METHOD SUITABLE FOR DRIVER TAKEOVER TRAINING OF MAN-MACHINE SHARED DRIVING VEHICLES

Abstract

A method suitable for driver takeover training of man-machine shared driving vehicle relates to the field of man-machine shared driving technology. The method includes: establishing database, situation-creation, establish a teaching model, take over training, evaluation and analysis of takeover ability. The method divides the driver's takeover behavior into a small operation action through the driver takeover training of the man-machine shared driving vehicle, defines where the driver's eyes need to observe when the takeover reminder appears, how the hands and feet need to be operated, and the sequence of these operations, solves the problems of the driver's tension and being in a flurry in the current sudden takeover reminder. In addition, through the evaluation and analysis of the takeover capability, the method can solve the problem that the existing technology cannot objectively evaluate the driver's takeover capability level.

Claims

1. A method suitable for driver takeover training of a man-machine shared driving vehicle, comprising the following steps: (1) establishing database forming a takeover scene library, and establishing a virtual simulation training scene model and a virtual simulation equipment model; (2) situation-creation according to the takeover scene library described in step (1), using the virtual simulation training scene model and the virtual simulation equipment model to simulate a takeover situation of the man-machine shared driving vehicle under different scenarios and different road events; (3) establish a teaching model according to the takeover situation of the man-machine shared driving vehicle simulated in step (2) under different scenarios and different road events, establishing the teaching model; (4) takeover training according to the takeover situation of the man-machine shared driving vehicle in different scenarios and different road events, carrying out the takeover training of a driver of the man-machine shared driving vehicle through the teaching model, and (5) evaluation and analysis of takeover ability.

2. The method suitable for driver takeover training of the man-machine shared driving vehicle according to claim 1, wherein the teaching model described in step (3) is a guided teaching model.

3. The method suitable for driver takeover training of the man-machine shared driving vehicle according to claim 2, wherein the establishment of the guided teaching model comprises the following steps: (3.1) making training courseware according to training needs; (3.2) selecting the training courseware, and establishing the virtual simulation training scene based on a training process of takeover behavior spectrum in a courseware content; (3.3) simulating a vehicle state and a takeover reminder mode when a virtual simulation takeover event occurs; and (3.4) conducting guided training through voice prompts in the virtual simulation training scene.

4. The method suitable for driver takeover training of the man-machine shared driving vehicle according to claim 3, wherein the man-machine shared driving vehicle driver takes over the training, comprising the following steps: (4.1) entering a virtual simulation guided training mode; (4.2) in a virtual simulation automatic driving environment, carrying out a preparation work before taking over; (4.3) takeover request issued, take over; the specific takeover steps comprise: a) observing a road environment and forming a preliminary understanding of the virtual simulation automatic driving environment; b) putting a right foot on a brake pedal, while a left hand on a steering wheel, preparing to control the man-machine shared driving vehicle in advance; c) the driver moves a line of his sight to an exit button, presses the exit button with his right hand and exits an automation system; and d) the driver moves the line of sight back to a front of a road, looks around, and observes left and right rearview mirrors, at the same time, the right hand is placed on the steering wheel, according to a mastery and judgment of the virtual simulation automatic driving environment, the subsequent vehicle handling is performed; and (4.4) the driver takeover training of the man-machine shared driving vehicle is over.

5. The method suitable for driver takeover training of the man-machine shared driving vehicle according to claim 1, wherein a comprehensive evaluation method of takeover ability described in step (5) comprises the following steps: (5.1) collecting reference index data collecting index data of the takeover evaluation of m-celebrity drivers of man-machine shared driving as reference index data, comprising the driver's eye movement characteristics, physiological characteristics, vehicle handling indicators and takeover behavior index; the eye movement characteristics comprise a percentage of fixation time and an average fixation time; the physiological characteristic indexes comprise RR interval and heart rate; the vehicle handling indicators comprise brake pedal force and lane shift amount; the takeover behavior index comprises a first fixation road time; (5.2) collecting training driver's index data in a process of man-machine shared driving vehicle driver takeover training, the percentage of driver's fixation time, the average fixation time, the RR interval, the heart rate, the brake pedal force, the lane shift amount, and the index data of the first fixation on the road are obtained as the index data to be evaluated; (5.3) standardization of data processing standardized processing the above m+1 drivers P.sub.i's (i=1, 2 . . . m+1, m is a natural number) n evaluation indexes X.sub.ij (j=1, 2, . . . n, n is a natural number), converting to a range of [0, 1], and obtaining standardized dimensionless quantity X.sub.ij′, the data standardization processing formula is as follows: positive indexes: $\begin{array}{cc}{X}_{\mathrm{ij}}^{\prime }=\frac{\mathrm{Xij}-\min \left[\mathrm{Xj}\right]}{\max \left[\mathrm{Xj}\right]-\min \left[\mathrm{Xj}\right]}& \left(5.1\right)\end{array}$ negative indexes: $\begin{array}{cc}{X}_{\mathrm{ij}}^{\prime }=\frac{\max \left[\mathrm{Xj}\right]-\mathrm{Xij}}{\max \left[\mathrm{Xj}\right]-\min \left[\mathrm{Xj}\right]}& \left(5.2\right)\end{array}$ moderate indexes: $\begin{array}{cc}{X}_{\mathrm{ij}}^{\prime }=\{\begin{array}{cc}\frac{\mathrm{Xij}-\min \left[\mathrm{Xj}\right]}{X0-\min \left[\mathrm{Xj}\right]},& \mathrm{Xij}<X0\\ \frac{\max \left[\mathrm{Xj}\right]-\mathrm{Xij}}{\max \left[\mathrm{Xj}\right]-X0},& \mathrm{Xij}\ge X0\end{array}& \left(5.3\right)\end{array}$ in formulas 5.1, 5.2, 5.3, X.sub.ij′ refers to standardized dimensionless data, X.sub.ij refers to raw data, X.sub.0 refers to a moderate value specified in an original data set. (5.4) calculating a proportion of a value of a j-th index of an i-th person: $\begin{array}{cc}{Y}_{\mathrm{ij}}=\frac{X_{\mathrm{ij}}}{{.Math.}_{i=1}^{m+1}{X}_{\mathrm{ij}}}& \left(5.4\right)\end{array}$ (5.5) calculating index information entropy:
e.sub.j=−kΣ.sub.i=1.sup.m+1(Y.sub.ij+lnY.sub.ij)  (5.5) (5.6) calculating information entropy redundancy:
d.sub.j=1−e.sub.j  (5.6) (5.7) calculating index weight
W.sub.j=d.sub.j/Σ.sub.j=1dj.sup.n  (5.7) (5.8) calculating an output of takeover capability evaluation normalized dimensionless data of the index data X.sub.j′ to be evaluated are input into Equation 5.8,
S=Σ.sub.j-1.sup.nW.sub.j*X.sub.j′  (5.8) calculating the m+1 driver's takeover ability evaluation index value S, if the closer S is to 1, it indicates that the driver's ability to take over is stronger.

Description

DETAILED DESCRIPTION OF THE EMBODIMENTS

[0056] To make the purpose, technical scheme and advantages of the present invention more clear, the following steps are described in detail in combination with the implementation examples. It should be understood that the specific implementation examples described here are used to explain the present invention and are not used to limit the present invention.

[0057] A method suitable for driver takeover training of man-machine shared driving vehicle,

[0058] (1) establishing database [0059] forming the takeover scene library, and establishing the virtual simulation training scene model and virtual simulation equipment model; [0060] the takeover scene takes a double-way six-lane highway as an example, with the speed limit of 120 km/h, double-way traffic flow applicable; [0061] typical takeover events include: front road construction, system failure, obstacles in front, disappearance of front lane lines, etc., and the takeover time is 7 s in advance.

[0062] (2) situation-creation [0063] according to the takeover scene library described in step (1), using the virtual simulation training scene model and the virtual simulation equipment model to simulate the takeover of the vehicle under different scenarios and different road events;

[0064] (3) establish a teaching model [0065] according to the takeover situation of the vehicle simulated in step (2) under different scenarios and different road events, establishing the teaching model;

[0066] (4) takeover training [0067] according to the takeover of the vehicle in different scenarios and different road events, carrying out the takeover training of the driver of the man-machine shared driving vehicle through the teaching model; training the trainers on the web or immersive VR equipment, when using the web side, through the PC device to operate the web side, realizing the simulation of virtual simulation training scene model, making the virtual simulation equipment model produce corresponding feedback; and the data acquisition device is used to collect a number of index data of eye movement characteristics, physiological characteristics, vehicle handling and takeover behavior during the training process.

[0068] (5) evaluation and analysis of takeover ability [0069] the teaching model described in step (3) is a guided teaching model. [0070] the establishment of the guided teaching model, including the following steps:

[0071] (3.1) making training courseware according to training needs;

[0072] (3.2) selecting the training courseware, and establishing the virtual simulation training scene based on the training process of takeover behavior spectrum in the courseware content;

[0073] (3.3) simulating the vehicle state and takeover reminder mode when the virtual simulation takeover event occurs;

[0074] (3.4) conducting guided training through voice prompts in the virtual simulation training scene.

[0075] Further, the man-machine shared driving vehicle takeover training, including the following steps:

[0076] (4.1) entering the virtual simulation guided training mode;

[0077] (4.2) in the virtual simulation automatic driving environment, carrying out the preparation work before taking over;

[0078] (4.3) takeover request issued, take over; the specific takeover steps include: [0079] a) observing the road environment and forming a preliminary understanding of the driving environment; [0080] b) putting the right foot on the brake pedal, while the left hand on the steering wheel, preparing to control the vehicle in advance; [0081] c) the driver moves his sight to the exit button, presses the button with his right hand and exits the automation system; [0082] d) the driver moves the line of sight back to the front of the road, looks around, and observes the left and right rearview mirrors, at the same time, the right hand is placed on the steering wheel, according to the mastery and judgment of the driving environment, the subsequent vehicle handling is performed;

[0083] (4.4) the driver takeover training of man-machine shared driving vehicle is over.

[0084] The comprehensive evaluation method of takeover ability described in step (5) includes the following steps:

[0085] (5.1) collecting reference index data [0086] collecting the data indicators of the takeover evaluation of 14 man-machine shared driving vehicle drivers; percentage of fixation time, average fixation time, RR interval, heart rate, brake pedal force, lane shift amount, first fixation road time, as shown in Table 1, as a reference indicator data.

TABLE-US-00001 TABLE 1 Percentage Average HR Brake Lane First of fixation fixation RR heart pedal shift fixation time time interval rate force amount road time 0.12 0.40 0.86 67.43 0.00 0.05 0.90 0.18 0.25 0.71 85.08 0.17 0.04 0.65 0.16 0.69 0.78 75.38 0.07 0.05 0.26 0.10 0.33 0.73 87.01 0.00 0.04 0.69 0.20 0.30 0.95 63.27 0.03 0.10 0.75 0.16 0.35 0.77 78.20 0.20 0.07 0.42 0.30 0.35 0.71 86.61 0.02 0.10 0.74 0.18 0.32 0.85 71.08 0.04 0.05 0.68 0.93 0.27 0.74 84.17 0.01 0.05 1.39 0.43 0.55 0.82 73.17 0.17 0.07 1.23 0.20 0.47 0.86 68.31 0.07 0.08 1.07 0.23 0.42 0.78 79.08 0.01 0.09 0.42 0.05 0.23 0.77 80.51 0.22 0.06 0.69 0.22 0.12 0.99 61.28 0.10 0.07 1.02

[0087] (5.2) collecting training driver's index data [0088] in the process of driver takeover training for the man-machine shared driving vehicles, obtaining the driver's index data including percentage of fixation time, average fixation time, RR interval, heart rate, brake pedal force, lane shift amount, and first fixation road time, as index data to be evaluated, as shown in Table 2

TABLE-US-00002 Percentage Average HR Brake Lane First of fixation fixation RR heart pedal shift fixation time time interval rate force amount road time 0.37 0.43 0.80 75.67 0.06 0.08 0.38

[0089] (5.3) the standardization of data processing [0090] standardized processing the above 15 drivers P.sub.i's(i=1, 2, . . . 15) 7 evaluation indexes X.sub.ij (j=1, 2, . . . 7), converting to the range of [0-1], and obtaining standardized dimensionless quantity X.sub.ij′, as shown in Table 3, the data standardization processing formula is as follows:

[0091] positive indexes:

[00005]$\begin{array}{cc}{X}_{\mathrm{ij}}^{\prime }=\frac{\mathrm{Xij}-\min \left[\mathrm{Xj}\right]}{\max \left[\mathrm{Xj}\right]-\min \left[\mathrm{Xj}\right]}& \left(5.1\right)\end{array}$

[0092] negative indexes:

[00006]$\begin{array}{cc}{X}_{\mathrm{ij}}^{\prime }=\frac{\max \left[\mathrm{Xj}\right]-\mathrm{Xij}}{\max \left[\mathrm{Xj}\right]-\min \left[\mathrm{Xj}\right]}& \left(5.2\right)\end{array}$

[0093] moderate indexes:

[00007]$\begin{array}{cc}{X}_{\mathrm{ij}}^{\prime }=\{\begin{array}{cc}\frac{\mathrm{Xij}-\min \left[\mathrm{Xj}\right]}{X0-\min \left[\mathrm{Xj}\right]},& \mathrm{Xij}<X0\\ \frac{\max \left[\mathrm{Xj}\right]-\mathrm{Xij}}{\max \left[\mathrm{Xj}\right]-X0},& \mathrm{Xij}\ge X0\end{array}& \left(5.3\right)\end{array}$

[0094] In formulas 5.1, 5.2, 5.3, X.sub.ij′ refers to the standardized dimensionless data, X.sub.ij refers to raw data, X.sub.0 refers to the moderate value specified in the original data set;

TABLE-US-00003 TABLE 3 Percentage Average HR Brake Lane First of fixation fixation RR heart pedal shift fixation time time interval rate force amount road time 0.07 0.49 0.53 0.76 1.00 0.88 0.43 0.15 0.23 0.00 0.07 0.23 1.00 0.66 0.13 1.00 0.25 0.45 0.69 0.92 1.00 0.06 0.37 0.07 0.00 1.00 0.96 0.62 0.18 0.31 0.86 0.92 0.85 0.00 0.57 0.13 0.40 0.20 0.34 0.11 0.50 0.86 0.29 0.41 0.00 0.02 0.89 0.00 0.58 0.15 0.35 0.51 0.62 0.84 0.80 0.63 1.00 0.26 0.11 0.11 0.94 0.92 0.00 0.43 0.75 0.40 0.54 0.24 0.52 0.14 0.17 0.61 0.54 0.73 0.69 0.36 0.29 0.20 0.52 0.25 0.31 0.94 0.16 0.86 0.00 0.20 0.21 0.25 0.00 0.60 0.62 0.20 0.00 1.00 1.00 0.55 0.49 0.33 0.36 0.54 0.32 0.44 0.72 0.41 0.90

[0095] (5.4) calculating the proportion of the value of the j-th index of the i-th person:

[00008]$\begin{array}{cc}{Y}_{\mathrm{ij}}=\frac{X_{\mathrm{ij}}}{{.Math.}_{i=1}^{m+1}{X}_{\mathrm{ij}}}& \left(5.4\right)\end{array}$

[0096] (5.5) calculating index information entropy:

e.sub.j=−kΣ.sub.i=1.sup.m+1(Y.sub.ij×lnY.sub.ij)  (5.5)

[0097] (5.6) calculating information entropy redundancy:

d.sub.j=1−e.sub.j  (5.6)

[0098] (5.7) calculating index weight

W.sub.j=d.sub.j/Σ.sub.j=1dj.sup.n  (5.7)

[0099] Obtaining the information entropy value, information utility value and weight coefficient of each index, as shown in table 4 below.

TABLE-US-00004 Information Information weight entropy utility coefficient Item value e value d W Percentage of fixation 0.8757 0.1243 20.66% time Average fixation time 0.9432 0.0568 9.44% RR interval 0.8794 0.1206 20.03% HR heart rate 0.8960 0.1040 17.28% Brake pedal force 0.9375 0.0625 10.38% Lane shift amount 0.9203 0.0797 13.23% First fixation road 0.9459 0.0541 8.98% time

[0100] (5.8) calculating the output of takeover capability evaluation

[0101] the normalized dimensionless data of the index data X.sub.j′ to be evaluated are input into Equation 5.8,

S=Σ.sup.n.sub.j=1W.sub.j*X.sub.j′  (5.8)

S=0.36*20.66%+0.54*9.44%+0.32*20.03%+0.44*17.28%+0.72*10.38%+0.41*13.23%+0.90*8.98%=0.48 [0102] calculating the driver's takeover ability evaluation index value S, if the closer S is to 1, it indicates that the driver's ability to take over is stronger.

[0103] The present invention is inspired by the behavior spectrum theory, the driver of the man-machine shared driving vehicle also has its specific ‘behavior spectrum’ when taking over, that is, a set of steps that enable the driver to take over the control of the autonomous vehicle correctly and safely. The takeover behavior spectrum divides the driver's takeover behavior into small operational actions, and defines where the driver's eyes need to observe when the takeover reminder appears, how the hands and feet need to be operated, and the sequence of these operations. Based on this, this application provides a method suitable for driver takeover training of man-machine shared driving vehicle, in the present invention, through the establishment of database and situation generation, the simulation vehicle takeover under different road events in different scenarios, in the virtual simulation training scene, the driver carries out the human-machine co-driving vehicle driver takeover training according to the teaching model, and realizes the evaluation and analysis of takeover capability according to the index data collected during the training process.

[0104] Although the embodiments of the invention have been shown and described, it is understandable to ordinary technicians in the field that these embodiments can be varied, modified, replaced and modified without departing from the principle and spirit of the invention, and the scope of the invention is limited by the accompanying claims and their equivalents.