This paper presents a probabilistic framework for road geometry estimation using a millimetre wave radar. It aims at estimating the geometry of roads without assuming any particular infrastructure such as lane marks. It provides also the vehicle location with respect to the edges of the road. This system employs a radar sensor in view of its robustness to weather conditions such as fog, dust, rain and snow. The proposed approach is robust to noisy measurements since the radar target locations are modelled as Gaussian distributions. These observations are integrated into a Kalman Particle filter to estimate the posterior distribution of the parameters that best describe the geometry of the road. Experimental results using data acquired on a highway road are presented. The effectiveness of the proposed approach is demonstrated by a qualitative analysis of the results.
|Number of pages||7|
|Publication status||Published - 25 Oct 2011|
|Event||2011 IEEE/RSJ International Conference on Intelligent Robots and Systems - San Francisco, United States|
Duration: 25 Sep 2011 → …
|Conference||2011 IEEE/RSJ International Conference on Intelligent Robots and Systems|
|Period||25/9/11 → …|