# Reinforcement Learning Decision-Making for Autonomous Vehicles Based on Semantic Segmentation
## 1 Introduction
Due to the complexity of scene structural information, constructing rules was challenging, making it difficult to form complete and effective system decision models [8]. Therefore, vehicles in the past primarily relied on advanced driver-assistance systems (ADAS) to actively improve vehicle safety by assisting in acceleration/deceleration and steering control to reduce accidents and injuries [9]. For example, [PERSON] et al. [10] proposed a car-following model that worked in a human-like manner, utilizing speed, relative speed, and vehicle distance. However, ADAS could only be applied to relatively simple scenarios, such as highways. With the development of artificial intelligence in the field of autonomous driving, the understanding and decision-making in complex scenarios were synthesized by neural networks, and traditional advanced driver-assistance systems transitioned to deep reinforcement learning (DRL) agent-based decision-control methods. Reinforcement learning decision algorithms had the advantages of a simple structure and excellent performance [11]. Some studies combined deep reinforcement learning methods with autonomous driving to achieve advanced decision-making in simulation tests [12]. [PERSON] et al. [13] proposed a framework that combined modular approaches with DRL methods to generate DRL strategies for urban driving tasks such as vehicle following and intersection navigation. [PERSON] et al. [14] proposed an end-to-end autonomous driving decision-making method based on an improved TD3 algorithm. This method used forward-facing cameras to capture data and introduced a new critic network to form a triple-critic structure, combining it with target-maximization operations to address the underestimation problem in the TD3 algorithm. [PERSON] et al. [15] utilized forward-facing camera images, current vehicle speed, and steering angles as inputs to train lane-keeping strategies, demonstrating the applicability of DRL in real-world autonomous driving scenarios. [PERSON] et al. [16] introduced a method for safely navigating autonomous vehicles on highways by combining insights from deep Q-networks and control theory. The deep Q-network was trained in simulations and acted as the central decision-making unit by setting targets for the trajectory planner. However, traditional rule-based autonomous driving systems have not been able to effectively construct decision models for complex road conditions, making it difficult to address decision-making demands in urban traffic scenarios. Other reinforcement learning studies have mostly focused on the comparative evaluation of DRL algorithm strategies, which has impacted decision-making accuracy. Given this, this paper integrates semantic segmentation technology with a DRL decision model to propose a decision-control method. It fully leverages semantic segmentation to enhance perception performance, thereby helping the DRL algorithm capitalize on its advantages in strategy update stability and balancing short-term and long-term rewards, ultimately strengthening autonomous driving decision-making capabilities in complex urban traffic environments. This study generates high-quality perception data through semantic segmentation, which provides pixel-level classification of the environment, offering detailed semantic information about drivable areas, obstacles, and dynamic objects. Rule-based methods, due to their reliance on predefined logic, lack of semantic understanding, and inflexibility, are inherently limited in handling complex and dynamic environments. These methods often fail to generalize to new scenarios and are sensitive to noise and edge cases. Semantic segmentation allows for a more comprehensive understanding of the scene, enabling the RL agent to effectively perceive and respond to dynamic and unpredictable situations. This study generates high-quality perception data through semantic segmentation, simplifying the complexity of the state space and significantly improving the efficiency and performance of the reinforcement learning algorithms.