Sensing apparatus for vehicles

• US 20060220912A1
• Filed: 01/31/2006
• Published: 10/05/2006
• Est. Priority Date: 07/31/2003
• Status: Abandoned Application

• Current Assignee: TRW Limited (Zeppelin - Stiftung der Stadt Friedrichshafen)
• Original Assignee: TRW Limited (Zeppelin - Stiftung der Stadt Friedrichshafen)
• Inventors: Heenan, Adam John, Oyaide, Andrew Oghenovo
• Application Number:
  US11/345,598
• Publication Number:
  US 20060220912A1
• Time in Patent Office: Days
• US Class Current: 340/933
• CPC Class Codes: B60T 2201/08 Lane monitoring; Lane Keeping Systems
• CPC Class Codes: B60T 2201/089 using optical detection
• CPC Class Codes: G05D 1/0248 in combination with a laser lasers per se H01S

Abstract

A lane detection apparatus for a host vehicle, the apparatus comprising: a first sensing means, which provides a first set of data dependent upon features of a part of the road ahead of the host vehicle; a second sensing means, which provides a second set of data dependent upon features of a part of the road ahead of the host vehicle; and a processing means arranged to estimate the location of lane boundaries by interpreting the data captured by both sensing means. The second sensing means may have different performance characteristics to the first sensing means. One or more of the sensing means may include a pre-processing means, which is arranged to process the “raw” data provided by the sensing means to produce estimated lane boundary position data indicative of an estimate of the location of lane boundaries. The fusion of the data points can be performed in many ways, but in each case the principle is that more reliable raw data points or de-constructed data points are given preference over, or are more dominant than, less reliable data points. How reliable the points are at a given range is determined by allocating a weighting to the data values according to which sensing means produces the data and to what range the data values correspond.

Claims

1. A lane detection apparatus for a host vehicle, the apparatus comprising:

a means sensor which provides a first set of data dependent upon features of a part of a road ahead of the host vehicle;

a second sensor which provides a second set of data dependent upon features of a part of the road ahead of the host vehicle; and

a processor arranged to estimate the location of lane boundaries by interpreting the captured by both sensors.

2. The apparatus of claim 1 in which wherein the second sensor has different performance characteristics to the first sensor.

3. The apparatus of claim 1 wherein the processor is arranged to analyse the data to generate a set of data points indicative of the position of points on the lane boundaries at a plurality of preset ranges.

4. The apparatus of claim 1, wherein at least one of the sensors includes a pre-processing means, which is arranged to process raw data provided by the sensors to produce estimated lane boundary position data indicative of an estimate of location of the lane boundaries.

5. The apparatus of claim 4 wherein the pre-processing means is arranged to produce the estimate of a lane position by fitting points in the data believed to be part of a lane boundary into one or more of a curve and a line.

6. The apparatus of claim 4 wherein the pre-processing means is arranged to process data local to capture of the data, the apparatus fuirther comprising a network over which the estimates can be passed to the processor.

7. The apparatus of claim 4, wherein the processor is arranged to receive estimates of lane boundary position from the pre-processing means and to de-construct these estimates to produce data points indicative of the position of points on the estimated boundaries at a plurality of preset ranges.

8. The apparatus of claim 7, wherein the processing means is arranged to combine data from the two sensors to produce a modified set of data points indicative of the location of points on the boundary at the preset ranges.

9. The apparatus of claim 8 wherein the processor is arranged to fit the modified points to a suitable set of equations to establish one or more of a curve and a line which express the location of the lane boundaries.

10. The apparatus of claim 1, wherein the processor is arranged to give preference to data points determined to be more reliable over less reliable data points.

11. The apparatus of claim 10 wherein the processor is arranged to allocate a weighting to the data values according to which the sensor produced the data and to the range to which the data values correspond.

12. The apparatus of claim 10 in which the wherein performance characteristics of the two sensors differ in that the first sensor is more accurate for the measurement of distant objects than the second sensor, which in turn is more accurate for the measurement of objects at close range than the first sensor.

13. The apparatus of claim 12 wherein the processor is arranged to give distant objects identified by the first sensor a higher weighting than the same object identified by the second sensor.

14. The apparatus of claim 12 wherein the processor is arranged to give near objects detected by the second sensor a higher weighting.

15. The apparatus of claim 10, wherein the apparatus includes a memory, which is arranged to be accessed by the processor and arranged to store information needed to allocate the weightings to the data points.

16. The apparatus of claim 3, wherein the pre-processing means is arranged to perform an edge detection technique or an image enhancement technique to modify the raw data.

17. The apparatus of claim 10, wherein, in addition to being arranged to apply to weightings to the data points the processing means is arranged to apply a confidence value to the data value being determined independently of the weighting values according to how confident the apparatus is about the data from each sensing means sensor.

18. The apparatus of claim 17 wherein the processor is arranged to fix the weightings for a given range and location of a data point in an image from the sensors but to allow the confidence values to vary over time depending upon the operating environment.

19. The apparatus of claim 11 wherein the processor is adapted to fuse the data points and weightings using at least one recursive processing technique.

20. The apparatus of claim 1 wherein first sensor comprises a range finder.

21. The apparatus of claim 20 in whichl1 wherein the second sensor comprises a video camera.

22. The apparatus of claim 21 wherein, with respect to the range finder, the video camera has a relatively narrow field of view and a relatively long range.

23. The apparatus of claim 1 wherein both sensors are arranged to be fitted to part of the vehicle.

24. The apparatus of claim 1 wherein one sensing means sensor is arranged to be remote from the vehicle.

25. The apparatus of claim 20 wherein the range finder is a laser range finder.

35. A computer program which, when running on a suitable processor, causes the processor to act as apparatus of claim 1.

26. A method of estimating the position of lane boundaries on a road ahead comprising:

capturing a first frame of data from a first sensor and a second frame of data from a second sensor; and

fusing the data captured by both sensors to produce an estimate of the a location of lane boundaries on said road.

27. The method of claim 26 wherein the first sensor has different performance characteristics to the second sensor.

28. The method of claim 26 wherein the fusing step includes the steps of allocating weightings to data points indicative of points on the lane boundaries estimated by both sensors at a plurality of ranges and processing the data points together with the weightings to provide a set of modified data points.

30. The method of claim 28, further comprising allocating a confidence value to each sensing means dependent upon the operating environment in which data was captured and modifying the weightings using the confidence values.

31. The method of claim 26, comprising generating the data points for at least one of the sensors by producing higher level data in which the lane boundaries are expressed as curves and subsequently deconstructing the curves by calculating a location in real space of data points on the curves at a plurality of preset ranges.

32. The method of claim 31 wherein the de-constructed data points are fused with other de-constructed data points or raw data points to establish estimates of lane boundary positions.

33. A computer program which when running on a processor causes the processor to perform a method of claim 26.

34. The program of claim 33 wherein the program is distributed across a number of different processors, located at different areas.

36. A data carrier carrying the program of claim 33.

29. The method of any fusion step comprises passing the data points and weightings through a filter.

39. The method of claim 29 wherein the filter is an RLS estimator.

37. A processing means which is adapted to receive data from at least two different sensors, the data being dependent upon features of a highway on which a vehicle including the processing means is located and which fuses the data from the sensors to produce an estimate of a location of lane boundaries of the highway relative to the vehicle.

38. The processing means of claim 37 wherein the processing means is distributed across a number of different locations on the vehicle.