Enroll Course: https://www.coursera.org/learn/motion-planning-self-driving-cars
In the rapidly evolving world of autonomous vehicles, understanding motion planning is crucial for anyone looking to dive into this field. The course ‘Motion Planning for Self-Driving Cars,’ offered by the University of Toronto on Coursera, is an excellent resource for both beginners and those with some background in robotics or computer science. This course is the fourth in the Self-Driving Cars Specialization and provides a comprehensive overview of the key planning tasks involved in autonomous driving.
### Course Overview
The course is structured into seven modules, each focusing on different aspects of motion planning. From the planning problem to dynamic object interactions, the curriculum is designed to build your knowledge progressively.
1. **The Planning Problem**: This module sets the stage by discussing the complexities of motion planning, including loss functions and constraints. It introduces a hierarchical motion planning optimization formulation that will be expanded upon in later modules.
2. **Mapping for Planning**: Here, you will learn about occupancy grids, a fundamental data structure in robotics. The module covers how to efficiently compress and filter 3D LIDAR scans to create 2D maps, which are essential for planning.
3. **Mission Planning in Driving Environments**: This module focuses on finding the shortest path using Dijkstra’s and A* algorithms. You will learn how to navigate a vehicle from its current location to a destination by understanding the roadmap graph.
4. **Dynamic Object Interactions**: This section introduces the concept of dynamic obstacles and teaches you how to assess the time to collision with other vehicles and pedestrians.
5. **Principles of Behaviour Planning**: Here, you will develop a rule-based behaviour planning system that helps in making high-level decisions about driving behaviours, such as lane changes and navigating intersections.
6. **Reactive Planning in Static Environments**: This module covers how to create a locally feasible path that is collision-free, using local information and global objectives.
7. **Putting it all together – Smooth Local Planning**: The final module focuses on parameterized curves and continuous curve path optimization, which is crucial for defining paths through the environment.
### Why You Should Take This Course
The ‘Motion Planning for Self-Driving Cars’ course is not just about theory; it provides practical insights and hands-on experience with algorithms that are foundational in the field of autonomous driving. The course is well-structured, with clear explanations and a logical progression from basic concepts to more complex applications.
By the end of the course, you will have a solid understanding of how to implement motion planning algorithms and how they interact with various driving scenarios. This knowledge is invaluable for anyone looking to work in the field of self-driving technology.
### Conclusion
If you’re passionate about robotics, AI, or the future of transportation, I highly recommend enrolling in this course. It will equip you with the necessary skills and knowledge to contribute to the exciting world of self-driving cars. Whether you’re a student, a professional looking to upskill, or simply curious about autonomous vehicles, this course is a great investment in your future.
### Tags
1. Self-Driving Cars
2. Motion Planning
3. Autonomous Vehicles
4. Robotics
5. AI
6. Coursera
7. University of Toronto
8. Dijkstra’s Algorithm
9. A* Algorithm
10. Behavior Planning
### Topic
Motion Planning for Self-Driving Cars
Enroll Course: https://www.coursera.org/learn/motion-planning-self-driving-cars