## 1 Introduction  
Road detection is an important yet challenging task in driverless technology. It is a crucial component of the visual navigation system for autonomous vehicles. Currently, deep-learning-based road detection algorithms are widely used for autonomous driving. These algorithms employ fully convolutional neural networks (CNNs) to extract features and classify image regions or pixels [1; 2; 3]. This enables the extraction of more abstract features and high detection accuracy. With the application of deep learning in image semantic segmentation, road detection methods using deep neural networks and segmentation [4; 5] architectures emerge. This propels progress in road detection. However, such networks focus on semantic segmentation accuracy over real-time performance. Given hardware constraints in vehicles, lightweight real-time algorithm models are essential for various driving tasks in autonomous scenarios.  
On the basis of the road structure characteristics of the current autonomous driving scenarios mainly studied, the road structure can be roughly divided into structured roads and unstructured roads. Structured roads usually have road markings and clear boundaries, with flat and less or no defects on the road surface, while unstructured roads typically do not have clear lane lines or road edges, and the road surface may be uneven or the road composition elements may be complex and varied. At this stage, the research technology for structured road detection under normal weather and light conditions is relatively well established. However, in reality, weather conditions and lighting conditions are not always constant, and road conditions are not always well-structured, so it is difficult for autonomous driving systems based on structured roads to cope with the demands of further development of their research, and the algorithms need to be strengthened to detect and recognize the complex environment of unstructured roads. There are two main problems with the current unstructured path:  
1. The boundary between the drivable area and the non-drivable area of the unstructured road may be blurred. The road edge is irregular and discontinuous, and there is no clear lane line. Its complexity has a great influence on the accurate extraction of the edge points, which poses a challenge to the traditional edge fitting method.
2. The structured road has fewer obstacles, while the unstructured road environment has more complex road conditions and often has various kinds of interference, which may appear at random locations, vehicles, people or animals, and non-motorized vehicles suddenly appear behind buildings, with higher environmental variability, requiring real-time detection of interference information and differentiation from the feasible driving area.  
Considering the accuracy and real-time performance of the feasible region detection of the model, as well as the value of unstructured road research in civil, commercial, and military fields, this paper aims at the drivable area detection of unstructured roads for autonomous driving. Based on the characteristics of unstructured roads and the theory of neural networks and deep learning, a lightweight convolutional neural network model for the identification of drivable areas in complex situations of autonomous driving is established.  
The remainder of this paper is organized as follows. Section 2 describes the background. Section 3 introduces the proposed method. Thereafter, the proposed dataset is presented in Section 4. Section 5 presents the results of the experiments. Finally, Section 6 concludes the paper.