Self-Driving Cars with Duckietown
- Understand the foundational principles of autonomous driving
- Develop and implement algorithms for vehicle localization, perception, and control
- Work with computer vision techniques for road detection, object tracking, and navigation
- Apply control strategies to manage autonomous vehicle motion and trajectory planning
- Build a mini self-driving car using the Duckietown platform and program it to navigate a cityscape
Module 1: Introduction to Self-Driving Cars and Duckietown
Overview of Autonomous Vehicles:
The five levels of autonomy in self-driving systems
Hardware setup: Duckiebot assembly and components (camera, motors, sensors)
Software overview: Duckietown operating system, ROS (Robot Operating System) basics
Module 2: Robot Kinematics and Motion Control
Introduction to Robot Kinematics:
Differential drive kinematics and odometry
Robot motion and steering
Controlling speed and direction of Duckiebots
Module 3: Computer Vision for Autonomous Driving
Image Processing Fundamentals:
Pre-processing techniques: Thresholding, edge detection, and contour detection
Applying the Hough Transform for line detection
Identifying lane markings and keeping the Duckiebot on track
Using bounding boxes and color segmentation for obstacle avoidance
Module 4: Localization and Mapping
Introduction to Localization:
Understanding sensor fusion techniques (e.g., combining camera data with odometry)
How Duckiebot uses SLAM to create and update maps of the Duckietown environment
Module 5: Path Planning and Obstacle Avoidance (10 Hours)
Path Planning Algorithms:
How to plan and execute paths for autonomous navigation in a dynamic environment
Reactive control methods: Handling dynamic obstacles such as moving cars or pedestrians
Module 6: Deep Learning for Autonomous Driving (5 Hours)
Introduction to Deep Learning for Self-Driving Cars:
Using Convolutional Neural Networks (CNNs) for object recognition and lane detection
Training models for visual navigation and obstacle detection
How Duckiebot communicates with traffic lights, stop signs, and other infrastructure
Implementing traffic management rules in Duckietown (e.g., stop at red lights)
Course Prerequisites:
Basic programming knowledge in Python or a similar language
Familiarity with Linux and command-line interfaces is helpful but not required
Basic understanding of robotics, sensors, and control systems is beneficial but not mandatory
Career Path After Completion:
Upon completing this course, participants can pursue roles in fields such as:
Autonomous Vehicle Engineer: Work on developing self-driving technology for real-world applications.
Job Interview Preparation (Soft Skills Questions & Answers)
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Job Interview Question- What are You Passionate About?
How to Prepare for a Job Promotion Interview
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