## 1 Introduction  
As autonomous driving technology continues to evolve and find wider applications, the reliability and safety of unmanned systems in complex environments are garnering increasing attention [1]. Environmental perception is a critical research focus in autonomous driving technology, with most unmanned systems relying on a combination of visual and radar sensing systems [2, 3]. In certain special circumstances, it is necessary to process images captured by cameras to mitigate the effects of poor visibility, such as in adverse weather conditions like fog or dust storms [4]. One of the primary tasks of the visual system is to identify drivable areas under various road conditions to determine a safe driving route. However, existing road recognition algorithms for autonomous driving primarily focus on lane detection [5] and are designed for favorable road conditions, with limited research on compatibility across different road conditions. Therefore, identifying drivable areas on unpaved roads is crucial for unmanned systems. Current unstructured road recognition algorithms [6] do not integrate dehazing techniques to handle varying visibility conditions.  
Deep learning [7] can be utilized to develop autonomous driving technology capable of unstructured road recognition under diverse visibility conditions, thereby improving system reliability and safety in complex environments and expanding its application scope. In the design of road condition feature extraction modules, the traditional Pyramid Scene Parsing Network (PSP-Net) [8] proposes a pyramid pooling structure that balances semantic information while being relatively fast, though it performs poorly in handling details. U-Net [9, 10] improves accuracy through skip connections, but its computational complexity and time cost are high. DeepLabv3+ [11] outperforms the aforementioned methods in both effectiveness and efficiency, but it is prone to overfitting, leading to mis-segmentation of small areas, which affects detection accuracy. These issues are examined in the experimental section, where a comparison with the proposed improved method is presented.  
In image dehazing, the Dark Channel Prior (DCP) method [12] is a more traditional approach but suffers from edge effects and performs poorly in estimating atmospheric light in sky regions [13]. Cycle-GAN [14; 15] employs two generators and two discriminators for adversarial training, generating more realistic images; however, this can cause image distortion, which negatively impacts subsequent detection. AOD-Net [16] is a lightweight dehazing network that offers fast detection and strong robustness post-training. However, due to the lack of specialized loss functions and a realistic, effective dataset, its dehazing capability is limited, performing well under light fog conditions but less effectively under dense fog or with greater depth of field. These limitations are reflected in the algorithm comparison section of the experiments. Consequently, the road detection algorithm and the semantic segmentation model trained in this study, which integrates dehazing and drivable area recognition, have significant practical value. The specific process of the proposed algorithm is as follows (Figure 1).