## 1 Introduction  
Road traffic safety hazards are a social security problem that the world is facing. According to the statistics from the WHO, approximately 1.35 million people are killed and 20-50 million people are injured by road traffic accidents every year across the world [1]. In terms of traffic accident categories, the proportion related to automobiles is more than 2/3, and the harm is also the most serious. In order to improve the level of road traffic safety, the automobile industry has developed favorable tools, such as airbags, anti-lock braking systems and electronic stability systems at an early stage. In recent years, research in the field of automobile safety has developed towards a more intelligent Advanced Driving Assistance System (ADAS). The ADAS can perceive the surrounding environment and remind the driver or automatically control the vehicle when danger occurs in order to reduce or avoid the harm of traffic accidents and it belongs to a new generation of active safety systems [2]. As most of the hazards caused by traffic accidents originate from collisions between vehicles and obstacles, obstacle avoidance is the primary task of the ADAS, and the accurate detection of obstacles is the basis for the ADAS to take obstacle avoidance measures [3]. In the field of the environment perception of automobile driving assistance systems, there are currently two solutions in the industry [4]; namely, machine vision solution [5, 6] and radar solution [7, 8]. Both solutions have their own advantages, but the machine vision solution is closer to the human driving form and has significant potential for development. Compared with the radar solution, the machine vision solution can obtain richer information, and has advantages in device installation, usage restrictions and costs. Researchshows that about 90% of the information obtained by the driver comes from vision [9]. As a sensor of machine vision, the image obtained by the camera is most similar to the real world, as it is perceived by the human eye, and the rich information contained in it can be used to complete various tasks in the field of environmental perception. Multiple view geometry and deep learning are the two main detection methods in vision schemes. The multiple view geometry method [10; 11] exploits the relationship between images captured at different viewpoints to detect image content through the geometric structural features of objects. The deep learning method [12; 13; 14] relies on a large amount of data to train the network to identify specific objects. It is limited by the richness of the data set and has a poor detection ability for object types not included in the training set. The multiple view geometry method is not limited by the object types and has wider application scenes. Multiple view geometry methods are further divided into monocular vision methods and stereo vision methods. The monocular vision method uses a monocular camera as a sensor, it cannot determine the absolute depth of the object, and there is a problem of scale ambiguity. The stereo vision method uses a binocular or multi-eye camera as a sensor. It uses a fixed baseline between the cameras to obtain depth information, which can avoid the scale ambiguity of monocular vision. This paper mainly studies the obstacle detection method based on stereo vision. This paper proposes an Obstacle Detection method based on the Local Spatial Feature (OD-LSF). As a general obstacle detection method, the OD-LSF method is able to classify traffic scenes into an obstacle class and free space class, and transform the classification results into efficient stixel representation. The main contributions of this paper are as follows:  
1. An Obstacle Detection method based on the Plane Normal Vector (OD-PNV) is proposed. Compared with the traditional obstacle detection method, based on a geometric structure, this method does not need to establish the road surface model, so the detection results are not affected by the road surface fitting accuracy.