Skip to content

Latest commit

 

History

History
179 lines (134 loc) · 6.04 KB

readme.md

File metadata and controls

179 lines (134 loc) · 6.04 KB
Raw

Advanced Driver Assistance Systems (ADAS) are designed to enhance vehicle safety and provide a better driving experience by automating and improving certain aspects of driving. ADAS can include features like adaptive cruise control, lane departure warning, collision avoidance, and more. Implementing ADAS functionalities on a platform like the NVIDIA Jetson Nano involves combining hardware components and software algorithms.

Implementing ADAS with Jetson Nano

Here is a step-by-step guide on how you can get started with an ADAS project using the NVIDIA Jetson Nano:

1. Setting Up the Jetson Nano

Hardware Requirements:

  • NVIDIA Jetson Nano Developer Kit
  • MicroSD card (32GB or larger)
  • Power supply (5V 4A recommended)
  • Camera (preferably a stereo camera or depth camera)
  • Optional: Ultrasonic sensors, LiDAR, or Radar for advanced features
  • Monitor, keyboard, and mouse

Software Requirements:

  • JetPack SDK
  • Python libraries: OpenCV, NumPy, etc.
  • Deep learning frameworks: TensorFlow or PyTorch
  • ROS (Robot Operating System) for integrating different sensors and controls

2. Initial Setup

  1. Flash the MicroSD Card:

    • Download and flash the JetPack image onto the MicroSD card.

  2. Boot the Jetson Nano:

    • Insert the MicroSD card, connect peripherals, and power on the Jetson Nano.

  3. Install Required Libraries:

    • Update the system and install essential libraries: 
      sudo apt-get update
sudo apt-get upgrade
sudo apt-get install python3-opencv
sudo apt-get install python3-numpy
sudo apt-get install ros-melodic-desktop-full
pip3 install tensorflow

3. Developing ADAS Features

Step 1: Lane Detection

  1. Capture Video Frames:

    import cv2

cap = cv2.VideoCapture(0)
if not cap.isOpened():
    print("Error: Could not open video stream.")
    exit()

while True:
    ret, frame = cap.read()
    if not ret:
        break

    cv2.imshow('ADAS - Lane Detection', frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

  2. Implement Lane Detection:

    import numpy as np

def region_of_interest(img, vertices):
    mask = np.zeros_like(img)
    cv2.fillPoly(mask, vertices, 255)
    masked_image = cv2.bitwise_and(img, mask)
    return masked_image

def draw_lines(img, lines):
    if lines is not None:
        for line in lines:
            for x1, y1, x2, y2 in line:
                cv2.line(img, (x1, y1), (x2, y2), (0, 255, 0), 5)

while True:
    ret, frame = cap.read()
    if not ret:
        break

    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    blur = cv2.GaussianBlur(gray, (5, 5), 0)
    edges = cv2.Canny(blur, 50, 150)

    height, width = edges.shape
    roi_vertices = [(0, height), (width / 2, height / 2), (width, height)]
    cropped_edges = region_of_interest(edges, np.array([roi_vertices], np.int32))

    lines = cv2.HoughLinesP(cropped_edges, 1, np.pi / 180, 50, maxLineGap=50)
    draw_lines(frame, lines)

    cv2.imshow('ADAS - Lane Detection', frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

Step 2: Object Detection

  1. Load a Pretrained Model:

    • Use TensorFlow or PyTorch to load a pretrained object detection model (e.g., SSD, YOLO).

  2. Perform Object Detection:

    import tensorflow as tf

model = tf.saved_model.load("ssd_mobilenet_v2_fpnlite_320x320/saved_model")
detection_fn = model.signatures['serving_default']

def detect_objects(image):
    input_tensor = tf.convert_to_tensor(image)
    input_tensor = input_tensor[tf.newaxis, ...]
    detections = detection_fn(input_tensor)
    return detections

while True:
    ret, frame = cap.read()
    if not ret:
        break

    image_np = np.array(frame)
    detections = detect_objects(image_np)

    for i in range(int(detections.pop('num_detections'))):
        score = detections['detection_scores'][i].numpy()
        if score > 0.5:
            bbox = detections['detection_boxes'][i].numpy()
            ymin, xmin, ymax, xmax = bbox
            (left, right, top, bottom) = (xmin * width, xmax * width,
                                          ymin * height, ymax * height)
            cv2.rectangle(frame, (int(left), int(top)), (int(right), int(bottom)), (0, 255, 0), 2)

    cv2.imshow('ADAS - Object Detection', frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

Step 3: Integrate ADAS Features

  1. Combine Lane Detection and Object Detection:

    • Run both algorithms in parallel to provide comprehensive ADAS functionality.

  2. Add Collision Avoidance:

    • Use depth cameras or additional sensors (e.g., ultrasonic sensors) to measure the distance to detected objects.
    • Implement logic to alert the driver or take corrective action if a collision is imminent.

Step 4: Optimize and Deploy

  1. Optimize Models:

    • Use NVIDIA TensorRT for optimizing the deep learning models for real-time performance on the Jetson Nano.

  2. Deployment:

    • Package the ADAS application for deployment.
    • Ensure the system can start automatically and run efficiently in a vehicle environment.

4. Further Improvements

  • Enhance Detection Algorithms:

    • Experiment with more advanced models and techniques to improve detection accuracy.
    • Use a diverse and extensive dataset for training your models.

  • Expand ADAS Capabilities:

    • Integrate additional features such as adaptive cruise control, traffic sign recognition, and pedestrian detection.
    • Use more sophisticated sensors (e.g., LiDAR, Radar) for improved perception.

By following these steps, you can develop a functional ADAS project using the NVIDIA Jetson Nano. This project will help you understand and implement key features of advanced driver assistance systems, contributing to safer and more intelligent vehicles.