WO2019095887A1 - Procédé et système permettant de réaliser un dispositif de détection anti-oubli universel pour des passagers dans un véhicule - Google Patents

Procédé et système permettant de réaliser un dispositif de détection anti-oubli universel pour des passagers dans un véhicule Download PDF

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WO2019095887A1
WO2019095887A1 PCT/CN2018/109440 CN2018109440W WO2019095887A1 WO 2019095887 A1 WO2019095887 A1 WO 2019095887A1 CN 2018109440 W CN2018109440 W CN 2018109440W WO 2019095887 A1 WO2019095887 A1 WO 2019095887A1
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event
vehicle
passenger
recognition model
candidate
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PCT/CN2018/109440
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English (en)
Chinese (zh)
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孙宝石
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苏州数言信息技术有限公司
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60R—VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
    • B60R21/00—Arrangements or fittings on vehicles for protecting or preventing injuries to occupants or pedestrians in case of accidents or other traffic risks
    • B60R21/01—Electrical circuits for triggering passive safety arrangements, e.g. airbags, safety belt tighteners, in case of vehicle accidents or impending vehicle accidents
    • B60R21/015—Electrical circuits for triggering passive safety arrangements, e.g. airbags, safety belt tighteners, in case of vehicle accidents or impending vehicle accidents including means for detecting the presence or position of passengers, passenger seats or child seats, and the related safety parameters therefor, e.g. speed or timing of airbag inflation in relation to occupant position or seat belt use
    • B60R21/01512—Passenger detection systems
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60R—VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
    • B60R21/00—Arrangements or fittings on vehicles for protecting or preventing injuries to occupants or pedestrians in case of accidents or other traffic risks
    • B60R21/01—Electrical circuits for triggering passive safety arrangements, e.g. airbags, safety belt tighteners, in case of vehicle accidents or impending vehicle accidents
    • B60R21/015—Electrical circuits for triggering passive safety arrangements, e.g. airbags, safety belt tighteners, in case of vehicle accidents or impending vehicle accidents including means for detecting the presence or position of passengers, passenger seats or child seats, and the related safety parameters therefor, e.g. speed or timing of airbag inflation in relation to occupant position or seat belt use
    • B60R21/01512—Passenger detection systems
    • B60R21/0153—Passenger detection systems using field detection presence sensors
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60R—VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
    • B60R21/00—Arrangements or fittings on vehicles for protecting or preventing injuries to occupants or pedestrians in case of accidents or other traffic risks
    • B60R21/01—Electrical circuits for triggering passive safety arrangements, e.g. airbags, safety belt tighteners, in case of vehicle accidents or impending vehicle accidents
    • B60R21/015—Electrical circuits for triggering passive safety arrangements, e.g. airbags, safety belt tighteners, in case of vehicle accidents or impending vehicle accidents including means for detecting the presence or position of passengers, passenger seats or child seats, and the related safety parameters therefor, e.g. speed or timing of airbag inflation in relation to occupant position or seat belt use
    • B60R21/01512—Passenger detection systems
    • B60R21/01542—Passenger detection systems detecting passenger motion
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
    • G08B21/02—Alarms for ensuring the safety of persons

Definitions

  • the present invention relates to a method and system for implementing a universal sensing device for in-vehicle passenger anti-forgetting.
  • the installation is cumbersome: the installation of sensors on the seat, by (the type of seat, the material varies widely, the installation of pressure sensors involves a lot of engineering work. For example, a school bus will be to modify dozens of seats.
  • High cost the system itself has high cost (more module components), high cost of vehicle modification and seat modification, and high maintenance cost.
  • an object of the present invention is to provide a sensor data acquired by a non-contact sensor, calculate various events occurring in the vehicle, and more accurately determine the car according to a logical relationship between various events.
  • the method for realizing the universal anti-forgotten sensing device for an in-vehicle passenger of the present invention includes:
  • the vehicle The related events include at least: vehicle engine starting, vehicle driving, vehicle waiting, starting and extinguishing, opening the door, closing the door;
  • passenger-related events include at least: human voice, human body movement, passengers getting on the bus, passengers getting off;
  • the event includes at least: at least one of a high or a low temperature, humidity, air pressure, temperature, humidity, and air pressure in the vehicle;
  • the event classifier is generated by supervising machine learning, and the process of identifying the event type by the event classifier includes:
  • the obtained sensor data is characterized to obtain characterization data, and the characterization data includes: performing fast Fourier transform, sliding time window, calculating maximum value, minimum value, median, and average value , sample number, standard variance 6 characteristic values;
  • the characterization data is sequentially substituted into m reserved candidate event recognition models for calculation, and the identified event types and credibility are output.
  • the screening method of the m highly correlated candidate event recognition model specifically includes:
  • the candidate event recognition model satisfying the activation condition is activated. If the candidate event recognition model satisfying the predetermined activation condition is greater than m, the m models with the smallest deviation score are selected as the screening result; the activation conditions of each candidate event recognition model are: (F x -F 0 )>2* ⁇ 0 , (F i -F x )/ ⁇ x is the deviation score of the candidate event recognition model;
  • calculating the average value of the sensor data acquired during the predetermined time period and the eigenvalue vector of the standard deviation are respectively recorded as F 0 and ⁇ 0 ; respectively, the sensing of the candidate event recognition model data during the training process
  • the eigenvalue vectors of the mean and standard deviation of the data are denoted as F x and ⁇ x , respectively; x is the number of the candidate event recognition model.
  • the predetermined time period includes a plurality of different event calculation cycles
  • the duration of the next event calculation cycle is determined based on the probability of a child forgetting event occurring within the next event calculation cycle.
  • the method further includes: acquiring vehicle information, loading the event classifier based on the vehicle information, and if the corresponding event classifier is stored locally, using a locally stored event classifier; if the corresponding event classifier is not stored locally And loading, by the wireless network, an event classifier of the remote service terminal, wherein the vehicle information includes one or a combination of a vehicle type, a vehicle brand, a number of passengers on the vehicle, and a model of the vehicle.
  • the method further includes determining the child The emergency priority of the forgotten event, different alarm modes and alarm frequencies are set according to the emergency priority.
  • the first level is urgent
  • the alarm information is sent to the bound private electronic terminal at the first frequency
  • the predetermined public receiving platform sends the alarm information.
  • Second-level emergency sending alarm information to the bound private electronic terminal with secondary frequency
  • third-level emergency sending alarm information to the bound private electronic terminal with three-level frequency
  • ...K-level emergency with K-level frequency
  • the bound private electronic terminal sends out the hug information, the emergency priority of the child forgetting event, level 1 > level 2 > level 3 > > level K, level 1 frequency > level 2 frequency > level 3 frequency > level K frequency.
  • the implementation system of the universal anti-forgotten sensing device for an in-vehicle passenger of the present invention comprises:
  • the sensors including an acceleration sensor, a gyroscope, a microphone, an electromagnetic induction, a temperature/humidity, a gas pressure, a Doppler radar, and/or a passive infrared PIR sensor;
  • the event classifier is configured to perform an analysis operation on the sensor data acquired by the plurality of non-contact sensor groups, and obtain a vehicle-related event, a passenger-related event, an in-vehicle environment-related event, and a user-defined event that occur within a predetermined time period;
  • Vehicle-related events include at least: vehicle engine start, vehicle travel, vehicle waiting, starting and extinguishing, opening the door, closing the door;
  • passenger-related events include at least: human voice, body movement, passengers getting on, passengers getting off; interior environment
  • the related events include at least: at least one of the temperature, humidity, normal air pressure, interior temperature, humidity, and air pressure of the vehicle is high or low;
  • the forgetting event determining unit is configured to determine whether the logical relationship between the vehicle related event, the passenger related event, the in-vehicle environment related event, and/or the user customized event satisfies a predetermined logical relationship of the child forgetting event on the vehicle,
  • the alarm unit sends an alarm message; if it is not satisfied, it will not process it.
  • the event classifier includes:
  • a characterization processing module configured to perform characterization processing on the acquired sensing data to obtain characterization data
  • a recognition model screening module configured to filter the selected event recognition model of the event classifier based on the sensing data, filter out a candidate event recognition model with low correlation, and retain m candidate events with high correlation model;
  • a result output module configured to substitute the characterization data into m reserved candidate event recognition models for calculation, and output the identified event type and credibility
  • Specific screening methods for identifying model screening modules include:
  • the candidate event recognition model satisfying the activation condition is activated. If the candidate event recognition model satisfying the predetermined activation condition is greater than m, the m models with the smallest deviation score are selected as the screening result; the activation conditions of each candidate event recognition model are: (F x -F 0 )>2* ⁇ 0 , (F i -F x )/ ⁇ x is the deviation score of the candidate event recognition model;
  • calculating the average value of the sensor data acquired during the predetermined time period and the eigenvalue vector of the standard deviation are respectively recorded as F 0 and ⁇ 0 ; respectively, the sensing of the candidate event recognition model data during the training process
  • the eigenvalue vectors of the mean and standard deviation of the data are denoted as F x and ⁇ x , respectively; x is the number of the candidate event recognition model.
  • the method further includes: an event calculation cycle duration determining module, configured to predict a next event based on a logical relationship between the vehicle related event and/or the in-vehicle environment related event occurring in the previous m event operation cycles Calculating a probability of a child forgetting event occurring in a next event calculation period based on the vehicle related event and/or the in-vehicle environment related event described in the calculation cycle;
  • the correspondence table between the vehicle-related event and/or the in-vehicle environment-related event and the occurrence probability of the child forgetting in-vehicle event is obtained by machine learning the historical data.
  • an event classifier loading module is further configured to acquire vehicle information, and load the event classifier based on the vehicle information. If a corresponding event classifier is stored locally, the locally stored event classifier is used; If the corresponding event classifier is not stored, the event classifier of the remote service terminal is loaded through the wireless network, wherein the vehicle information includes one or a combination of a vehicle type, a vehicle brand, a number of passengers on the vehicle, and a model of the vehicle. ;
  • the alarm unit includes an emergency priority determination module for determining an emergency priority of the child forgetting event, and setting different alarm modes and alarm frequencies according to the emergency priority, wherein the first level emergency is tied to the first frequency
  • the fixed private electronic terminal sends out the alarm information and the predetermined public receiving platform sends out the alarm information; the second-level emergency sends the alarm information to the bound private electronic terminal with the secondary frequency; the third-level emergency, the third-level frequency to the bound private
  • the electronic terminal issues an alarm message; ... K-level emergency, sending a tight message to the bound private electronic terminal at the K-level frequency, the emergency priority of the child forgetting event, level 1 > level 2 > level 3 > level K, Primary frequency > secondary frequency > tertiary frequency > K frequency.
  • the method and system for realizing the in-vehicle passenger anti-forgotten sensing device of the present invention have at least the following advantages:
  • the anti-forgetting integrated sensing device of the invention is composed of a 32-bit single chip microcomputer, a communication module, various physical sensors and a power supply module.
  • the communication module can be used for sending event notifications and alarm messages by using Bluetooth, wireless (Wi-Fi), 3G/4G, NB-IoT, eMTC, and the like.
  • Physical sensors include accelerometers, gyroscopes, microphones, electromagnetic induction, temperature and humidity, air pressure, and one or two sets of Doppler radar and passive infrared (PIR) sensors.
  • the power supply module is composed of a power management module, a battery, and a battery power detecting module, and supports power supply of the USB interface and power supply of the cigarette lighter and the power port (12V or 24V);
  • a set of event classifiers is integrated on the sensing device, corresponding to each in-vehicle event that needs to be identified, including: starting, starting, vehicle driving, waiting for the vehicle, starting and extinguishing, opening the door, closing the door, moving the human body, speaking, getting on and off Etc; the event classifier is generated by supervising machine learning;
  • the sensor raw data is characterized, including: fast Fourier transform, sliding time window to calculate six eigenvalues (maximum value, minimum value, median, average value, sample number, standard deviation);
  • the recognition speed is improved by the pre-screening algorithm of the classifier
  • Event notifications, raw data, and feature values are output as configurable items
  • the invention has wide application range, convenient installation, low cost and high reliability. Not only can accurate passenger forgetting detection be achieved, but a wide variety of in-vehicle events can be detected to support more applications.
  • FIG. 1 is a flow chart showing a method for realizing a general-purpose in-vehicle passenger anti-forgotten sensing device
  • FIG. 2 is a block diagram showing an implementation system of a general-purpose in-vehicle passenger anti-forgotten sensing device
  • Figure 3 is an anti-forgotten discrimination processing logic
  • Figure 4 is an event recognition model training process
  • Figure 5 is a production environment event identification process.
  • the method and system for realizing the in-vehicle passenger anti-forgotten sensing device of the present invention are based on the sensing data collected by a plurality of non-contact sensors, and the event classifier is obtained through data training, and the predetermined time period is obtained based on the event classifier.
  • the events occurring inside the vehicle determine the passenger forgetting event through the logical relationship between these events.
  • the invention utilizes multiple sets of non-contact sensors to acquire multiple sets of different sensor data, and performs comprehensive operations on multiple sensing data to obtain an event, and logical analysis of multiple events to obtain a final result.
  • a method for implementing an anti-forgotten sensing device for a passenger in a vehicle includes:
  • the vehicle The related events include at least: vehicle engine starting, vehicle driving, vehicle waiting, starting and extinguishing, opening the door, closing the door;
  • passenger-related events include at least: human voice, human body movement, passengers getting on the bus, passengers getting off;
  • the event includes at least: at least one of a high or a low temperature, humidity, air pressure, temperature, humidity, and air pressure in the vehicle;
  • the event classifier is generated by supervising machine learning, and the process of identifying the event type by the event classifier includes:
  • the characterization data is sequentially substituted into m reserved candidate event recognition models for calculation, and the identified event types and credibility are output.
  • the screening method of the m highly correlated candidate event recognition model specifically includes:
  • the candidate event recognition model satisfying the activation condition is activated. If the candidate event recognition model satisfying the predetermined activation condition is greater than m, the m models with the smallest deviation score are selected as the screening result; wherein the activation conditions of each candidate event recognition model Is: (F x -F 0 )>2* ⁇ 0 , (F i -F x )/ ⁇ x is the deviation score of the candidate event recognition model;
  • this background environment calculates the average value of the sensor data acquired in the predetermined time period and the eigenvalue vector of the standard deviation, respectively, F 0 and ⁇ 0 ; each candidate event recognition model data training process, the average value of the acquired sensor data and the eigenvalue vector of the standard deviation are respectively recorded as F x and ⁇ x ; x is the number of the candidate event recognition model.
  • the sensors are all non-contact sensors.
  • the acquisition of each event type is based on the results of the combined operation of the sensor data acquired by multiple sensors, rather than the result of a single sensor data.
  • the final conclusion of the passenger forgetting event is obtained by a logical relationship between multiple events, rather than pure sensory data.
  • the microphone detects the sound of someone in the car
  • the passive infrared (PIR) sensor detects that there is a human body in the car
  • the electromagnetic induction detects the change of the electromagnetic inside the car
  • the temperature sensor detects the temperature inside the car (the inside of the car and the inside of the car) There is no difference in temperature when there is no one.)
  • the event in the car is based on the data integration of the above sensors. After the three events of stopping the vehicle, some people in the car, and no one in the driver's seat, based on the logical relationship between the three events, it can be determined that the passenger forgetting event has occurred. For another example, if the vehicle is turned off for more than a preset time event, a door closing event, a sound event of a person in the vehicle, or a movement event in the vehicle, it can be determined that a passenger forgetting event has occurred. For another example, if the vehicle is in a normal range of speed driving state and there is an event in the vehicle, it can be judged that no passenger forgetting event has occurred.
  • a family car is taken as an example, and an anti-forgetting integrated sensing device is disposed at the front panel of the family car, wherein the physical sensor includes an acceleration sensor, a gyroscope, a microphone, electromagnetic induction, temperature and humidity, air pressure, and a group of Doppler. Radar and passive infrared (PIR) sensors.
  • the sensing device draws power through the USB port or the cigarette lighter.
  • the sensing device detects the door opening event and the boarding event; when the vehicle starts, the sensing device detects the engine starting event and sets the vehicle state to start; the vehicle starts to drive, and the sensing device sets the vehicle state For driving; the vehicle stops, the sensing device sets the vehicle state to stop; the driver turns off the fire, the sensing device sets the vehicle state to the engine stop state; the driver opens the door, gets off the vehicle, closes the door, and the sensing device detects the opening event and gets off the vehicle. Events, closing events. At this time, there are still passengers in the car. After 1 minute (assuming the anti-forgetting alarm delay is set to 1 minute), if the passenger makes a sound or moves the body, the sensing device will detect the human activity event, and an alarm message will be sent through the communication module.
  • the method for implementing the in-vehicle passenger anti-forgotten sensing device is obtained.
  • the vehicle information is acquired, and the event classifier is loaded based on the vehicle information, and the corresponding event classification is stored locally.
  • the event classifier is stored locally; if the corresponding event classifier is not stored locally, the event classifier of the remote service terminal is loaded through the wireless network, wherein the vehicle information includes a model, a vehicle brand, and a vehicle passenger A combination of one or several of the number of people and the model of the vehicle.
  • the predetermined time period includes a plurality of different event calculation periods, and the correspondence table between the vehicle related event and/or the vehicle environment related event and the occurrence probability of the child forgetting vehicle event is obtained based on the historical data training; A logical relationship between the vehicle-related event and/or the in-vehicle environment-related event occurring in an event computing cycle, predicting a vehicle-related event and/or an in-vehicle environment-related event within the next event computing cycle, based on The correspondence table obtains the probability of occurrence of the child forgetting event in the next event computing period; and determines the duration of the next event computing period based on the probability of the child forgetting event occurring in the next event computing period.
  • the probability of the passenger forgetting the event in the car is not large, and the time of the next event calculation cycle can be relatively long, that is, the time interval for acquiring the sensor data can be longer, which can reduce the data transmission frequency and Reduce the amount of calculations and save power.
  • a vehicle deceleration event (vehicle-related event) occurs, and the interior temperature is higher than a set threshold (in-vehicle environment-related event).
  • the next cycle is predicted to stop at this time. The possibility of coming down is relatively large.
  • the probability of the passenger forgetting event is higher than that of the vehicle.
  • the event of the next event calculation cycle is relatively short, so that it is convenient to find the passenger forgetting event in time.
  • the interior environment is also considered as a consideration. When the interior environment threatens human health, this is defined as a high probability of passenger forgetting events.
  • the generation of a specific probability correspondence table is generated by specific machine learning.
  • the method for realizing the in-vehicle passenger anti-forgotten sensing device in the embodiment is determined on the basis of the embodiment 1 or 2, and the vehicle-related event, the passenger-related event, the in-vehicle environment-related event, and/or the user is determined.
  • Defining the logical relationship of the event satisfies the predetermined logical relationship of the child forgetting the event on the vehicle and further includes determining the emergency priority of the child forgetting event, and setting different alarm modes and alarm frequencies according to the emergency priority, wherein Urgently, the alarm message is sent to the bound private electronic terminal at the first-level frequency and the alarm message is sent by the predetermined public receiving platform; the second-level emergency sends the alarm information to the bound private electronic terminal at the secondary frequency; The third-level frequency sends an alarm message to the bound private electronic terminal; ...
  • K-level emergency sending a tight message to the bound private electronic terminal with a K-level frequency, the emergency priority of the child forgetting event, level 1 > level 2 > Level 3...>K level, first order frequency>2nd frequency>3rd order frequency>K stage frequency.
  • the first-level frequency is bound to the private electronic terminal (mobile phone, notebook computer, PAD, smart).
  • the watch sends an alarm message and sends an alarm message to the public receiving platform (the server of the background operator and/or 110).
  • What is included in the alarm information can be set according to the program.
  • the vehicle has stopped, detecting that the driver's getting off the vehicle exceeds the predetermined time, and the passenger's voice and/or passenger movement in the rear seat of the vehicle may be defined as a secondary emergency, which is sent to the bound private electronic terminal.
  • Alarm information Specifically, the setting of the emergency situation and the setting of the alarm mode can be specifically trained according to the actual situation to generate a corresponding training model.
  • the specific binding quantity of the private electronic terminal is set by the user.
  • the output of the alarm signal also includes an audible and visual alarm signal.
  • the event classifier is configured to perform an analysis operation on the sensor data acquired by the plurality of non-contact sensor groups, and obtain a vehicle-related event, a passenger-related event, an in-vehicle environment-related event, and a user-defined event that occur within a predetermined time period;
  • Vehicle-related events include at least: vehicle engine start, vehicle travel, vehicle waiting, starting and extinguishing, opening the door, closing the door;
  • passenger-related events include at least: human voice, body movement, passengers getting on, passengers getting off; interior environment
  • the related events include at least: at least one of the temperature, humidity, normal air pressure, interior temperature, humidity, and air pressure of the vehicle is high or low;
  • the forgetting event determining unit is configured to determine whether the logical relationship between the vehicle related event, the passenger related event, the in-vehicle environment related event, and/or the user customized event satisfies a predetermined logical relationship of the child forgetting event on the vehicle,
  • the alarm unit sends an alarm message; if it is not satisfied, it will not process it.
  • the event classifier includes:
  • a characterization processing module configured to perform characterization processing on the acquired sensing data to obtain characterization data
  • a recognition model screening module configured to filter the selected event recognition model of the event classifier based on the sensing data, filter out a candidate event recognition model with low correlation, and retain m candidate events with high correlation model;
  • the result output module is configured to sequentially substitute the characterization data into the m reserved candidate event recognition models, and output the identified event type and credibility.
  • the screening method of the identification model screening module specifically includes:
  • the candidate event recognition model satisfying the activation condition is activated. If the candidate event recognition model satisfying the predetermined activation condition is greater than m, the m models with the smallest deviation score are selected as the screening result; wherein the activation conditions of each candidate event recognition model Is: (F x -F 0 )>2* ⁇ 0 , (F i -F x )/ ⁇ x is the deviation score of the candidate event recognition model;
  • the eigenvalue vectors of the average value and the standard deviation of the sensor data acquired in the predetermined time period are calculated as F 0 and ⁇ 0 respectively; in the training process of each candidate event recognition model data, the acquired sensor data
  • the eigenvalue vectors of the mean and standard deviation are denoted as F x and ⁇ x , respectively; x is the number of the candidate event recognition model.
  • a school bus is taken as an example, and an anti-forgetting integrated sensing device is deployed in the middle of the school bus.
  • the physical sensors include an acceleration sensor, a gyroscope, a microphone, an electromagnetic induction, a temperature and humidity, a gas pressure, and two sets of Doppler radar and passive infrared. (PIR) sensor.
  • the sensing device is powered by a 12V lighting circuit inside the vehicle.
  • the sensing device detects the door closing event, the door opening event, the getting off event and the closing event. But one student fell asleep in the car and was forgotten in the car. After 3 minutes (assuming the anti-forgetting alarm delay is set to 3 minutes), if the student makes a sound or moves the body, the sensing device detects the human activity event and sends an alarm message through the communication module.
  • the implementation system of the in-vehicle passenger anti-forgotten sensing device of the present embodiment further includes, on the basis of Embodiment 4, an event calculation cycle duration determining module, which is configured to be based on the occurrence of the previous m event calculation cycles.
  • an event calculation cycle duration determining module which is configured to be based on the occurrence of the previous m event calculation cycles.
  • the correspondence table between the vehicle-related event and/or the in-vehicle environment-related event and the occurrence probability of the child forgetting in-vehicle event is obtained by machine learning the historical data.
  • the implementation system of the in-vehicle passenger anti-forgotten sensing device of the present embodiment further includes an emergency priority determining module for determining the vehicle-related event and the passenger-related event on the basis of Embodiment 4 or 5.
  • the logical relationship between the environment related events in the vehicle and/or the user-defined event satisfies the predetermined logical relationship of the child forgetting event on the vehicle, determines the emergency priority of the child forgetting event, and sets different alarms according to the emergency priority.
  • Mode and alarm frequency wherein, the first level is urgent, the sound and light alarm is issued; the second level emergency sends an alarm message to the predetermined receiving terminal through the wireless network; the third level is urgent, and the alarm message is sent to the predetermined receiving terminal, and the predetermined public receiving is sent.
  • the platform sends an alarm message.
  • the method further includes: acquiring vehicle information, loading the event classifier based on the vehicle information, and if the corresponding event classifier is stored locally, using a locally stored event classifier; if the local does not store the corresponding The event classifier loads the event classifier of the remote service terminal through the wireless network, wherein the vehicle information includes one or a combination of a vehicle type, a vehicle brand, a number of passengers on the vehicle, and a model of the vehicle.
  • the senor includes an acceleration sensor, a gyroscope, a microphone, an electromagnetic induction, a temperature and humidity, a gas pressure, a Doppler radar, and a passive infrared (PIR) sensor.
  • the sensor data characterization is to extract the collected sensor raw data, which not only retains the key information, but also plays the role of data compression and confidentiality. Furthermore, for sensors with a sampling frequency lower than 1 kHz, such as Doppler radar, passive infrared, temperature and humidity, air pressure, etc., six characteristic values are calculated with a sliding time window of 1 to 2 seconds (maximum, minimum). Value, median, average, sample number, standard deviation); for sensors with sampling frequency higher than 1 kHz, such as: acceleration sensor, gyroscope, microphone, electromagnetic induction, etc. Leaf transformation (FFT, 256 sample sliding window), then calculate 6 eigenvalues.
  • FFT Fast Fourier transform
  • the characterization processed data packet (X matrix) is transmitted to the background machine learning tool, and the event recognition model is trained together with the event type tag (Y vector).
  • Machine learning tools are only used during the model training phase, support conventional machine learning algorithms (such as: SVM support vector machine, random forest, neural network), can be offline tools, or can be used online through automated scripts.
  • the m models with the smallest deviation score are selected, and the specific value of m may be 5 or may be specifically set according to a specific situation, and the specific data and the calculation amount that the device can support are specifically set.
  • the result of “passengers being forgotten in the car” is reflected in a series of related events. Therefore, by logically detecting the event for a period of time, it is possible to accurately recognize that a passenger is forgotten in the vehicle and issue an alarm message through the communication module.
  • the anti-forgotten related discriminating and processing logic is shown in Figure 3.
  • the vehicle status includes: starting from start, vehicle driving, vehicle waiting, starting and extinguishing, etc.; environmental events in the vehicle include: normal, over temperature, low temperature, humidity Excessively high, low humidity, high air pressure, low air pressure, etc.; other events include opening the door, closing the door, getting on the bus, getting off the bus, etc.

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Abstract

L'invention concerne un procédé et un système permettant de réaliser un dispositif de détection anti-oubli universel pour des passagers dans un véhicule, lesquels procédé et système sont conçus pour améliorer la précision d'une reconnaissance anti-oubli pour des enfants dans un véhicule. Dans le cadre du procédé et du système permettant de réaliser un dispositif de détection anti-oubli universel pour des passagers dans un véhicule, le procédé consiste : à déterminer si un événement d'état de véhicule se rapporte à un événement d'alarme prédéterminé, si tel est le cas, à déterminer si l'environnement actuel dans le véhicule satisfait la situation dans laquelle un corps humain se déplace et quelqu'un émet un son, et/ou si les données environnementales actuelles dans le véhicule dépassent une plage de seuil prédéterminée, et si tel est le cas, à déterminer qu'une personne a été oubliée dans le véhicule et à envoyer des informations d'alarme ; sinon, à n'effectuer aucun traitement. Le système peut être appliqué à tous les types et marques de véhicules sans qu'il soit nécessaire de modifier le véhicule, et le système est véritablement prêt à l'emploi, et le coût est fortement réduit.
PCT/CN2018/109440 2017-11-14 2018-10-09 Procédé et système permettant de réaliser un dispositif de détection anti-oubli universel pour des passagers dans un véhicule WO2019095887A1 (fr)

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