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+# Test Safety and Comfort Metrics Documentation
+
+## Files/Scripts
+- `testing/test_comfort_metrics.py`
+
+## Purpose
+
+This script analyzes log files and reports vehicle comfort and pedestrian safety with plots. It extracts:
+
+- **Vehicle Data:** Time, acceleration, heading rate from `behavior.json`.
+- **Pedestrian Data:** Time and pedestrian distance to car from `behavior.json`.
+- **Pure Pursuit Tracker Data (Optional):** Vehicle time, cross-track error, and position (actual vs. desired) from `PurePursuitTrajectoryTracker_debug.csv`.
+
+The script will include 5 plots:
+- Vehicle jerk vs. time.
+- Vehicle heading acceleration vs. time.
+- Vehicle cross-track error vs. time.
+- Vehicle actual vs. desired position.
+- Pedestrian distance vs. time.
+
+The script also prints key metrics:
+- RMS Jerk
+- RMS Heading acceleration
+- RMS Cross track error
+- RMS Position error
+- Minimum pedestrian distance to car
+
+## Usage
+
+1. **Check log directory:**
+   - Ensure your log directory contains `behavior.json` (required).
+   - Optionally include `PurePursuitTrajectoryTracker_debug.csv` (if missing, some plots are skipped).
+
+2. **Run the script:**
+
+   ```bash
+   python test_comfort_metrics.py <log_directory>
+   ```
+   Replace `<log_directory>` with the path to the folder containing the log files.
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diff --git a/testing/test_comfort_metrics.py b/testing/test_comfort_metrics.py
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+++ b/testing/test_comfort_metrics.py
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+import sys
+import os
+sys.path.append(os.getcwd())
+
+from GEMstack.state import AgentEnum
+import json
+import matplotlib.pyplot as plt
+import numpy as np
+
+CMAP = "RdYlGn"
+
+def compute_safety_factor(value, safe_thresh, unsafe_thresh, flip=False):
+    """
+    Computes a safety factor between 0(unsafe) and 1(safe)
+    If flip is True, the threshold bounds are reversed.
+    """
+    abs_val = abs(value)
+    if abs_val <= safe_thresh:
+        factor = 1.0
+    elif abs_val >= unsafe_thresh:
+        factor = 0.0
+    else:
+        factor = 1.0 - (abs_val - safe_thresh) / (unsafe_thresh - safe_thresh)
+    
+    if flip:
+        return 1.0 - factor
+    return factor
+
+def parse_behavior_log(filename):
+    """
+    Parses the behavior log file and extracts the following data:
+    - vehicle time
+    - vehicle acceleration
+    - vehicle heading rate
+    - pedestrian time
+    - pedestrian distance
+    """
+    times = []
+    accelerations = []
+    heading_rates = []
+    pedestrian_times = []
+    pedestrian_distances = []
+    
+    with open(filename, 'r') as f:
+        for line in f:
+            try:
+                entry = json.loads(line)
+            except json.JSONDecodeError:
+                print(f"Skipping invalid JSON line: {line.strip()}")
+                continue
+            # Process vehicle state data
+            if "vehicle" in entry:
+                t = entry.get("time")
+                vehicle_data = entry["vehicle"].get("data", {})
+                acceleration = vehicle_data.get("acceleration")
+                heading_rate = vehicle_data.get("heading_rate")
+                # Only add if all fields are available
+                if None not in (t, acceleration, heading_rate):
+                    times.append(t)
+                    accelerations.append(acceleration)
+                    heading_rates.append(heading_rate)
+            # Process agent state data
+            if "agents" in entry:
+                for agent in entry["agents"].values():
+                    agent_data = agent.get("data", {})
+                    # Check if the agent is a pedestrian
+                    if agent_data.get("type") == AgentEnum.PEDESTRIAN.value:
+                        t = entry.get("time")
+                        pose = agent_data.get("pose", {})
+                        x_agent = pose.get("x")
+                        y_agent = pose.get("y")
+                        if None not in (t, x_agent, y_agent):
+                            pedestrian_times.append(t)
+                            dist = np.sqrt(x_agent**2 + y_agent**2)
+                            pedestrian_distances.append(dist)
+    
+    return (np.array(times), np.array(accelerations), np.array(heading_rates), 
+            np.array(pedestrian_times), np.array(pedestrian_distances))
+
+def parse_tracker_csv(filename):
+    """
+    Parses the pure pursuit tracker log file and extracts the following data:
+      - vehicle time (from column index 18)
+      - Crosstrack error (from column index 20)
+      - X position actual (from column index 2)
+      - Y position actual (from column index 5)
+      - X position desired (from column index 11)
+      - Y position desired (from column index 14)
+    """
+    data = np.genfromtxt(filename, delimiter=',', skip_header=1)
+    vehicle_time = data[:, 18]
+    cte = data[:, 20]
+    x_actual, y_actual = data[:, 2], data[:, 5]
+    x_desired, y_desired = data[:, 11], data[:, 14]
+    return vehicle_time, cte, x_actual, y_actual, x_desired, y_desired
+
+def compute_derivative(times, values):
+    """
+    Computes the derivative of array with respect to time.
+    Returns the time array and derivative values.
+    """
+    dt = np.diff(times)
+    dv = np.diff(values)
+    derivative = dv / dt
+    return times[1:], derivative
+
+def add_safety_colorbar(figure):
+    """Adds a colorbar to the right of the figure"""
+    cbar_ax = figure.add_axes([0.92, 0.2, 0.02, 0.6])
+    sm = plt.cm.ScalarMappable(cmap=CMAP)
+    cbar = figure.colorbar(sm, cax=cbar_ax)
+    cbar.set_label("Comfort/Safety Level")
+
+def plot_metrics(time_jerk, jerk, time_heading_acc, heading_acc, vehicle_time, cte, 
+                 x_actual, y_actual, x_desired, y_desired, pedestrian_times, pedestrian_distances):
+    """Plots all metrics in 2x3 grid"""
+    fig, axs = plt.subplots(2, 3, figsize=(12, 8))
+    fig.subplots_adjust(hspace=0.375, wspace=0.35)
+    axs[1,2].axis('off')
+
+    plot_jerk(axs[0,0], time_jerk, jerk)
+    plot_heading_acceleration(axs[0,1], time_heading_acc, heading_acc)
+    plot_crosstrack_error(axs[1,0], vehicle_time, cte)
+    plot_position(axs[1,1], x_actual, y_actual, x_desired, y_desired)
+    plot_pedestrian_dist(axs[0,2], pedestrian_times, pedestrian_distances)
+    
+    # Colorbar on the right side
+    add_safety_colorbar(fig)
+    
+    plt.show()
+
+def plot_jerk(axis, time, jerk, safe_thresh=1.0, unsafe_thresh=2.5):
+    """Plots vehicle jerk (rate of acceleration) vs. time"""
+    safety_scores = np.vectorize(compute_safety_factor)(jerk, safe_thresh, unsafe_thresh)
+    
+    axis.plot(time, jerk, color="black", linewidth=0.8, alpha=0.5)
+    axis.scatter(time, jerk, c=safety_scores, cmap=CMAP, vmin=0, vmax=1, edgecolors="black")
+
+    axis.set_xlabel("Time (s)")
+    axis.set_ylabel("Jerk (m/s³)")
+    axis.set_title("Vehicle Jerk Over Time")
+    axis.grid(True)
+
+def plot_heading_acceleration(axis, time, heading_acc, safe_thresh=0.0075, unsafe_thresh=0.25):
+    """Plots vehicle heading acceleration vs. time"""
+    safety_scores = np.vectorize(compute_safety_factor)(heading_acc, safe_thresh, unsafe_thresh)
+    
+    axis.plot(time, heading_acc, color="black", linewidth=0.8, alpha=0.5)
+    axis.scatter(time, heading_acc, c=safety_scores, cmap=CMAP, vmin=0, vmax=1, edgecolors="black")
+    
+    axis.set_xlabel("Time (s)")
+    axis.set_ylabel("Heading Acceleration (rad/s²)")
+    axis.set_title("Vehicle Heading Acceleration Over Time")
+    axis.grid(True)
+
+def plot_crosstrack_error(axis, time, cte, safe_thresh=0.1, unsafe_thresh=0.4):
+    """Plots vehicle cross track error vs. time"""
+    safety_scores = np.vectorize(compute_safety_factor)(cte, safe_thresh, unsafe_thresh)
+    
+    axis.plot(time, cte, color="black", linewidth=0.8, alpha=0.5)
+    axis.scatter(time, cte, c=safety_scores, cmap=CMAP, vmin=0, vmax=1, edgecolors="black")
+    
+    axis.set_xlabel("Time (s)")
+    axis.set_ylabel("Cross Track Error")
+    axis.set_title("Vehicle Cross Track Error Over Time")
+    axis.grid(True)
+
+def plot_position(axis, x_actual, y_actual, x_desired, y_desired, safe_thresh=1, unsafe_thresh=2.5):
+    """Plots vehicle actual and desired positions vs. time"""
+    position_error = np.sqrt((x_desired - x_actual) ** 2 + (y_desired - y_actual) ** 2)
+    safety_scores = np.vectorize(compute_safety_factor)(position_error, safe_thresh, unsafe_thresh)
+    
+    axis.plot(y_desired, x_desired, marker='.', linestyle=':', color='blue', label='Desired')
+    axis.plot(y_actual, x_actual, color="black", linewidth=0.8, alpha=0.5)
+    axis.scatter(y_actual, x_actual, c=safety_scores, cmap=CMAP, vmin=0, vmax=1, edgecolors="black")
+    
+    axis.set_xlabel("Y Position (m)")
+    axis.set_ylabel("X Position (m)")
+    axis.set_title("Vehicle Position vs. Desired Position")
+    axis.legend()
+    axis.grid(True)
+
+def plot_pedestrian_dist(axis, pedestrian_times, pedestrian_distances, safe_thresh=5.0, unsafe_thresh=2.0):
+    """Plots pedestrian distance to vehicle vs. time"""
+    if len(pedestrian_times) > 0:
+        safety_scores = np.vectorize(compute_safety_factor)(pedestrian_distances, safe_thresh, unsafe_thresh, flip=True)
+        axis.plot(pedestrian_times, pedestrian_distances, color="black", linewidth=0.8, alpha=0.5)
+        axis.scatter(pedestrian_times, pedestrian_distances, c=safety_scores, cmap=CMAP, vmin=0, vmax=1, edgecolors="black")
+    
+    axis.set_xlabel("Time (s)")
+    axis.set_ylabel("Pedestrian Distance (m)")
+    axis.set_title("Pedestrian Distance Over Time")
+    axis.grid(True)
+
+if __name__=='__main__':
+    if len(sys.argv) != 2:
+        print("Usage: python test_comfort_metrics.py <log_directory>")
+        sys.exit(1)
+    
+    log_dir = sys.argv[1]
+    behavior_file = os.path.join(log_dir, "behavior.json")
+    tracker_file = os.path.join(log_dir, "PurePursuitTrajectoryTracker_debug.csv")
+    
+    # if behavior.json doesn't exist, print error and exit
+    if not os.path.exists(behavior_file):
+        print("Error: behavior.json file is missing in log folder.")
+        sys.exit(1)
+    
+    # Parse behavior log file and compute metrics
+    times, accelerations, heading_rates, ped_times, ped_distances = parse_behavior_log(behavior_file)
+    time_jerk, jerk = compute_derivative(times, accelerations)
+    time_heading_acc, heading_acc = compute_derivative(times, heading_rates)
+    
+    # Pure pursuit tracker file exists: parse and plot all metrics
+    if os.path.exists(tracker_file):
+        vehicle_time, cte, x_actual, y_actual, x_desired, y_desired = parse_tracker_csv(tracker_file)
+        plot_metrics(time_jerk, jerk, time_heading_acc, heading_acc, vehicle_time, cte, 
+                     x_actual, y_actual, x_desired, y_desired, ped_times, ped_distances)
+        
+        print("RMS Cross Track Error:", np.sqrt(np.mean(cte**2)), "m")
+        print("RMS Position Error:", np.sqrt(np.mean((x_actual - x_desired)**2 + (y_actual - y_desired)**2)), 'm')
+    # Pure pursuit tracker file is missing: plot only behavior.json metrics
+    else:
+        print("Tracker file is missing. Skipping cross track error and vehicle position plots.")
+        # Plot only jerk, heading acceleration, and pedestrian distance
+        fig, axs = plt.subplots(1, 3, figsize=(12, 4))
+        plot_jerk(axs[0], time_jerk, jerk)
+        plot_heading_acceleration(axs[1], time_heading_acc, heading_acc)
+        plot_pedestrian_dist(axs[2], ped_times, ped_distances)
+        add_safety_colorbar(fig)
+        plt.show()
+        
+    print("RMS Jerk:", np.sqrt(np.mean(jerk**2)), "m/s³")
+    print("RMS Heading Acceleration:", np.sqrt(np.mean(heading_acc**2)), "rad/s²")
+    if len(ped_distances) > 0:
+        print("Minimum Pedestrian Distance:", np.min(ped_distances), "m")
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