Data processing: vehicles – navigation – and relative location – Navigation – Employing position determining equipment
Reexamination Certificate
2002-01-08
2004-08-03
Black, Thomas G. (Department: 3663)
Data processing: vehicles, navigation, and relative location
Navigation
Employing position determining equipment
C180S168000, C702S152000, C702S095000, C700S061000, C700S066000, C700S068000, C700S301000
Reexamination Certificate
active
06772062
ABSTRACT:
REFERENCE TO A COMPUTER PROGRAM APPENDIX
Not Applicable
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A portion of the material in this patent document may be subject to copyright protection under the copyright laws of the United States and of other countries. The owner of the copyright rights has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the United States Patent and Trademark Office files or records, but otherwise reserves all copyright rights whatsoever. The copyright owner does not hereby waive any of its rights to have this patent document maintained in secrecy, including without limitation its rights pursuant to 37 C.F.R. §1.14.
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention generally relates to sensing roadway markers for driver assistance and vehicle control, and more particularly to an intelligent sensing system for sensing roadway markers for driver assistance and vehicle control.
2. Description of the Background Art
Over the years, numerous systems have been developed to provide automatic vehicle control to provide hands-free and feet-free driving of automobiles. These systems automate either steering control (referred to as lateral control), throttle and/or brake control (longitudinal control), or all of the above (complete vehicle automatic control, also referred to as an Automated Highway System or AHS).
Longitudinal control typically relies on some form of forward-looking sensor, e.g. radar, to provide collision warning and/or distance to preceding vehicles, and takes the form of either adaptive cruise control or platooning, wherein vehicles are formed into electronically coupled groups, similar to a train, but without the mechanical coupling. Lateral control relies on a variety of sensors to determine the vehicle's lateral offset from the lane centerline, and usually an estimate of upcoming roadway geometry, e.g. curvature.
Sensors for lateral control can be based infrastructure-based or infrastructure-independent. Examples of infrastructure-based systems include systems based on detection of embedded infrastructure, such as discrete magnetic reference markers or continuous magnetic tape. Infrastructure-independent methods include vision-based sensing and Global Positioning System (GPS) sensing; however, these systems rely on infrastructure in the sense of reliable roadway markings in the former case, and a reliable and accurate roadway Geographical Information System (GIS) database in the latter.
In recent years, systems have been developed using similar sensing technologies, but with the purpose of providing visual, audible, or tactile feedback and warning (i.e. driver assistance) to the driver of the vehicle, thus enhancing the driver's ability to operate the vehicle in degraded visibility conditions (e.g. in dense fog or snow-induced whiteout conditions). For these types of conditions, visual sensing for lateral control is not the ideal primary sensing system, as the performance of these systems is degraded in poor optical conditions. Infrastructure-based systems appear to provide the most reliable and robust solution for these conditions, particularly in areas that may be subject to satellite obscuration (e.g., mountainous regions). These systems also must include a forward collision warning system, as they are intended to enable the vehicle operator to drive in degraded visibility conditions, and must provide sufficient warning of upcoming obstacles, in order to protect this vehicle's driver, as well as others on the roadway.
There are several well-developed technologies for vehicle lateral guidance. They may be classified as vision-based, roadway reference system based, and radio wave signal based methods. Vision-based or other optical systems are generally considered inappropriate in poor visibility conditions such as fog, rain, and particularly snow. Roadway reference systems include induction wires, radar-reflective tape, magnetic tape, and discrete reference markers. Reference systems may be passive or active elements. Wire-guided vehicle control represents one active system; construction and maintenance issues preclude its use in a highway environment. Example markers include magnets, colored paint marks, retroreflective raised pavement markers, and radar-reflective materials. However, any optic-based marker detection system faces the same problem as any other vision-based system in low visibility environments; as such, these systems are not feasible here. Magnetic markers and overhead induction wires are possible all weather solutions for lateral guidance.
Magnetic markers for lateral control have been found to have a maximum lateral sensing error of 1.5 cm with 1 cm standard deviation, which is well within the 3 cm needed for commercially viable systems. Discrete magnetic markers embedded in the roadway can be used for longitudinal position measurement as well as lateral control. Moreover, magnetic markers can be coded with other roadway information, which each vehicle can read via onboard magnetometers. Each magnet is capable of storing one bit of information. The coded sequence consists of header code to initialize and uniquely identify the message, followed by the roadway information. Error detection codes can be placed at the end of the message as well. Magnetic pavement marker tape has been shown to have similar performance for lateral position measurement. However, it cannot be coded to provide roadway information. Furthermore, retrofitting magnetic tape to current highways may be difficult, and possibly more costly than installing magnetic markers; further study is warranted here. Nonetheless, concern has been expressed that magnetic markers may lead to temperature induced stress concentration and faster road deterioration. However, the maturity and robustness of the magnetic marker technology warrants its use in the current development for snowplow guidance. On the other hand, experience gained in recent research indicated other deficiencies in the state of the art in the magnetic sensing system.
For example, the use of continuous magnetic marking material to provide vehicle guidance and control is well known. However, such systems cannot provide information coding, and thus cannot provide upcoming roadway geometry (e.g. curvature), or other infrastructure information (e.g. upcoming bridge abutments). In addition, the algorithms used to detect the magnetic “tape” and determine lateral offset are typically based on the use of frequency and phase information, which assists in separating signal from noise, but cannot provide lateral offset down to zero vehicle speed. Furthermore, these systems appear to be limited to vehicle speeds of about five MPH and above. In addition, since these systems typically use a square-wave magnetization pattern to support the signal processing, at least one wavelength must be detected before lateral offset can be obtained; this introduces a signal processing delay of ½ wavelength.
Systems that employ discrete analog magnetometers suffer from a number of deficiencies as well. For example, information from the sensors (magnetic field strength) is transmitted to a central computer's data acquisition system over analog lines. This introduces serious noise issues in a vehicle environment. In addition, due to the large number of sensors and channels required, the conductor and part count is very high, leading to difficulties in installation, maintenance, and trouble-shooting. The high channel count also introduces the need for custom data acquisition and signal conditioning boards, further aggravating the problem. These and other issues make it difficult to protect subsystems against harsh environments (e.g., snow, ice, and salt). In addition, some of the algorithms used are subject to a number of problems. For example, typical algorithms rely on vehicle-specific calibration, and the calibration table is magnet-type or magnet-strength dependent, so that different calibration tables
Donecker Stephen M.
Lasky Ty A.
Ravani Bahram
Yen Kin S.
Black Thomas G.
Mancho Ronnie
O'Banion John P.
The Regents of the University of California
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