.. _key_concepts:

Key Concepts Explained
======================

Stereo Vision and Disparity
---------------------------

*   **Stereo Vision:** Uses two cameras to capture slightly different views, mimicking human vision.
*   **Disparity:** The difference in horizontal position of a corresponding point in left/right images. Inversely proportional to depth.
*   **Application:** Calculating a disparity map allows depth estimation, crucial for 3D understanding. The ``CREStereo`` class is designed for this.

Object Detection (YOLO)
-----------------------

*   **YOLO (You Only Look Once):** Fast and accurate real-time object detection models.
*   **Principle:** Divides an image into a grid, predicts bounding boxes, confidence scores, and class probabilities in a single pass.
*   **Application:** Used in :doc:`src/perception/object_detection.py <../../src/perception/object_detection>` to identify road users and objects.

Lane Detection
--------------

*   **Purpose:** Identifying lane markings on the road.
*   **Importance:** Essential for features like Lane Keeping Assist and Lane Departure Warning.
*   **Method:** The ``UltrafastLaneDetector`` (:doc:`src/perception/lane_detection.py <../../src/perception/lane_detection>`) uses a deep learning model.

Object Tracking (SORT-like)
---------------------------

*   **Purpose:** Maintain a consistent identity for detected objects across frames.
*   **SORT (Simple Online and Realtime Tracking):**
    *   Relies on an external object detector.
    *   Matches new detections to existing tracks using motion prediction, an affinity metric (e.g., IoU), and an assignment algorithm (e.g., Hungarian algorithm).
*   **Application:** :doc:`src/tracker.py <../../src/tracker>` assigns stable IDs to detected objects.

NLP for Q&A (Intent Recognition)
--------------------------------

*   **NLP (Natural Language Processing):** Enabling computers to understand human language.
*   **Intent Recognition:** Determining the underlying purpose of a user's query.
*   **Process in** :doc:`NLP/adas_chatbot.py <../../NLP/adas_chatbot>`:
    1.  **Data:** Predefined intents, patterns, and responses (``moroccan_traffic_code.json``).
    2.  **Preprocessing:** Tokenization, lemmatization.
    3.  **Bag-of-Words (BoW):** Input converted to a numerical vector.
    4.  **Neural Network Classifier:** Trained to map BoW vectors to intents.
    5.  **Response Generation:** Selects a response for the predicted intent.

CARLA Simulator
---------------

*   **CARLA:** Open-source simulator for autonomous driving research.
*   **Features:** Realistic 3D environments, vehicle physics, sensor models, API.
*   **Role in this Project:** Allows development and testing in a controlled virtual environment.