Creating Autonomous Vehicles like Tesla: Technology that Sees the World with Only Cameras

The innovator of autonomous driving, Tesla. Why did they choose to see the world with only cameras, without LiDAR and radar? This is not just a choice for cost reduction, but a bold vision to create an autonomous driving system that recognizes and judges the world like humans. Especially, Tesla’s End-to-End deep learning-based autonomous driving, introduced from version 12, is an innovative attempt that is completely different from the traditional modular approach.

Traditional autonomous driving systems consist of several modules, such as perception, which processes sensor data, prediction, which determines the situation, planning, which plans the route, and control, which controls the vehicle. Each module is developed independently and connected, but this process can cause information loss or errors, making it difficult to optimize the entire system.

On the other hand, Tesla’s End-to-End deep learning approach takes image data collected from camera sensors as input and directly outputs control signals for steering, acceleration, and deceleration. This is like a human seeing with their eyes and judging with their brain to move their body, integrating the entire autonomous driving system into one large neural network. This approach has the potential to simplify the data processing process, maximize the efficiency of the entire system, and improve the ability to cope with unpredictable situations.

In the following chapters, we will deeply analyze Tesla’s unique vision-centered, End-to-End deep learning autonomous driving approach and present a way to implement it directly using the CARLA simulator.

This includes:

• Tesla Autonomous Driving Philosophy:
  • Why not use LiDAR and radar?
  • What is End-to-End learning and why is it important?
  • What is the core of HydraNet architecture?
  • How do auto-labeling and fleet learning systems work?
• Building a Tesla-Style Vision System
  • Setting up 8 cameras in the CARLA simulator and optimizing each camera’s role and placement.
  • Building an image preprocessing pipeline to efficiently process camera sensor data.
  • Implementing object detection and lane detection algorithms to recognize surroundings, such as lanes, traffic lights, and other vehicles.
  • Using monocular camera depth estimation technology to restore the 3D environment.
• Catching up with Tesla in CARLA
  • Implementing BEV (Bird’s Eye View) transformation, a key technology of Tesla, to grasp the 360-degree situation around the vehicle at a glance.
  • Predicting the trajectory and speed of moving objects through spatiotemporal feature extraction.
  • Implementing probabilistic state estimation to achieve stable autonomous driving even in uncertain situations.

Here, you will:

• Deeply understand the core principles and technologies of Tesla’s autonomous driving system.
• Directly implement a Tesla-style vision-centered autonomous driving system in the CARLA simulator.
• Experience the possibilities and limitations of autonomous driving technology that recognizes, judges, and controls the world with only cameras.
• Open up new horizons for autonomous driving technology through Tesla’s unique approach.

Without LiDAR, without radar, with only cameras. Experience Tesla’s bold challenge directly in the CARLA simulator.