基于深度学习的自适应场景路面提取方法

Adaptive Road Extraction Method in Different Scene Based on Deep Learning

为满足无人驾驶车辆对越野环境的适应能力,提高无人驾驶车辆对环境的理解能力,必须对环境感知层面提出更高的要求.而环境感知中最为关键的一点就是车道线提取或者路面提取.与城市环境下的结构化道路相比,越野环境下的路面提取更加复杂.综合对多种越野场景展开研究,提出了一种能够自适应场景变化的路面分割方法.文中在越野环境下采集了大量的数据,并且制作了相应的数据集;应用深度学习技术对这些场景进行识别;应用语义分割算法对不同场景下的路面进行分割;最后统一了整个算法模块,给出测试结果.

In order to meet the adaptability of autonomous vehicles to the cross-country environment and to improve the understanding ability of autonomous vehicles to the environment, higher requirements for environmental awareness system must be put forward.The most critical point in environmental awareness system is lane extraction or road extraction.However, the cross-country environment is more complicated in comparison with the structured road in the urban environment.The main reason lies in high complexity of the cross-country environment, and the extraction algorithms are different for different scene.A variety of cross-country scenes were studied, and a road segmentation method that adaptable to different scene was proposed.Firstly, a large number of data were collected for the cross-country environment, and the corresponding datasets were established.Secondly, these scenes were arranged to be identified by using deep learning method.And then a semantic segmentation algorithm was applied to segment the roads under different scenes.Finally, the whole algorithm modules were unified to obtain test results.