Adaptive Cruise Control

Adaptive Cruise Control System Simulation

Overview

This project focuses on the analysis and evaluation of an adaptive cruise control (ACC) system under dynamic traffic conditions. The simulation is implemented using MATLAB and Simulink, integrating multiple components like sensors, controllers, and vehicle dynamics to ensure accurate performance evaluation.

Key Objectives

1. Optimize fuel consumption.
2. Enhance driving safety.
3. Reduce driver fatigue.
4. Evaluate and improve control algorithms under realistic traffic conditions.

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Project Details

Simulation Scenario

The simulation involves:

• Road Environment: A curved highway section with a radius of 850 meters.
• Vehicles:
  • Ego Vehicle (purple): Equipped with the ACC system.
  • Other Vehicles: Includes one vehicle moving in the same direction and another in the opposite direction.

Dynamic Conditions

1. The ego vehicle adjusts its speed to maintain a safe distance from the vehicle ahead.
2. The vehicle ahead changes lanes, allowing the ego vehicle to return to its original speed.

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Key Features

1. Sensor Fusion: Utilizes sensor data for real-time adjustments.
2. Algorithm Efficiency: Evaluates the stability and adaptability of the ACC algorithm.
3. Traffic Scenarios:
   • Acceleration.
   • Constant-speed driving.
   • Deceleration.

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Simulation Results

• Initial phase: Ego vehicle maintains a constant speed of 20 m/s with safe distancing.
• Mid-phase: Reduced speed to maintain safe following distance.
• Final phase: Ego vehicle accelerates to target speed after the lead vehicle changes lanes.

Conclusion

The ACC system demonstrated reliable responses to dynamic traffic scenarios, maintaining safety and efficiency.

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How to Use the Repository

1. Clone the repository:

   git clone https://github.com/spacealab/acc-simulation.git