CN109035121B - Single-sensor data association preprocessing method - Google Patents
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Abstract
The invention discloses a single-sensor data association preprocessing method, which comprises the following steps: step 1, receiving a sensor CAN message, analyzing the sensor CAN message, calculating the relative speed of a vehicle and a target object detected by a sensor, adding a timestamp to the sensor CAN message, and obtaining a target detection position inputV with the timestamp; step 2, judging whether the target expected position trackV is empty; step 3, if the target expected position trackV is empty, trackV = inputV; otherwise, entering step 4; step 4, judging whether the timestamp difference T _ input-T _ track is smaller than a preset value A or not; if not, then rack v = inputV; if yes, entering step 5; and 5, estimating the trackV at the T _ input moment according to the timestamp difference and the relative speed of the vehicle and the target object. The invention can improve the stability of the system.
Description
Technical Field
The invention belongs to the technical field of sensor data processing, and particularly relates to a single sensor data association preprocessing method.
Background
With the modern technology progress and the scientific and technological development, people have higher and higher requirements on vehicles, and intelligent control of the vehicles, such as unmanned vehicles, self-adaptive cruise, active safety of the vehicles, full-automatic parking and other functions, is required to be realized on the basis of meeting the traditional driving. The sensor modules (such as laser radar, millimeter wave radar, camera and the like) for sensing the environment and the situation are important components in intelligent control, and are important for processing data acquired by a single sensor and fusing data acquired by a plurality of sensors.
Therefore, it is necessary to develop a new single-sensor data correlation preprocessing method.
Disclosure of Invention
The invention aims to provide a single-sensor data association preprocessing method, which is used for reducing the stability of a system, reducing the data processing amount and improving the efficiency of data preprocessing.
The single-sensor data association preprocessing method comprises the following steps of:
the method comprises the following steps:
step a, receiving a sensor CAN message, analyzing the sensor CAN message, calculating the relative speed of a vehicle and a target object detected by a sensor, adding a timestamp to the sensor CAN message, and obtaining a target detection position inputV with the timestamp, wherein the target detection position inputV = { input1, input2, input3, …, input x, … and input } and m represents the number of the target object contained in the sensor data of the current frame of the sensor;
step b, judging whether the target expected position trackV is empty, wherein the target expected position trackV = { track1, track2, track3, track x, …, track }, and n represents the number of target objects contained in the previous frame of sensor data;
step c, if the target expected position trackV is empty, setting the target expected position trackV as a current target detection position inputV; if the target expected position trackV is not empty, entering step d;
step d, judging whether the timestamp difference between the target expected position trackV and the target detection position inputV is smaller than a preset value A or not, wherein the timestamp of the target expected position trackV is T _ track, and the timestamp of the target detection position inputV is T _ input; if not, setting the target expected position trackV as the current target detection position inputV; if yes, entering step e;
and e, estimating the target expected position inputV at the time of T _ input according to the time stamp difference and the relative speed of the vehicle and the target object.
Further, in the step a, calculating a relative speed between the vehicle and the target object detected by the sensor, specifically:
acquiring the absolute speed of a target object from a sensor;
acquiring the absolute speed of the vehicle from the CAN bus;
the relative speed of the target object and the host vehicle is calculated according to the absolute speed of the target object and the host vehicle.
Further, in step e, a calculation formula of trackV at the time of T _ input is as follows:
target expected position trackV at time T _ input = target expected position trackV + (T _ input-T _ track) at time T _ track, relative speed of the host vehicle and the target object.
Further, the preset value A is 0.3 s.
The invention has the following advantages: before the data association between the target detection position and the target prediction position is performed, a precondition judgment condition is set, that is, whether the timestamp difference between the target expected position and the target detection position is smaller than a preset value A or not is judged, if the timestamp difference is larger than the preset value A, the sensor is in a fault (for example, the sensor has a communication fault), and if the sensor continues to be estimated at the moment, the estimated result is inaccurate and has a large error. The invention adopts the processing mode of directly discarding the estimated value when the time stamp difference is more than or equal to the preset value A, can avoid the influence of transient fault of the sensor on the system, and thus provides the stability of the system. In addition, the invention estimates the time difference only when the time difference is less than the preset value A, thereby reducing the data processing amount and improving the efficiency of data preprocessing.
Drawings
FIG. 1 is a flow chart of the present invention.
Detailed Description
The invention will be further explained with reference to the drawings.
As shown in fig. 1, the single-sensor data association preprocessing method of the present invention includes the following steps:
step a, the sensor sends sensed information to the processor through the CAN bus, the processor receives the CAN message of the sensor, analyzes the CAN message of the sensor, calculates the relative speed of the vehicle and a target object detected by the sensor, and adds a timestamp to the CAN message of the sensor to obtain a target detection position inputV with the timestamp. Target detection position inputV = { input1, input2, input3, …, input x, …, input m }, where m denotes the number of target objects included in sensor data of the sensor current frame;
and b, judging whether the target expected position trackV is empty or not. Where the target expected position trackV represents a set of target expected positions, the target expected position trackV = { track1, track2, track3, track x, …, track }, and n represents the number of target objects included in the last frame of sensor data. Only in the initial state, the trackV is empty.
Step c, if the target expected position trackV is empty, setting the target expected position trackV as a current target detection position inputV, namely, trackV = inputV; if the target expected position trackV is not empty, entering step d;
step d, judging whether a timestamp difference (i.e. T _ input-T _ track) between the target expected position trackV (timestamp is T _ track) and the target detection position inputV (timestamp is T _ input) is smaller than a preset value a (in this embodiment, a is 0.3 seconds); if T _ input-T _ track is less than A, the target position detected by the current sensor is considered to be a credible target position, and then the step e is carried out; otherwise, T _ input-T _ track ≧ A, which indicates that the sensor is out of order (e.g., sensor communication failure), the target expected location trackV is set to the current target detection location inputV, i.e., the estimate is discarded directly.
E, synchronizing the existing target expected position trackV (with the timestamp of T _ track) to the current target detection position inputV time (T _ input): and estimating the trackV at the T _ input moment according to the timestamp difference and the relative speed of the host vehicle and the target object. Such as: t _ input-T _ track = as, the relative velocity is bm/s, and the target expected position trackV at time T _ input = target expected position trackV + a × b at time T _ track.
In this embodiment, in the step a, the calculating the relative speed between the host vehicle and the target object detected by the sensor specifically includes: acquiring the absolute speed of a target object from a sensor; acquiring the absolute speed of the vehicle from the CAN bus; the relative speed of the target object and the host vehicle is calculated according to the absolute speed of the target object and the host vehicle.
In this embodiment, a precondition is set before the data association between the target detection position and the target predicted position is performed, that is, whether the timestamp difference between the target expected position and the target detection position is smaller than a preset value a is determined, if the timestamp difference is greater than the preset value a, it indicates that the sensor has a fault (for example, the sensor has a communication fault), and if the estimation is continued, the estimated result is inaccurate and has a large error. The invention adopts the processing mode of directly discarding the estimated value when the time stamp difference is more than or equal to the preset value A, can avoid the influence of transient fault of the sensor on the system, and thus provides the stability of the system. In addition, the invention estimates the time difference only when the time difference is less than the preset value A, thereby reducing the data processing amount and improving the efficiency of data preprocessing.
Claims (4)
1. A single-sensor data association preprocessing method is characterized by comprising the following steps:
step a, receiving a sensor CAN message, analyzing the sensor CAN message, calculating the relative speed of a vehicle and a target object detected by a sensor, adding a timestamp to the sensor CAN message, and obtaining a target detection position inputV with the timestamp, wherein the target detection position inputV = { input1, input2, input3, …, input x, … and input } and m represents the number of the target object contained in the sensor data of the current frame of the sensor;
step b, judging whether the target expected position trackV is empty, wherein the target expected position trackV = { track1, track2, track3, track x, …, track }, and n represents the number of target objects contained in the previous frame of sensor data;
step c, if the target expected position trackV is empty, setting the target expected position trackV as a current target detection position inputV; if the target expected position trackV is not empty, entering step d;
step d, judging whether the timestamp difference between the target expected position trackV and the target detection position inputV is smaller than a preset value A or not, wherein the timestamp of the target expected position trackV is T _ track, and the timestamp of the target detection position inputV is T _ input; if not, setting the target expected position trackV as the current target detection position inputV; if yes, entering step e;
and e, estimating the target expected position inputV at the time of T _ input according to the time stamp difference and the relative speed of the vehicle and the target object.
2. The single-sensor data association preprocessing method according to claim 1, characterized in that: in the step a, calculating the relative speed between the vehicle and the target object detected by the sensor, specifically:
acquiring the absolute speed of a target object from a sensor;
acquiring the absolute speed of the vehicle from the CAN bus;
the relative speed of the target object and the host vehicle is calculated according to the absolute speed of the target object and the host vehicle.
3. The single-sensor data association preprocessing method according to claim 1 or 2, characterized in that: in step e, a calculation formula of trackV at the time of T _ input is as follows:
target expected position trackV at time T _ input = target expected position trackV + (T _ input-T _ track) at time T _ track, relative speed of the host vehicle and the target object.
4. The single-sensor data association preprocessing method according to claim 1 or 2, characterized in that: the preset value A is 0.3.
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| WO2020139967A1 (en) | 2018-12-28 | 2020-07-02 | Didi Research America, Llc | Distributed system execution using a serial timeline |
| US11550623B2 (en) | 2018-12-28 | 2023-01-10 | Beijing Voyager Technology Co., Ltd. | Distributed system task management using a simulated clock |
| US11397610B2 (en) | 2018-12-28 | 2022-07-26 | Beijing Voyager Technology Co., Ltd. | Architecture for simulation clock-based simulation of distributed systems |
| CN115599330B (en) * | 2022-09-28 | 2023-08-01 | 北京亚控科技发展有限公司 | Fault diagnosis method and related device of data acquisition system |
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| US7705743B2 (en) * | 2006-03-01 | 2010-04-27 | L-3 Communications Corporation | Self-assembling wireless network, vehicle communications system, railroad wheel and bearing monitoring system and methods therefor |
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