## 1 Introduction  
Road traffic safety hazards are currently a common social safety issue all over the world. In terms of vehicle type, car-related accidents account for more than two-thirds of all and are the most harmful. To improve traffic safety, the automotive industry developed airbags, anti-lock braking systems, and electronic stability systems were used in the early days. In recent years, research in the field of automotive safety has moved toward a more intelligent Advanced Driving Assistance System (ADAS). ADAS can perceive the surrounding environment and alert the driver or control the vehicle automatically when danger is present, thus reducing or avoiding the hazards of traffic accidents. It is a new generation of active safety systems [1]. Since most accidents originate from collisions between vehicles and obstacles, obstacle avoidance is the primary task of ADAS, and accurate detection of obstacles is the basis for ADAS to take obstacle avoidance measures. Unexpected hazards on the road (e.g., lost goods, loose stones, etc.) are an easily overlooked problem for traffic safety, and they can easily cause traffic accidents, which have a great impact on people's property and even their lives. US traffic reports show that approximately 150 people die each year in the US due to lost goods on the road [2]. As such, obstacles usually have small sizes and complex shapes, and achieving accurate and effective detection is a hard task for both drivers and general detection systems. Therefore, it is greatly significant to include the detection of unexpected obstacles in vehicle environment perception systems and develop an effective detection method. There are currently two solutions in the field of automotive environment perception, namely computer vision solution and radar solution. At the current research stage, both solutions have certain advantages and disadvantages, but the computer vision solution is closer to the human driving form and has great potential for development. Vision is the most important way for humans to obtain information, and studies have shown that about 90% of the information obtained by drivers comes from vision [3]. Computer vision takes a camera as a sensor, and the images acquired by the camera are most similar to the real world perceived by the human eyes, which contains a wealth of information that can be used for a variety of tasks in the field of environment perception. In addition, computer vision solutions are low-cost and more easily marketable. Stereo vision and deep learning are the two main detection methods used in vision solutions. Stereo vision methods [4; 5] mainly rely on the stereo information contained in stereo image pairs captured by binocular cameras and use the differences in the geometric structure of objects to recognize the image content. However, stereo vision techniques are strongly influenced by distance and are not effective in detecting obstacles at long distances. Deep learning methods can handle complex feature information thanks to the powerful feature extraction capabilities of convolutional neural networks. In addition, they have certain advantages over stereo vision methods in terms of detection distance and accuracy, and semantic information can be obtained. Consequently, this paper studies an unexpected obstacle detection method based on deep learning, and it mainly has the following three contributions:  
1. A semantic segmentation method suitable for detecting unexpected obstacles on the road is proposed. Traditional supervised machine learning systems can only identify the classes of obstacles present in the training set but are powerless for unexpected obstacles. A powerful advantage of DeepLabV3+ is its ability to use the learned contextual features of the images to generalize information far beyond their training data.