Accurate Lane Detection Research Articles

Precomputation‐based radix‐4 CORDIC for approximate rotations and Hough transform

Vector rotation is an important component of algorithms in digital signal processing and robotics. Often, the rotation does not require very high accuracy. This study presents a lowoverhead sign-precomputation-based architecture for approximate rotation using the coordinate rotation digital computer (CORDIC) algorithm. The proposed architecture is independent of Z -datapath, and involves precomputation of the direction of rotation for each micro-rotation angle. The approach involves selecting the optimal micro-rotation angles from a set of elementary angles in run time. Careful selection and elimination of the redundant micro-rotation angles leads to a maximum of three iterations for a majority of the input angles while also simultaneously reaching within 0:45 (of the desired rotation angle). An field programmable gate array (FPGA) implementation of the proposed rotation mode CORDIC on XC7K70T-3FBG676 Kintex-7 using Xilinx ISE 13.2 achieves roughly 50% reduction in slice-delay product and power-delay product compared to recent designs. An application of approximate rotation to Hough transform-based lane detection is presented. An efficient algorithm for generation of vote addresses in the parameter space is proposed. It is shown that accurate lane detection is possible along with resource savings using the proposed CORDIC. The proposed architecture reduces the number of additions roughly by a factor of 20 compared with the conventional method of computing a parameter for each feature point.

Robust lane detection algorithm based on triangular lane model

Recently, various technologies and sciences are developed for fourth industrial revolution which includes artificial intelligence, robotics, internet of things, 3-D printing, and autonomous transportation. The autonomous transportation is one of the fourth industrial technology, and has been studying for safety driving. Accurate lane detection and lane departure warning system is very important for autonomous transportation. However, conventional methods have some problems of applying real-driving such as extracting miss lanes under cloudy weather and vehicle disturbance situations. In order to solve the real driving problems, we propose a new lane detection technique for lane departure and forward collision warning system using a single in-vehicle camera. The proposed method consists of triangular lane model, feature points extraction method. In the near field, a triangular lane model is used to approximate a pair of lane boundaries. Subsequently, feature points extraction method based on hyperbola curve model is applied to obtain lane curvature in the far field. Mathematical B-spline applied to feature points for curved lane fitting. Simulation results show that the proposed lane detection and tracking method has good performance.