Bayesian curved lane estimation for autonomous driving using Python

Several pieces of research during the last decade in intelligent perception are focused on the development of algorithms allowing vehicles to move efficiently in complex environments. Most of existing approaches suffer from either processing time which do not meet real-time requirements, or inefficient in real complex environment, which also doesn’t meet the full availability constraint of such a critical function. To improve the existing solutions, an algorithm based on curved lane detection by using a Bayesian framework for the estimation of multi-hyperbola parameters is proposed to detect curved lane under challenging conditions. The general idea is to divide a captured image into several parts. The trajectory is modeled by a hyperbola over each part, whose parameters are estimated using the proposed hierarchical Bayesian model. Compared to the existing works in the state of the art, experimental results prove that our approach is more efficient and more precise in road marking detection.

Autonomous driving is one of the main concerns which allows a step by step evolution of Artificial Intelligence.

Indeed, it is important to ensure the proper functioning of autonomous vehicle mechanisms by making appropriate and accurate decisions in real time by identifying and distinguishing the different objects of the road. In this direction, several studies have been carried out on autonomous driving in order to improve road security. The methods of obtaining intelligent vehicles systems are solely based on artificial intelligence, by trying reproducing human intelligence, and more precisely Machine Learning / Deep leaning approaches. The main goal of research and development of smart vehicles is to reduce accident rates, as well as improve the efficiency of traffic use by detecting different dangerous situations.