# Reinforcement Learning Decision-Making for Autonomous Vehicles Based on Semantic Segmentation
## 1 Introduction  
In the development of autonomous driving technology, safety is the primary consideration. Decision-control, as one of the core modules of autonomous driving, serves as a bridge between perception and execution, directly influencing the vehicle's behavior decisions and safety in complex traffic environments. To prove that autonomous vehicles were safer than human drivers, approximately five billion miles of road testing were required [1], which consumed substantial human, material, and time resources for real-world testing. With the continuous development of autonomous driving technology, simulation experiments not only save resources but also allow for the simulation of various complex and dangerous traffic scenarios in a virtual environment [2]. Powerful perception and understanding capabilities provide accurate environmental information for simulation-based decision-control, thereby supporting complex decision-making processes. In this process, the application of semantic segmentation network technology becomes an important means of achieving environmental understanding. Semantic segmentation, as one of the technologies in intelligent driving perception and advanced driver-assistance systems (ADAS) [3], has played a role in classifying environmental elements in scene images into various category labels [4]. Compared to single-objectrecognition tasks and image segmentation, scene semantic segmentation technology has provided granular and high-level semantic information for subsequent scene analysis and visual understanding. This has helped autonomous vehicles simplify the extraction of environmental features and achieve an understanding of complex scenes during driving decision-making [5]. In the application of semantic segmentation in autonomous driving scenarios, it was necessary to design semantic segmentation models that balance efficiency and accuracy. [PERSON] et al. [6] proposed a video prediction-based method that expanded the training set by synthesizing new training samples to improve the accuracy of semantic segmentation networks. This method utilized the ability of video prediction models to predict future frames to generate future labels. [PERSON] et al. [7], addressing the issue of semantic segmentation networks being unable to effectively balance segmentation efficiency and accuracy, designed a lightweight semantic segmentation network, the LMBA convolutional unit (LCV), which enabled efficient feature extraction and helped semantic segmentation networks achieve better perception and recognition capabilities in autonomous driving scenarios. However, although the aforementioned studies have improved the accuracy of semantic segmentation in specific scenarios, they have not thoroughly explored its application in perception and recognition within complex road environments, nor have they comprehensively evaluated its practicality and stability. Common perception modules typically relied on raw images or object detection using bounding boxes, which introduced significant limitations in terms of efficiency and semantic richness. Raw images have high dimensionality, making feature extraction computationally expensive and slow to converge. Bounding box methods, while identifying the position and category of objects, failed to capture the detailed spatial and semantic information needed for complex decision-making tasks in autonomous driving. Semantic segmentation addressed these gaps by providing pixel-level classification of the entire scene. This allowed for precise identification of object contours and enabled the separation of drivable and non-drivable areas, pedestrians, vehicles, and other key scene elements. Compared to bounding box methods, semantic segmentation provided richer global scene understanding, reduced input complexity, and enhanced decision robustness, especially in dynamic and complex environments. Traditional autonomous driving systems were rule-based.