | 东风汽车集团股份有限公司 | Intelligent driving system and method for identifying pit and upheaval of road |
| CN116620296A (en) * | 2023-05-18 | 2023-08-22 | 中公高远(北京)汽车检测技术有限公司 | Danger avoiding method and system for intelligent automobile |
| CN116620296B (en) * | 2023-05-18 | 2024-03-12 | 中公高远(北京)汽车检测技术有限公司 | Danger avoiding method and system for intelligent automobile |
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Application publication date: 20200204 |