城市交通场景的随机性和复杂性使得无人车的可行驶区域检测成为难题。受人类驾驶行为的启发，本文提出了一种无人车的可行驶区域检测的新方法，该方法利用了单目相机得到的像素信息和激光传感器得到的空间点云信息的融合，与共线中的双射类似，该方法引入了'共点映射'的新概念，其定义为：将来自激光雷达的点映射到图像分割边缘上的点的双射。该方法通过将障碍物与超像素融合得到初始可行驶区域，并基于该初始可行驶区域，利用自学习模型定位候选可行驶区域。此外，为了提升算法的鲁棒性，本文融合了四种特征，特别提出了一种称为可行驶程度（drivable degree, DD）的特征，该特征定义了激光点的可行驶程度。经过自学习四种特征得到初始可行驶区域之后，利用贝叶斯框架建立可行驶区域的最终概率图模型。本文的方法没有引入强假设条件，也不需要训练过程，但在ROAD-KITTI benchmark 测试中获得了最佳的性能。实验结果表明，该方法是一种泛化性强且有效的可行驶区域的检测方法。

The randomness and complexity of urban traffic scenes make it a difficult task for self-driving cars to detect drivable areas. Inspired by human driving behaviors, we propose a novel method of drivable area detection for self-driving cars based on fusing pixel information from a monocular camera with spatial information from a light detection and ranging (LIDAR) scanner. Similar to the bijection of collineation, a new concept called co-point mapping, which is a bijection that maps points from the LIDAR scanner to points on the edge of the image segmentation, is introduced in the proposed method. Our method positions candidate drivable areas through self-learning models based on the initial drivable areas that are obtained by fusing obstacle information with superpixels. In addition, a fusion of four features is applied in order to achieve a more robust performance. In particular, a feature called drivable degree (DD) is proposed to characterize the drivable degree of the LIDAR points. After the initial drivable area is characterized by the features obtained through self-learning, a Bayesian framework is utilized to calculate the final probability map of the drivable area. Our approach introduces no common hypothesis and requires no training steps; yet it yields a state-of-art performance when tested on the ROAD-KITTI benchmark. Experimental results demonstrate that the proposed method is a general and efficient approach for detecting drivable area.