# Reinforcement Learning Decision-Making for Autonomous Vehicles Based on Semantic Segmentation
## 4 Discussion and Future Works  
While the proposed framework shows improved decision-control performance in simulated autonomous driving tasks, it faces several limitations. The DeepLabv3P segmentation model, although precise, introduces computational overhead that may limit its real-time applicability, especially on embedded systems. The combined pipeline of semantic segmentation and RL also introduces inference delays, which can affect decision-making in time-critical scenarios. The semantic segmentation network is primarily trained on synthetic CARLA data, which, while diverse, lack the noise and variability of real-world environments. This domain gap may reduce the model's effectiveness when deployed in real-world settings. Furthermore, the framework's ability to handle multi-lane changes and complex urban intersections has not been widely tested. Scenarios like highway merging or multi-lane roundabouts with dynamic traffic signals remain challenging. Although CNNs show high performance and generalization in semantic segmentation, their explainability and application-specific evaluation are crucial for ensuring reliable decision-making in autonomous systems. Future work will focus on using visualization techniques, such as Grad-CAM, to analyze the internal decision-making process of the CNN and assess its robustness against noise and adversarial disturbances in input images. Experiments will be conducted to evaluate the performance of the semantic segmentation network in various scenarios, including highway driving and adverse weather conditions, to ensure its adaptability and reliability. Furthermore, the impact of segmentation errors on RL decisions will be analyzed, and mechanisms to mitigate their effect on the overall decision-control framework will be proposed. In this study, semantic segmentation and deep reinforcement learning (DRL) were integrated to enhance decision-control performance in autonomous driving. PPO (Proximal Policy Optimization) and DDQN (Double Deep Q-Network) were chosen as the DRL algorithms due to their simplicity, robustness, and proven effectiveness in simulation-based tasks. While algorithms like SAC (Soft Actor-Critic) and TD3 (Twin Delayed Deep Deterministic Policy Gradient) offer State-of-the-Art performance in continuous action spaces, they are computationally more demanding and were not the primary focus of this work. The choice of PPO and DDQN aligns with the study's objective of demonstrating the impact of semantic segmentation on decision-making performance. Both algorithms serve as strong baselines, allowing for a clear comparison of results with and without segmentation integration. Furthermore, PPO's policy stability and DDQN's suitability for discrete action spaces made them ideal for the experimental setup, where tasks involved categorical decisions such as lane-keeping and turning. The simulation results demonstrate the effectiveness of the proposed framework in improving autonomous driving decision-making control. However, simulation-based evaluation has inherent limitations. Simulators like CARLA provide a safe and cost-effective way to test algorithms across various scenarios, but they may not fully capture the complexity and uncertainty of real-world driving conditions. For example, sensor data in real environments often contain noise and distortion, and the predictability of traffic participants' behavior is lower than in simulated models. Additionally, the performance of the framework relies on the accuracy of the semantic segmentation network, which may degrade under conditions like occlusion or adverse weather. The computational overhead of combining semantic segmentation with reinforcement learning (RL) may limit its deployment on resource-constrained platforms. Future work will address these limitations by conducting real-world experiments to validate the framework's generalizability, improve robustness to sensor noise and environmental uncertainty, and optimize computational efficiency for real-time applications.