1. Technical Field
The present invention relates to vehicular collision avoidance and mitigation systems, and more particularly, to a digital map and sensory based collision avoidance system that utilizes data fusion to identify overpasses and modify a threat assessment algorithm, so as to maintain sufficient warning distances, and reduce false alerts.
2. Background Art
A prevailing concern in current implementations of collision avoidance and warning systems in vehicles is that they typically present a significant number of false alerts (i.e. warnings of imminent collisions with objects that are not in fact within the vehicle path). This concern is especially perpetuated by the proximity of stationary objects, the current limitations in accurate prediction of forward path, and the inability of the radar to discriminate between objects present at different elevations. False alerts in conventional systems are often caused by overpasses, mailboxes on the roadside, staled vehicles, etc.
Overpasses are of particular concern for various reasons. First, they are present in great numbers on interstate highways and other thoroughfares. Second, they are typically found traversing the path of thoroughfares having a relatively high speed limit. Third, they are difficult to distinguish from in-path objects that present true potential collisions. Fourth, and perhaps most concerning, current overpass detection algorithms that analyze the signal-strength trend of the approaching object are generally unable to provide sufficient warning distances, when a true potential collision, and not an overpass, is determined.
With respect to the later, once an object is detected at an initial threshold distance, the trend in the radar return signal strength over a plurality of diminishing distances (see,
Thus, to be effective a collision avoidance system must provide reliable and efficient warning distances to the operator, and, therefore, be capable of timely distinguishing false concerns caused by overpasses from potential collisions caused by true in-path objects.
Responsive to these and other concerns caused by conventional collision avoidance and mitigation systems, the present invention presents an improved collision avoidance system that utilizes data fusion to more rapidly and accurately determine the presence of overpasses.
A first aspect of the present invention concerns a collision avoidance system adapted for use with a host vehicle, and by an operator. The system includes at least one sensor configured to detect an object located a minimum threshold distance from the vehicle, so as to determine a detected object location, and a map database including a plurality of intersecting links, and denoting overpass locations. The system further includes a locator device communicatively coupled to the map database, and configured to detect the current position coordinates of the vehicle within the map database. Finally, the system includes an electronic control unit communicatively coupled to the sensor, database, and device, and programmably configured to autonomously execute a warning assessment algorithm, compare the detected object location with the overpass locations, so as to determine whether the detected object location is generally at an overpass location, modify the warning assessment algorithm, when the detected object location is at a general overpass location, and cause a warning perceivable by the operator to be generated or a mitigating action to be initiated, when the execution of the algorithm detects a potential collision.
A second aspect of the present invention concerns a method of modifying a first warning assessment algorithm of the system, so as to reduce false alerts caused by overpasses, while maintaining sufficient warning distances. The method generally begins with the steps of autonomously determining the current position coordinates, and heading of the vehicle, and retrieving the position coordinates of at least one overpass location within a predetermined vicinity ahead of the vehicle from a database. Next, an approaching object at least a minimum threshold distance from the vehicle is detected, and the detected position coordinates of the object are determined. The detected position coordinates are compared to the position coordinates of said at least one overpass location from the database. Finally, a second algorithm is executed, if the detected coordinates generally match the position coordinates of a database overpass location, and a third algorithm is executed, if the detected coordinates do not match the position coordinates of a database overpass location, wherein said third algorithm is executable over a shorter period than the second, and the second algorithm is executable over a shorter period than the first.
It will be understood and appreciated that the present invention provides a number of advantages over the prior art, including, for example, further utilizing pre-existing in-vehicle navigation and map database systems, enabling more efficient, reliable, and accurate overpass determination, allowing the full radar range to be utilized for warning or mitigation, and adding redundancy where a plurality of overlapping sensors are utilized. Other aspects and advantages of the present invention will be apparent from the following detailed description of the preferred embodiment(s) and the accompanying drawing figures.
A preferred embodiment of the present invention is described in detail below with reference to the attached drawing figures, wherein:
a is a plan view of the vehicle and approaching object shown in
a is a plan view of the vehicle and approaching object shown in
a is a plan view of the vehicle and approaching object shown in
As shown in
The system 10 includes an in-vehicle navigation system and updateable map database 20 that is communicatively coupled to the ECU 18. As shown in
The system 10 also includes a locator device 26 configured to locate the absolute position (e.g., latitude, longitude, and height) and preferably the heading of the host vehicle 12. As shown in
As previously mentioned, the system 10 further includes at least one sensor 30 configured to detect the in-path object or overpass 16 at a minimum threshold distance. The sensor 30 may employ any suitable technology, including vision/camera, infrared, radar, lidar, or laser technology. For example, a long-range radar detector capable of detecting a single lane overpass from a minimum threshold distance of at least 150 meters, and more preferably 250 meters, may be utilized.
As described in
1. Radar and Map Based Determination
In a first embodiment, a preferably pre-existing in-vehicle navigation system map database 20 is combined with a conventional radar-based overpass detection system. Once an object 16 is detected by the sensor 30, a sensor-detected range and relative object location are determined. The ECU 18, locator device 26, and map database 20 are cooperatively configured to search the forward map preview of the map database 20 for overpass locations 24 in the general vicinity (e.g., within 50 meters) of the detected object location. If a matching overpass location 24 is not found in the forward map preview, the preferred system 10 issues the warning immediately, so that sufficient distance separates the vehicle 12 from the object 16.
If, however, a matching overpass location 24 is found in the forward map preview, then the radar signal trend analysis module uses a lower threshold to look for a signature trend of diminishing amplitude (i.e. decay) of the radar return signal. That is to say, the radar signal analysis in this configuration may be performed over a period shorter than conventional assessment periods (e.g., a sample of two return signal strengths versus a sampling of three), so that the warning is issued to the vehicle 12 at a greater distance from the object 16. For example, if the trend presents a significant decay rate over a sample of Xo . . . Xn strengths, wherein the rate is taken from the differences between progressively succeeding strengths (i.e. Xn-Xn-1, etc.), then the object 16 is deemed an overpass; but if a significant decay trend is absent (e.g., the differences are positive), the object is deemed in-path, and a warning is issued, and/or mitigation action, such as actuating the braking module 32 of the vehicle 12, is initiated. It is appreciated that, despite a matching overpass location determination, radar-trend analysis is necessary to detect in-path objects that are located under the overpass.
As shown in
At a step 110, a radar subsystem detects an object, determines a detected object location, and communicates it to the data fusion module. At a step 112, the module compares the detected object location to the overpass locations 24, such that if the detected object location does not correspond to a map-identified overpass location 24, then, at a step 114a, the detected object 16 is deemed in-path without considering signal strength trend data, and the warning is caused to be generated or mitigation is initiated. If, however, the detected object location does correspond to an overpass location 24, then, at step 114b, the radar subsystem and ECU 18 proceed with the process of analyzing the signal strength trend data of the object 16 over a truncated period, to decide whether it is an overpass. At a step 116, the trend is compared to a threshold to determine whether it presents a true in-path object. If the threshold is met, then the object 16 is deemed in-path, and a warning is caused to be generated, or a mitigating maneuver is caused to be initiated as per 114a; else the method returns to step 102.
2. Radar, Vision, and Map Determination
In a second preferred embodiment, the ECU 18 fuses input from a plurality of different sensors 30 and the map database 20 during overpass determination, to add redundancy and capability. In the illustrated embodiment shown in
More particularly, the vision sensor 30b is configured to determine whether an overpass signature pattern is present, wherein, for example, the pattern may include the detection of a wide object across the field of view, a horizontal object relative to the ground plane, higher light intensity above the object (during daytime), and/or lower light intensity below the object (during daytime). Alternatively, a reflective surface, or other indicia can be positioned on the overpass, so as to directly communicate its presence to the sensor 30b. If an overpass signature pattern is determined, and/or the radar subsystem detects a moving object through a stationary track, then the map database 20 is consulted.
Referring to
Concurrently, at a step 200a, a radar subsystem 30a is used to track a plurality of objects by determining their relative object locations over a period. At a step 202a, the individual track data is examined to determine if there is a wide stationary object 16 that spans the width of the thoroughfare, and/or to detect the presence of a moving object 16m through the stationary object location. If a moving object is found to have traversed the stationary object location, then the radar-detected stationary object 16 is deemed an overpass, and correlated input data is communicated to the data fusion module proceeding to step 204; else, the radar subsystem returns to step 200a.
At a step 204, the data fusion module will combine overpass identified locations from each sensor 30a,b, and more preferably, attribute a weighted factor to those overpass locations detected by both sensors. At a step 206, the current position coordinates of the vehicle 12 are determined using a locator device 26, and links in the vicinity of the vehicle 12 are retrieved from the map database 20. From the current position coordinates, absolute position coordinates for the objects 16,16m can be determined from their relative positioning. Next, at a step 208, the heading, and forward travel direction of the vehicle 12 are determined, and links in the vicinity of the forward travel path of the vehicle 12 are retrieved from the map database 20. At a step 210, the geometry of the retrieved roads is determined from their map points, and approaching intersection points therewith are identified. At a step 212, intersection points are classified as either “at grade” or “overpass” based on the grade level indicia provided at the points. At step 214, the overpass determined intersection points are communicated to the data fusion module, and at step 216, compared to the sensor determined overpass locations.
Finally, at a step 216a, if a sensor-detected overpass location does not correspond to a map-identified overpass location 24, then the object 16 is deemed in-path and at-grade without considering signal strength trend data to eliminate the possibility that it is an overpass. In other words, where an detected overpass is not corroborated by the database 20, the system 10 will immediately issue a warning, even if both sensors 30a,b detected an overpass location. If, however, a sensor-detected overpass location does correspond to a map database overpass location 24, then the signal strength trend data is considered, at step 216b, to determine whether the object is in-path at grade level, or out of the grade level path, or where detected by the vision sensor only, further analysis can be made to determine whether an in-path object pattern is also present.
The preferred forms of the invention described above are to be used as illustration only, and should not be utilized in a limiting sense in interpreting the scope of the present invention. Obvious modifications to the exemplary embodiments and methods of operation, as set forth herein, could be readily made by those skilled in the art without departing from the spirit of the present invention. The inventor hereby state his intent to rely on the Doctrine of Equivalents to determine and assess the reasonably fair scope of the present invention as pertains to any system or method not materially departing from but outside the literal scope of the invention as set forth in the following claims.
Number | Name | Date | Kind |
---|---|---|---|
5587929 | League et al. | Dec 1996 | A |
6047234 | Cherveny et al. | Apr 2000 | A |
6317691 | Narayan et al. | Nov 2001 | B1 |
6438491 | Farmer | Aug 2002 | B1 |
6658336 | Browne et al. | Dec 2003 | B2 |
6832156 | Farmer | Dec 2004 | B2 |
6897802 | Daniell et al. | May 2005 | B1 |
7027615 | Chen | Apr 2006 | B2 |
7584047 | Igarashi | Sep 2009 | B2 |
7592945 | Colburn et al. | Sep 2009 | B2 |
20020106135 | Iwane | Aug 2002 | A1 |
20020161513 | Bechtolsheim et al. | Oct 2002 | A1 |
20020169533 | Browne et al. | Nov 2002 | A1 |
20030002713 | Chen | Jan 2003 | A1 |
20030004644 | Farmer | Jan 2003 | A1 |
20050149251 | Donath et al. | Jul 2005 | A1 |
20060106538 | Browne et al. | May 2006 | A1 |
20070016372 | Browne et al. | Jan 2007 | A1 |
20090002222 | Colburn et al. | Jan 2009 | A1 |
Number | Date | Country |
---|---|---|
6-206507 | Oct 2006 | JP |
Entry |
---|
Yang Chen. “Highway Overhead Structure Detection Using Video Image Sequences.” IEEE Transactions on Intelligent Transportation Systems. vol. 4, No. 2. pp. 67-77. Jun. 2003. |
Srinivasa, N.; Yang Chen; Daniell, C. “A fusion system for real-time forward collision warning in automobiles.” IEEE Transactions on Intelligent Transportation Systems. vol. 1, pp. 457-462. Oct. 2003. |
NHTSA. United States Department of Transportation, National Highway Traffic Safety Administration. (2000). Automotive collision avoidance systems (acas) program final report (DOT HS 809 080). Springfield, VA: National Technical Information Service. |
Number | Date | Country | |
---|---|---|---|
20080189039 A1 | Aug 2008 | US |