PSO algorithm particle filters for improving the performance of lane detection and tracking systems in difficult roads.
PSO algorithm particle filters for improving the performance of lane detection and tracking systems in difficult roads.
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PSO算法粒子过滤器，用于改善困难道路中车道检测和跟踪系统的性能。
DOI：
10.3390/s121217168
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发表时间：
2012-12-12
期刊：
影响因子：
--
通讯作者：
Cheng WC
中科院分区：
文献类型：
--
作者：
Cheng WC
本文提出了一种结合粒子滤波和粒子群优化的鲁棒车道检测与跟踪方法。该方法主要利用粒子滤波器检测和跟踪输入图像中车道模型的局部最优，然后利用粒子群优化方法寻求车道模型的全局最优解。粒子滤波可以在复杂或可变的车道环境中有效地完成车道检测和跟踪。然而，得到的结果通常是系统的局部最优状态，而不是全局最优状态。因此，在所有系统状态下，采用粒子群优化方法进一步细化全局最优系统状态。由于粒子群优化方法是一种基于迭代计算的全局优化算法，它可以通过模拟鱼群或昆虫在所有粒子相互合作下的寻食方式来找到全局最优的通道模型。在验证测试中，测试环境包括高速公路和普通道路，直道和弯道，上坡和下坡车道，变道等。该方法比现有方法更准确、有效地完成车道检测和跟踪。
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.
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