CN111038511B - Method and system for selecting target during vehicle cornering for ADAS and vehicle - Google Patents
Method and system for selecting target during vehicle cornering for ADAS and vehicle Download PDFInfo
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- CN111038511B CN111038511B CN201911338790.3A CN201911338790A CN111038511B CN 111038511 B CN111038511 B CN 111038511B CN 201911338790 A CN201911338790 A CN 201911338790A CN 111038511 B CN111038511 B CN 111038511B
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units, or advanced driver assistance systems for ensuring comfort, stability and safety or drive control systems for propelling or retarding the vehicle
- B60W30/18—Propelling the vehicle
- B60W30/18009—Propelling the vehicle related to particular drive situations
- B60W30/18145—Cornering
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
- B60W40/06—Road conditions
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Abstract
The invention relates to the field of driving assistance systems, in particular to a method and a system for selecting a target when an ADAS vehicle passes a curve and the vehicle. The invention discloses a method for selecting a target when an ADAS vehicle passes a curve, which comprises the following steps: detecting a lane line in front of the vehicle to obtain lane line information; judging whether the lane line information meets a lane line judgment condition; if so, calculating the curvature of the lane line in front of the vehicle in real time; if the curvature of the lane line exceeds a curvature threshold, judging that the vehicle is about to enter a curve; judging whether the historical driving track of the vehicle is close to the historical lane line information or not; and if the distance between the vehicle and the lane line is close, selecting the vehicle closest to the vehicle in the lane line as the target vehicle. Judging whether the historical driving track of the vehicle is close to the historical lane line information or not by detecting the lane line in front of the vehicle; if the vehicle is close to the target vehicle, the vehicle closest to the vehicle in the lane line is selected as the target vehicle, so that the probability of mistakenly selecting the target vehicle when the vehicle passes a curve can be reduced, and the safety of the vehicle in the driving process is improved.
Description
Technical Field
The application relates to the field of driving assistance systems, in particular to a method and a system for selecting a target when an ADAS vehicle passes a curve and the vehicle.
Background
An Advanced Driving Assistance System (ADAS) senses the surrounding environment at any time during the Driving process of an automobile by using various sensors (millimeter wave radar, laser radar, single/binocular camera and satellite navigation) installed on the automobile, collects data, identifies, detects and tracks static and dynamic objects, and performs systematic operation and analysis by combining with navigator map data, thereby enabling drivers to perceive possible dangers in advance and effectively increasing the comfort and safety of automobile Driving. The ADAS comprises: automatic Emergency Braking (AEB) and Adaptive Cruise Control (ACC).
The AEB system measures the distance between the automobile and a front automobile or an obstacle by adopting a radar and a camera, then compares the measured distance with an alarm distance and a safety distance by utilizing a data analysis module, carries out alarm prompt when the measured distance is less than the alarm distance, and starts the AEB system to automatically brake the automobile even if a driver does not have time to step on a brake pedal when the measured distance is less than the safety distance, thereby protecting the driving for safe travel.
The ACC is controlled by a central control system of the automobile and is responsible for recording whether the automobile exists in front of the automobile or not, automatically keeping the distance between the automobile and a front automobile and automatically braking the automobile in emergency.
In the prior art, the running track of the vehicle is generally predicted by only using the current state (speed, steering wheel angle, lateral acceleration and the like) of the vehicle. When a vehicle in front appears on the predicted driving track of the vehicle, the vehicle closest to the vehicle is selected as a target vehicle to track the ACC or an early warning and braking object in an AEB emergency.
However, AEB and ACC in ADAS systems are prone to falsely identifying vehicles in adjacent lanes as target vehicles as they pass through a curve. As shown in fig. 1, when the vehicle is turning over, if the traveling locus is predicted according to the current vehicle state, the target vehicle may be lost. And if other vehicles exist on the adjacent lane, the vehicles in the adjacent lane can be mistakenly identified as target vehicles, so that the system can perform wrong automatic braking. During driving, the automatic braking caused by the error recognition easily causes other potential safety hazards.
In view of the foregoing, it would be desirable to provide a method, system and vehicle that avoids false identifications when the vehicle is negotiating a curve.
Disclosure of Invention
In order to solve the above problems, the present application proposes a method, system and vehicle for selecting a target when a vehicle of an ADAS is cornering.
In one aspect, the present application provides a method for selecting a target when a vehicle of an ADAS is cornering, comprising:
detecting a lane line in front of the vehicle to obtain lane line information;
judging whether the lane line information meets lane line judgment conditions or not;
if so, calculating the curvature of the lane line in front of the vehicle in real time;
if the curvature of the lane line exceeds a curvature threshold, judging that the vehicle is about to enter a curve;
judging whether the historical driving track of the vehicle is close to the historical lane line information or not;
if the distance is close, the vehicle closest to the vehicle in the lane line is selected as the target vehicle.
Further, the method for selecting a target when the ADAS vehicle passes a curve as described above, before detecting a lane line in front of the host vehicle and obtaining lane line information, further includes:
and calculating the predicted track of the vehicle in real time according to the historical running track of the vehicle.
Further, the method for selecting a target when the ADAS vehicle passes a curve as described above, after detecting a lane line in front of the host vehicle and obtaining lane line information, further includes:
and storing the lane line information to obtain historical lane line information.
Further, the method for selecting a target when the ADAS vehicle passes a curve as described above, which determines whether the lane line information satisfies a lane line determination condition, includes:
judging whether the lane line definition in the lane line information is greater than or equal to a definition threshold value or not;
judging whether the length of the lane line in the lane line information is larger than or equal to an AEB or ACC detection distance threshold value at the current speed;
and if the number of the lane lines is larger than or equal to the preset value, the lane line judgment condition is met.
Further, the method for selecting a target when the ADAS vehicle passes a curve as described above, after the determining whether the lane line information satisfies the lane line determination condition, further includes:
and if not, selecting the target vehicle according to the predicted track.
Further, the method for selecting a target when the ADAS vehicle passes a curve as described above, after determining whether the historical driving trajectory of the host vehicle is close to the historical lane line information, further includes:
and if not, selecting the target vehicle according to the predicted track.
In a second aspect, the present application provides a system for selecting a target when a vehicle of an ADAS is cornering, comprising:
the acquisition module is used for detecting a lane line in front of the vehicle to obtain lane line information;
and the processing module is used for judging whether the lane line information meets the lane line judging condition or not, calculating the curvature of the lane line in front of the vehicle in real time if the lane line information meets the lane line judging condition, judging that the vehicle is about to enter a curve if the curvature of the lane line exceeds a curvature threshold value, judging whether the historical driving track of the vehicle is close to the historical lane line information or not, and selecting the vehicle closest to the vehicle in the lane line as the target vehicle if the historical driving track of the vehicle is close to the historical lane line information.
Further, as described above, in the system for selecting a target when the ADAS vehicle passes a curve, the processing module is further configured to calculate the predicted trajectory of the vehicle in real time according to the historical driving trajectory of the vehicle.
Further, the system for selecting a target when the ADAS vehicle passes a curve as described above further includes a storage module, configured to store the lane line information to obtain historical lane line information.
In a third aspect, the present application is directed to a motor vehicle including a system for selecting a target when a vehicle of an ADAS is cornering.
The application has the advantages that: judging whether the historical driving track of the vehicle is close to the historical lane line information or not by detecting the lane line in front of the vehicle; if the vehicle is close to the target vehicle, the vehicle closest to the vehicle in the lane line is selected as the target vehicle, so that the probability of mistakenly selecting the target vehicle when the vehicle is bent over can be greatly reduced, and the safety of the vehicle in the driving process is improved.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating preferred embodiments and are not to be construed as limiting the application. Also, like reference numerals are used to denote like parts throughout the drawings. In the drawings:
FIG. 1 is a schematic view of a vehicle passing a curve;
FIG. 2 is a schematic representation of the steps of a method for selecting targets for a vehicle cornering of an ADAS according to the present application;
FIG. 3 is a schematic flow chart diagram illustrating a method for selecting targets during a vehicle turn-by-turn for an ADAS as provided herein;
fig. 4 is a schematic diagram of a system for selecting targets during vehicle cornering for ADAS according to the present application.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
In a first aspect, according to an embodiment of the present application, there is provided a method for selecting a target when a vehicle of an ADAS passes a curve, as shown in fig. 2, including:
s101, detecting a lane line in front of the vehicle to obtain lane line information;
s102, judging whether the lane line information meets the lane line judgment condition;
s103, if yes, calculating the curvature of the lane line in front of the vehicle in real time;
s104, if the curvature of the lane line exceeds a curvature threshold, judging that the vehicle is about to enter a curve;
s105, judging whether the historical driving track of the vehicle is close to the historical lane line information;
and S106, if the vehicle approaches to the lane line, selecting the vehicle closest to the vehicle in the lane line as the target vehicle.
If the curvature of the lane line is equal to or less than the curvature threshold, the process returns to S101.
Before detecting the lane line in front of the vehicle and obtaining the lane line information, the method further comprises the following steps:
and calculating the predicted track of the vehicle in real time according to the historical running track of the vehicle.
After the lane line in front of the vehicle is detected and the lane line information is obtained, the method further comprises the following steps:
and storing the lane line information to obtain historical lane line information.
Judging whether the lane line information meets the lane line judgment condition or not, including:
judging whether the lane line definition in the lane line information is greater than or equal to a definition threshold value or not;
judging whether the length of the lane line in the lane line information is larger than or equal to an AEB or ACC detection distance threshold value at the current speed;
if the number of the lane lines is larger than or equal to the number of the lane lines, the lane line judgment condition is met.
After judging whether lane line information satisfies the lane line judgment condition, the method further includes:
if not, the target vehicle is selected according to the predicted track of the vehicle.
After judging whether the historical driving track of the vehicle is close to the historical lane line information, the method further comprises the following steps:
and if not, selecting the target vehicle according to the predicted track.
Wherein, S105 includes: and judging whether the historical driving track of the vehicle is close to the lane line track in the historical lane line information. For judging whether the vehicle is running according to the lane line.
The target vehicle includes: the target vehicle of AEB warning and braking and/or the target vehicle of ACC following.
In the embodiment of the present application, it is preferable that the lane line and the curvature of the lane line are recognized by using a camera. When the historical driving track of the vehicle is close to the lane line track in the lane line information, the front target is selected to mainly recognize the in-line of the lane line of the vehicle. When the historical travel track of the host vehicle is greatly different from the lane line track, the target vehicle is selected mainly based on the predicted travel track (predicted track) of the host vehicle.
Next, as shown in fig. 3, the present embodiment will be further explained.
The vehicle detects the lane line in front of the vehicle in real time to obtain lane line information, records the running track of the vehicle in real time to obtain a historical running track, and calculates the predicted track of the vehicle in real time according to the historical running track of the vehicle.
And if the definition of the front lane line is greater than or equal to the definition threshold, the confidence of the camera for identifying the lane line information is higher. And if the length of the identified lane line is enough to meet (is more than or equal to) the AEB or ACC detection distance requirement (detection distance threshold value) at the current speed, calculating the curvature of the lane line in front of the vehicle in real time, and recording the driving track of the vehicle.
And if the identified lane line is not clear, or the current identification length is not enough to meet the detection distance requirement under the current vehicle speed, or the confidence coefficient of the identified lane line is not high, selecting the target by using the predicted track of the vehicle.
When the curvature of the identified lane line exceeds the curvature threshold, the host vehicle is considered to be about to enter the curve.
When it is recognized that a curve is to be entered, the history travel trajectory of the host vehicle and the lane line trajectory in the history lane line information are calculated. If the two are close, the driver is considered to drive according to the lane line.
And detecting targets in the lane lines in real time, and selecting the vehicle closest to the vehicle in the lane lines as an AEB alarming and braking target vehicle and/or an ACC following target vehicle.
And if the difference between the historical driving track of the vehicle and the lane line track in the historical lane line information is far, selecting the target vehicle by using the predicted track of the vehicle.
In a second aspect, according to an embodiment of the present application, there is further provided a system for selecting a target when a vehicle of an ADAS makes a curve, as shown in fig. 4, including:
the acquisition module 101 is used for detecting a lane line in front of the vehicle to obtain lane line information;
the processing module 102 is configured to determine whether the lane line information satisfies a lane line determination condition, calculate a lane line curvature in front of the host vehicle in real time if the lane line information satisfies the lane line determination condition, determine that the host vehicle will enter a curve if the lane line curvature exceeds a curvature threshold, determine whether a historical driving trajectory of the host vehicle is close to the historical lane line information, and select a vehicle closest to the host vehicle in the lane line as a target vehicle if the historical driving trajectory of the host vehicle is close to the historical lane line information.
And the processing module is also used for calculating the predicted track of the vehicle in real time according to the historical running track of the vehicle.
The system also comprises a storage module used for storing the lane line information to obtain historical lane line information.
In a third aspect, according to an embodiment of the present application, there is also provided a motor vehicle comprising a system for selecting a target when a vehicle of an ADAS is cornering.
In the method, whether the historical driving track of the vehicle is close to the historical lane line information is judged by detecting the lane line in front of the vehicle; if the vehicle is close to the target vehicle, the vehicle closest to the vehicle in the lane line is selected as the target vehicle, so that the probability of mistakenly selecting the target vehicle when the vehicle is bent over can be greatly reduced, and the safety of the vehicle in the driving process is improved. The conventional method can only estimate the running track according to the current vehicle state, and can cause target selection errors. However, it is difficult to confirm that the vehicle can actually travel along the lane line only by selecting the target based on the lane line recognized by the camera, and a target recognition error may also be caused. The embodiment of the application judges whether the vehicle runs according to the lane line by comparing the historical running track of the vehicle with the lane line track in the historical lane line information. If the vehicle is judged to run according to the lane line, when the lane line is long enough and the recognition confidence coefficient is high, the lane line is adopted to select the target, and when the vehicle is judged not to run according to the lane line, the target is selected according to the predicted track, so that target recognition errors caused by fixedly using one mode can be avoided, the number of modes for selecting the target is large, the adaptability is high, and the safety is high.
The above description is only for the preferred embodiment of the present application, but the scope of the present application 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 application should be covered within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (9)
1. A method for selecting a target for use in a vehicle cornering situation for an ADAS, comprising:
detecting a lane line in front of the vehicle to obtain lane line information;
judging whether the lane line information meets lane line judgment conditions or not; judging whether the lane line information meets the lane line judgment condition or not, including: judging whether the lane line definition in the lane line information is greater than or equal to a definition threshold value or not; judging whether the length of the lane line in the lane line information is larger than or equal to an AEB or ACC detection distance threshold value at the current speed; if the number of the lane lines is larger than or equal to the number of the lane lines, the lane line judgment condition is met;
if so, calculating the curvature of the lane line in front of the vehicle in real time;
if the curvature of the lane line exceeds a curvature threshold, judging that the vehicle is about to enter a curve;
judging whether the historical driving track of the vehicle is close to the historical lane line information or not;
if the distance is close, the vehicle closest to the vehicle in the lane line is selected as the target vehicle.
2. The method of claim 1, wherein before detecting a lane line in front of the vehicle to obtain the lane line information, the method further comprises:
and calculating the predicted track of the vehicle in real time according to the historical driving track of the vehicle.
3. The method of claim 1, wherein after detecting a lane line in front of the vehicle to obtain the lane line information, the method further comprises:
and storing the lane line information to obtain historical lane line information.
4. The method of claim 1, wherein after determining whether the lane marking information satisfies a lane marking determination condition, further comprising:
and if not, selecting the target vehicle according to the predicted track.
5. The method of claim 1, wherein after determining whether the historical driving path of the vehicle is similar to the historical lane line information, the method further comprises:
and if not, selecting the target vehicle according to the predicted track.
6. A system for selecting a target for a vehicle passing a curve for ADAS, comprising:
the acquisition module is used for detecting a lane line in front of the vehicle to obtain lane line information;
the processing module is used for judging whether the lane line information meets lane line judging conditions or not, if so, calculating the curvature of the lane line in front of the vehicle in real time, if the curvature of the lane line exceeds a curvature threshold value, judging that the vehicle is about to enter a curve, judging whether the historical driving track of the vehicle is close to the historical lane line information or not, and if so, selecting the vehicle closest to the vehicle in the lane line as a target vehicle; judging whether the lane line information meets the lane line judgment condition or not, including: judging whether the lane line definition in the lane line information is greater than or equal to a definition threshold value or not; judging whether the length of the lane line in the lane line information is larger than or equal to an AEB or ACC detection distance threshold value at the current speed; and if the number of the lane lines is larger than or equal to the preset value, the lane line judgment condition is met.
7. The system for selecting a target when a vehicle of an ADAS turns round as claimed in claim 6, wherein the processing module is further configured to calculate the predicted trajectory of the host vehicle in real time according to the historical driving trajectory of the host vehicle.
8. The system for selecting a target when a vehicle of an ADAS passes a curve as claimed in claim 6, further comprising a storage module for storing the lane line information to obtain historical lane line information.
9. A motor vehicle comprising a system for selecting a target when a vehicle for an ADAS passes a curve according to any one of claims 6 to 8.
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| CN201911338790.3A CN111038511B (en) | 2019-12-23 | 2019-12-23 | Method and system for selecting target during vehicle cornering for ADAS and vehicle |
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| CN201911338790.3A CN111038511B (en) | 2019-12-23 | 2019-12-23 | Method and system for selecting target during vehicle cornering for ADAS and vehicle |
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| CN111038511B true CN111038511B (en) | 2021-03-16 |
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| CN114454888B (en) * | 2022-02-22 | 2023-10-13 | 福思(杭州)智能科技有限公司 | Lane line prediction method and device, electronic equipment and vehicle |
| CN116101327A (en) * | 2023-04-14 | 2023-05-12 | 北京集度科技有限公司 | Driving path planning method, device, vehicle and storage medium |
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| CN105667509A (en) * | 2015-12-30 | 2016-06-15 | 苏州安智汽车零部件有限公司 | Curve control system and method applied to automobile adaptive cruise control (ACC) system |
| CN106114511B (en) * | 2016-07-21 | 2018-03-06 | 辽宁工业大学 | A kind of automobile cruise system core target identification method |
| JP2019003234A (en) * | 2017-06-09 | 2019-01-10 | トヨタ自動車株式会社 | Driving support device |
| CN110562253B (en) * | 2018-06-05 | 2020-12-11 | 广州汽车集团股份有限公司 | Automobile self-adaptive cruise curve following control device and control method |
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