This paper presents a comprehensive and quantitative framework for enhancing the safety of autonomous vehicles by integrating sensor detection performance with braking dynamics under extreme weather conditions and variable road slopes. Using high-fidelity simulations in CARLA and PyChrono, extreme weather datasets are generated, sensor detection distances are evaluated and braking distances are simulated under varying friction coefficients and slopes. This study quantifies the relationship between detection distance and safe braking performance, establishing objective metrics for the dynamic Operational Design Domain of AVs. Furthermore, an adaptive speed control strategy based on reinforcement learning — implemented via the Soft Actor-Critic algorithm — is proposed, which continuously adjusts vehicle speed to ensure that braking distances remain within sensor detection limits. Experimental results demonstrate that models trained with extreme weather data significantly outperform general-purpose detectors and that integrating real-time braking distance predictions into the control strategy improves safety margins and operational efficiency across diverse driving scenarios.