Enroll Course: https://www.coursera.org/learn/motion-planning-self-driving-cars
The ‘Motion Planning for Self-Driving Cars’ course offered by the University of Toronto on Coursera is an exceptional resource for anyone interested in autonomous vehicle technology. As part of the Self-Driving Cars Specialization, this course delves into the core aspects of motion planning, including mission planning, behavior planning, and local planning. Throughout the course, learners are introduced to fundamental algorithms such as Dijkstra’s and A*, which are essential for finding the shortest paths across road networks. Additionally, the course covers critical concepts like finite state machines for behavior selection and trajectory optimization.
One of the standout features of this course is its structured approach to complex topics, beginning with the basics of the planning problem, mapping techniques like occupancy grids, and advancing to dynamic object interactions. The modules on mission planning and reactive planning provide practical insights into how autonomous vehicles navigate real-world environments, addressing challenges posed by static and dynamic obstacles.
The course also emphasizes the integration of various planning components, culminating in the development of smooth local planning strategies using parameterized curves. This holistic approach ensures that learners gain not only theoretical knowledge but also practical skills applicable in real-world scenarios.
I highly recommend this course for students, engineers, and enthusiasts aiming to deepen their understanding of autonomous vehicle motion planning. The comprehensive syllabus, combined with hands-on problem-solving exercises, makes it an invaluable addition to any learning portfolio in robotics and automotive engineering.
Enroll Course: https://www.coursera.org/learn/motion-planning-self-driving-cars