Reproduce advay summer 2025

.gitignore

@@ -152,6 +152,10 @@ dmypy.json
 # Cython debug symbols
 cython_debug/
 
+# ROAR competition run instrumentation
+competition_code/telemetry/runs/
+experiment_results/
+
 # PyCharm
 #  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
 #  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore

competition_code/LateralController.py

@@ -0,0 +1,42 @@
+import numpy as np
+import math
+
+
+def normalize_rad(rad: float):
+    return rad % (2 * np.pi)
+
+
+class LatController:
+    def run(self, vehicle_location, vehicle_rotation, next_waypoint) -> float:
+        """
+        Calculates the steering command using the pure pursuit algorithm.
+
+        Args:
+            vehicle_location (np.array): Current vehicle location [x, y].
+            vehicle_rotation (float): Current vehicle rotation (yaw) in radians.
+            next_waypoint (Waypoint): Next waypoint to track.
+
+        Returns:
+            steering_command (float): Steering command in radians.
+        """
+
+        # Calculate vector pointing from vehicle to next waypoint
+        waypoint_vector = np.array(next_waypoint.location) - np.array(vehicle_location)
+
+        # Project waypoint vector onto heading vector to find lookahead point
+        distance_to_waypoint = np.linalg.norm(waypoint_vector)
+        if distance_to_waypoint == 0:
+            return 0  # Prevent division by zero
+
+        waypoint_vector_normalized = waypoint_vector / distance_to_waypoint
+
+        # Calculate steering command
+        alpha = normalize_rad(vehicle_rotation[2]) - normalize_rad(
+            math.atan2(waypoint_vector_normalized[1], waypoint_vector_normalized[0])
+        )
+
+        steering_command = 1.5 * math.atan2(
+            2.0 * 4.7 * math.sin(alpha) / distance_to_waypoint, 1.0
+        )
+
+        return float(steering_command)

competition_code/SpeedData.py

@@ -0,0 +1,9 @@
+class SpeedData:
+    def __init__(
+        self, distance_to_section, current_speed, target_speed, recommended_speed
+    ):
+        self.current_speed = current_speed
+        self.distance_to_section = distance_to_section
+        self.target_speed_at_distance = target_speed
+        self.recommended_speed_now = recommended_speed
+        self.speed_diff = current_speed - recommended_speed