Autonomous Vehicles

Build the self-driving stack. Master Perception, Localization (SLAM), Path Planning, and Control logic using C++ and ROS.

Self-driving cars are the peak of robotics. This course breaks down the autonomous stack. You will learn Perception (using Cameras/Lidar to see), Localization (knowing where the car is), and Path Planning (deciding where to go). We implement PID controllers to steer the vehicle smoothly. Using the CARLA simulator, you will write code that drives a virtual vehicle through traffic, adhering to traffic laws and avoiding obstacles.

✅ 100% Free & Lifetime Access

⏱️ 5-Minute Lessons (Bite-sized learning)

🚀 21-Lesson Path (Independent modules)

📱 Mobile Friendly (Learn anywhere)

Robotics

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Complete Course Syllabus

1

The AV Stack

Sensors, Perception, Planning, and Control layers.
2

Computer Vision

Detecting lanes and traffic lights with OpenCV.
3

Sensor Fusion

Combining Lidar and Radar data for accuracy.
4

Localization

Particle Filters and knowing where you are on the map.
5

Path Planning

Generating smooth trajectories through traffic.

Estimated completion time: 21 lessons • Self-paced learning • Lifetime access

Career Outlook

Estimated Salary

$130k - $190k

Career Paths

Autonomous Driving Eng $140k-$200k

Perception Engineer $135k-$190k

Robotics Scientist $130k-$180k

What You Will Learn

Implement Computer Vision for lane and object detection

Perform Localization using Kalman Filters and SLAM

Plan paths using A* and behavioral logic

Control vehicle steering/throttle using PID controllers

Test algorithms in the CARLA high-fidelity simulator

Skills You Will Gain

Computer Vision Sensor Fusion Path Planning Control Theory C++ / Python

Who Is This For

Robotics Engineers

CV Specialists

Auto Engineers

Prerequisites

C++

Linear Algebra