Abstract
In this paper we propose a robust lane detection and tracking method by combining particle filters with the particle swarm optimization method. This method mainly uses the particle filters to detect and track the local optimum of the lane model in the input image and then seeks the global optimal solution of the lane model by a particle swarm optimization method. The particle filter can effectively complete lane detection and tracking in complicated or variable lane environments. However, the result obtained is usually a local optimal system status rather than the global optimal system status. Thus, the particle swarm optimization method is used to further refine the global optimal system status in all system statuses. Since the particle swarm optimization method is a global optimization algorithm based on iterative computing, it can find the global optimal lane model by simulating the food finding way of fish school or insects under the mutual cooperation of all particles. In verification testing, the test environments included highways and ordinary roads as well as straight and curved lanes, uphill and downhill lanes, lane changes, etc. Our proposed method can complete the lane detection and tracking more accurately and effectively then existing options.
Highlights
Lane detection plays an important role in many driver assistance systems such as lane departure warning systems, lane following systems, lane collision warning systems, etc. [1,2,3,4,5,6]
Algorithm 3 shows the pseudo code of the particle swarm optimization particle filter (PSO-PF) algorithm, which firstly initializes all particle statuses and uses the particle filter to carry out the prediction, measurement and resample of particle status, and it further searches for the optimal status from particle statuses after re-sampling by the particle swarm optimization method
We have proposed a new algorithm to improve the efficiency of the particle filter, which is known as the particle swarm optimization particle filter (PSO-PF)
Summary
Lane detection plays an important role in many driver assistance systems such as lane departure warning systems, lane following systems, lane collision warning systems, etc. [1,2,3,4,5,6]. Lane detection plays an important role in many driver assistance systems such as lane departure warning systems, lane following systems, lane collision warning systems, etc. A lane departure warning system mainly determines whether the vehicle is deviating from the current lane or crosses the lane based on the geometrical relationship between the lane and the vehicle. When the vehicle departs from or crosses the lane, a warning is given for the first time to draw the driver’s attention or the turn signal is automatically activated when changing lanes; this can effectively reduce the incidence of accidents. The lane following system is usually used to monitor whether the driver is focused on driving or not. From the above discussion it can be concluded that lane detection plays an important role in driving assistance systems
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