On-vehicle situation detection apparatus and method

• US 9,776,644 B2
• Filed: 03/12/2015
• Issued: 10/03/2017
• Est. Priority Date: 07/10/2014
• Status: Active Grant

• Current Assignee: Hyundai Mobis Co., Ltd.
• Original Assignee: Hyundai Mobis Co., Ltd.
• Inventors: Chun, In-Sung, Lee, Jun-Han
• Primary Examiner(s): Black, Thomas G
• Assistant Examiner(s): Kong, Sze-Hon
• Application Number:
  US14/656,378
• Publication Number:
  US 20160009295A1
• Time in Patent Office: 936 Days
• CPC Class Codes: A61B 5/0077 Devices for viewing the surface of the body, e.g. camera, magnifying lens
• CPC Class Codes: A61B 5/163 by tracking eye movement, gaze, or pupil change
• CPC Class Codes: A61B 5/18 for vehicle drivers or machine operators
• CPC Class Codes: A61B 5/6893 Cars
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: B60C 5/005 Ballast tyres
• CPC Class Codes: B60Q 5/005 automatically actuated
• CPC Class Codes: B60W 2040/0818 Inactivity or incapacity of driver
• CPC Class Codes: B60W 2050/0025 Transfer function weighting factor
• CPC Class Codes: B60W 2050/143 Alarm means
• CPC Class Codes: B60W 2050/146 Display means
• CPC Class Codes: B60W 2420/403 Image sensing, e.g. optical camera
• CPC Class Codes: B60W 2420/408 Radar; Laser, e.g. lidar
• CPC Class Codes: B60W 2420/54 Audio sensitive means, e.g. ultrasound
• CPC Class Codes: B60W 2422/90 on bumper, e.g. collision sensor
• CPC Class Codes: B60W 2540/00 Input parameters relating to occupants
• CPC Class Codes: B60W 2540/043 Identity of occupants
• CPC Class Codes: B60W 2540/10 Accelerator pedal position
• CPC Class Codes: B60W 2540/12 Brake pedal position
• CPC Class Codes: B60W 2540/16 : Ratio selector position
• CPC Class Codes: B60W 2540/18 : Steering angle
• CPC Class Codes: B60W 2540/22 : Psychological state; Stress level or workload
• CPC Class Codes: B60W 2540/30 : Driving style
• CPC Class Codes: B60W 2554/00 : Input parameters relating to objects
• CPC Class Codes: B60W 40/02 : related to ambient conditions
• CPC Class Codes: B60W 40/09 : Driving style or behaviour
• CPC Class Codes: B60W 50/14 : Means for informing the driver, warning the driver or prompting a driver intervention
• CPC Class Codes: B60W 50/16 : Tactile feedback to the driver, e.g. vibration or force feedback to the driver on the steering wheel or the accelerator pedal
• CPC Class Codes: G07C 5/0808 : Diagnosing performance data testing of vehicles G01M17/00; testing of electrical installation on vehicles G01R31/005
• CPC Class Codes: G07C 5/0816 : Indicating performance data, e.g. occurrence of a malfunction
• CPC Class Codes: G07C 5/085 : using electronic data carriers
• CPC Class Codes: G16H 50/70 : for mining of medical data, e.g. analysing previous cases of other patients

Abstract

An on-vehicle situation detection apparatus may include a detection unit to identify a driver and acquire driver status data and data about vehicle driving information or vehicle surrounding obstacles, a driving pattern learning unit to learn and store a driving pattern of a driver, based on the data acquired by the detection unit, a weighted value determination unit to determine a weighted value assigned to the information data acquired by the detection unit, based on the driving pattern learned by the driving pattern learning unit, a determination unit to determine a safe driving state of the driver, based on the data to which the weighted value determined by the weighted value determination unit is assigned, and a warning unit to warn the driver when the driver is determined to be not in the safe driving state.

Claims

1. An on-vehicle situation detection method comprising:

performing driver recognition of calling pre-stored driver information to compare whether the driver information coincides with a current driver of a vehicle;

performing driving pattern calling of calling a pre-stored driving pattern of the driver identified by the performing driver recognition from a memory unit;

performing detection using at least one sensor, of collecting driver status information and vehicle driving information or vehicle surrounding obstacle information;

performing driving pattern learning of learning the pre-stored driving pattern called by the performing driving pattern calling and a driving pattern derived from the performing detection;

performing weight value determination of determining a weight value for data acquired from the performing detection, based on the driving pattern learned in the performing driving pattern learning;

performing determination of determining a safe driving state of the driver, based on the data to which the weight value determined in the performing weight value determination is assigned; and

performing warning of warning the driver when the driver is determined to be not in the safe driving state in the performing determination,

wherein the performing weight value determination assigns a weight value determined according to impact on a driver'"'"'s integrated risk index to the data acquired by the performing detection,

wherein the performing weight value determination compares the acquired data and the integrated risk index with an information data reference value and an integrated risk index reference value for updating the weight value,

wherein the performing weight value determination changes a weight value according to the following equation;

α

(n+1)=α

(n), if W<

d_W and R≧

d_R

α

(n+1)=α

(n)+δ

, if W≧

d_W

α

(n+1)=α

(n)−

δ

, if W<

d_W and R<

d_R,

where R=integrated risk index,

α

=weight value,

α

(n+1)=weight value after update,

α

(n)=weight value before update,

W=acquired data,

d_R=integrated risk index reference value,

d_W=information data reference value, and

δ

=α

(n)/R.

2. The on-vehicle situation detection method of claim 1, wherein the performing driver recognition comprises performing driver data acquisition of acquiring image data of pupils or faces of the current driver through an imaging device, and performing driver identification of comparing the image data of pupils and faces acquired by the performing driver data acquisition with the pre-stored driver information in the memory unit to identify the current driver.

3. The on-vehicle situation detection method of claim 1, wherein in the performing driving pattern calling a pre-stored default driving pattern is called when a driver coinciding with the pre-stored driver information in the performing driver recognition is not present.

4. The on-vehicle situation detection method of claim 1, wherein the performing detection comprises performing driver status detection of detecting one or more of a driver eyelid, a driver pupils, a steering wheel speed, a steering wheel angle, a suspension movement, whether or not an accelerator pedal is operated, whether or not a brake pedal is operated, whether or not a multifunctional switch is operated, whether or not a driver makes conversation, whether or not an Audio Video Navigation (AVN) of the vehicle, is operated, whether or not an air conditioning device is operated, whether or not a gearbox is operated, whether or not a console box is operated, and whether or not a glove box is operated, and performing vehicle surrounding situation detection of detecting a vehicle surrounding situation using one or more of an SCC (Smart Cruise Control), an LKAS (Lane Keeping Assistant System), an SPAS (Smart Parking Assistant System), an AVM (Around View Monitoring), a camera, and a radar.

5. The on-vehicle situation detection method of claim 1, wherein the performing driving pattern learning comprises:

performing driving pattern comparison of comparing the pre-stored driving pattern of the driver called by the performing driving pattern calling with a current driving pattern by the data acquired from the performing detection;

performing driving pattern determination of determining whether difference between the pre-stored driving pattern and the current driving pattern is within a preset noise range;

performing driving pattern storage of learning a current driving pattern when the difference is equal to or less than the preset noise range in the performing driving pattern determination; and

performing noise removal of excluding a current driving pattern from a subject to be learned when the difference exceeds the preset noise range in the performing driving pattern determination.

6. The on-vehicle situation detection method of claim 1, wherein the performing determination comprises selecting from the data acquired from the performing detection a plurality of high-impact data in view of individual datum'"'"'s impact on the integrated risk index.

7. The on-vehicle situation detection method of claim 6, wherein the performing determination comprises performing calculation of calculating a driver'"'"'s integrated risk index by adding up respective risk indexes which multiply the selected high-impact data, to which the weight value determined in the performing weigh value determination is assign, by a weight value for each data assigned in the performing weight value determination.

8. The on-vehicle situation detection method of claim 7, wherein the performing determination comprises performing examination of comparing the integrated risk index calculated by the performing calculation with an examination risk index calculated based on the pre-stored driving pattern to determine whether a difference of both indexes is within a preset error range.

9. The on-vehicle situation detection method of claim 8, wherein the performing determination comprises performing control of comparing the integrated risk index with a preset reference risk index when the difference between the integrated risk index and the examination risk index is determined to be within the preset error range in the performing examination, to determine whether or not to provide warning to the current driver.

10. The on-vehicle situation detection method of claim 1, wherein the performing warning comprises:

performing first warning of executing one or more of warning sound generation through a speaker, warning display through an Audio Video Navigation (AVN) or a Head Up Display (HUD) of the vehicle, and vibration notification through vibration of a steering wheel or a seat when an integrated risk index is equal to or greater than a preset first reference risk index and is less than a preset second reference risk index;

performing second warning of holding at least one function of the AVN when the integrated risk index is equal to or greater than the preset second reference risk index and is less than a preset third reference risk index;

performing third warning of forcibly stopping a vehicle when the integrated risk index is equal to or greater than the preset third reference risk index; and

performing information transfer of transferring information for use in the performing weight value determination for an update of the weight value.