Unleashing the Future: Decoding the Best Path for Self-Driving Cars and Robotics – Machine Learning or Deep Learning?
As we usher into an exciting era of technology, the debate between Machine Learning and Deep Learning in the world of robotics and autonomous vehicles or self-driving cars gains supremacy. The question that emerges now is – Which of the two learning algorithms – Machine Learning or Deep Learning- ensures a foolproof future for driverless cars and robotics?
How Machine Learning in Automotive Makes Self-Driving Cars a Reality
First, let’s understand how Machine Learning, a type of artificial intelligence, contributes to autonomous vehicle development. Machine Learning, in essence, leverages a vast amount of data to help the machine learning algorithm understand and recognize various scenarios and situations. This enables, for instance, a self-driving car to detect and classify objects – be it a pedestrian, a traffic light or another car – using sensors like Lidar and radars thereby enhancing autonomous driving.
Adding another layer, Supervised Learning, a subset of machine learning, uses labelled data to make the learning process more effective. The algorithm is trained with example input-output pairs, giving autonomous cars the power to accurately map their surroundings, leading to efficient path planning and localization.
What is Artificial Intelligence (AI)?
AI or Artificial Intelligence is a broad term embracing all techniques that enable machines to mimic human intelligence, including but not limited to Machine Learning. Essentially, AI involves teaching machines how to learn, reason, perceive, infer, communicate and make decisions like a human would. Of course, when we talk of AI and Machine Learning in the same vein, it’s clear that Machine Learning is a subset of AI, providing the statistical tools to computers for them to learn from observations.
Deep Learning vs. Machine Learning
Deep Learning, too, is a subset of machine learning. However, it brings a twist to the tale. Instead of using the traditional machine learning algorithms, Deep Learning implements deep neural networks. This makes deep learning more precise in tasks like object detection and segmentation, critical for autonomous driving. But one must note that to use Deep Learning effectively, a colossal amount of data is necessary.
Like supervised learning, deep learning leverages labeled data to teach neural networks. However, it also employs unsupervised learning, utilizing unclassified data for the learning process. Its gem, though, is deep reinforcement learning which allows a self-driving car to learn from its environment, get feedback, modify its actions accordingly, and improve its performance over time.
How Automotive Artificial Intelligence algorithms are used for Self-Driving Cars
AI and machine learning are at the heart of autonomous vehicle functions, whether it’s Tesla, Waymo, or any other driverless car on the road. The AI software in self-driving cars utilizes machine learning algorithms to make self-driving cars more independent and efficient. It makes use of deep learning systems for computer vision and detection, allowing the self driving car to detect and recognize objects and pedestrians, read traffic signs, and more.
Going beyond, Deep Learning algorithms are not just used in self-driving cars but also extensively in robotics. Robots can use deep reinforcement learning to improve how they grasp and manipulate objects or navigate environments. Autonomous cars, too, adopt this method in path planning, making use of multiple convolutional neural networks to identify environmental factors and adjust the car’s path accordingly.
Ultimately, choosing between machine learning or deep learning for self-driving cars and robotics depends largely on the goal, availability of data, and computational resources. But in the current scenario where data is abundant, Deep Learning proves to be more effective and is increasingly being employed by many autonomous cars and robotics companies.
