Probabilistic lane estimation for autonomous driving using basis curves

Lane estimation for autonomous driving can be formulated as a curve estimation problem, where local sensor data provides partial and noisy observations of spatial curves forming lane boundaries. The number of lanes to estimate are initially unknown and many observations may be outliers or false detections (due e.g. to shadows or non-boundary road paint). The challenges lie in detecting lanes when and where they exist, and updating lane estimates as new observations are made. This paper describes an efficient probabilistic lane estimation algorithm based on a novel curve representation. The key advance is a principled mechanism to describe many similar curves as variations of a single basis curve. Locally observed road paint and curb features are then fused to detect and estimate all nearby travel lanes. The system handles roads with complex multi-lane geometries and makes no assumptions about the position and orientation of the vehicle with respect to the roadway. We evaluate our algorithm using a ground truth dataset containing manually-labeled, fine-grained lane geometries for vehicle travel in two large and diverse datasets that include more than 300,000 images and 44 km of roadway. The results illustrate the capabilities of our algorithm for robust lane estimation in the face of challenging conditions and unknown roadways.United States. Defense Advanced Research Projects Agency (Urban Challenge, ARPA Order No. W369/00, Program Code DIRO, issued by DARPA/CMO under Contract No. HR0011-06-C-0149

Deblurring a Camera- Shake Image Using a Thinning Kernel

[[abstract]]The task of blind deblurring usually consists of estimation of interim images and blur kernels. Due to the lack of information in kernels compared to that in interim images, when only a blurred image is available, most of deblurring methods emphasis the estimation of interim images. However, the resulting kernel is often wider than it should be, thus degrading the quality of the deconvolved image. To remedy the problem of wide kernels, we present a thinning scheme to better estimate a kernel. In this way, a clear image can be recovered from a camera-shake blurred image. To mitigate the insufficient information of blur kernels, we make simple inferences and assumptions for kernels based on the trajectory of the camera shake. Under these inferences and assumptions, we use a three-step approach to estimate the blur kernel. Firstly, we relax the condition to find the shape of the blur kernel. Next, we use a thinning algorithm to obtain the skeleton of the blur kernel. Thirdly, we reweight the blur kernel by Gaussian distribution. By repeating these steps a few times we can get a more accurate blur kernel. Finally, we can reconstruct a high quality deblurred image by using the blur kernel. The proposed method is tested by a public database and our results outperform those of two similar methods.[[notice]]補正完