Lab A: Agentic Driving Coach

Agent type: Large Language Model (LLM) agent

Goal

Place a small local LLM in the control loop of a simulated car approaching a stop sign. The coach reasons about the situation and produces driving guidance or emergency actuation while the system tracks both logical time and real inference latency.

The central question is not only whether an LLM response is sensible, but also whether it arrives in time to affect the physical outcome. Late, malformed, or unsafe responses trigger a deterministic fallback.

System topology

The driver, car, environment, hierarchical coach, inference deadline race, and planner form a closed loop. Driver commands cross 500 ms logical delays, while coach emergency actuation returns over a 200 ms delay. Click the diagram for the full-size topology from the project repository.

Focus

• LLM reasoning inside a cyber-physical control loop
• Xronos reactors, logical time, wall-clock lag, and delayed connections
• Inference deadlines, response validation, and deterministic fallback
• Reproducible comparison of model latency, output quality, and stopping outcome

What you will do

1. Build timing intuition with small Xronos reactor exercises.
2. Run deterministic rule and replay baselines.
3. Compare live local LLMs under a fixed inference deadline.
4. Analyze how driver behavior, model timing, and fallback decisions change the simulated car’s physical outcome.

Learning outcomes

By the end of this lab, you should be able to:

• distinguish logical time from wall-clock inference time, lag, and slack;
• measure latency, deadline misses, and malformed model responses;
• explain why deterministic fallback is needed in an agentic control loop; and
• connect software timing and model behavior to a physical stopping result.

Project repository

Use the public embodied-ai-lab/agentic-driving-coach template to create your group’s private repository. Read its README.md for the ASU Sol workflow and its ASSIGNMENT.md for the current tasks, deliverables, and rubric.