Lane Detection and Trajectory Generation System

International audienceThis paper presents the development of a perception system that enables an Ackermann-type autonomous vehicle to move through urban environments using control commands based on short-term trajectory planning. We propose a lane detection and keeping system based on computer vision techniques that are computationally efficient. Also, a Kalman filter-based estimation module was added to gain robustness against illumination changes and shadows. Additionally, the simulation and control of the Autónomo Uno robot gave good results following the steering commands to keep the position. In the simulation the controllers had some slight noise problems but the robot executed the given steering commands and it moved following the road. This behavior was also seen in the physical implementation

Optimizations in Dynamic Origin Technique for Efficient Lane Detection for Autonomous Vehicles

Driver assistance systems have started becoming a key differentiator in automotive space and all major automotive manufacturers have such systems with various capabilities and stages of implementation. The main building blocks of such systems are similar in nature and one of the major building blocks is road lane detection. Even though lane detection technology has been around for decades, it is still an ongoing area of research and there are still several improvements and optimizations that are possible. This paper offers an Optimized Dynamic Origin Technique (Optimized DOT) for lane detection. The proposed optimization algorithm of optimized DOT gives better results in performance and accuracy compared to other methods of lane detection. Analysis of proposed optimized DOT with various edge detection techniques, various threshold levels, various sample dataset and various lane detection methods were done and the results are discussed in this paper. The proposed optimized DOT lane detection average processing time increases by 9.21 % when compared to previous Dynamic Origin Technique (DOT) and 59.09 % compared to traditional hough transform