DocumentCode :
2367673
Title :
Accurate forward road geometry estimation for collision warning applications
Author :
Khosla, Deepak
Author_Institution :
Hughes Res. Labs., Malibu, CA, USA
fYear :
2002
fDate :
2002
Firstpage :
254
Lastpage :
258
Abstract :
This paper describes a new model and method for accurate estimation of forward path geometry of an automobile. In this work, the forward geometry is modeled by two contiguous clothoid segments with different geometries, but continuous curvature across the transition between them. This results in a closed-form parametric expression of the same polynomial order as previous models. A recursive estimation method based on the new road model is also described. The performance evaluation of the proposed method on various simulated road geometries and comparisons with previous approaches demonstrate the feasibility and higher accuracy of the proposed method. This higher accuracy comes without a concomitant increase in computational cost or/and sensitivity to noise. The high accuracy estimation of forward path or road geometry is directly useful in applications that rely on detecting targets in the forward path of the host vehicle, e.g., adaptive cruise control and automotive collision warning.
Keywords :
Kalman filters; collision avoidance; computational geometry; computer vision; computerised navigation; object recognition; recursive estimation; road vehicles; Kalman filtering; automobile; clothoid segments; collision warning; computer vision; forward path geometry; look-ahead range; recursive estimation; road geometry estimation; target detection; Automobiles; Computational efficiency; Computational modeling; Geometry; Polynomials; Recursive estimation; Road accidents; Road vehicles; Solid modeling; Vehicle detection;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Transportation Systems, 2002. Proceedings. The IEEE 5th International Conference on
Print_ISBN :
0-7803-7389-8
Type :
conf
DOI :
10.1109/ITSC.2002.1041224
Filename :
1041224
Link To Document :
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