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Motion Planning for Self-Driving Cars

Motion Planning for Self-Driving Cars

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8.9/10 (Our Score)
Product is rated as #25 in category Artificial Intelligence

Welcome to Motion Planning for Self–Driving Cars, the fourth course in University of Toronto’s Self–Driving Cars Specialization. This course will introduce you to the main planning tasks in autonomous driving, including mission planning, behavior planning and local planning. By the end of this course, you will be able to find the shortest path over a graph or road network using Dijkstra’s and the A* algorithm, use finite state machines to select safe behaviors to execute, and design optimal, smooth paths and velocity profiles to navigate safely around obstacles while obeying traffic laws. You’ll also build occupancy grid maps of static elements in the environment and learn how to use them for efficient collision checking. This course will give you the ability to construct a full self–driving planning solution, to take you from home to work while behaving like a typical driving and keeping the vehicle safe at all times. For the final project in this course, you will implement a hierarchical motion planner to navigate through a sequence of scenarios in the CARLA simulator, including avoiding a vehicle parked in your lane, following a lead vehicle and safely navigating an intersection. You’ll face real–world randomness and need to work to ensure your …

Instructor Details

Prof. Steven Waslander is a leading authority on autonomous aerial and ground vehicles, including multirotor drones and autonomous driving, Simultaneous Localization and Mapping (SLAM) and multi-vehicle systems. He received his B.Sc.E.in 1998 from Queen’s University, his M.S. in 2002 and his Ph.D. in 2007, both from Stanford University in Aeronautics and Astronautics, where as a graduate student he created the Stanford Testbed of Autonomous Rotorcraft for Multi-Agent Control (STARMAC), the world’s most capable outdoor multi-vehicle quadrotor platform at the time. He was recruited to Waterloo from Stanford in 2008, where he founded and directs the Waterloo Autonomous Vehicle Laboratory (WAVELab), extending the state of the art in autonomous drones and autonomous driving through advances in localization and mapping, object detection and tracking, integrated planning and control methods and multi-robot coordination. In 2018, he joined the University of Toronto Institute for Aerospace Studies (UTIAS), and founded the Toronto Robotics and Artificial Intelligence Laboratory (TRAILab). Prof. Waslander’s innovations in drone research were recognized by the Ontario Centres of Excellence Mind to Market award for the best Industry/Academia collaboration (2012, with Aeryon Labs), best paper and best poster awards at the Computer and Robot Vision Conference (2018), and through two Outstanding Performance Awards, and two Distinguished Performance Awards while at the University of Waterloo. His work on autonomous vehicles has resulted in the Autonomoose, the first autonomous vehicle created at a Canadian University to drive over 100 km on public roads. His insights into autonomous driving have been featured in the Globe and Mail, Toronto Star, National Post, the Rick Mercer Report, and on national CBC Radio. He is Associate Editor of the IEEE Transactions on Aerospace and Electronic Systems, has served as the General Chair for the International Autonomous Robot Racing Competition (IARRC 2012-15), as the program chair for the 13th and 14th Conference on Computer and Robot Vision (CRV 2016-17), and as the Competitions Chair for the International Conference on Intelligent Robots and Systems (IROS 2017).

Specification: Motion Planning for Self-Driving Cars

Duration

18 hours

Year

2019

Level

Expert

Certificate

Yes

Quizzes

No

14 reviews for Motion Planning for Self-Driving Cars

4.9 out of 5
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  1. Wei H

    It is a very nice and meaningful course.

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  2. AmirHossein H P

    perfect

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  3. Omar E A

    very interesting course. highly recommended

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  4. Joachim S

    Again a course from the specialization that I can highly recommend. I has the same high standard on content, presentation and assignments as the other courses, The final project is absolutely gorgeous as you make your car drive around a parking other car, follow a lead vehicles and halt at a stop sign. I loved it,

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  5. Maksym B

    The course is great! Content is very advanced and this course is a good introduction to the topic, but it would require to keep learning additional material to go deeper into Motion Planning. The final project is complex but interesting at the same time as it is performed with Carla simulator which is fun.

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  6. juan.hu

    Although I am not working for self driving car, I have got the general knowledge of the development of self driving car. and combine what I am doing, it will be good for me to start learning from motion planning.

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  7. nicholas m

    Valuable survey course summarizing the high level architecture of motion planning, from high–level map planning, down to path lattices. Note: this is not going to make you an expert (unless you really go above and beyond and try to re–build the project) but it’s very well structured and I do feel much better prepared to dive deeper.

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  8. Irfan K

    I think it is one of the best courses for learning the motion planning algorithms for Autonomous driving. The concepts are well explained with lots of examples.

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  9. Neeraj P

    The final project gives a great overview into the challenges needed for a full fledged self driving car

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  10. Nikhil U D

    It was really well informative course and the assignments and projects were really helped me to understand the in real scenario implementation. Thanks.

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  11. Igor S

    Good lectures, but badly prepared assignments.

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  12. Unmesh P

    Very nice course. Very good to start with but the supplementary reading is a must.

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  13. REVANTH B

    Amazing journey came to an end. Than you Prof Waslander and Prof Kelly.

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  14. YanDing

    The course is very good for the basic knowledge of self driving. There are a lot of good examples of different parts. I have learned a lot from it. Thank you for your excellent job!

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