Drivable Road Boundary Detection for Intelligent Vehicles Based on Stereovision with Plane-induced Homography
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摘要: 道路检测是智能车辆及先进驾驶辅助系统(Advanced driver assistance systems, ADAS) 研究的关键问题之一.本文提出了一种基于立体视觉的可行驶道路区域与非道路区域间边界的检测方法. 该方法基于立体视觉平面单应性建立了一个隐马尔科夫模型(Hidden Markov model, HMM).针对该模型,我们应用Viterbi算法,并提出了一种巧妙的状态序列的观测概率函数,以寻找道路/非道路边界的最优状态序列. 实验结果证明了该方法在各种典型且复杂的实际道路场景中的有效性和鲁棒性.Abstract: Road detection is one of the key issues for intelligent vehicles and advanced driver assistance systems (ADAS). In this paper, we present a stereovision-based approach for estimating the boundary between the drivable road region and the non-road region. It is based on a formulation of stereo with the ground-plane-induced homography in a hidden Markov model (HMM). Under this formulation, we employ the Viterbi algorithm and propose a sophisticated measure of the probability of the state sequence to find the most likely road/non-road boundary. Experimental results on a wide variety of typical but challenging real road scenes have substantiated the effectiveness as well as robustness of the proposed approach.
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Key words:
- Intelligent vehicles /
- road detection /
- stereovision /
- homography /
- hidden Markov models /
- global optimization
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