## 2 Related Work
This model can separate the foreground and background in images and achieve good segmentation results on the SYNTHIA-AL dataset [23]. The above-mentioned semantic segmentation methods are only used to detect the types of obstacles that already exist in the training set, while unexpected obstacles (which do not exist in the training set) are not detected. [PERSON] et al. [24] first explored this particular problem, and the paper used a restricted Boltzmann machine neural network to detect anomalous regions on the road. There are currently some methods for the detection of unexpected classes, such as open-set recognition, out-of-distribution detection, anomaly detection, and novelty detection, etc. However, the difference between the latter three methods and open-set recognition is that they cannot distinguish known classes in the training set [25]. The methods to solve the open-set recognition problem are referred to as open-set recognition algorithms [26]. [PERSON] et al. [27] conducted pioneering research on the open-set recognition problem and defined it as a constrained minimization problem. The article proposes a novel method to deal with the task of open-set recognition, which sculpts a decision space from the marginal distances of a 1-class or binary SVM with a linear kernel. [PERSON] et al. [PERSON] et al. (2018) investigated the use of deep neural networks for open-set recognition to provide deep-learning-based recognition systems with the ability to reject unexpected class. However, the above two approaches have not carried out targeted research in the area of unexpected obstacle detection. As a matter of fact, using a single method for obstacle detection in practical problems is very limited. If different detection methods can be integrated, the purpose of complementing each other's advantages can be achieved, thereby improving the detection quality. In recent years, many researchers have adopted this idea. For example, [PERSON] et al. [PERSON] et al. (2018) defined the model as an energy minimization problem and used semantics and geometry as unary data terms in it. This model can jointly infer semantic and geometric clues to achieve better detection results. [PERSON] et al. [PERSON] et al. (2019) proposed an obstacle detection method using fusion of radar and visual data, and its data fusion methods are divided into two categories: spatial fusion and time fusion. The former can realize the unification between coordinate systems through a series of coordinate transformations, and the latter can ensure the synchronization of data fusion in time. It can be seen that the fusion method can consider a variety of data information, which effectively improves the detection quality. For fusion strategies, Bayesian is a probability-based fusion framework, which is very classic and widely used. Therefore, this paper adopts Bayesian as the fusion framework. In fact, semantic segmentation models have inherently powerful generalization capabilities and are capable of acquiring much more information than the types of data provided in the training dataset. For example, semantic segmentation models can learn the contextual information of an image from a large amount of training data [PERSON] et al. (2014); [PERSON] et al. (2014). Pixels in an image are usually not isolated, and contextual information reflects some connections between a pixel and its neighboring pixels. The information generalization capability of semantic segmentation models provides powerful technical support for the detection of unexpected obstacles. In this paper, a semantic segmentation method that can highlight the contextual information of unexpected obstacles is designed for such a complex problem of unexpected obstacle detection. In addition, as the semantic method only utilizes the semantic information of the image, the detection accuracy is limited, so this paper also designs an open-set recognition algorithm based on the uncertainty. Unexpected obstacles are detected by incorporating the two methods into the Bayesian framework for joint inference.