Introduction
Adaptive Cruise Control (ACC) has emerged as a cornerstone in the development of modern automotive safety systems. By dynamically adjusting vehicle speed based on road conditions, weather, and surrounding traffic, ACC systems reduce the risk of collisions while enhancing driver comfort. This paper presents a comprehensive approach to the simulation, modification, and testing of ACC functionality using a robust software framework. The methods focus on key factors such as weather, road curvature, vehicle dynamics, and real-world scenarios like emergency cut-ins and overtaking.

Simulation Framework
The simulation framework employs Python to integrate adaptive cruise control algorithms with realistic road and traffic conditions. At its core, the system utilizes the ESmini library, a powerful simulation tool compatible across multiple platforms, including Linux, Windows, and macOS. The modular structure allows for easy configuration and expansion to test diverse driving scenarios.

Key Components of the ACC System

Dynamic Safe Distance Calculation
The ACC system calculates a dynamic safe following distance based on the vehicle’s speed. This distance ensures that the ego vehicle maintains a safe buffer from the lead vehicle, accounting for factors like sudden deceleration and traffic density. The implemented algorithm ensures a minimum safe distance of 5 meters, which scales with the vehicle’s speed.

Speed Adjustment Mechanisms

Smooth Speed Transition: To enhance passenger comfort, the system employs jerk-limited transitions when adjusting the vehicle’s speed toward the target. By capping acceleration and deceleration rates, the system avoids abrupt changes that could compromise stability.

Weather and Road Conditions: Speed is dynamically adjusted to account for reduced traction in adverse weather conditions, such as rain or snow. Similarly, the system reduces speed on sharp curves to enhance safety.

Emergency Handling
Emergency cut-in scenarios are addressed through an aggressive deceleration mechanism that ensures the vehicle reacts within 0.5 seconds to imminent dangers. This capability is complemented by algorithms that maintain the headway time and mitigate risks during sudden events.

Simulation Modifications
The system provides tools for modifying simulation parameters, such as road curvature, lane width, and vehicle start positions. These modifications are implemented through XML parsing and manipulation of XODR and XOSC files. For instance, changes to road geometry are reflected in real-time, enabling scenario-specific tests.

Testing Framework
A comprehensive unittest framework validates the functionality of the ACC system under various scenarios. Key test cases include:

Default Conditions: Validating baseline performance with standard speed and weather settings.

Adverse Weather Scenarios: Testing the system’s response to rain and snow to ensure traction-based speed adjustments.

Emergency Situations: Evaluating system behavior during abrupt lead vehicle decelerations and cut-ins.

Dynamic Road Configurations: Assessing the impact of road curvature and elevation on vehicle stability and speed control.

Video Demonstration
A video demonstration showcasing the simulation and key functionalities of the ACC system is available at the following link:

This video provides a visual representation of how the system operates under different scenarios, enhancing the understanding of its real-world applicability.

Results and Discussion
Preliminary results indicate that the ACC system effectively adapts to changing environmental conditions and maintains safety margins in various scenarios. The modularity of the framework allows seamless integration of additional parameters, such as vehicle-to-vehicle communication and advanced machine learning models for predictive analytics.

Conclusion
This work highlights the potential of simulation-based frameworks to advance adaptive cruise control systems. By incorporating dynamic safe distance calculations, speed adjustment mechanisms, and robust testing frameworks, the system demonstrates improved safety and reliability. Future directions include the integration of real-world sensor data to bridge the gap between simulation and on-road performance.