Lane Detection Algorithm for Intelligent Vehicles in Complex Road Conditions and Dynamic Environments

Abstract

Lane detection is an important foundation in the development of intelligent vehicles. To address problems such as low detection accuracy of traditional methods and poor real-time performance of deep learning-based methodologies, a lane detection algorithm for intelligent vehicles in complex road conditions and dynamic environments was proposed. Firstly, converting the distorted image and using the superposition threshold algorithm for edge detection, an aerial view of the lane was obtained via region of interest extraction and inverse perspective transformation. Secondly, the random sample consensus algorithm was adopted to fit the curves of lane lines based on the third-order B-spline curve model, and fitting evaluation and curvature radius calculation were then carried out on the curve. Lastly, by using the road driving video under complex road conditions and the Tusimple dataset, simulation test experiments for lane detection algorithm were performed. The experimental results show that the average detection accuracy based on road driving video reached 98.49%, and the average processing time reached 21.5 ms. The average detection accuracy based on the Tusimple dataset reached 98.42%, and the average processing time reached 22.2 ms. Compared with traditional methods and deep learning-based methodologies, this lane detection algorithm had excellent accuracy and real-time performance, a high detection efficiency and a strong anti-interference ability. The accurate recognition rate and average processing time were significantly improved. The proposed algorithm is crucial in promoting the technological level of intelligent vehicle driving assistance and conducive to the further improvement of the driving safety of intelligent vehicles.

Identification of Road-Surface Type Using Deep Neural Networks for Friction Coefficient Estimation

“…Sensors in the automated vehicle are used for perception, and most of them are cause-based: an ultrasonic sensor; video, thermal and stereo cameras; radars; laser-based radar (LIDAR), a global positioning system (GPS), etc. Video cameras are usually used for determining path [17] and obstacles [18], line detection and road edge recognition [19,20]. Also, there are developments where image analysis methods are used for road distress, cracks and other road damage [21][22][23].…”

Estratégia de Detecção de Faixas de Trânsito Baseada em Câmera Monocular para Sistemas Embarcados

“…Para realizar a transformação de perspectiva são necessários oito pontos na imagem, quatro para cada plano de transformação (Cao et al, 2019 Oúltimo critério adotado considera a estrutura geométrica das faixas de transito. Na imagem em perspectiva aérea,é esperado que as faixas obtidas estejam aproximadamente paralelas.…”

Occlusion-Free Road Segmentation Leveraging Semantics for Autonomous Vehicles

“…Generally, the road is represented by its boundaries [9,10] or regions [1,2,11]. Moreover, road lane [12,13,14] and drivable area [15,16] detection also attract much attention from researchers, which concern the ego lane and the obstacle-free region of the road, respectively. The learning-based methods usually outperform the model-based methods due to the developed segmentation techniques.…”