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🧠 Specifications Teen
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.. spec:: Implement RADAR system
   :id: TEEN_RADAR
   :reqs: TEEN_SAFE
   :status: closed
   :author: SARAH

   The RADAR sensor software for the car must accurately detect and track surrounding objects 
   within a specified range. It should employ signal processing algorithms to filter out noise 
   nd interference, ensuring reliable object detection in various weather and road conditions. 
   The software should integrate seamlessly with the car's control system, providing real-time 
   data on detected objects to enable collision avoidance and adaptive cruise control functionalities. 
   Additionally, it should adhere to industry standards for safety and reliability, with robust 
   error handling mechanisms in place.


.. spec:: Implement distant detection
   :id: TEEN_DIST
   :reqs: TEEN_SAFE
   :status: open
   :author: SARAH, STEVEN

   Software Specification for Distance Detection Algorithm:

   Objective: The distance detection algorithm aims to accurately measure the distance between the car and surrounding objects to ensure safe navigation and collision avoidance.

   Input:
       Sensor Data: Raw data from distance sensors mounted on the car, providing information about the distance to nearby objects.
       Vehicle Speed: Speed of the car to adjust detection thresholds dynamically.

   Output:
       Distance Measurement: Precise estimation of the distance between the car and nearby objects, expressed in meters or feet.
       Collision Warning: Alerts or notifications triggered when objects are within a predefined proximity threshold.

   Algorithm:
       Sensor Fusion: Integrate data from multiple sensors (e.g., RADAR, LiDAR, ultrasonic) for improved accuracy and reliability.
       Filtering: Apply signal processing techniques (e.g., Kalman filtering) to reduce noise and errors in sensor measurements.
       Dynamic Thresholding: Adjust detection thresholds based on vehicle speed to account for varying stopping distances.
       Object Tracking: Implement algorithms to track the movement of detected objects over time, enabling predictive collision avoidance.
       Obstacle Classification: Classify detected objects (e.g., vehicles, pedestrians, static obstacles) to prioritize collision warnings and adaptive control actions.

   Performance Requirements:
       Accuracy: The algorithm should provide distance measurements with high accuracy, typically within a tolerance of ±5%.
       Real-Time Processing: Process sensor data and compute distance measurements in real-time to enable timely collision warnings and control interventions.
       Scalability: Ensure the algorithm's scalability to accommodate different sensor configurations and vehicle platforms.

   Safety Considerations:
       Fault Tolerance: Implement error handling mechanisms to handle sensor failures or data inconsistencies gracefully.
       Redundancy: Incorporate redundant sensor systems or fallback mechanisms to maintain functionality in case of sensor failures.
       Validation and Verification: Thoroughly test the algorithm in simulated and real-world scenarios to validate its performance and safety.

   Interface Requirements:
       Integration: Provide APIs or interfaces for seamless integration with the car's control system and Human-Machine Interface (HMI).
       Configuration: Support configurable parameters (e.g., detection range, sensitivity) to adapt to different driving environments and user preferences.

   Compliance:
       Adherence to Automotive Safety Standards: Ensure compliance with relevant automotive safety standards (e.g., ISO 26262) to guarantee the algorithm's safety integrity level (SIL).

   Documentation:
       Detailed documentation including design specifications, algorithms descriptions, interface definitions, and testing procedures for reference and maintenance purposes.

   This software specification outlines the requirements and considerations for developing a robust and reliable distance detection algorithm for automotive applications.