CN110992709A - Active speed limiting system based on fatigue state of driver - Google Patents
Active speed limiting system based on fatigue state of driver Download PDFInfo
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- CN110992709A CN110992709A CN201911407279.4A CN201911407279A CN110992709A CN 110992709 A CN110992709 A CN 110992709A CN 201911407279 A CN201911407279 A CN 201911407279A CN 110992709 A CN110992709 A CN 110992709A
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/052—Detecting movement of traffic to be counted or controlled with provision for determining speed or overspeed
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
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- G06V20/50—Context or environment of the image
- G06V20/59—Context or environment of the image inside of a vehicle, e.g. relating to seat occupancy, driver state or inner lighting conditions
- G06V20/597—Recognising the driver's state or behaviour, e.g. attention or drowsiness
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- G—PHYSICS
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- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C5/00—Registering or indicating the working of vehicles
- G07C5/08—Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time
Abstract
An active speed limiting system based on a fatigue state of a driver comprises a driver fatigue grade analysis device, an audible and visual alarm device, a speed limiting device and a vehicle speed sensor; the driver fatigue grade analysis device is used for analyzing the fatigue grade of a driver and sending an acousto-optic alarm signal to the acousto-optic alarm device when the fatigue grade of the driver is mild fatigue driving; when the fatigue grade of the driver is deep fatigue driving, an activation instruction is sent to the speed limiting device; the speed limiting device is used for activating the speed limiting device by an activation instruction, intercepting an accelerator opening value signal sent to a vehicle-mounted computer ECU by an accelerator pedal position sensor in real time when the speed limiting device is in an activated state, and acquiring the real-time speed of a vehicle by a vehicle speed sensor; when the real-time speed of the vehicle is less than or equal to a preset limit speed, transmitting the intercepted throttle opening value signal to a vehicle-mounted computer ECU in real time; and when the real-time speed of the vehicle is greater than the preset limit speed, sending an accelerator opening value signal corresponding to the limit speed to the vehicle-mounted computer ECU.
Description
Technical Field
The invention relates to the field of emergency braking, in particular to an active speed limiting system based on a fatigue state of a driver.
Background
The data of the national statistical bureau show that the number of the traffic accidents per year in nearly five years in China exceeds 12 thousands, wherein the truck traffic accidents are particularly serious, 5.04 thousands of the traffic accidents of truck responsibility roads occur in 2016, 2.5 thousands of people die and 4.68 thousands of people are injured, the truck accident rate is higher than that of common motor vehicles, and the caused loss is also higher than the average level. Among them, traffic accidents due to fatigue driving cause significant losses to people's life and property safety every year, and various studies have shown that about 20% of all road accidents are associated with fatigue, and up to 50% on some roads. The sampling survey results of the freight vehicle drivers by relevant departments in China are displayed: 84% of the freight vehicle drivers are driving for a daily average time of more than 8 hours, 40% of them for more than 12 hours, and 64% of the freight vehicles are equipped with only 1 driver.
Therefore, the fatigue driving detecting system can help prevent accidents caused by drowsiness of the driver. At present, a single fatigue driving detection means in the market has a plurality of defects, for example, when driving behavior characteristic analysis is utilized, the types of bus data are multiple, the data volume is large, the characteristic behaviors of fatigue driving are extremely difficult to identify and extract, and the driving habits of each driver are different, so that intelligent identification cannot be realized; by using the method of facial state recognition, the actions of closing eyes, yawning, making a call and the like of a driver can be effectively recognized, but the driver cannot normally work in a poor light environment in a cabin or when a camera is shielded.
Disclosure of Invention
In order to solve the technical problems, the invention provides an active speed limiting system based on a fatigue state of a driver, which comprises a driver fatigue grade analysis device, an audible and visual alarm device, a speed limiting device and a vehicle speed sensor, wherein the driver fatigue grade analysis device is respectively connected with the audible and visual alarm device and the speed limiting device;
the driver fatigue grade analysis device is used for analyzing the fatigue grade of a driver, the fatigue grade comprises non-fatigue driving, light fatigue driving and deep fatigue driving, and when the fatigue grade of the driver is the light fatigue driving, an acousto-optic alarm signal is sent to the acousto-optic alarm device; when the fatigue grade of the driver is deep fatigue driving, an activation instruction is sent to the speed limiting device;
the sound and light alarm device is used for outputting sound and light alarm after receiving the sound and light alarm signal so as to remind a driver of being in a fatigue driving state;
the speed limiting device is used for activating the speed limiting device through the activation instruction, intercepting an accelerator opening value signal sent to a vehicle-mounted computer ECU by an accelerator pedal position sensor in real time when the speed limiting device is in an activated state, and acquiring the real-time speed of a vehicle through a vehicle speed sensor; when the real-time speed of the vehicle is less than or equal to a preset limit speed, transmitting the intercepted throttle opening value signal to a vehicle-mounted computer ECU in real time; and when the real-time speed of the vehicle is greater than the preset limit speed, sending an accelerator opening value signal corresponding to the limit speed to the vehicle-mounted computer ECU.
Furthermore, the device also comprises a normally closed switch, and the accelerator pedal position sensor is connected with the vehicle-mounted computer ECU through a normally closed contact of the normally closed switch; the speed limiting device comprises a central processing unit, a signal acquisition unit, a signal output unit and a switch control unit, wherein the signal acquisition unit, the signal output unit, the switch control unit and the driver fatigue grade analysis device are all connected with the central processing unit;
the central processing unit is respectively connected with a vehicle speed sensor and an accelerator pedal position sensor of a vehicle through a signal acquisition unit, is connected with a normally open contact of a normally closed switch through a signal output unit, and is connected with a relay coil of the normally closed switch through a switch control unit; when the speed limiting device receives an activation instruction sent by the driver fatigue grade analysis device, a state switching signal is sent to the normally closed switch through the switch control unit, so that the normally closed contact of the normally closed switch is disconnected, and the speed limiting device is connected with the vehicle-mounted computer ECU through the normally open contact of the normally closed switch, so that the vehicle-mounted computer ECU cannot directly receive an electric signal of the accelerator pedal position sensor.
Furthermore, the driver fatigue grade analysis device comprises an image acquisition module, a facial behavior recognition module and a driving recorder host, wherein the image acquisition module and the driving recorder host are both connected with the facial behavior recognition module, and the driving recorder host is also connected with a CAN (controller area network) bus of a vehicle;
the image acquisition module is used for acquiring a face dynamic image of a driver;
the facial behavior recognition module is used for recognizing eye closing characteristics and mouth opening and closing characteristics from the acquired dynamic human face image;
the driving recorder host is used for acquiring and analyzing vehicle operation information from a vehicle CAN bus, calculating driving behavior characteristics through the vehicle operation information, and analyzing the fatigue level of a driver through fusion of eye closing characteristics, mouth opening and closing characteristics and driving behavior characteristics.
Further, the eye closure characteristics include a maximum closed-eye time within a time window, a frequency of blinking within a time window, and a percentage of closed-eye time within a time window, and the mouth opening characteristics include a maximum mouth opening time within a time window, a frequency of yawning within a time window, and a percentage of mouth opening time within a time window.
Further, the time window corresponding to the maximum eye closing time, the percentage of eye closing time, the maximum mouth opening time and the percentage of mouth opening time is 10 seconds, and the time window corresponding to the blink frequency and the yawning frequency is 60 seconds.
Further, the driving recorder host comprises a vehicle operation information acquisition module and a driving behavior feature calculation module, the driving recorder host is used for acquiring and analyzing vehicle operation information from a vehicle CAN bus, the driving behavior feature calculation module is used for calculating driving behavior features through the vehicle operation information, the vehicle operation information comprises a steering wheel angle SA and a steering wheel angular velocity SAR, and the driving behavior features comprise a steering wheel angle absolute MEAN value SAMEAN, a steering wheel angle standard difference SASTD, a steering wheel angle lower quartile value MEAN value SAQ1MEAN, a steering wheel angle upper quartile value MEAN value SAQ3MEAN, a steering wheel angle entropy SE, a steering wheel angular velocity absolute value MEAN value SAVMEAN, a steering wheel angular velocity standard difference SAVSTD and zero speed percentage PNS accumulated running duration.
Further, the driving behavior feature calculation module calculates the driving behavior feature through the vehicle operation information, specifically:
the absolute steering wheel angle mean value SAMEAN is an average value of absolute steering wheel angles, and a calculation formula is shown as formula one:
the formula I is as follows:
；
wherein N is the steering wheel angle sample number, SAiIs the ith steering wheel angle sample;
the calculation formula of the steering wheel angle standard deviation SASTD is shown as a formula II:
the formula II is as follows:
；
wherein, SAmThe calculation formula is shown as formula three:
the formula III is as follows:
；
arranging N numerical values in a steering wheel corner sample from small to large, counting the numerical values from small to large, wherein the numerical value at the fourth quarter is a lower quartile value SAQ1 of the steering wheel corner, the numerical value at the third quarter is an upper quartile value SAQ3 of the steering wheel corner, the MEAN value SAQ1MEAN of the lower quartile value of the steering wheel corner is the MEAN value smaller than the lower quartile value SAQ1 of the steering wheel corner sample, and the MEAN value SAQ3MEAN of the upper quartile value 383 MEAN of the steering wheel corner sample is the MEAN value larger than the upper quartile value SAQ3 of the steering wheel corner sample;
steering wheel corner entropy SEThe degree of disorder and randomness of the operation of the steering wheel by a driver are reflected, the larger the steering wheel corner entropy SE is, the larger the randomness of the operation of the steering wheel by the driver is, the higher the fatigue degree of the driver is, the steering wheel corner entropy SE is calculated according to the probability of the occurrence of the predicted deviation of the steering wheel corner, and the predicted value theta of the steering wheel corner is firstly calculated according to the formula IVp(n)：
The formula four is as follows:
；
then, the actual value theta is calculated according to the steering wheel angle(n)Predicted value theta of steering wheel anglep(n)Calculating a steering wheel angle prediction deviation e from the differencenThe calculation formula is the following formula five:
the formula five is as follows:
；
steering wheel angle predicted deviation enObeying a normal distribution N (mu, sigma)2) Predicting the deviation e of the steering wheel anglenDivided into 9 intervals, (- ∞, -5. mu.)]，（−5μ，− 2.5μ]，（−2.5μ，−μ]，（−μ，−0.5μ](-0.5 μ, 0.5 μ), [0.5 μ, μ), [ μ, 2.5 μ), [2.5 μ, 5 μ), [5 μ, + ∞) and then calculates the probability values for each intervalp i And finally, calculating the steering wheel corner entropy SE according to a formula six:
formula six:
；
the mean value SAVMEAN and the standard deviation SAVSTD of the absolute values of the steering wheel angular velocity reflect the fluctuation condition of the vehicle, and the steering wheel angular velocity SA in the first formula and the second formula is replaced by the SAR of the steering wheel, so that the mean value SAVMEAN and the standard deviation SAVSTD of the absolute values of the steering wheel angular velocity are calculated;
the zero-speed percentage PNS detects the continuous and immovable operation characteristic PNS of the steering wheel, and the calculation formula is shown as formula seven:
formula (II)Seventhly, the method comprises the following steps:
；
wherein N is the total number of samples of angular velocity in the selected time, and N is the sample of angular velocity between + -0.1 deg./s in the total sample.
Further, the driving recorder host also comprises a fatigue prediction neural network model, and the fatigue grade of the driver is analyzed through fusion of eye closing characteristics, mouth opening and closing characteristics and driving behavior characteristics, specifically:
forming a fusion feature vector set by fusing eye closing features, mouth opening and closing features and driving behavior features, wherein X, X = { X1, X2, …, X15}, X1 is the longest eye closing time, X2 is the blink frequency, X3 is the percentage eye closing time, X4 is the longest mouth opening time, X5 is the yawning time, X6 is the percentage mouth opening time, X7 is the absolute MEAN SAMEAN of the steering wheel rotation angle, X8 is the standard deviation SASTD of the steering wheel rotation angle, X9 is the MEAN SAQ1MEAN of the quartiles under the steering wheel rotation angle, X10 is the MEAN SAQ3MEAN of the quartiles under the steering wheel rotation angle, X11 is the steering wheel rotation angle SE, X12 is the MEAN savnean of the steering wheel rotation angle speed absolute value, X13 is the savtd, X14 is the zero speed percentage PNS, and X15 is the running total sum;
the method comprises the following steps of constructing a fatigue prediction neural network model, judging fatigue driving probability by using a full connection layer, inputting the model as a fusion feature vector set in a fusion window, carrying out vector product operation on the fusion feature vector set and a weight vector w of the full connection layer, inputting the vector product to a Sigmoid activation function (expressed as sigma), and outputting a fatigue probability value y between 0 and 1 through the Sigmoid activation function, wherein the specific formula is as follows:
furthermore, in the training process of the fatigue prediction neural network model, a cross-entropy (cross-entropy) function is adopted as the loss function Em,let the training set be N sample pairs<Xi,Oi>In which XiIs the fused feature vector set of the ith window sample, OiIs a label corresponding to the fused feature vector set of the ith window sample, OiThe value is 1 or 0, 1 represents fatigue driving, 0 represents non-fatigue driving, and when the fusion feature vector set of the ith window sample corresponds to fatigue driving, OiThe value is 1, and when the fusion feature vector set of the ith window sample corresponds to fatigue driving, OiWith a value of 0, the calculation formula of the loss function Em is as follows:
and dividing the training set into small batches to be used as input of each iteration in the training process, and performing model training by using a random gradient descent optimization algorithm through multiple iterations until a loss function is converged to obtain the trained fatigue prediction neural network model.
Further, the driving recorder host further comprises a fatigue grade judging module, wherein the fatigue grade judging module is used for judging the fatigue grade of the driver according to the fatigue probability value, and specifically comprises the following steps:
when the fatigue probability value is less than 0.6, judging that the vehicle is in non-fatigue driving;
when the fatigue probability value is more than or equal to 0.6 and less than 0.9, judging the driver is light fatigue driving;
and when the fatigue probability value is more than or equal to 0.9, judging the deep fatigue driving.
Further, the sample data of the fatigue prediction neural network model training is as follows: the method comprises the steps that eye closing characteristics, mouth opening and closing characteristics and driving behavior characteristics of a corresponding driver are obtained in the scene that the driver is determined to be in fatigue driving; the method comprises the steps that eye closing characteristics, mouth opening and closing characteristics and driving behavior characteristics of a corresponding driver are obtained in a scene that the driver is determined to be in non-fatigue driving;
inputting eye closing characteristics, mouth opening and closing characteristics and driving behavior characteristics of a driver in a fatigue driving state at an input layer of the fatigue prediction neural network model, and simultaneously inputting 1 at an output layer to perform fatigue preset training of the model;
and inputting and determining eye closing characteristics, mouth opening and closing characteristics and driving behavior characteristics of a driver in a non-fatigue driving state at an input layer of the fatigue prediction neural network model, and simultaneously inputting 0 at an output layer to perform non-fatigue preset training of the model.
The invention has the following beneficial effects:
1. compared with the emergency brake in the prior art, the method and the device have the advantages that when the driver is determined to be in a light fatigue driving state, sound and light alarm is carried out to remind the driver of being in the fatigue driving state, when the driver is in a deep fatigue driving state, the accelerator opening value signal sent to the vehicle-mounted computer ECU by the accelerator pedal position sensor is intercepted, the real-time speed of the vehicle is judged to be larger than the preset limit speed, if yes, the accelerator opening value signal corresponding to the limit speed is sent to the vehicle-mounted computer ECU, the speed of the vehicle is gradually reduced within the limit speed, and therefore the problem that the existing chain rear-end collision possibly caused by the emergency brake is solved.
2. Compared with the existing method for analyzing the fatigue level of the driver only through the driving behavior characteristics, the method for analyzing the fatigue level of the driver based on the eye closing characteristics, the mouth opening and closing characteristics and the driving behavior characteristics is combined to analyze the fatigue level of the driver, so that the limitation of a single information source is overcome, the relevance and the complementarity of each information source are fully considered, and the fatigue level analysis is more accurate.
3. The fatigue prediction method predicts the fatigue probability value through the fatigue prediction neural network model based on the obtained eye closing characteristics, mouth opening and closing characteristics and driving behavior characteristics, judges the fatigue grade through the fatigue probability value, and has strong real-time performance of fatigue prediction.
4. According to the invention, through analysis of fatigue driving, parameters which can reflect driving behaviors most are determined as driving behavior characteristics, including a steering wheel corner absolute MEAN value SAMEAN, a steering wheel corner standard difference SASTD, a steering wheel corner lower quartile value MEAN value SAQ1MEAN, a steering wheel corner upper quartile value MEAN value SAQ3MEAN, a steering wheel corner entropy SE, a steering wheel corner speed absolute MEAN value SAVMEAN, a steering wheel corner speed standard difference SAVSTD, a zero speed percentage PNS and the like are taken as the driving behavior characteristics, and the driving behavior characteristics can be calculated through the steering wheel corner SA and the steering wheel corner speed SAR, so that a large amount of unnecessary data acquisition and related calculation are reduced, and compared with the prior art, the fatigue grade analysis is more accurate.
Drawings
FIG. 1 is a schematic block diagram of an active speed limiting system based on a fatigue state of a driver according to an embodiment of the invention;
FIG. 2 is a schematic block diagram of a driver fatigue level analyzing apparatus according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a sliding time window provided by an embodiment of the present invention;
fig. 4 is a fatigue prediction neural network model provided in an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the active speed limiting system based on the fatigue state of the driver provided by the invention comprises a driver fatigue grade analysis device, an audible and visual alarm device, a speed limiting device and a vehicle speed sensor, wherein the driver fatigue grade analysis device is respectively connected with the audible and visual alarm device and the speed limiting device, and the speed limiting device is respectively connected with the vehicle speed sensor and an accelerator pedal position sensor of a vehicle;
the driver fatigue grade analysis device is used for analyzing the fatigue grade of a driver, the fatigue grade comprises non-fatigue driving, light fatigue driving and deep fatigue driving, and when the fatigue grade of the driver is the light fatigue driving, an acousto-optic alarm signal is sent to the acousto-optic alarm device; when the fatigue grade of the driver is deep fatigue driving, an activation instruction is sent to the speed limiting device;
the sound and light alarm device is used for outputting sound and light alarm after receiving the sound and light alarm signal so as to remind a driver of being in a fatigue driving state;
the speed limiting device is used for activating the speed limiting device through the activation instruction, intercepting an accelerator opening value signal sent to a vehicle-mounted computer ECU by an accelerator pedal position sensor in real time when the speed limiting device is in an activated state, and acquiring the real-time speed of a vehicle through a vehicle speed sensor; when the real-time speed of the vehicle is less than or equal to a preset limit speed, transmitting the intercepted throttle opening value signal to a vehicle-mounted computer ECU in real time; and when the real-time speed of the vehicle is greater than the preset limit speed, sending an accelerator opening value signal corresponding to the limit speed to the vehicle-mounted computer ECU.
Compared with the emergency brake in the prior art, the method and the device have the advantages that when the driver is determined to be in a light fatigue driving state, sound and light alarm is carried out to remind the driver of being in the fatigue driving state, when the driver is in a deep fatigue driving state, the accelerator opening value signal sent to the vehicle-mounted computer ECU by the accelerator pedal position sensor is intercepted, the real-time speed of the vehicle is judged to be larger than the preset limit speed, if yes, the accelerator opening value signal corresponding to the limit speed is sent to the vehicle-mounted computer ECU (the accelerator opening value signal is the accelerator opening value signal which is sent to the vehicle-mounted computer ECU by the accelerator pedal position sensor when the vehicle is about to be opened to the limit speed), the vehicle speed is gradually reduced within the limit speed, and the problem of the existing chain rear-end collision possibly caused by the emergency brake is solved.
Wherein the limit speed is a safe speed reasonably preset based on the deep fatigue driving state, such as 40 km/h.
In addition, when the driver fatigue grade analysis device analyzes that the driver fatigue grade is light fatigue driving or non-fatigue driving, a release instruction is sent to the speed limiting device; after receiving the releasing instruction, the speed limiting device enters an inactive state and does not intercept an accelerator opening value signal sent to the vehicle-mounted computer ECU by the accelerator pedal position sensor.
Preferably, the device also comprises a normally closed switch, and the accelerator pedal position sensor is connected with the vehicle-mounted computer ECU through a normally closed contact of the normally closed switch; the speed limiting device comprises a central processing unit, a signal acquisition unit, a signal output unit and a switch control unit, wherein the signal acquisition unit, the signal output unit, the switch control unit and the driver fatigue grade analysis device are all connected with the central processing unit;
the central processing unit is respectively connected with a vehicle speed sensor and an accelerator pedal position sensor of a vehicle through a signal acquisition unit, is connected with a normally open contact of a normally closed switch through a signal output unit, and is connected with a relay coil of the normally closed switch through a switch control unit; when the speed limiting device receives an activation instruction sent by the driver fatigue grade analysis device, a state switching signal is sent to the normally closed switch through the switch control unit, so that the normally closed contact of the normally closed switch is disconnected, and the speed limiting device is connected with the vehicle-mounted computer ECU through the normally open contact of the normally closed switch, so that the vehicle-mounted computer ECU cannot directly receive an electric signal of the accelerator pedal position sensor.
As shown in fig. 1, after the central processing unit receives an activation instruction sent by the driver fatigue level analysis device, the central processing unit sends a state switching signal to the normally closed switch through the switch control unit, so that the normally closed contacts 1 and 2 of the normally closed switch are disconnected, the normally closed contact 1 is connected with the normally open contact 3, the speed limiting device is connected with the vehicle-mounted computer ECU through the normally open contact of the normally closed switch, and an accelerator opening value signal sent by the accelerator pedal position sensor can only be transmitted to the central processing unit of the speed limiting device, so that the accelerator opening value signal sent to the vehicle-mounted computer ECU by the accelerator pedal position sensor is intercepted.
As shown in fig. 2, preferably, the driver fatigue level analyzing apparatus includes an image collecting module, a facial behavior recognition module and a driving recorder host, wherein the image collecting module and the driving recorder host are both connected with the facial behavior recognition module, and the driving recorder host is further connected with a CAN bus of the vehicle;
the image acquisition module is used for acquiring a face dynamic image of a driver;
the facial behavior recognition module is used for recognizing eye closing characteristics and mouth opening and closing characteristics from the acquired dynamic human face image;
the driving recorder host is used for acquiring and analyzing vehicle operation information from a vehicle CAN bus, calculating driving behavior characteristics through the vehicle operation information, and analyzing the fatigue level of a driver through fusion of eye closing characteristics, mouth opening and closing characteristics and driving behavior characteristics.
The driving recorder host comprises a CAN bus communication module, and the CAN bus communication module is connected with a vehicle CAN bus to acquire and analyze vehicle operation information in the vehicle CAN bus.
The eye closing characteristic and the mouth opening and closing characteristic are used as visual processing information characteristics of the driver, and the method and the device fuse the visual processing information and the driving behavior characteristics of the driver to judge whether the driver is in a fatigue driving state or not so as to overcome the limitation of a single information source, fully consider the relevance and complementarity of each information source and analyze the fatigue grade more accurately.
Preferably, the image acquisition module comprises a visible light camera and an infrared camera; the visible light camera is used for collecting a face dynamic image of the driver in the daytime, and the infrared camera is used for collecting the face dynamic image of the driver at night.
Preferably, the driving recorder host is further configured to fuse characteristics of two modalities, namely, a visual processing information characteristic and a driving behavior characteristic, specifically, a characteristic parameter group fusion method based on a sliding time window, as shown in fig. 3, where a current time is T, and an optimal time window of a characteristic parameter xi is TiAssuming a characteristic parameter xiThe next extraction time is T +. Δ, the time window slides forward to the current time period of [ T +. T-TiAt, T +. T ], data repetition is (T)i-∆t)/TiThe invention selects the sliding time window Δ t =4 s.
Preferably, the eye closure characteristics include a maximum closed-eye time within a time window, a frequency of blinking within a time window, and a percentage of closed-eye time within a time window, and the mouth opening and closing characteristics include a maximum mouth opening time within a time window, a frequency of yawning within a time window, and a percentage of mouth opening time within a time window.
Wherein the time window corresponding to the maximum eye closing time, the percentage of eye closing time, the maximum mouth opening time and the percentage of mouth opening time is 10 seconds, and the time window corresponding to the blink frequency and the yawning frequency is 60 seconds.
Preferably, the facial behavior recognition module recognizes the eye closing feature and the mouth opening and closing feature through a built-in face recognition algorithm.
Preferably, when the opening range of the mouth of the driver is detected to be one third of the face range and lasts for more than 2S, the driver is judged to be yawned, and when the eye closing of the driver is detected and lasts for 1.5 seconds, the driver is judged to be eye closing.
Preferably, the driving recorder host comprises a vehicle operation information acquisition module and a driving behavior feature calculation module, the driving recorder host is configured to acquire and analyze vehicle operation information from a vehicle CAN bus, and the driving behavior feature calculation module is configured to calculate driving behavior features through the vehicle operation information, where the vehicle operation information includes a steering wheel angle SA and a steering wheel angular velocity SAR, and the driving behavior features include a steering wheel angle absolute MEAN SAMEAN, a steering wheel angle standard deviation SASTD, a steering wheel angle lower quartile MEAN SAQ1MEAN, a steering wheel angle upper quartile MEAN SAQ3MEAN, a steering wheel angle entropy, a steering wheel angle absolute MEAN SAVMEAN, a steering wheel angular velocity standard deviation SAVSTD, a zero speed percentage PNS, and an accumulated driving duration.
The driving behavior feature calculation module specifically calculates the driving behavior features through vehicle operation information as follows:
the absolute steering wheel angle mean value SAMEAN is an average value of absolute steering wheel angles, and a calculation formula is shown as formula one:
the formula I is as follows:
；
wherein N is the steering wheel angle sample number, SAiIs the ith steering wheel angle sample;
the calculation formula of the steering wheel angle standard deviation SASTD is shown as a formula II:
the formula II is as follows:
；
wherein, SAmThe calculation formula is shown as formula three:
the formula III is as follows:
；
arranging N numerical values in a steering wheel corner sample from small to large, counting the numerical values from small to large, wherein the numerical value at the fourth quarter is a lower quartile value SAQ1 of the steering wheel corner, the numerical value at the third quarter is an upper quartile value SAQ3 of the steering wheel corner, the MEAN value SAQ1MEAN of the lower quartile value of the steering wheel corner is the MEAN value smaller than the lower quartile value SAQ1 of the steering wheel corner sample, and the MEAN value SAQ3MEAN of the upper quartile value 383 MEAN of the steering wheel corner sample is the MEAN value larger than the upper quartile value SAQ3 of the steering wheel corner sample;
the steering wheel corner entropy SE reflects the chaos degree and randomness of the operation of a driver on a steering wheel, the larger the steering wheel corner entropy SE is, the larger the randomness of the operation of the driver on the steering wheel is, the higher the fatigue degree of the driver is, the steering wheel corner entropy SE is calculated according to the probability of the occurrence of the prediction deviation of the steering wheel corner, and the predicted value theta of the steering wheel corner is firstly calculated according to the formula IVp(n)：
The formula four is as follows:
；
then, the actual value theta is calculated according to the steering wheel angle(n)Predicted value theta of steering wheel anglep(n)Calculating a steering wheel angle prediction deviation e from the differencenThe calculation formula is the following formula five:
the formula five is as follows:
；
steering wheel angle predicted deviation enObeying a normal distribution N (mu, sigma)2) Predicting the deviation e of the steering wheel anglenDivided into 9 intervals, (- ∞, -5. mu.)]，（−5μ，− 2.5μ]，（−2.5μ，−μ]，（−μ，−0.5μ](-0.5 μ, 0.5 μ), [0.5 μ, μ), [ μ, 2.5 μ), [2.5 μ, 5 μ), [5 μ, + ∞) and then calculates the probability values for each intervalp i And finally, calculating the steering wheel corner entropy SE according to a formula six:
formula six:
；
the mean value SAVMEAN and the standard deviation SAVSTD of the absolute values of the steering wheel angular velocity reflect the fluctuation condition of the vehicle, and the steering wheel angular velocity SA in the first formula and the second formula is replaced by the SAR of the steering wheel, so that the mean value SAVMEAN and the standard deviation SAVSTD of the absolute values of the steering wheel angular velocity are calculated;
the zero-speed percentage PNS detects the continuous and immovable operation characteristic PNS of the steering wheel, and the calculation formula is shown as formula seven:
the formula seven:
；
wherein N is the total number of samples of angular velocity in the selected time, and N is the sample of angular velocity between + -0.1 deg./s in the total sample.
Preferably, the driving recorder host comprises a fatigue prediction neural network model, the driving recorder host further comprises a fatigue prediction neural network model, and the analysis of the fatigue level of the driver through fusion of the eye closing feature, the mouth opening and closing feature and the driving behavior feature specifically comprises:
forming a fusion feature vector set by fusing eye closing features, mouth opening and closing features and driving behavior features, wherein X, X = { X1, X2, …, X15}, X1 is the longest eye closing time, X2 is the blink frequency, X3 is the percentage eye closing time, X4 is the longest mouth opening time, X5 is the yawning time, X6 is the percentage mouth opening time, X7 is the absolute MEAN SAMEAN of the steering wheel rotation angle, X8 is the standard deviation SASTD of the steering wheel rotation angle, X9 is the MEAN SAQ1MEAN of the quartiles under the steering wheel rotation angle, X10 is the MEAN SAQ3MEAN of the quartiles under the steering wheel rotation angle, X11 is the steering wheel rotation angle SE, X12 is the MEAN savnean of the steering wheel rotation angle speed absolute value, X13 is the savtd, X14 is the zero speed percentage PNS, and X15 is the running total sum;
constructing a fatigue prediction neural network model, wherein the fatigue prediction neural network model uses a full connection layer to judge fatigue driving probability, as shown in fig. 4, the input of the model is a fusion feature vector set in a fusion window, the fusion feature vector set and a weight vector w of the full connection layer perform vector product operation, the vector product is input to a Sigmoid activation function (expressed as σ), and a fatigue probability value y between 0 and 1 is output through the Sigmoid activation function, and the specific formula is as follows:
further, in the training process of the fatigue prediction neural network model, a cross-entropy (cross-entropy) function is adopted as the loss function Em, so that a training set is N sample pairs<Xi,Oi>In which XiIs the fused feature vector set of the ith window sample, OiIs a label corresponding to the fused feature vector set of the ith window sample, OiThe value is 1 or 0, 1 represents fatigue driving, 0 represents non-fatigue driving, and when the fusion feature vector set of the ith window sample corresponds to fatigue driving, OiThe value is 1, and when the fusion feature vector set of the ith window sample corresponds to fatigue driving, OiWith a value of 0, the calculation formula of the loss function Em is as follows:
wherein the content of the first and second substances,
；
and dividing the training set into small batches to be used as input of each iteration in the training process, and performing model training by using a random gradient descent optimization algorithm through multiple iterations until a loss function is converged to obtain the trained fatigue prediction neural network model.
Wherein, the training set is: under a plurality of scenes that the driver is determined to be in fatigue driving, acquiring a set of fusion feature vector sets formed by fusing eye closing features, mouth opening and closing features and driving behavior features of the corresponding driver; and under a plurality of scenes that the driver is determined to be in non-fatigue driving, acquiring a set of fusion feature vector sets formed by eye closing features, mouth opening and closing features and driving behavior features of the corresponding driver.
Preferably, the driving recorder host further includes a fatigue level determination module, where the fatigue level determination module is configured to determine a fatigue level by using a sequential regression network model according to a fatigue probability value, and specifically includes:
when the fatigue probability value is less than 0.6, judging that the vehicle is in non-fatigue driving;
when the fatigue probability value is more than or equal to 0.6 and less than 0.9, judging the driver is light fatigue driving;
and when the fatigue probability value is more than or equal to 0.9, judging the deep fatigue driving.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (10)
1. An active speed limiting system based on a fatigue state of a driver is characterized by comprising a driver fatigue grade analysis device, an audible and visual alarm device, a speed limiting device and a vehicle speed sensor, wherein the driver fatigue grade analysis device is respectively connected with the audible and visual alarm device and the speed limiting device;
the driver fatigue grade analysis device is used for analyzing the fatigue grade of a driver, the fatigue grade comprises non-fatigue driving, light fatigue driving and deep fatigue driving, and when the fatigue grade of the driver is the light fatigue driving, an acousto-optic alarm signal is sent to the acousto-optic alarm device; when the fatigue grade of the driver is deep fatigue driving, an activation instruction is sent to the speed limiting device;
the sound and light alarm device is used for outputting sound and light alarm after receiving the sound and light alarm signal so as to remind a driver of being in a fatigue driving state;
the speed limiting device is used for activating the speed limiting device through the activation instruction, intercepting an accelerator opening value signal sent to a vehicle-mounted computer ECU by an accelerator pedal position sensor in real time when the speed limiting device is in an activated state, and acquiring the real-time speed of a vehicle through a vehicle speed sensor; when the real-time speed of the vehicle is less than or equal to a preset limit speed, transmitting the intercepted throttle opening value signal to a vehicle-mounted computer ECU in real time; and when the real-time speed of the vehicle is greater than the preset limit speed, sending an accelerator opening value signal corresponding to the limit speed to the vehicle-mounted computer ECU.
2. The active speed limiting system based on the fatigue state of the driver as claimed in claim 1, wherein the device further comprises a normally closed switch, and the accelerator pedal position sensor is connected with the vehicle-mounted computer ECU through a normally closed contact of the normally closed switch; the speed limiting device comprises a central processing unit, a signal acquisition unit, a signal output unit and a switch control unit, wherein the signal acquisition unit, the signal output unit, the switch control unit and the driver fatigue grade analysis device are all connected with the central processing unit;
the central processing unit is respectively connected with a vehicle speed sensor and an accelerator pedal position sensor of a vehicle through a signal acquisition unit, is connected with a normally open contact of a normally closed switch through a signal output unit, and is connected with a relay coil of the normally closed switch through a switch control unit; when the speed limiting device receives an activation instruction sent by the driver fatigue grade analysis device, a state switching signal is sent to the normally closed switch through the switch control unit, so that the normally closed contact of the normally closed switch is disconnected, and the speed limiting device is connected with the vehicle-mounted computer ECU through the normally open contact of the normally closed switch, so that the vehicle-mounted computer ECU cannot directly receive an electric signal of the accelerator pedal position sensor.
3. The active speed limiting system based on the fatigue state of the driver as claimed in claim 1, wherein the driver fatigue grade analyzing device comprises an image acquisition module, a facial behavior recognition module and a driving recorder host, wherein the image acquisition module and the driving recorder host are both connected with the facial behavior recognition module, and the driving recorder host is also connected with a CAN bus of the vehicle;
the image acquisition module is used for acquiring a face dynamic image of a driver;
the facial behavior recognition module is used for recognizing eye closing characteristics and mouth opening and closing characteristics from the acquired dynamic human face image;
the driving recorder host is used for acquiring and analyzing vehicle operation information from a vehicle CAN bus, calculating driving behavior characteristics through the vehicle operation information, and analyzing the fatigue level of a driver through fusion of eye closing characteristics, mouth opening and closing characteristics and driving behavior characteristics.
4. The active speed limit system based on driver fatigue status of claim 3, wherein the eye closure characteristics comprise a maximum closed-eye time in a time window, a frequency of blinking in a time window, and a percentage of closed-eye time in a time window, and the mouth opening and closing characteristics comprise a maximum mouth opening time in a time window, a frequency of yawning in a time window, and a percentage of mouth opening time in a time window.
5. The active speed limit system based on driver fatigue status of claim 4, wherein the time window corresponding to the maximum eye-closing time, the percentage of eye-closing time, the maximum mouth opening time and the percentage of mouth opening time is 10 seconds, and the time window corresponding to the blink frequency and the yawning frequency is 60 seconds.
6. The active speed limit system based on the driver fatigue state according to claim 3, the driving recorder host comprises a vehicle running information acquisition module and a driving behavior characteristic calculation module, the driving recorder host is used for acquiring and analyzing vehicle operation information from a vehicle CAN bus, the driving behavior feature calculation module is used for calculating the driving behavior feature according to the vehicle running information, the vehicle running information comprises a steering wheel corner SA and a steering wheel corner speed SAR, and the driving behavior characteristics comprise an absolute steering wheel corner MEAN value SAMEAN, a standard steering wheel corner difference SASTD, a lower steering wheel corner quartile value MEAN value SAQ1MEAN, an upper steering wheel corner quartile value MEAN value SAQ3MEAN, a steering wheel corner entropy SE, an absolute steering wheel corner speed MEAN value SAVMEAN, a standard steering wheel corner speed difference SAVSTD, a zero-speed percentage PNS and accumulated running duration.
7. The active speed limit system based on the fatigue state of the driver as claimed in claim 6, wherein the driving behavior feature calculation module calculates the driving behavior feature by the vehicle operation information specifically as follows:
the absolute steering wheel angle mean value SAMEAN is an average value of absolute steering wheel angles, and a calculation formula is shown as formula one:
the formula I is as follows:
；
wherein N is the steering wheel angle sample number, SAiIs the ith steering wheel angle sample;
the calculation formula of the steering wheel angle standard deviation SASTD is shown as a formula II:
the formula II is as follows:
；
wherein, SAmThe calculation formula is shown as formula three:
the formula III is as follows:
；
arranging N numerical values in a steering wheel corner sample from small to large, counting the numerical values from small to large, wherein the numerical value at the fourth quarter is a lower quartile value SAQ1 of the steering wheel corner, the numerical value at the third quarter is an upper quartile value SAQ3 of the steering wheel corner, the MEAN value SAQ1MEAN of the lower quartile value of the steering wheel corner is the MEAN value smaller than the lower quartile value SAQ1 of the steering wheel corner sample, and the MEAN value SAQ3MEAN of the upper quartile value 383 MEAN of the steering wheel corner sample is the MEAN value larger than the upper quartile value SAQ3 of the steering wheel corner sample;
the steering wheel corner entropy SE reflects the chaos degree and randomness of the operation of a driver on a steering wheel, the larger the steering wheel corner entropy SE is, the larger the randomness of the operation of the driver on the steering wheel is, the higher the fatigue degree of the driver is, the steering wheel corner entropy SE is calculated according to the probability of the occurrence of the prediction deviation of the steering wheel corner, and the predicted value theta of the steering wheel corner is firstly calculated according to the formula IVp(n)：
The formula four is as follows:
；
then, the actual value theta is calculated according to the steering wheel angle(n)Predicted value theta of steering wheel anglep(n)Calculating a steering wheel angle prediction deviation e from the differencenThe calculation formula is the following formula five:
the formula five is as follows:
；
steering wheel cornerPredicted deviation enObeying a normal distribution N (mu, sigma)2) Predicting the deviation e of the steering wheel anglenDivided into 9 intervals, (- ∞, -5. mu.)]，（−5μ，− 2.5μ]，（−2.5μ，−μ]，（−μ，−0.5μ](-0.5 μ, 0.5 μ), [0.5 μ, μ), [ μ, 2.5 μ), [2.5 μ, 5 μ), [5 μ, + ∞) and then calculates the probability values for each intervalp i And finally, calculating the steering wheel corner entropy SE according to a formula six:
formula six:
；
the mean value SAVMEAN and the standard deviation SAVSTD of the absolute values of the steering wheel angular velocity reflect the fluctuation condition of the vehicle, and the steering wheel angular velocity SA in the first formula and the second formula is replaced by the SAR of the steering wheel, so that the mean value SAVMEAN and the standard deviation SAVSTD of the absolute values of the steering wheel angular velocity are calculated;
the zero-speed percentage PNS detects the continuous and immovable operation characteristic PNS of the steering wheel, and the calculation formula is shown as formula seven:
the formula seven:
；
wherein N is the total number of samples of angular velocity in the selected time, and N is the sample of angular velocity between + -0.1 deg./s in the total sample.
8. The active speed limiting system based on the fatigue state of the driver as claimed in claim 3, wherein the driving recorder host further comprises a fatigue prediction neural network model, and the analysis of the fatigue level of the driver by fusion of the eye closing feature, the mouth opening and closing feature and the driving behavior feature is specifically as follows:
forming a fusion feature vector set by fusing eye closing features, mouth opening and closing features and driving behavior features, wherein X, X = { X1, X2, …, X15}, X1 is the longest eye closing time, X2 is the blink frequency, X3 is the percentage eye closing time, X4 is the longest mouth opening time, X5 is the yawning time, X6 is the percentage mouth opening time, X7 is the absolute MEAN SAMEAN of the steering wheel rotation angle, X8 is the standard deviation SASTD of the steering wheel rotation angle, X9 is the MEAN SAQ1MEAN of the quartiles under the steering wheel rotation angle, X10 is the MEAN SAQ3MEAN of the quartiles under the steering wheel rotation angle, X11 is the steering wheel rotation angle SE, X12 is the MEAN savnean of the steering wheel rotation angle speed absolute value, X13 is the savtd, X14 is the zero speed percentage PNS, and X15 is the running total sum;
the method comprises the following steps of constructing a fatigue prediction neural network model, judging fatigue driving probability by using a full connection layer, inputting the model as a fusion feature vector set in a fusion window, carrying out vector product operation on the fusion feature vector set and a weight vector w of the full connection layer, inputting the vector product to a Sigmoid activation function (expressed as sigma), and outputting a fatigue probability value y between 0 and 1 through the Sigmoid activation function, wherein the specific formula is as follows:
9. the active speed limit system based on the fatigue status of the driver as claimed in claim 8, wherein the cross-entropy (cross-entropy) function is used as the loss function Em during the training process of the fatigue prediction neural network model, so that the training set is N sample pairs<Xi,Oi>In which XiIs the fused feature vector set of the ith window sample, OiIs a label corresponding to the fused feature vector set of the ith window sample, OiThe value is 1 or 0, 1 represents fatigue driving, 0 represents non-fatigue driving, and when the fusion feature vector set of the ith window sample corresponds to fatigue driving, OiThe value is 1, and when the fusion feature vector set of the ith window sample corresponds to fatigue driving, OiWith a value of 0, the calculation formula of the loss function Em is as follows:
and dividing the training set into small batches to be used as input of each iteration in the training process, and performing model training by using a random gradient descent optimization algorithm through multiple iterations until a loss function is converged to obtain the trained fatigue prediction neural network model.
10. The active speed limiting system based on the fatigue state of the driver as claimed in claim 8, wherein the driving recorder host further comprises a fatigue level judging module, the fatigue level judging module is used for judging the fatigue level of the driver according to the fatigue probability value, and specifically comprises:
when the fatigue probability value is less than 0.6, judging that the vehicle is in non-fatigue driving;
when the fatigue probability value is more than or equal to 0.6 and less than 0.9, judging the driver is light fatigue driving;
and when the fatigue probability value is more than or equal to 0.9, judging the deep fatigue driving.
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