Planning and decision-making are critical aspects of autonomous vehicle technology, enabling safe, efficient, and reliable navigation in dynamic environments. This study explores advanced algorithms and strategies for path planning, obstacle avoidance, and real-time decision-making under uncertainty. It highlights the integration of sensor data, machine learning models, and optimization techniques to enhance situational awareness and adapt to complex traffic scenarios. Additionally, the paper reviews recent developments in ethical decision-making frameworks and regulatory challenges associated with autonomous systems. The findings contribute to the development of robust methodologies that support the deployment of autonomous vehicles in real-world conditions.