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.
Highlights
The annual increase in car ownerships has caused traffic safety to become an important factor affecting the development of a city
The results show that many research methods improved the effective recognition rate of lane detection, but advantages and disadvantages still remain between algorithms that will be limited by various conditions [27,28]
This study proposed a lane detection algorithm for intelligent vehicles in complex road conditions and dynamic environments
Summary
The annual increase in car ownerships has caused traffic safety to become an important factor affecting the development of a city. Visual sensors have essentially become the eyes of a smart car that capture road scenes in front of vehicles through cameras Such sensors can work continuously for long periods of time with strong adaptability. Deep learning-based methodologies have been used for lane detection He et al [24] proposed an algorithm for lane detection based on convolution neural network which converted the input detection image into aerial view. A lane detection algorithm for intelligent vehicles in complex road conditions and dynamic environments was proposed. Converting the distorted image and using the superposition threshold algorithm for edge detection, an aerial view of the lane was obtained by using region of interest (ROI) extraction and inverse perspective transformation.
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