Systems and methods involving features of adaptive and/or autonomous traffic control
원문보기
IPC분류정보
국가/구분
United States(US) Patent
등록
국제특허분류(IPC7판)
G06F-019/00
G06G-007/70
G06G-007/76
G08G-001/00
G08G-001/01
G08G-001/042
G06K-009/00
출원번호
US-0474232
(2014-09-01)
등록번호
US-9761131
(2017-09-12)
발명자
/ 주소
Robinson, Kurt B.
출원인 / 주소
FASTec International, LLC
대리인 / 주소
Knobbe, Martens, Olson & Bear LLP
인용정보
피인용 횟수 :
0인용 특허 :
49
초록▼
Systems and method are disclosed for adaptive and/or autonomous traffic control. In one illustrative implementation, there is provided a method for processing traffic information. Moreover, the method may include receiving data regarding travel of vehicles associated with an intersection, using neur
Systems and method are disclosed for adaptive and/or autonomous traffic control. In one illustrative implementation, there is provided a method for processing traffic information. Moreover, the method may include receiving data regarding travel of vehicles associated with an intersection, using neural network technology to recognize types and/or states of traffic, and using the neural network technology to process/determine/memorize optimal traffic flow decisions as a function of experience information. Exemplary implementations may also include using the neural network technology to achieve efficient traffic flow via recognition of the optimal traffic flow decisions.
대표청구항▼
1. A method for processing traffic information, the method comprising: at a computer with a processor and memory in communication with a data storage, to control automated traffic lights, receiving data regarding travel of vehicles associated with an intersection;processing the data using neural net
1. A method for processing traffic information, the method comprising: at a computer with a processor and memory in communication with a data storage, to control automated traffic lights, receiving data regarding travel of vehicles associated with an intersection;processing the data using neural network technology to recognize traffic;performing processing using the neural network technology to recognize states of the traffic;processing the traffic and the states of traffic using the neural network technology to memorize optimal traffic flow decisions as a function of prior experience;determining optimal traffic flow using the neural network technology to achieve efficient traffic flow via recognition of the optimal traffic flow decisions;enhancing the traffic type recognition using an array having a plurality of sensor inputs beyond inputs from video sensors;processing other sensor inputs including: a traffic radar input providing enhanced capability from transmission of various data;detailed radar signatures of detected traffic objects independent from adverse weather or light conditions for recognition of vehicle position and vehicle type of one or more of the vehicles associated with an intersection; andradar measurement of velocities of incoming traffic vehicles;wherein prediction of traffic flow is enhanced for traffic light control decisions at a higher level of the neural network array, including prioritization of incoming traffic and collision-avoidance traffic light hold. 2. The method of claim 1 wherein the traffic flow determination includes state information inputs from nearby traffic controllers comparable with intermediate level traffic state recognition at a local traffic controller which are combined at higher levels of the neural network to optimize traffic flow decision-making at a local traffic light. 3. The method of claim 1, further comprising recognizing driver intentions without use of communication or transmissions between a vehicle associated with the driver and the traffic lights. 4. The method of claim 3, wherein digital logic is included to provide an output or warning of current- and next-state information configured to be utilized by in-vehicle communication devices, as known, that transmit information to or from one or more of the traffic lights. 5. The method of claim 1, wherein digital logic is included to provide an output or warning of current- and next-state information configured to be utilized by in-vehicle communication devices, as known, that transmit information to or from one or more of the traffic lights. 6. The method of claim 1 wherein the other sensor inputs include an infrared traffic input providing enhanced capability from transmission of various types of data; and wherein the infrared traffic input comprises: detailed infrared signatures of detected traffic objects independent from adverse weather or light conditions for recognition of the vehicle position and the vehicle type of at least one of the vehicles associated with an intersection; anddetection of the vehicle passenger occupancy which is used in traffic flow decision making at a higher level of the neural network array; andwherein the method further comprises: giving priority to passenger-weighted incoming traffic. 7. The method of claim 1, wherein neural network recognition is deployed to recognize various standardized signal types of at least one high-priority vehicle of the vehicles associated with an intersection, which, once recognized, is used to initiate required authentication and prioritize traffic flow decisions. 8. A method for processing traffic information, the method comprising: at a computer with a processor and memory in communication with a data storage, to control automated traffic lights,receiving data regarding travel of vehicles associated with an intersection;processing the data using neural network technology to recognize traffic;performing processing using the neural network technology to recognize states of the traffic;processing the traffic and the states of traffic using the neural network technology to memorize optimal traffic flow decisions as a function of prior experience;determining optimal traffic flow using the neural network technology to achieve efficient traffic flow via recognition of the optimal traffic flow decisions; utilizing 2 or more layers of neural network neuron storage elements for unique recognition, classification, and traffic flow decision-making taskswherein the unique recognition includes feeding from the low level to the higher levels of the neural network array, wherein assigned neurons are: trained to aggregate total numbers of incoming traffic vehicles; andtrained to recognize position over fixed time sequences resulting in recognition of relative velocity. 9. The method of claim 8 wherein the e traffic flow determination includes state information inputs from nearby traffic controllers comparable with intermediate level traffic state recognition at a local traffic controller which are combined at higher levels of the neural network array to optimize traffic flow decision- making at a local traffic light. 10. The method of claim 8, further comprising recognizing driver intentions without use of communication or transmissions between a vehicle associated with the driver and the traffic lights. 11. The method of claim 8, wherein digital logic is included to provide an output or warning of current- and next-state information configured to be utilized by in-vehicle communication devices, as known, that transmit information to or from one or more of the traffic lights. 12. The method of claim 8, wherein the other sensor inputs include an infrared traffic input providing enhanced capability from transmission of various types of data; and wherein the infrared traffic input comprises: detailed infrared signatures of detected traffic objects independent from adverse weather or light conditions for recognition of vehicle position and vehicle type of one or more of the vehicles associated with an intersection; anddetection of the vehicle passenger occupancy which is used in traffic flow decision making at a higher level of the neural network array; andwherein the method further comprises: giving priority to passenger-weighted incoming traffic. 13. The method of claim 12, wherein digital logic is included to provide an output or warning of current- and next-state information configured to be utilized by in-vehicle communication devices, as known, that transmit information to or from one or more of the traffic lights. 14. The method of claim 8, wherein neural network recognition is deployed to recognize various standardized signal types of at least one high-priority vehicle of the vehicles associated with an intersection, which, once recognized, is used to initiate required authentication and prioritize traffic flow decisions. 15. A method for processing traffic information, the method comprising: at a computer with a processor and memory in communication with a data storage, to control automated traffic lights,receiving data regarding travel of vehicles associated with an intersection;processing the data using neural network technology to recognize traffic;performing processing using the neural network technology to recognize states of the traffic;processing the traffic and the states of traffic using the neural network technology to memorize optimal traffic flow decisions as a function of prior experience;determining optimal traffic flow using the neural network technology to achieve efficient traffic flow via recognition of the optimal traffic flow decisions;utilizing 2 or more layers of neural network neuron storage elements for one or more of recognition, classification, and traffic flow decision-making tasks; wherein the classification includes compiling aggregate traffic information as a function of neurons assigned to first-level data of the neural network array including one or more of:processing weight of traffic within each zone;processing data regarding vehicle occupancy; andusing composite weighting of all such inputs for higher level traffic flow decision-making. 16. The method of claim 15 wherein the traffic flow determination includes state information inputs from nearby traffic controllers comparable with intermediate level traffic state recognition at a local traffic controller which are combined at higher levels of the neural network array to optimize traffic flow decision-making at a local traffic light. 17. The method of claim 16, wherein digital logic is included to provide an output or warning of current- and next-state information configured to be utilized by in-vehicle communication devices, as known, that transmit information to or from one or more of the traffic lights. 18. The method of claim 15, further comprising recognizing driver intentions without use of communication or transmissions between a vehicle associated with the driver and the traffic light. 19. The method of claim 15, wherein digital logic is included to provide an output or warning of current- and next-state information configured to be utilized by in-vehicle communication devices, as known, that transmit information to or from one or more of the traffic lights. 20. The method of claim 15 wherein the other sensor inputs include an infrared traffic input providing enhanced capability from transmission of various types of data; and wherein the infrared traffic input comprises: detailed infrared signatures of detected traffic objects independent from adverse weather or light conditions for recognition of vehicle position and vehicle type of one or more of the vehicles associated with an intersection; anddetection of the vehicle passenger occupancy which is used in traffic flow decision making at a higher level of the neural network array; andwherein the method further comprises: giving priority to passenger-weighted incoming traffic.
연구과제 타임라인
LOADING...
LOADING...
LOADING...
LOADING...
LOADING...
이 특허에 인용된 특허 (49)
Pearson,Jeremiah W., Automated traffic control system having an interactive emergency vehicle warning therein.
Glier Michael T. ; Laird Mark D. ; Tinnemeier Michael T. ; Small Steven I. ; Sybel Randall T. ; Hsieh Steven S. ; Johnson Greg D. ; Coimbatore Lalitha R., Integrated traffic light violation citation generation and court date scheduling system.
Pierowicz, John A.; Pirson, Herbert A.; Yuhnke, David, Method and apparatus for determination and warning of potential violation of intersection traffic control devices.
Bullock Darcy M. (3592 Beechwood Blvd. Pittsburgh PA 15217) Garrett ; Jr. James H. (307 S. Highlander Heights Dr. Glenshaw PA 15116) Hendrickson Chris T. (6933 Rosewood St. Pittsburgh PA 15208), Neural network-based vehicle detection system and method.
Park, Young Jin, Terminal and computer program product for receiving traffic information, method of providing signal light information, and method of guiding signal.
Glier Michael T. ; Laird Mark D. ; Tinnemeier Michael T. ; Small Steven I. ; Sybel Randall T. ; Reilly Douglas L., Traffic light collision avoidance system.
Glier, Michael T.; Laird, Mark D.; Tinnemeier, Michael T.; Small, Steven I.; Sybel, Randall T., Traffic light violation prediction and recording system.
Masafumi Kobayashi JP; Tsutomu Usami JP; Toshifumi Oota JP, Traffic signal control apparatus optimizing signal control parameter by rolling horizon scheme.
Fritzinger George H. (15 Standish Ave. West Orange NJ 07052), Traffic-actuated control systems providing an advance signal to indicate when the direction of traffic will change.
MacPhail, Margaret Gardner; Kumhyr, David Bruce, Use of vehicle permissions to control individual operator parameters in a hierarchical traffic control system.
Small, Steven I.; Sybel, Randall T.; Johnson, Greg D.; Coimbatore, Lalitha R., Video-file based citation generation system for traffic light violations.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.