**Unlocking the Power of Autonomous Vehicles: A Deep Dive in

Unlocking the Power of Autonomous Vehicles: A Deep Dive into 3-Line TensorFlow Code for Sensor Fusion

Autonomous vehicles rely on a multitude of sensors to navigate and make informed decisions. These sensors can include cameras, lidar, radar, and ultrasonic sensors, each providing a unique perspective on the environment. However, processing and fusing data from these diverse sources is a daunting task. This is where TensorFlow comes in, providing a powerful tool for building and training machine learning models that can effectively merge sensor data.

Sensor Fusion using 3-Line TensorFlow Code

Below is a simplified example of how we can implement sensor fusion using TensorFlow and Keras:

tf
import tensorflow as tf
model = tf.keras.models.Sequential([
    tf.keras.layers.Concatenate([
        tf.keras.layers.InputLayer(input_shape=(12, 3)),  # Camera data (3 channels)
        tf.keras.layers.InputLayer(input_shape=(64, 1)),  # Lidar data (1 channel)
        tf.keras.lay...

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