Simulate Real-time Obstacle Reaction for DecisionMaker Agent

Design a specific simulation scenario where a sudden, unexpected obstacle (e.g., a child running into the street) appears. Implement the logic for your `SensorInterpreter` to detect this event and for the `DecisionMaker` agent (using Claude 4 Sonnet, deployed on Cerebrium) to react appropriately and safely. Evaluate its response time and outcome (e.g., collision avoided, emergency braking performed). Add this scenario to your MLflow tracking. ```python
# Assuming a simulated environment function
def simulate_obstacle_event(current_speed: float, distance_to_obstacle: float) -> dict: # Simulate sensor data and environment update return {"obstacle_detected": True, "distance": distance_to_obstacle, "speed_obstacle": 0.0} def run_obstacle_scenario(config: dict): with mlflow.start_run(run_name="Obstacle_Reaction_Test"): # Specific MLflow run mlflow.log_params(config) # Simulate initial state # sensor_data = {"road_clear": True, "speed": 60.0} # path_plan = ModalGPT5ProClient().plan_path(sensor_data, ...) # Trigger obstacle event after some time event_data = simulate_obstacle_event(60.0, 20.0) # 20 meters ahead # Simulate DecisionMaker's reaction using CerebriumClaude4SonnetClient # decision = CerebriumClaude4SonnetClient().make_tactical_decision("obstacle_ahead", path_plan_segment) # Log metrics mlflow.log_metric("reaction_time_ms", random.randint(100, 300)) mlflow.log_metric("collision_avoided", True) mlflow.log_metric("final_speed", random.uniform(0.0, 10.0)) # Log conversation or decision details
```