Hierarchical Planning in Autonomous Driving

Let’s consider an example of autonomous driving car, here hierarchical planning is employed in the following manner:

1. High-Level Goal: safely navigate from A to B, following the traffic rules
2. Major Steps:
   1. Route Planning: determine optimal route to B
   2. Obstacle Avoidance: identify obstacles like vehicle, people, etc.
   3. Traffic Signal Recognition: detect traffic signals and signs
   4. Lane Keeping: stay in the designated lane and adjust the vehicle’s position to avoid collision
3. Minor Steps:
   1. Route Planning:
      • Map Analysis: analyze maps to find the optimal route
      • Traffic Prediction: predict traffic patterns to avoid traffic jams.
   2. Obstacle Avoidance:
      • Sensor Data Processing: process the data from onboard sensor to detect nearby objects
      • Path Planning: generate paths to avoid obstacles
   3. Traffic Signal Recognition:
      • Image Recognition: analyze images to detect traffic lights
      • Traffic Rule Interpretation: interpret and detect signal to determine the action
   4. Lane Keeping:
      • Lane Detection: use computer vision algorithms to detect lane markings
      • Control Systems: adjust the speed, steering, break command to keep the vehicle within the detected lane.
4. Hierarchical Planning:
   1. First Level Plan: Define the high-level goals and major steps, such as route planning, obstacle avoidance, traffic signal recognition, and lane keeping.
   2. Second Level Plan: Break down each major step into subtasks and minor steps, as described above, to handle the complexity of each component.
   3. Third Level Plan: Further decompose the minor steps into detailed actions and algorithms necessary to execute them effectively.

Hierarchical Planning Techniques in Autonomous Driving

In autonomous driving, hierarchical planning techniques are crucial for safe navigation.

• HTN Planning: Decomposes route planning into subtasks like map analysis and traffic prediction, ensuring optimal routes.
• Hierarchical Reinforcement Learning (HRL): Learns hierarchical policies for obstacle avoidance, adjusting vehicle trajectory to avoid collisions.
• Hierarchical Task Networks (HTNs): Decomposes traffic signal recognition into subtasks for accurate detection and rule interpretation.
• Hierarchical State Space Search: Explores state space of lane keeping, adjusting vehicle commands for effective lane-keeping strategies.

Hierarchical Planning in Artificial Intelligence is a problem-solving and decision-making technique employed to reduce the computational expense associated with planning. The article provides an overview of hierarchical planning in AI, discussing its components, techniques, applications in autonomous driving and robotics, advantages, and challenges.