Multiple Lane Detection Algorithm Based on Optimised Dense Disparity Map Estimation
This work addresses lane detection for self-driving vehicles, but it is incremental as it builds on existing methods with optimizations.
The paper tackled lane detection for self-driving vehicles by using an optimized dense disparity map estimation that leverages previous time-step information to constrain the search range, reducing disparity estimation runtime by around 37% and achieving lane detection accuracy of about 99%.
Lane detection is very important for self-driving vehicles. In recent years, computer stereo vision has been prevalently used to enhance the accuracy of the lane detection systems. This paper mainly presents a multiple lane detection algorithm developed based on optimised dense disparity map estimation, where the disparity information obtained at time t_{n} is utilised to optimise the process of disparity estimation at time t_{n+1}. This is achieved by estimating the road model at time t_{n} and then controlling the search range for the disparity estimation at time t_{n+1}. The lanes are then detected using our previously published algorithm, where the vanishing point information is used to model the lanes. The experimental results illustrate that the runtime of the disparity estimation is reduced by around 37% and the accuracy of the lane detection is about 99%.