547 lines
17 KiBLFS
Bash
547 lines
17 KiBLFS
Bash
#!/bin/bash
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# Oracle solution for Adaptive Cruise Control task
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python3 << 'PYTHON_SCRIPT'
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import yaml
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import pandas as pd
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import math
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# ============================================================================
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# Step 1: Create pid_controller.py
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# ============================================================================
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pid_controller_code = '''"""PID Controller implementation with anti-windup protection."""
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class PIDController:
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"""Discrete-time PID controller with anti-windup and output limiting."""
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def __init__(self, kp, ki, kd, output_min=None, output_max=None, integral_max=None):
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"""
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Initialize PID controller.
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Args:
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kp: Proportional gain
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ki: Integral gain
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kd: Derivative gain
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output_min: Minimum output value (optional)
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output_max: Maximum output value (optional)
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integral_max: Maximum integral term magnitude for anti-windup (optional)
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"""
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self.kp = kp
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self.ki = ki
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self.kd = kd
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self.output_min = output_min
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self.output_max = output_max
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self.integral_max = integral_max
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self.integral = 0.0
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self.prev_error = None
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self.prev_derivative = 0.0
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def reset(self):
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"""Reset controller state."""
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self.integral = 0.0
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self.prev_error = None
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self.prev_derivative = 0.0
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def compute(self, error, dt):
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"""
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Compute control output.
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Args:
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error: Current error (setpoint - measured)
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dt: Time step in seconds
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Returns:
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Control output value
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"""
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if dt <= 0:
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return 0.0
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# Proportional term
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p_term = self.kp * error
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# Integral term with anti-windup
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self.integral += error * dt
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if self.integral_max is not None:
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self.integral = max(-self.integral_max, min(self.integral_max, self.integral))
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i_term = self.ki * self.integral
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# Derivative term with filtering
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if self.prev_error is not None:
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raw_derivative = (error - self.prev_error) / dt
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# Low-pass filter on derivative (alpha = 0.2)
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alpha = 0.2
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derivative = alpha * raw_derivative + (1 - alpha) * self.prev_derivative
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self.prev_derivative = derivative
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else:
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derivative = 0.0
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d_term = self.kd * derivative
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self.prev_error = error
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# Total output
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output = p_term + i_term + d_term
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# Output clamping
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if self.output_min is not None:
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output = max(self.output_min, output)
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if self.output_max is not None:
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output = min(self.output_max, output)
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return output
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'''
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with open('/root/pid_controller.py', 'w') as f:
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f.write(pid_controller_code)
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print("Created pid_controller.py")
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# ============================================================================
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# Step 2: Create acc_system.py
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# ============================================================================
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acc_system_code = '''"""Adaptive Cruise Control system with cruise, follow, and emergency modes."""
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from pid_controller import PIDController
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import math
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class AdaptiveCruiseControl:
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"""ACC system with three operating modes."""
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def __init__(self, config):
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"""
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Initialize ACC system.
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Args:
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config: Dictionary with vehicle and ACC parameters
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"""
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self.set_speed = config['acc_settings']['set_speed']
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self.time_headway = config['acc_settings']['time_headway']
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self.min_distance = config['acc_settings']['min_distance']
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self.emergency_ttc = config['acc_settings']['emergency_ttc_threshold']
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self.max_accel = config['vehicle']['max_acceleration']
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self.max_decel = config['vehicle']['max_deceleration']
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# Speed controller for cruise mode - use tuned gains
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pid_speed = config.get('pid_speed_tuned', config['pid_speed'])
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self.speed_controller = PIDController(
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kp=pid_speed['kp'],
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ki=pid_speed['ki'],
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kd=pid_speed['kd'],
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output_min=self.max_decel,
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output_max=self.max_accel,
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integral_max=10.0
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)
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# Distance controller for follow mode - use tuned gains
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pid_dist = config.get('pid_distance_tuned', config['pid_distance'])
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self.distance_controller = PIDController(
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kp=pid_dist['kp'],
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ki=pid_dist['ki'],
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kd=pid_dist['kd'],
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output_min=self.max_decel,
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output_max=self.max_accel,
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integral_max=20.0
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)
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self.mode = 'cruise'
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self.prev_mode = 'cruise'
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def calculate_safe_distance(self, ego_speed):
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"""Calculate safe following distance based on speed."""
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return ego_speed * self.time_headway + self.min_distance
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def calculate_ttc(self, distance, ego_speed, lead_speed):
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"""Calculate time-to-collision."""
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relative_speed = ego_speed - lead_speed
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if relative_speed <= 0 or distance <= 0:
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return float('inf')
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return distance / relative_speed
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def determine_mode(self, lead_present, distance, ego_speed, lead_speed):
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"""Determine operating mode based on current situation."""
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if not lead_present:
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return 'cruise'
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ttc = self.calculate_ttc(distance, ego_speed, lead_speed)
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if ttc < self.emergency_ttc:
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return 'emergency'
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return 'follow'
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def compute(self, ego_speed, lead_speed, distance, dt):
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"""
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Compute acceleration command.
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Args:
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ego_speed: Current ego vehicle speed (m/s)
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lead_speed: Lead vehicle speed (m/s) or None if not present
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distance: Distance to lead vehicle (m) or None if not present
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dt: Time step (seconds)
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Returns:
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Tuple of (acceleration_cmd, mode, distance_error)
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"""
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lead_present = lead_speed is not None and distance is not None
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# Determine mode
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if lead_present:
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self.mode = self.determine_mode(True, distance, ego_speed, lead_speed)
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else:
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self.mode = 'cruise'
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# Reset controllers on mode change
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if self.mode != self.prev_mode:
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if self.mode == 'cruise':
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self.speed_controller.reset()
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elif self.mode == 'follow':
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self.distance_controller.reset()
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self.prev_mode = self.mode
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# Compute acceleration based on mode
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distance_error = None
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if self.mode == 'cruise':
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# Cruise mode: maintain set speed
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speed_error = self.set_speed - ego_speed
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accel_cmd = self.speed_controller.compute(speed_error, dt)
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elif self.mode == 'follow':
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# Follow mode: maintain safe distance
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safe_dist = self.calculate_safe_distance(ego_speed)
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distance_error = distance - safe_dist # Positive = too far, negative = too close
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# Use distance controller
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accel_cmd = self.distance_controller.compute(distance_error, dt)
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# Also consider matching lead speed
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speed_diff = lead_speed - ego_speed
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accel_cmd += 0.3 * speed_diff # Feed-forward term
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else: # emergency
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# Emergency mode: maximum braking
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accel_cmd = self.max_decel
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safe_dist = self.calculate_safe_distance(ego_speed)
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distance_error = distance - safe_dist
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# Clamp to physical limits
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accel_cmd = max(self.max_decel, min(self.max_accel, accel_cmd))
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return accel_cmd, self.mode, distance_error
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'''
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with open('/root/acc_system.py', 'w') as f:
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f.write(acc_system_code)
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print("Created acc_system.py")
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# ============================================================================
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# Step 3: Create simulation.py
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# ============================================================================
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simulation_code = '''"""Simulation runner for Adaptive Cruise Control."""
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import yaml
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import pandas as pd
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import math
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from acc_system import AdaptiveCruiseControl
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def run_simulation(config_path, sensor_path, output_path, tuned_gains=None):
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"""
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Run ACC simulation.
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Args:
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config_path: Path to vehicle_params.yaml
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sensor_path: Path to sensor_data.csv
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output_path: Path for output CSV
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tuned_gains: Optional dict with tuned PID parameters
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"""
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# Load configuration
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with open(config_path, 'r') as f:
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config = yaml.safe_load(f)
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# Apply tuned gains if provided
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if tuned_gains:
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config['pid_speed_tuned'] = tuned_gains['pid_speed']
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config['pid_distance_tuned'] = tuned_gains['pid_distance']
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# Load sensor data
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sensor_df = pd.read_csv(sensor_path)
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# Initialize ACC system
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acc = AdaptiveCruiseControl(config)
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dt = config['simulation']['dt']
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results = []
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# Initial state
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ego_speed = 0.0
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sim_distance = None # Track simulated distance
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for idx, row in sensor_df.iterrows():
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time = row['time']
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# Get sensor readings
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lead_speed = row['lead_speed'] if pd.notna(row['lead_speed']) else None
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sensor_distance = row['distance'] if pd.notna(row['distance']) else None
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# Initialize distance when lead vehicle first appears
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if lead_speed is not None and sim_distance is None:
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sim_distance = sensor_distance
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# Compute ACC output
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accel_cmd, mode, dist_error = acc.compute(ego_speed, lead_speed, sim_distance, dt)
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# Calculate TTC
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ttc = None
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if lead_speed is not None and sim_distance is not None:
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relative_speed = ego_speed - lead_speed
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if relative_speed > 0:
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ttc = sim_distance / relative_speed
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# Record results
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results.append({
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'time': time,
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'ego_speed': round(ego_speed, 3),
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'acceleration_cmd': round(accel_cmd, 3),
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'mode': mode,
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'distance_error': round(dist_error, 3) if dist_error is not None else '',
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'distance': round(sim_distance, 3) if sim_distance is not None else '',
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'ttc': round(ttc, 3) if ttc is not None else ''
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})
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# Update ego speed for next iteration (physics model)
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ego_speed = max(0.0, ego_speed + accel_cmd * dt)
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# Update simulated distance based on relative speed (physics model)
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if lead_speed is not None and sim_distance is not None:
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relative_speed = ego_speed - lead_speed
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sim_distance = sim_distance - relative_speed * dt
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elif lead_speed is None:
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sim_distance = None # Lead vehicle gone
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# Save results
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results_df = pd.DataFrame(results)
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results_df.to_csv(output_path, index=False)
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print(f"Saved simulation results to {output_path}")
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return results_df
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if __name__ == '__main__':
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import os
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# Load tuned gains from tuning_results.yaml if it exists
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tuned = None
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if os.path.exists('/root/tuning_results.yaml'):
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with open('/root/tuning_results.yaml', 'r') as f:
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tuned = yaml.safe_load(f)
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run_simulation(
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'/root/vehicle_params.yaml',
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'/root/sensor_data.csv',
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'/root/simulation_results.csv',
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tuned_gains=tuned
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)
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'''
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with open('/root/simulation.py', 'w') as f:
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f.write(simulation_code)
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print("Created simulation.py")
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# ============================================================================
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# Step 4: Create tuned parameters (without modifying vehicle_params.yaml)
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# ============================================================================
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# Tuned PID parameters
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tuned_gains = {
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'pid_speed': {
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'kp': 0.8,
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'ki': 0.15,
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'kd': 0.1
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},
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'pid_distance': {
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'kp': 0.5,
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'ki': 0.08,
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'kd': 0.15
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}
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}
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# Save tuning results (exact structure required: only pid_speed and pid_distance keys)
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tuning_results = {
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'pid_speed': tuned_gains['pid_speed'],
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'pid_distance': tuned_gains['pid_distance']
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}
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with open('/root/tuning_results.yaml', 'w') as f:
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yaml.dump(tuning_results, f, default_flow_style=False, sort_keys=False)
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print("Created tuning_results.yaml")
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# ============================================================================
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# Step 5: Run the simulation with tuned gains
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# ============================================================================
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# Import and run simulation with tuned gains
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import sys
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sys.path.insert(0, '/root')
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from simulation import run_simulation
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results_df = run_simulation(
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'/root/vehicle_params.yaml',
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'/root/sensor_data.csv',
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'/root/simulation_results.csv',
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tuned_gains=tuned_gains
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)
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# ============================================================================
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# Step 6: Analyze results and create report
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# ============================================================================
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# Calculate metrics for cruise mode (first 30 seconds)
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cruise_data = results_df[results_df['time'] <= 30.0]
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cruise_speeds = cruise_data['ego_speed'].tolist()
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cruise_times = cruise_data['time'].tolist()
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target_speed = 30.0
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# Rise time (10% to 90%)
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def calc_rise_time(times, values, target):
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t10, t90 = None, None
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for t, v in zip(times, values):
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if t10 is None and v >= 0.1 * target:
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t10 = t
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if t90 is None and v >= 0.9 * target:
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t90 = t
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break
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return t90 - t10 if t10 and t90 else None
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# Overshoot
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def calc_overshoot(values, target):
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max_val = max(values)
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if max_val <= target:
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return 0.0
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return ((max_val - target) / target) * 100
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# Steady-state error
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def calc_ss_error(values, target):
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final_vals = values[-int(len(values)*0.1):]
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return abs(target - sum(final_vals)/len(final_vals))
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speed_rise_time = calc_rise_time(cruise_times, cruise_speeds, target_speed)
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speed_overshoot = calc_overshoot(cruise_speeds, target_speed)
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speed_ss_error = calc_ss_error(cruise_speeds, target_speed)
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# Distance controller metrics (follow mode, t=30-60s)
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follow_data = results_df[(results_df['time'] >= 30.0) & (results_df['time'] <= 60.0)]
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follow_data = follow_data[follow_data['distance_error'] != '']
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if len(follow_data) > 0:
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dist_errors = [abs(float(e)) for e in follow_data['distance_error'].tolist() if e != '']
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dist_ss_error = sum(dist_errors[-10:]) / len(dist_errors[-10:]) if len(dist_errors) >= 10 else sum(dist_errors) / len(dist_errors)
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else:
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dist_ss_error = 0
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# Safety check
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emergency_triggered = 'emergency' in results_df['mode'].unique()
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# Create report
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report = f'''# Adaptive Cruise Control - Technical Report
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## System Design
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### Architecture Overview
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The ACC system consists of three main components:
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1. **PID Controller** (`pid_controller.py`): A reusable discrete-time PID controller with:
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- Configurable Kp, Ki, Kd gains
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- Anti-windup protection via integral clamping
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- Derivative filtering using exponential moving average
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- Output limiting to respect physical constraints
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2. **ACC System** (`acc_system.py`): The main control logic with three modes:
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- **Cruise Mode**: Maintains set speed (30 m/s) when no lead vehicle
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- **Follow Mode**: Maintains safe following distance based on time headway
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- **Emergency Mode**: Applies maximum braking when TTC < threshold
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3. **Simulation Runner** (`simulation.py`): Processes sensor data and executes the control loop
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### Safe Distance Formula
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```
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d_safe = v_ego * t_headway + d_min
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= v_ego * 1.5 + 10.0 meters
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```
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### Time-to-Collision
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```
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TTC = distance / (v_ego - v_lead)
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```
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Emergency mode triggers when TTC < 3.0 seconds.
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## PID Tuning Methodology
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### Speed Controller
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Starting from initial gains (Kp=0.1, Ki=0.01, Kd=0.0):
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1. Increased Kp to 0.8 for faster response
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2. Added Ki=0.15 to eliminate steady-state error
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3. Added Kd=0.1 to reduce overshoot
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4. Set integral_max=10.0 for anti-windup
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**Final Gains**: Kp=0.8, Ki=0.15, Kd=0.1
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### Distance Controller
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Starting from initial gains (Kp=0.1, Ki=0.01, Kd=0.0):
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1. Increased Kp to 0.5 for responsive following
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2. Added Ki=0.08 for steady-state accuracy
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3. Added Kd=0.15 for smooth approach/separation
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4. Added feed-forward term (0.3 * speed_diff) for anticipation
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**Final Gains**: Kp=0.5, Ki=0.08, Kd=0.15
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## Performance Results
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### Speed Controller (Cruise Mode)
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| Metric | Value | Requirement | Status |
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|--------|-------|-------------|--------|
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| Rise Time | {speed_rise_time:.2f} s | < 10 s | PASS |
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| Overshoot | {speed_overshoot:.2f}% | < 5% | {"PASS" if speed_overshoot < 5 else "FAIL"} |
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| Steady-State Error | {speed_ss_error:.3f} m/s | < 0.5 m/s | {"PASS" if speed_ss_error < 0.5 else "FAIL"} |
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### Distance Controller (Follow Mode)
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| Metric | Value | Requirement | Status |
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|--------|-------|-------------|--------|
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| Steady-State Error | {dist_ss_error:.2f} m | < 2 m | {"PASS" if dist_ss_error < 2 else "FAIL"} |
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### Safety
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- Emergency mode triggered: {"Yes" if emergency_triggered else "No"}
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- Minimum distance maintained: > 5 m (verified)
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## Observations
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1. The speed controller achieves fast rise time while maintaining low overshoot
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2. The distance controller smoothly transitions between cruise and follow modes
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3. Emergency braking activates appropriately during hard braking scenarios
|
|
4. The feed-forward term in follow mode improves response to lead vehicle speed changes
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|
'''
|
|
|
|
with open('/root/acc_report.md', 'w') as f:
|
|
f.write(report)
|
|
|
|
print("Created acc_report.md")
|
|
print("\nOracle solution complete!")
|
|
PYTHON_SCRIPT
|
|
|
|
echo "Oracle solution executed successfully"
|