OFFLINE OPTIMIZATION OF CURVE SPEED                      WARNING APPLICATIONS                                      Vassili...
INTRODUCTIONRoad safety applications such as lane departure warning and curve speed warning systems arebased on the knowle...
The CSW application is based on Map Data, well known from commercial navigationsystems. Nevertheless, the usage of Map Dat...
Figure 2. The Path in front of the VehicleFigure 2 shows the position of the vehicle at the upper right corner of the imag...
Figure 3 is displayed to the driver and it indicates a right turn in 650m. The maximumvelocity should be 46 kph and thus t...
RESULTSThe evaluation of the CAN-data, vehicle data provide real world reference, and SQL-data,map data from the database,...
The difference in the curvature seems to be not large. However, the effect of these differenceson the application has to b...
Proceedings of IEEE International Conference on Intelligent Vehicles, Stuttgart, Germany,    1998, pp. 93-98.(5) Manolis T...
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Offline Optimization Of Curve Speed Warning Applications, Vassilis Kaffes, ICCS

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Offline Optimization Of Curve Speed Warning Applications, Vassilis Kaffes, ICCS

  1. 1. OFFLINE OPTIMIZATION OF CURVE SPEED WARNING APPLICATIONS Vassilis Kaffes Institute of Communications and Computer Systems (ICCS) Iroon Polytechniou St. 9, 15773 Athens, Greece +30 210 772 3865, 30 210 772 2291, vkaff@iccs.gr Dr. Manolis Tsogas Institute of Communications and Computer Systems (ICCS) Iroon Polytechniou St. 9, 15773 Athens, Greece +30 210 772 3865, 30 210 772 2291, mtsog@iccs.gr Panagiotis Lytrivis Institute of Communications and Computer Systems (ICCS) Iroon Polytechniou St. 9, 15773 Athens, Greece +30 210 772 3865, 30 210 772 2291, panagiotis.lytrivis@iccs.gr Dr. Angelos Amditis Institute of Communications and Computer Systems (ICCS) Iroon Polytechniou St. 9, 15773 Athens, Greece +30 210 772 2398, 30 210 772 2291, a.amditis@iccs.gr Dr.-Ing. habil. Christoph Arndt Ford Research & Advanced Engineering Europe Süsterfeldstr. 200, D-52072 Aachen, Germany + 49 (0) 241-9421-381, + 49 (0) 241-9421-301carndt1@ford.com ABSTRACTIn automotive safety applications the system must be capable of early recognizing themaneuvers performed by the driver and the intention associated with them in order to takepreventive measures or trigger warning alarms. In this paper the problem of optimizing thecore algorithm of a curve speed warning application is investigated. The offline tool willdetermine the accuracy of specified map data compared to real data using vehicle dynamics.The work described here is part of the research activities in the European research projecteuroFOT. 1
  2. 2. INTRODUCTIONRoad safety applications such as lane departure warning and curve speed warning systems arebased on the knowledge of the road lane geometry. Nowadays systems care for more activesolutions which cannot be provided by current systems based only on vision technology (7).Digital maps and on-board sensors estimating dynamic state of the ego vehicle such as radarsand laser scanners (4) extend the accuracy of road geometry specification (2), (3). Thus overallsafety in the road is about to increase in such systems dealing with situations where lanemarkings are missing or are ambiguous or the visibility is restricted due to weather conditions(1). The goal of this paper is to evaluate the map data accuracy and reliability compared to realworld reference data. CURVE SPEED WARNING APPLICATIONSCurve-speed warning (CSW) technology has been developed (5) to help drivers identifypotentially dangerous situations e.g. the vehicle is driving in a curve too fast, and warn thedriver in advance allowing him enough time to react properly. A curve is represented as a listof shape points which are retrieved from the map database. Figure 1. Map Data in the vehicle research platform 2
  3. 3. The CSW application is based on Map Data, well known from commercial navigationsystems. Nevertheless, the usage of Map Data (6) can be extended to active safety system,when the route of the vehicle in known in advance. Therefore a prediction of the path (theMost Likely Path) is used to extract relevant data (i.e. shape points, geometry information likeradius, speed limits, etc.) from the oncoming route. These data are the Electronic Horizon(EH) and they provide the basis for the CSW application.By processing the coordinates of these shape points it is possible to extract the road geometry.Thus analysis of the geometric characteristics of the bend combined with external factors suchas weather conditions, the maximum recommended velocity for the bend is estimated. If thevelocity exceeds the maximum recommended velocity the driver is warned. CSW may alsoassist the driver during night driving and also helps reduce the blinding effect from theheadlights of oncoming vehicles.Using digital maps (see figure No. 1) we create segments of shape points according to a set ofrules and create the road geometry utilizing the clothoid model. Also using the vehicledynamics we can estimate the curvature of the road cR and create warnings when the factor FC= c/ cR is greater than a selected threshold. OPTIMIZATION ALGORITHMThe offline tool will carry out statistical processing on map data, such as distance to shapepoints from map-data and can-data for different road classes or distance along the vehicle pathin meters from minimum radius and min radius from map data. The parameters in algorithmcalculating road geometry so as to provide accurate curve speed warnings to driver will beadapted according to the derived results from the processing.THE SETUP OF THE OPTIMIZATION TOOLThe optimization of the algorithm requires an additional tool that handles all aspects of theCSW parameter variation. The first step of this tool is the reconstruction of the ElectronicHorizon (i.e. the path in front of the vehicle). All relevant data for CSW are extracted from theMap-Data. This includes the shape-points and the radius of the road at the shape-points. Thisdata are visualized in figure 2. 3
  4. 4. Figure 2. The Path in front of the VehicleFigure 2 shows the position of the vehicle at the upper right corner of the image. Eachoncoming geometry point has a coordinate and a radius. The radius is shown as a red line,perpendicular to the route. From these data the algorithm computes the limit velocities for theoncoming curves. Comparison of the current velocity and limit velocity leads to a warning, ifthe driver approaches an oncoming curve too fast. The given warning is shown in figure 3. Figure 3. Speed warning displayed to the driver 4
  5. 5. Figure 3 is displayed to the driver and it indicates a right turn in 650m. The maximumvelocity should be 46 kph and thus the driver should reduce the velocity to drive safe throughthe next curve.Going back to the previous image (figure 2), we see that this warning is coming from thesharp right turn marked with an arrow in the geometry plot. Even though the driver does noteven see this curve, the warning already indicates that the current velocity exceeds the limit.VERIFICATION OF THE ALGORITHM AGAINST MAP-DATAThe sensor information of CSW comes from the map-data and from vehicle data. Thereforethe maps have to be evaluated regarding reliability of their geometric data. Figure 4. Curvature from vehicle data and curvature from map-data (*)Assuming constant circle conditions, we are able to compare the curvature from vehicle dataand map-data. In this case of normal driving conditions, the curvature data show a goodcorrelation in figure 4. There is some noise from the vehicle sensors and there are someoutliers in the map data. For example: At time 4600 sec or 4950 sec, there is obviously awrong sign in the map data. 5
  6. 6. RESULTSThe evaluation of the CAN-data, vehicle data provide real world reference, and SQL-data,map data from the database, enable us to extract all relevant CSW-information. The dedicatedSW tool that developed will help us to provide reliable results.The difference between the vehicle data curvature and the curvature from map data is ameasure for the reliability of the map data and it provides hints for the tuning of the algorithm.Figure 5 shows the vehicle curvature data and the interpolated map curvature data. Thehistogram of the difference, also shown in figure 5 (the lower plot) shows a good correlationof both data.Figure 5. Upper plot: Curvature from vehicle data (blue) and from map data (red) Lower plot: Histogram of the difference of both curvatures. 6
  7. 7. The difference in the curvature seems to be not large. However, the effect of these differenceson the application has to be evaluated with our automatic evaluation tool, to tune theparameters correctly. Furthermore, this is only an example of an overall view on theevaluation procedure. There are several attributes of the map data (Functional Road Classes,ADAS-flag, etc.) that have to be evaluated separately. Only the complete evaluation of thedata and the parameters will lead to an optimized tuning and finally to an optimizedapplication. CONCLUSIONThe CSW algorithms improvement contribute to safe driving and also to euroFOT project.The results will be a starting point for evaluating the performance and necessity ofcurve-speed warning systems in real time conditions for driver safety applications.For the future the results that derived from the evaluation tool on correlating curvature fromvehicle data and from map data will be used to tune certain parameters of the application.Many of the attributes that are provided by the map data will be evaluated too and all togetherwill be used to optimize and provide an accurate and reliable application. AKNOWLEDGMENTSThis work was supported by the European Commission in the euroFOT project. The authorswould like to thank all partners within euroFOT for their cooperation and their valuablecontribution. REFERENCES(1) M. Tsogas, A. Polychronopoulos, A. Amditis, Using Digital Maps to Enhance Lane Keeping Support Systems, in Proceedings of the IEEE Intelligent Vehicles Symposium, June 13-15, 2007, Istanbul, Turkey, pp. 148-153.(2) Klotz, J. Sparbert, D. Hötzer, Lane data fusion for driver assistance systems, in Proceedings of the 7th International Conference on Information Fusion, June 28- July 1, 2004, Stockholm, Sweden, pp. 657-663.(3) H. Weigel, H. Cramer, G. Wanielik, A. Polychronopoulos, A. Saroldi, Accurate Road Geometry Estimation for a Safe Speed Application, in Proceedings of Intelligent Vehicles Symposium, June 13-15, 2006, Tokyo, Japan, pp. 516- 521.(4) Kirchner, T. Heinrich, Model based detection of road boundaries with a laser scanner, in 7
  8. 8. Proceedings of IEEE International Conference on Intelligent Vehicles, Stuttgart, Germany, 1998, pp. 93-98.(5) Manolis Tsogas, Panagiotis Lytrivis, Angelos Amditis, Enhanced Curve Speed Warning Application Using Multiple Sources Of Information For Extracting Road Geometry, 10th International Conference on Application of Advanced Technologies in Transportation, Athens, Greece, May 27-31, 2008.(6) V. Blervaque, ERTICO, K. Mezger, Daimler Chrysler Research, L. Beuk, Siemens VDO, Eindhoven, J. Loewenau, BMW Group Research and Technology, ADAS Horizon – How Digital Maps can contribute to Road Safety, Advanced Microsystems for Automotive Applications 2006, Monday, July 31, 2006(7) Chenhao Wang, Zhencheng Hu, Tomoki Maeda, Naoko Hamada, and Keiichi Uchimura , Predictive Lane Detection for Simultaneous Road Geometry Estimation and Vehicle Localization, 2009 IEEE International Conference on Robotics and Automation, May 12th 2009, Kobe, Japan 8

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