CN110816542A - Method for providing driver assistance - Google Patents
Method for providing driver assistance Download PDFInfo
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- CN110816542A CN110816542A CN201910660737.9A CN201910660737A CN110816542A CN 110816542 A CN110816542 A CN 110816542A CN 201910660737 A CN201910660737 A CN 201910660737A CN 110816542 A CN110816542 A CN 110816542A
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- 238000000034 method Methods 0.000 title claims abstract description 30
- 230000002776 aggregation Effects 0.000 claims abstract description 5
- 238000004220 aggregation Methods 0.000 claims abstract description 5
- 230000001133 acceleration Effects 0.000 claims description 3
- 238000012544 monitoring process Methods 0.000 claims 1
- 238000001514 detection method Methods 0.000 description 4
- 206010020772 Hypertension Diseases 0.000 description 3
- 208000024172 Cardiovascular disease Diseases 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 208000012902 Nervous system disease Diseases 0.000 description 1
- 208000025966 Neurological disease Diseases 0.000 description 1
- 230000036626 alertness Effects 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 206010012601 diabetes mellitus Diseases 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 208000020016 psychiatric disease Diseases 0.000 description 1
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Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/08—Interaction between the driver and the control system
- B60W50/14—Means for informing the driver, warning the driver or prompting a driver intervention
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- 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
- G08B21/18—Status alarms
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
- H04W4/46—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for vehicle-to-vehicle communication [V2V]
Abstract
A method of providing driver assistance is disclosed. The method includes receiving (105), by a processor, a plurality of input parameters from a vehicle, the input parameters including at least one of real-time driving patterns associated with a driver of the vehicle; retrieving (110) a training data set comprising an aggregation of a plurality of driving patterns classified as corresponding to one of a plurality of medical conditions and a plurality of road conditions; comparing (115) the input parameters to the training data set to determine a match between the real-time driving pattern and at least one of a plurality of driving patterns present in the training data set; and if a match is determined, sending (120) warning information to the vehicle and at least one of the plurality of vehicles around the vehicle.
Description
Technical Field
The present invention relates to the field of providing driver assistance to a driver of a vehicle, and more particularly to the field of providing driver assistance based on the health of the driver.
Background
During driving, many medical conditions can negatively impact driving. Therefore, it is necessary to recognize the health condition of the driver. According to chinese patent application CN104207791, a driving method for detecting fatigue is disclosed. In CN104207791, a method for detecting fatigue driving belongs to the field of safe driving of automobiles, which obtains a fatigue detection physiological parameter as an index variable by taking a behavior index (steering wheel master data, driving speed and driving time) of a driver, pre-information, and trains a BP (back propagation) neural network by building a fatigue detection network model using the fatigue index corresponding variable, and performs fatigue detection using the model.
However, in the chinese application, only one condition of fatigue detection is addressed.
Drawings
Various modes of the invention are disclosed in detail in the specification and illustrated in the accompanying drawings:
FIG. 1 illustrates a flow chart describing a method of providing driver assistance in accordance with one embodiment of the present disclosure.
Detailed Description
A method of providing driver assistance is disclosed. The method includes receiving (105), by a processor, a plurality of input parameters from a vehicle, the input parameters including at least one of a first data set, a second data set, and a third data set, the first data set including a real-time driving pattern associated with a driver of the vehicle, the second data set including a health record of the driver, and the third data set including a real-time road condition of the vehicle. The method further includes retrieving (110), by the processor, a training data set, the training data stored in the memory unit and including an aggregation of a plurality of driving patterns classified as corresponding to one of a plurality of medical conditions and a plurality of road conditions, the classification based on one of a supervised learning technique and an unsupervised learning technique. Further, the method includes comparing (115), by the processor, the input parameters to the training data set to determine a match between the real-time driving pattern and at least one of a plurality of driving patterns present in the training data set, and if a match is determined between the real-time driving pattern and the at least one of the plurality of driving patterns, sending (120), by the processor, warning information to at least one of the vehicle and a plurality of vehicles surrounding the vehicle.
The method is performed by a processor. In one embodiment, the processor is embedded in a server device present in the cloud network. The electronic control unit of the vehicle may be connected to a server device existing in the cloud network. Further, each vehicle is adapted to communicate with other vehicles through V2V or through a cloud network.
At step 105, the processor receives a plurality of input parameters from the vehicle. An electronic control unit in the vehicle is connected to a processor in the cloud network through a wireless communication interface. The input parameters include, but are not limited to, a first data set including a real-time driving pattern associated with a driver of the vehicle, a second data set including a health record of the driver, and a third data set including real-time road conditions of the vehicle.
The real-time driving mode includes data associated with acceleration, deceleration, braking force, and turning radius of the vehicle. Various sensors present in the vehicle are used to retrieve this data. Data retrieved from the various sensors is sent to the processor by an electronic control unit in the vehicle. The health of the driver affects the driving pattern. For example, if the driver has high blood pressure, the driving mode may include a sharp deceleration due to an emergency brake. Similarly, various other medical conditions are associated with corresponding driving patterns. Therefore, it is necessary to develop a real-time driving mode. Thus, the real-time driving pattern is sent to the processor.
Another input parameter includes a health record of the driver. In one embodiment, the health record is obtained from an insurance database. That is, the processor may access the insurance database for a particular driver and may thus determine the medical condition of the driver. In one embodiment, the health record may be obtained using health inputs provided by the driver himself. The health input may be provided by entering a medical condition on a dashboard that is sent to the processor, or a voice input describing the health condition may be provided that is sent to the processor present in the cloud network.
In addition, another input parameter includes real-time road conditions of the vehicle. The real-time road conditions indicate the environment in which the vehicle is driven. Examples of real-time road conditions include, but are not limited to, highway, city driving, icy road, uphill driving, and terrain driving. Real-time road conditions are also sent to the processor so that the driving mode associated with each of these road conditions can be retrieved from a memory unit connected to the processor.
At step 110, the processor retrieves a training data set. The training data set is stored in a memory unit accessible by the processor. The training data set includes an aggregation of a plurality of driving patterns classified as corresponding to one of a plurality of medical conditions and a plurality of road conditions. Examples of medical conditions include, but are not limited to, conditions affecting alertness and memory, learning and judgment, hypertension, physical disabilities, cardiovascular disease, neurological disorders, psychiatric disorders, diabetes and vision problems. In other words, the training data set includes a driving pattern associated with each medical condition. For example, if the medical condition is hypertension, the condition will include an associated driving mode. Similarly, if the medical condition includes a vision problem, it will include an associated driving pattern. Thus, each medical condition and its associated driving pattern form part of a training data set.
Similarly, each road condition is associated with a corresponding driving mode that is stored in the memory unit and accessible by the processor. For example, an icy road will have a particular driving pattern. Similarly, a highway will be associated with a particular driving pattern. Thus, the driving patterns are classified based on the various road conditions forming part of the training data set.
Classifying numerous driving patterns into various medical conditions or road conditions is performed using supervised or unsupervised learning techniques. In other words, driving patterns from a large number of drivers with various medical conditions are obtained. Further, supervised or unsupervised learning techniques are used to group the driving patterns of drivers with similar medical conditions. Thus, the more driving patterns analyzed, the more accurate the grouping will be. Similarly, driving patterns from numerous drivers driving under various road conditions are obtained and grouped. Thus, the more driving pattern data that is obtained, the easier it will be to classify the driving pattern as a specific medical condition or a specific road condition or both. It should be noted that supervised learning may be used to detect patterns corresponding to medical conditions, and unsupervised learning may be used to detect anomalies in driving patterns.
Thus, training data sets are obtained from the driving patterns of a large number of drivers. In addition, a supervised learning technique or an unsupervised learning technique is applied to the data (driving patterns of many drivers) to classify the driving patterns into various medical conditions or various road conditions.
Such training data is used as a reference or model for classifying the real-time driving pattern associated with the driver as any medical condition or any road condition. Such references may be used to predict the health of the driver in real time, thereby avoiding an impending disaster.
At step 115, the processor compares the input parameters to the training data set to determine a match between the real-time driving pattern and at least one of the plurality of driving patterns present in the training data set. A comparator present in the processor may be used for such comparison. If there is a match, the corresponding medical condition associated with the matching data of the training data is retrieved by the processor. A match of the real-time driving pattern to one of the driving patterns in the training data set indicates that the user has a medical condition associated with that driving pattern in the training data set.
Further, if the real-time driving pattern does not match any driving pattern in the training data set, the processor checks the health record of the driver. If the health record of the driver indicates that the driver is healthy, the processor will not take any action. However, if the health record of the driver indicates that the driver has a certain medical condition, the real-time driving pattern is tracked and forms part of the training data set. Further, in the future, if a similar driving pattern is seen on another driver, that driver may be grouped under that particular medical condition. Thus, the training data set will automatically and continuously learn and update, thereby improving the accuracy and reliability of the training data.
At step 120, if a match between the real-time driving pattern and at least one of the plurality of driving patterns is determined, the processor sends warning information to at least the vehicle or a plurality of vehicles around the vehicle or both.
The warning information includes one of health alert data, autopilot control instructions, and early warning data. The health alert data is sent by the processor to the driver, alerting him that he may have a certain medical condition based on his driving pattern and therefore has to perform a medical examination to avoid any unfortunate events related to his health.
Similarly, the autopilot control commands are sent by the processor to the vehicle based on the road conditions. For example, based on the driving mode, if it is determined that the vehicle is on an icy road, the processor sends an automatic driving control instruction such as "deceleration" or "braking instruction" to the vehicle.
Further, the early warning data may be transmitted by the processor to a plurality of vehicles around the vehicle. For example, if the vehicle occupant is an elderly person with various medical conditions, such as cardiovascular disorders, the processor may send an "no whistle" or "no overtaking" warning data indication to a plurality of vehicles around the vehicle carrying the elderly person.
Thus, the method is able to predict the health of the driver and thereby alert the driver about any medical emergency that is safety critical. Furthermore, the method supports warning the surrounding vehicle based on the health of the driver, such that the surrounding vehicle is alerted. In addition, by updating the training data, the reliability and accuracy of the prediction is enhanced.
It should be understood that the embodiments explained in the above description are merely illustrative, and do not limit the scope of the present invention in learning the type of technology or the method of determining the driving pattern and the technology for classification. Many other modifications and variations of such embodiments and the embodiments explained in the specification are conceivable. The scope of the invention is limited only by the scope of the claims.
Claims (8)
1. A method of providing driver assistance, the method comprising:
receiving (105), by a processor, a plurality of input parameters from a vehicle, the input parameters including at least one of a first data set, a second data set, and a third data set, the first data set including a real-time driving pattern associated with a driver of the vehicle, the second data set including a health record of the driver, and the third data set including real-time road conditions of the vehicle;
retrieving (110), by the processor, a training data set, the training data stored in a memory unit and comprising an aggregation of a plurality of driving patterns classified as corresponding to one of a plurality of medical conditions and a plurality of road conditions, the classification based on one of a supervised learning technique and an unsupervised learning technique;
comparing (115), by the processor, the input parameters to the training data set to determine a match between the real-time driving pattern and at least one driving pattern of the plurality of driving patterns present in the training data set; and
sending (120), by the processor, a warning message to at least one of the vehicle and a plurality of vehicles surrounding the vehicle if the match is determined between the real-time driving pattern and at least one of the plurality of driving patterns.
2. The method of claim 1, wherein the real-time driving pattern includes data associated with acceleration, deceleration, braking force, and turning radius of the vehicle.
3. The method of claim 1, wherein the health record is derived from an insurance database and health input provided by the driver.
4. The method of claim 1, wherein the plurality of road conditions comprises highway, city driving, icy road, uphill driving, and terrain driving.
5. The method of claim 1, wherein the warning information includes one of health alert data, autopilot control instructions, and early warning data.
6. A processor for providing driver assistance by monitoring a driver's health and a surrounding area of the driver's vehicle, the processor comprising:
receiving a plurality of input parameters from a vehicle, the input parameters including at least one of a first data set, a second data set, and a third data set, the first data set including a real-time driving pattern associated with a driver of the vehicle, the second data set including a health record of the driver, and the third data set including real-time road conditions of the vehicle;
retrieving a training data set, the training data stored in a memory unit and comprising an aggregation of a plurality of driving patterns classified as corresponding to a plurality of medical conditions, the classification based on one of supervised and unsupervised learning techniques;
comparing the input parameters to the training data set to determine a match between the real-time driving pattern and at least one of the plurality of driving patterns present in the training data set; and
transmitting warning information to at least one of the vehicle and a plurality of vehicles around the vehicle in a case where the match is determined between the real-time driving pattern and at least one of the plurality of driving patterns.
7. The processor of claim 6, wherein the real-time driving modes include acceleration, deceleration, braking force, and turning radius.
8. The processor of claim 6, wherein the health record is derived from an insurance database and health input provided by the driver.
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| IN201841027498 | 2018-07-23 | ||
| IN201841027498 | 2018-07-23 |
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| Publication Number | Publication Date |
|---|---|
| CN110816542A true CN110816542A (en) | 2020-02-21 |
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| CN201910660737.9A Pending CN110816542A (en) | 2018-07-23 | 2019-07-22 | Method for providing driver assistance |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111341106A (en) * | 2020-03-11 | 2020-06-26 | 北京汽车集团有限公司 | Traffic early warning method, device and equipment |
| CN111932892A (en) * | 2020-08-21 | 2020-11-13 | 重庆电子工程职业学院 | Driving management method and system for dangerous transport driver |
| CN114368392A (en) * | 2020-10-14 | 2022-04-19 | 通用汽车环球科技运作有限责任公司 | Method and system for autonomous driver training |
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2019
- 2019-07-22 CN CN201910660737.9A patent/CN110816542A/en active Pending
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| CN111341106A (en) * | 2020-03-11 | 2020-06-26 | 北京汽车集团有限公司 | Traffic early warning method, device and equipment |
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| CN111932892A (en) * | 2020-08-21 | 2020-11-13 | 重庆电子工程职业学院 | Driving management method and system for dangerous transport driver |
| CN114368392A (en) * | 2020-10-14 | 2022-04-19 | 通用汽车环球科技运作有限责任公司 | Method and system for autonomous driver training |
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