INTELLIGENT TRAFFIC SAFETY SYSTEM BASED ON COMPREHENSIVE STATE DETECTION AND DECISION METHOD THEREOF

• US 20130069773A1
• Filed: 11/12/2012
• Published: 03/21/2013
• Est. Priority Date: 05/12/2010
• Status: Active Grant

• Current Assignee: Beijing Its-Star Technology Co., Ltd.
• Original Assignee: Beijing Its-Star Technology Co., Ltd.
• Inventors: LI, Bin, LI, Ying, LI, Minghang, LI, Zhibin, SHAO, Rose, MA, Tao, LIN, Zongli, YAO, Zhengbin, MOU, Jianchao
• Granted Patent: US 8,841,994 B2
• Time in Patent Office: Days
• US Class Current: 340/426.1
• CPC Class Codes: B60K 28/02 responsive to conditions relating to the driver
• CPC Class Codes: B60K 28/10 responsive to conditions relating to the vehicle
• CPC Class Codes: B60W 2540/043 Identity of occupants
• CPC Class Codes: B60W 2540/045 Occupant permissions
• CPC Class Codes: B60W 2540/223 Posture, e.g. hand, foot, or seat position, turned or inclined
• CPC Class Codes: B60W 2540/229 Attention level, e.g. attentive to driving, reading or sleeping
• CPC Class Codes: B60W 2540/24 Drug level, e.g. alcohol
• CPC Class Codes: B60W 2540/26 Incapacity
• CPC Class Codes: B60W 2552/10 Number of lanes
• CPC Class Codes: B60W 2552/15 Road slope, i.e. the inclination of a road segment in the longitudinal direction
• CPC Class Codes: B60W 2552/35 Road bumpiness, e.g. potholes
• CPC Class Codes: B60W 2552/50 Barriers
• CPC Class Codes: B60W 2554/00 Input parameters relating to objects
• CPC Class Codes: B60W 2554/4029 Pedestrians
• CPC Class Codes: B60W 2554/406 Traffic density
• CPC Class Codes: B60W 2554/80 Spatial relation or speed relative to objects
• CPC Class Codes: B60W 30/08 Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
• CPC Class Codes: B60W 30/0953 the prediction being responsive to vehicle dynamic parameters
• CPC Class Codes: B60W 30/0956 the prediction being responsive to traffic or environmental parameters
• CPC Class Codes: B60W 40/09 Driving style or behaviour
• CPC Class Codes: Y02T 10/84 : Data processing systems or methods, management, administration

Abstract

The present invention discloses an intelligent traffic safety system based on comprehensive state detection and decision method thereof. The intelligent traffic safety system includes a person condition detection unit, a vehicle condition detection unit, a road condition detection unit, an intelligent decision unit, a driver warning unit, a current vehicle mandatory processing unit, a barrier warning unit, a pursuer warning unit and an after-crash warning unit. The person condition detection unit, the vehicle condition detection unit and the road condition detection unit are separately connected to the intelligent decision unit; the intelligent decision unit implements an intelligent traffic safety decision method based on comprehensive state detection, and respectively sends corresponding crash avoidance warnings and processing instructions to the driver warning unit, the current vehicle mandatory processing unit, the barrier warning unit, the pursuer warning unit and the after-crash warning unit connected to the intelligent decision unit.

Claims

1. An intelligent traffic safety decision method based on comprehensive state detection, wherein

before making an action decision, an avoidance requirement of a crash with a front single-target barrier is considered first;

if no intervention is applied, a crash occurs after the time expressed as follows;

2. An intelligent traffic safety decision method according to claim 1, wherein

the non-intervention crash incurrence time constraint criterion is obtained through the following formula:

t_dr=t_re(1+k<sub>alcoholicity—

re</sub>)(1+k<sub>tiredness—

re</sub>)+t_ex−

(1+k<sub>alcoholicity—

ex</sub>)(1+k<sub>tiredness—

ex</sub>)

Where, t_re is a reaction capability of driver, t_ex is a manipulation capability of driver, k<sub>alcoholicity—

re</sub> and k<sub>alcoholicity—

ex</sub> separately indicate influences of alcoholic strength on the reaction capability and the manipulation capability, k<sub>tiredness—

re</sub> and k<sub>tiredness—

ex</sub> separately indicate influences of fatigue degree on the reaction capability and the manipulation capability.

3. An intelligent traffic safety decision method according to claim 1, wherein

the intervention acceleration constraint criterion is obtained through the following formula:

4. An intelligent traffic safety system based on comprehensive state detection, wherein

the intelligent traffic safety system comprises a person condition detection unit, a vehicle condition detection unit, a road condition detection unit, an intelligent decision unit, a driver warning unit, a current vehicle mandatory processing unit, a barrier warning unit, a pursuer warning unit and an after-crash warning unit, wherein,

the person condition detection unit further comprises three underlying detection modules for driver identity recognition and basic capability detection, driver'"'"'s alcoholic strength monitoring, and driver'"'"'s fatigue degree detection;

the vehicle condition detection unit further comprises five underlying detection modules for vehicle basic parameter detection, vehicle dynamic parameter detection, gear position setting parameter detection, deceleration and braking parameter detection, and acceleration and starting parameter detection;

the road condition detection unit further comprises six underlying detection modules for road basic parameter detection, weather influencing factor detection, road busy degree detection, front barrier detection, rear pursuer detection, and opposite pursuer detection;

the person condition detection unit, the vehicle condition detection unit and the road condition detection unit are separately connected to the intelligent decision unit;

according to person condition information, vehicle condition information and road condition information, the intelligent decision unit executes the intelligent traffic safety decision method according to claim 1, and separately sends corresponding crash avoidance warning and processing instructions to the driver warning unit, the current vehicle mandatory processing unit, the barrier warning unit, the pursuer warning unit and the after-crash warning unit, which connected to the intelligent decision unit.

5. The intelligent traffic safety system according to claim 4, wherein

the intelligent traffic safety system is formed of a server supporting cloud computing, multiple vehicle-mounted electronic terminals mounted on vehicles and a cloud monitoring terminal mounted on a traffic infrastructure, wherein,

the server and the cloud monitoring terminal form a cloud computing platform, to provide elementary computing and management functions for the entire intelligent traffic safety system, comprising safety information learning, safety path warning and digital map loading service;

the cloud computing platform is connected to each vehicle-mounted electronic terminal through a wide area wireless network and/or cable network, and the vehicle-mounted electronic terminals are connected with each other through a wireless network;

the vehicle-mounted electronic terminals mounted on the vehicles are used for identifying information about person conditions comprising whether a driver is a legitimate driver, driver'"'"'s fatigue state, driver'"'"'s under-threat state, and driver'"'"'s drinking state.

6. The intelligent traffic safety system according to claim 5, wherein

a wireless network is constructed between the vehicle-mounted electronic terminals in a ZigBee manner.

7. The intelligent traffic safety system according to claim 5, wherein

a vehicle-mounted electronic terminal mounted on a certain vehicle sends a data packet indicating a condition of the vehicle through the wireless network, and after receiving the data packet, a vehicle-mounted electronic terminal mounted on a nearby vehicle takes countermeasures according to content of the data packet.

8. The intelligent traffic safety system according to claim 5, wherein

the driver identity recognition and basic capability detection module is implemented through the vehicle anti-theft and anti-robbery network apparatus, comprises a pyroelectric sensor, a Pan/Tilt/Zoom, a camera, a front-end embedded system and a front-end detection module, wherein,

the front-end embedded system comprises a front-end embedded central unit, a pyroelectric interface circuit, a Pan/Tilt/Zoom control circuit and a video input processing interface;

the front-end embedded central unit is separately connected to the pyroelectric interface circuit, the Pan/Tilt/Zoom control circuit and the video input processing interface, and is connected to the front-end detection module which used for executing a pattern recognition method for recognizing driver identity information;

the pyroelectric sensor, the Pan/Tilt/Zoom and the camera are mounted on a frame of a vehicle front windshield outside a driver, and are respectively connected to the pyroelectric interface circuit, the Pan/Tilt/Zoom control circuit and the video input processing interface.

9. The intelligent traffic safety system according to claim 8, wherein

the front-end detection module further comprises a face detection module, a face feature point positioning module, a liveness identification module, a face recognition module and an anti-robbery function module.

10. The intelligent traffic safety system according to claim 5, wherein

the driver fatigue degree detection module is implemented through the driver fatigue monitoring electronic terminal, comprises a camera, a Pan/Tilt/Zoom, a front-end embedded module and a front-end application module, wherein,

the camera and the Pan/Tilt/Zoom are separately connected to the front-end embedded module, the front-end embedded module is connected to the front-end application module, and the front-end application module is used for executing detection on an eye opening/closing state, a yawning state, and a head movement state, and making comprehensive judgment;

the camera is mounted on the Pan/Tilt/Zoom, and the Pan/Tilt/Zoom is mounted on a frame of a vehicle front windshield outside a driver.

11. The intelligent traffic safety system according to claim 10, wherein

the front-end embedded module comprises a front-end embedded central unit, a video interface and analog-to-digital conversion circuit and a Pan/Tilt/Zoom control circuit;

the front-end embedded central unit is separately connected to the video interface and analog-to-digital conversion circuit and the Pan/Tilt/Zoom control circuit;

the video interface and analog-to-digital conversion circuit is connected to the camera, and the Pan/Tilt/Zoom control circuit is connected to the Pan/Tilt/Zoom.

12. The intelligent traffic safety system according to claim 10, wherein

the front-end application module further comprises a face detection module, a face feature point positioning module, an eye opening/closing detection module, a yawning detection module, a head movement tracking module and a comprehensive judgment module, wherein,

the face detection module and the face feature point positioning module are connected, and are separately connected to the eye opening/closing detection module, the yawning detection module and the head movement tracking module;

the eye opening/closing detection module, the yawning detection module and the head movement tracking module are separately connected to the comprehensive judgment module.

13. The intelligent traffic safety system according to claim 5, wherein

the driver alcoholic strength detection module comprises a driver inductor, a visible blowpipe, an acousto-optic generator, an on-site embedded server and at least one remote wireless monitoring terminal;

the driver inductor, the visible blowpipe and the acousto-optic generator are separately connected to the on-site embedded server;

the on-site embedded server and the remote wireless monitoring terminal perform remote wireless communication;

the driver inductor is mounted under a driver'"'"'s seat in a vehicle.

14. The intelligent traffic safety system according to claim 5, wherein

the front barrier detection module, the rear pursuer detection module, and the opposite pursuer detection module are implemented through a vehicle active anti-crash apparatus, and the microwave radar in the vehicle active anti-crash apparatus is mounted over the whole body of a vehicle, the microwave radar comprises a transmitting/receiving front end and a signal processing rear end;

the transmitting/receiving front end and the signal processing rear end are connected with each other through a power line, a control line, an analog modulation signal line and an echo signal line;

the signal processing rear end accesses a vehicle bus, and is then connected to a transmission system and a braking system of the vehicle, so as to control a movement state of the vehicle.

15. The intelligent traffic safety system according to claim 5, wherein

the vehicle condition detection unit comprises a redundancy reliability monitoring apparatus for an internal working condition of a vehicle tire, the redundancy reliability monitoring apparatus comprises a vehicle upper module and multiple tire lower modules, wherein,

the vehicle upper module is mounted inside a vehicle, and is formed of a reliable starting unit, a multi-channel warning unit and an emergency processing unit;

the tire lower module is mounted inside each vehicle tire, and is formed of a redundancy measurement unit and an on-site processing unit;

multiple sensors used for implementing pressure measurement, temperature measurement, humidity measurement, electricity measurement and movement induction inside the vehicle tire is disposed in the redundancy measurement unit, and the sensors adopts a redundancy configuration manner;

the tire lower module and the vehicle upper module communicate with each other in a wireless manner.

16. The intelligent traffic safety system according to claim 15, wherein

the redundancy configuration manner means to set one primary sensor and at least one backup sensor for the same measurement objective, and each sensor separately sends a measurement signal independently.

17. A pre-estimation method for computing a camber/gradient of a front road, used in the intelligent traffic safety decision method according to claim 1 to determine the camber/gradient influencing coefficient, comprising the following steps:

first, establishing a two-dimensional dynamic azimuth coordinate system with a current position of a running vehicle as an origin, and searching for conditions of the origin and ambient roads;

secondly, determining an arc length interval according to a length of the vehicle and a road jam degree, selecting multiple points according to a predetermined time interval between two adjacent points in the front and back of the road, obtaining a coordinate value of each point in the two-dimensional dynamic azimuth coordinate system according to a geographic parameter of each point, and recording the values as a column vector;

thirdly, performing fitting based on the known column vector to obtain the following three-dimensional polynomial, and a first-order derived function and a second-order derived function thereof;

y=a₀+a₁x+a₂x²+a₃x³,=a₁+2a₂x+3a₃x²,=2a₂+6a₃x

a curvature radius is expressed as follows;

18. The pre-estimation method for computing a camber/gradient of a front road according to claim 17, wherein

when the camber of the front road needs to be pre-estimated, the geographic parameter is a longitude/latitude parameter.

19. The pre-estimation method for computing a camber/gradient of a front road according to claim 17, wherein

when the gradient of the front road needs to be pre-estimated, the geographic parameter is an altitude parameter.