Self– Driving Cars & Physical AI Build the Autonomy Stack

What you'll learn

Build an end-to-end self-driving car stack that turns sensor data into safe, drivable trajectories and closed-loop vehicle control.
Implement camera, LiDAR and radar perception, sensor fusion, object tracking, mapping, localization and motion prediction in Python.
Implement camera, LiDAR and radar perception, sensor fusion, object tracking, mapping, localization and motion prediction in Python.
Simulate, test and evaluate a complete Physical AI driving pipeline with Python, ROS 2, Gazebo and repeatable safety metrics.

Requirements

Basic Python knowledge is helpful, but the course includes guided, executable examples and tested student code.
High-school algebra and basic geometry are enough; no advanced robotics, AI or control-theory experience is required.
A computer that can run Python is required. Ubuntu with ROS 2 and Gazebo is recommended for the full simulation experience.

Description

This course contains the use of artificial intelligence.
How does a self-driving car turn camera, LiDAR and radar data into safe steering and speed commands?
Autonomous driving combines computer vision, robotics, artificial intelligence, sensor fusion, localization, mapping, planning and control. Learning these topics separately can make it difficult to understand how a complete self-driving system actually works.
This course builds that understanding from the ground up.
You will explore the complete autonomous-driving pipeline, beginning with raw sensor data and ending with the commands that control a simulated vehicle. Instead of studying disconnected algorithms, you will learn how every component works together to produce safe driving decisions.
In this course, you will learn how to
- Understand the complete architecture of a self-driving vehicle
- Process images from vehicle-mounted cameras
- Understand how LiDAR creates three-dimensional point clouds
- Use radar measurements for distance and relative-velocity estimation
- Combine camera, LiDAR and radar data through sensor fusion
- Detect lanes, vehicles, pedestrians and other obstacles
- Estimate the vehicle's position within its environment
- Generate smooth and feasible vehicle trajectories
- Control steering, acceleration and braking using feedback controllers
- Connect autonomous-driving components using ROS 2
- Test the vehicle inside a Gazebo simulation
- Measure accuracy, latency and overall system performance
- Design safety limits and failure-handling strategies
- Build a complete simulated autonomous-driving pipeline in Python
The full pipeline covered in this course is
Camera, LiDAR and radar sensing → perception → sensor fusion → localization and mapping → object tracking → motion prediction → behavior planning → path planning → trajectory generation → vehicle control → safety evaluation
Throughout the course, you will use
- Python for implementing autonomy algorithms
- Computer vision for understanding camera images
- LiDAR point clouds for three-dimensional perception
- Radar data for distance and velocity measurements
- Sensor-fusion techniques for combining multiple sources of information
- ROS 2 for connecting different components of the autonomy stack
- Gazebo for testing algorithms on a simulated vehicle
You will also learn how engineers evaluate an autonomy stack beyond a successful demonstration. The course covers
- Processing time and system latency
- Detection, localization and tracking accuracy
- Safety limits and operational constraints
- Sensor and component failures
- Repeatable simulation scenarios
By the end of the course, you will understand how the major components of a self-driving car work together. You will complete an integrated capstone project that combines sensing, estimation, planning and control into a clear and testable autonomous-driving pipeline.
This course is designed for
- Beginners interested in self-driving cars
- Robotics and engineering students
- Python developers entering autonomous systems
- Learners interested in robotics, AI or Physical AI
- Anyone who wants to understand the complete autonomous-driving stack
Basic familiarity with Python is helpful, but advanced knowledge of robotics, artificial intelligence, mathematics or control theory is not required.
If you want a practical and structured path into autonomous-vehicle engineering, this course will give you the technical foundation and project experience needed to begin.

Who this course is for

Beginners, students and Python developers who want to learn how self-driving cars work through practical, step-by-step projects.

Homepage

https://www.udemy.com/course/self-driving-cars-physical-ai-build-the-autonomy-stack/

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