COMFORT-BASED SELF-DRIVING PLANNING METHOD

Abstract

A comfort-based self-driving planning method, including the steps of: a) establishing a relationship model of vibration road surface quality and driving comfort on the basis of a vehicle type; b) obtaining road ahead condition parameters, including abnormal condition information, road flatness and road surface anti-slide performance; c) obtaining the road ahead condition parameters, and adjusting a vehicle expected driving trajectory; d) respectively designing vehicle acceleration, deceleration and constant speed processes, and generating a speed change curve; e) optimising the speed change curve. By means of changeable road surface quality and vehicle vibration action mechanism analysis and image-based road surface anti-slide coefficient evaluation technology, a GIS and vehicle-road communication technology are used to acquire road condition parameters, and vehicle acceleration, deceleration and constant speed processes are respectively designed on the basis of changes in the parameters.

Claims

1. A comfort-based self-driving planning method, the method comprising the following steps: a) Establishing a prediction model between vibration type pavement condition and driving comfort, based on type of vehicles, including: a1) Three-axis acceleration sensors are mounted to particular positions of a vehicle with selected model and make; a2) The vehicles are driven at different speeds on testing roads, respectively, to record the vibration of the three-axis acceleration; a3) The integrated weighted RMSA is calculated, as the comfort indicator; a4) Using comparative testing data, a multivariate linear regression was established with the comfort of the road segment as the dependent variable, the driving speed and the international roughness index IRI value as the independent variable; b) Obtaining road conditions, including road IRI, road surface anti-sliding performance, and abnormal conditions; c) Obtaining road conditions, thereby guiding the vehicle to travel; d) Designing acceleration process, deceleration process and uniform process, to generate speed curve; e) Optimizing the said speed curve.

2. The method according to claim 1, wherein the said testing roads in step a2) should meet the following conditions: a21) Said testing roads are straight-line segment of not less than 300 meters long; a22) The road roughness of said testing roads is 1 m/km, 2 m/km, 31(m/h, 4 m/km, 5 m/km, 6 m/km respectively;

3. The method according to claim 1, wherein methods to obtain road conditions include: b1) obtaining the following road condition information: measured road distress, road condition, abnormal traffic information, and road surface anti-sliding performance; b2) assigning GPS tags to said road conditions; b3) assigning said road condition information to GIS layers through GPS tags; b4) via vehicle road communication technology, the said road condition information is passed to automated vehicles; b5) vibrations are detected by the said automated vehicles using in-car sensors; b6) said vibrations are uploaded to GIS database via vehicle road communication technology; b7) said vibrations are analyzed, and road condition information in GIS database is updated and corrected.

4. The method according to claim 3, wherein the said road surface anti-sliding performance in b1) is obtained through the following sub-steps: b11) photos of the front road is obtained by cameras on automated vehicles; b12) each photo is converted into a local binary method (LBP) into an LBP matrix form; b13) after calculating the LBP values of all the elements in each photo, a histogram is drawn, and the LBP histogram fitting parameters are calculated, based on the mixed Gaussian distribution model; b14) obtaining road surface anti-sliding performance.

5. The method according to claim 3, wherein the step in b4) includes the following sub-steps: b41) ZIGBEE wireless transmission facilities are arranged along roadside at a distance of 1 km; b42) the wireless transmission facilities include a data storage part and a short-range wireless communication part; the data storage part stores road condition information with GPS tags; b43) when an automated vehicle travels within the range of the wireless network coverage of a roadside wireless communication facility, the wireless communication facility is automatically connected to vehicle, the said road condition information is passed to automated vehicles.

6. The method according to claim 3, wherein step b7 includes two conditions: b71) when a vibration beyond expectation is detected, recording the position of this vibration as a temporary data to be confirmed; if position matching degree is greater than 2.1, then add this data to GIS database; b72) when a vibration within expectation is not detected, recording the position of this vibration as a temporary data to be deleted; if position matching degree is greater than 2.1, then delete this data from GIS database;

7. The method according to claim 1, wherein the speed curve in step d) is obtained by the following method: When the differences of IRI between the ahead road section and the current position is less then 10%, and there is no abnormal condition, the speed curve is of constant speed; otherwise The speed curve is of hyperbolic tangent function, which includes two parameters: speed difference value and smooth parameter, the speed difference value is the difference between the current speed and the speed corresponding to the target comfort.

8. The method according to claim 2, wherein the speed curve in step d) is obtained by the following method: When the differences of IRI between the ahead road section and the current position is less then 10%, and there is no abnormal condition, the speed curve is of constant speed; otherwise The speed curve is of hyperbolic tangent function, which includes two parameters: speed difference value and smooth parameter, the speed difference value is the difference between the current speed and the speed corresponding to the target comfort.

9. The method according to claim 3, wherein the speed curve in step d) is obtained by the following method: When the differences of IRI between the ahead road section and the current position is less then 10%, and there is no abnormal condition, the speed curve is of constant speed; otherwise The speed curve is of hyperbolic tangent function, which includes two parameters: speed difference value and smooth parameter, the speed difference value is the difference between the current speed and the speed corresponding to the target comfort.

10. The method according to claim 4, wherein the speed curve in step d) is obtained by the following method: When the differences of IRI between the ahead road section and the current position is less then 10%, and there is no abnormal condition, the speed curve is of constant speed; otherwise The speed curve is of hyperbolic tangent function, which includes two parameters: speed difference value and smooth parameter, the speed difference value is the difference between the current speed and the speed corresponding to the target comfort.

11. The method according to claim 5, wherein the speed curve in step d) is obtained by the following method: When the differences of IRI between the ahead road section and the current position is less then 10%, and there is no abnormal condition, the speed curve is of constant speed; otherwise The speed curve is of hyperbolic tangent function, which includes two parameters: speed difference value and smooth parameter, the speed difference value is the difference between the current speed and the speed corresponding to the target comfort.

12. The method according to claim 6, wherein the speed curve in step d) is obtained by the following method: When the differences of IRI between the ahead road section and the current position is less then 10%, and there is no abnormal condition, the speed curve is of constant speed; otherwise The speed curve is of hyperbolic tangent function, which includes two parameters: speed difference value and smooth parameter, the speed difference value is the difference between the current speed and the speed corresponding to the target comfort.

13. The method according to claim 7, wherein the differences of IRI between the ahead road section and the current position is greater than 10%, but there is no abnormal condition, the smooth parameter stability coefficient is calculated as follows: e11) calculating maximum acceleration the maximum LBP value (K); e12) calculating the comfort under current speed, based on said prediction mode; e13) calculating the weighted RMSA of maximum acceleration and comfort, so that said comfort is less than the comfort threshold; calculating the range of stability coefficients; said stability coefficient equals to the maximum of the said range of stability coefficients.

14. The method according to claim 8, wherein the differences of IRI between the ahead road section and the current position is greater than 10%, but there is no abnormal condition, the smooth parameter stability coefficient is calculated as follows: e11) calculating maximum acceleration the maximum LBP value (K); e12) calculating the comfort under current speed, based on said prediction mode; e13) calculating the weighted RMSA of maximum acceleration and comfort, so that said comfort is less than the comfort threshold; calculating the range of stability coefficients; said stability coefficient equals to the maximum of the said range of stability coefficients.

15. The method according to claim 9, wherein the differences of IRI between the ahead road section and the current position is greater than 10%, but there is no abnormal condition, the smooth parameter stability coefficient is calculated as follows: e11) calculating maximum acceleration the maximum LBP value (K); e12) calculating the comfort under current speed, based on said prediction mode; e13) calculating the weighted RMSA of maximum acceleration and comfort, so that said comfort is less than the comfort threshold; calculating the range of stability coefficients; said stability coefficient equals to the maximum of the said range of stability coefficients.

16. The method according to claim 7, wherein the differences of IRI between the ahead road section and the current position is less than 10%, but there is abnormal condition, the smooth parameter stability coefficient is calculated as follows: e21) calculating acceleration jerk, ensuring the acceleration jerk is within the acceptable threshold; calculating the range of stability coefficients; e22) calculating maximum acceleration; establishing a nonlinear optimization equation, based on said prediction mode and the distance of the automated vehicle from the nearest abnormal condition; and calculating the range of stability coefficients; e23) comparing the two ranges of stability coefficients in e21) and e22); said stability coefficient equals to the maximum of the said two ranges.

17. The method according to claim 8, wherein the differences of IRI between the ahead road section and the current position is less than 10%, but there is abnormal condition, the smooth parameter stability coefficient is calculated as follows: e21) calculating acceleration jerk, ensuring the acceleration jerk is within the acceptable threshold; calculating the range of stability coefficients; e22) calculating maximum acceleration; establishing a nonlinear optimization equation, based on said prediction mode and the distance of the automated vehicle from the nearest abnormal condition; and calculating the range of stability coefficients; e23) comparing the two ranges of stability coefficients in e21) and e22); said stability coefficient equals to the maximum of the said two ranges.

18. The method according to claim 9, wherein the differences of IRI between the ahead road section and the current position is less than 10%, but there is abnormal condition, the smooth parameter stability coefficient is calculated as follows: e21) calculating acceleration jerk, ensuring the acceleration jerk is within the acceptable threshold; calculating the range of stability coefficients; e22) calculating maximum acceleration; establishing a nonlinear optimization equation, based on said prediction mode and the distance of the automated vehicle from the nearest abnormal condition; and calculating the range of stability coefficients; e23) comparing the two ranges of stability coefficients in e21) and e22); said stability coefficient equals to the maximum of the said two ranges.

19. The method according to claim 7, wherein the differences of IRI between the ahead road section and the current position is great than 10%, and there is abnormal condition, the smooth parameter stability coefficient is calculated as follows: e31) calculating maximum acceleration; calculating the comfort under current speed, based on said prediction mode; calculating weighted root-mean-square of maximum acceleration and comfort, so that it is less than comfort threshold; calculating range of stability coefficients, the maximum of which is the first coefficient; e32) calculating the acceleration jerk, so that it is less than acceleration jerk threshold; calculating range of stability coefficients, the maximum of which is the second coefficient; e33) calculating maximum acceleration; establishing a nonlinear optimization equation, based on said prediction mode and the distance of the automated vehicle from the nearest abnormal condition; and calculating the range of stability coefficients, the maximum of which is the third coefficient; e34) said stability coefficient equals to the maximum of the three coefficients in e31), e32), and e33).

20. The method according to claim 8, wherein the differences of IRI between the ahead road section and the current position is great than 10%, and there is abnormal condition, the smooth parameter stability coefficient is calculated as follows: e31) calculating maximum acceleration; calculating the comfort under current speed, based on said prediction mode; calculating weighted root-mean-square of maximum acceleration and comfort, so that it is less than comfort threshold; calculating range of stability coefficients, the maximum of which is the first coefficient; e32) calculating the acceleration jerk, so that it is less than acceleration jerk threshold; calculating range of stability coefficients, the maximum of which is the second coefficient; e33) calculating maximum acceleration; establishing a nonlinear optimization equation, based on said prediction mode and the distance of the automated vehicle from the nearest abnormal condition; and calculating the range of stability coefficients, the maximum of which is the third coefficient; e34) said stability coefficient equals to the maximum of the three coefficients in e31), e32), and e33).

Description

DESCRIPTION OF ATTACHED DRAWINGS

[0137] FIG. 1 shows the schematic diagram of road surface slip resistance;

[0138] FIG. 2 shows the mechanism diagram of the roadside communication device;

[0139] FIG. 3 shows the calculation flow chart of the comfort prediction model;

[0140] FIG. 4 shows the velocity change curve of the hyperbolic tangent function;

[0141] FIG. 5 shows the acceleration variation of the hyperbolic tangent function;

[0142] FIG. 6 shows the machine vision inspection road surface anti-sliding performance flow chart

[0143] FIG. 7 shows the LBP solution process diagram;

[0144] FIG. 8 shows the LBP statistical histogram diagram;

[0145] FIG. 9 shows the mixed Gaussian distribution diagram;

[0146] FIG. 10 shows the three-axis sensor placement position diagram;

[0147] FIG. 11 shows the scene diagram of the accident vehicle at the beginning and end of the road section when the accident occurs;

[0148] FIG. 12 is the scene diagram of the accident vehicle that can search other vehicles when the accident occurs;

[0149] FIG. 13 is the scene diagram of the accident vehicle that cannot search other vehicles when the accident occurs;

[0150] FIG. 14 shows the accident information transmission mechanism diagram;

[0151] FIG. 15 shows the scene diagram of the driving position of the vehicle receiving the information when receiving the accident information;

[0152] FIG. 16 shows the flow chart of rapid release of abnormal traffic status information;

EXAMPLE

[0153] According to the requirements of the patent description, the vehicle road communication equipment is arranged: the arrangement interval of adjacent equipment is 1000 meters, and the roadside communication equipment includes the roughness and abnormal data of the ahead road section, and the international roughness index value IRI of the test section is 1.2 m/km and 2.7 m/km, and there is a bridgehead jumping position in the second section of the road. The distance of the roadside communication facilities is 100 meters, and the speed limit of the road section is 70 km/h. After the vehicle travels to the control section, the road surface condition data is received, and a comfort-based speed control strategy is performed.

Step 1 Determine if the Vehicle is in Safe Driving State.

[0154] The environmental information collected by the sensors, cameras and probes of the self-driving vehicle is used to generate a safe speed curve by using the conventional technology. Since the flow rate of the road section is low, the test vehicle can use the highest speed limit for full speed running, that is, the vehicle speed is 70 km/h.

Step 2 Auto-Driving Vehicle Current Comfort Judgment

[0155] According to the correlation between driving comfort, vehicle speed and roughness IRI obtained by formula (4), the driving comfort is predicted as follows:

a.sub.v=0.008.Math.v+0.298.Math.IRI1.246

[0156] The calculated weighted the root-mean-square acceleration value is 0.3412 m/s.sup.2, which satisfies the comfort requirement that less than 0.63 m/s.sup.2, so the vehicle can continue to travel at 70 km/h

Step 3 Speed Strategy Under Changing Road Conditions

[0157] When the vehicle enters the second road, the roadside communication system will send the roughness and abnormality of the road ahead to the vehicle. When the vehicle receives a roughness of 3.7 m/km and there is a bridgehead jump, Speed changes are made to ensure driving comfort.

[0158] After the comfort calculation, it was found that the weighted root-mean-square acceleration was 1.9087 m/km at a roughness of 3.7 m/km, which exceeded the upper limit of comfort. Therefore, it is necessary to reduce the speed to ensure comfort. If the degree is within the range, i.e. a.sub.v 0.63 m/s.sup.2, then the calculated speed can not exceed 55 km/h, and the b value is 7055=15 km/h.

[00017]$k\frac{7.2.Math.\sqrt{0.396.Math.9-{\left(w_k.Math.\left(0.008.Math.v_0+0.298.Math.\mathrm{IRI}-1.246\right)\right)}^2}}{b.Math.w_d}$

[0159] In the formula, w.sub.k=1, w.sub.d=0.8, and the comfort upper limit of the k value obtained by the formula (9) is 0.3712.

[0160] On the other hand, in order to prevent the jerk from exceeding the comfort limit, the comfort upper limit of the k value can be obtained by the formula (12) to be 0.9400, so the k value of the hyperbolic curve is selected to be 0.3712*0.95=0.3526.

[0161] In addition, due to the close distance of the bridgehead, the deceleration distance is only 100 meters. According to GIS, the physical characteristics of the bridgehead will cause the vibration of the vehicle to be:

a.sub.v=0.5621.Math.e.sup.0.0378.Math.speed

[0162] Substituting the above formula into the nonlinear programming, the optimal k and b values of the hyperbolic tangent function can be obtained, b=31.05 km/h, k=0.6762. Therefore, the automated vehicle will achieve the best comfort by reducing the speed to about 39 km/h by a hyperbolic tangent function of k=0.6762.