The primary goal of planning in autonomous vehicles is to generate a safe and efficient path for the vehicle to follow, taking into account factors such as traffic conditions, road geometry, and the vehicle's own capabilities.

Model Predictive Control (MPC) is a common approach used for planning in autonomous vehicles due to its ability to handle complex constraints and its online optimization capabilities.

Decision making in autonomous vehicles involves selecting the best action to take given the current state of the vehicle and the environment, such as changing lanes, overtaking other vehicles, or adjusting the vehicle's speed.

Autonomous vehicles may employ a combination of rule-based decision making, machine learning-based decision making, and human-in-the-loop decision making, depending on the specific situation and the level of autonomy.

Planning and decision making for autonomous vehicles in dynamic and uncertain environments pose several challenges, including dealing with incomplete and noisy sensor data, handling complex traffic scenarios and interactions with other vehicles, and predicting the behavior of other road users.

Autonomous vehicles can improve their planning and decision-making capabilities by utilizing high-resolution maps and sensor data, employing advanced machine learning algorithms, and incorporating human feedback and experience.

Sensor fusion in planning and decision making for autonomous vehicles aims to combine data from multiple sensors, such as cameras, radar, and lidar, to create a more comprehensive and accurate representation of the environment, which can then be used for better decision-making.

Path planning for autonomous vehicles considers the vehicle's kinematics and dynamics by taking into account factors such as the vehicle's acceleration, velocity, turning radius, and energy consumption, as well as ensuring that the path is collision-free.

Trajectory optimization in planning and decision making for autonomous vehicles aims to find a path that minimizes the vehicle's travel time, energy consumption, and ensures safety and collision avoidance.

Decision making in autonomous vehicles handles uncertain and dynamic environments by employing probabilistic models to predict the behavior of other road users, using reinforcement learning to adapt to changing conditions, and incorporating human feedback and experience.

Risk assessment in planning and decision making for autonomous vehicles involves identifying potential hazards and risks in the environment, evaluating their likelihood and severity, and determining the best course of action to mitigate these risks.

Planning and decision making in autonomous vehicles address ethical considerations by prioritizing the safety of human life, considering the impact of decisions on other road users and the environment, and ensuring transparency and accountability in decision-making processes.

Planning and decision making for autonomous vehicles in urban environments face several challenges, including dealing with dense traffic and complex intersections, navigating through narrow streets and pedestrian areas, and coordinating with other vehicles and infrastructure.

Planning and decision making in autonomous vehicles handle adverse weather conditions by adjusting the vehicle's speed and acceleration, using sensors and algorithms to detect and respond to weather-related hazards, and incorporating weather forecasts and road condition data into decision-making.

Current limitations and future research directions in planning and decision making for autonomous vehicles include improving the accuracy and robustness of sensor data and perception algorithms, developing more efficient and scalable planning and decision-making algorithms, and addressing ethical and legal challenges related to autonomous vehicle decision-making.