  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%.
