IT 312 – Navigation techniques used in connected and automated vehicles

 Average 5 out of 5

This course introduces students to principles of navigation techniques used in connected and automated vehicles.


Course Description: This course introduces students to principles of navigation techniques used in connected and automated vehicles. Topics include autonomous navigation and connected vehicles, basic navigational mathematics, mobile robot positioning, inertial sensors and navigation systems, global positioning system, kalman-fitering techniques, integrated navigation system, multisensory integrated navigation, fault detection and integrity monitoring, and communication among connected vehicles.

Course Objectives: Upon completion of this course students will be able to:

  1. Explain the importance of navigation systems.
  2. Evaluate the levels of automated vehicles.
  3. Recognize and apply laboratory safety procedures.
  4. Explain the principles of a navigation system.
  5. Explain the fundamentals of basic navigational mathematics.
  6. Determine the detailed kinematic relationships between the 4 major frames of interest
  7. Explain the fundamental basics navigational mathematics.
  8. Demonstrate the understanding of the relationship between specific force, inertial acceleration, and gravitational attraction.
  9. Demonstrate the understanding of mobile robot positioning.
  10. Demonstrate the understanding of mobile robot positioning with respect to Sensors and techniques applications.
  11. Describe the basics of performing INS computations
  12. Describe the integration of the inertial navigation system (INS) with other sensors.
  13. Discuss various examples of practical INS applications.
  14. Describe the fundamentals of satellite navigation
  15. Explain the fundamentals of GPS
  16. Explain how to set up and operate the components of a mapping-grade GPS system
  17. Identify levels of GPS accuracy.
  18. Explore the integration of GPS with other technologies
  19. Identify sources of GPS errors
  20. Measure GPS accuracy
  21. Demonstrate understanding of the material covered by the learning outcomes in Lessons 1 through 6 on a mid-term exam.
  22. Demonstrate the ability to apply different Kalman filter (KF) techniques to combine noisy sensor outputs to estimate the state of a system with uncertain dynamics.
  23. Apply KF to estimate the errors introduced into the unaided INS system due to gyros and accelerometers.
  24. Discuss the fundamentals of the integrated navigation system (INS).
  25. Describe the different INS/GNSS integration architectures.
  26. Describe different integration architectures
  27. Combine different navigation sensors for different applications.
  28. Explain the limitations of incorporating terrestrial radio navigation
  29. Differentiate between loosely coupled integration and tightly coupled integration.
  30. Explain what a dead-reckoning reference incorporates.
  31. Describe feature matching techniques
  32. Discus the failure modes that can occur in navigation systems.
  33. Describe the certification that an integrity monitoring system fulfills.
  34. Demonstrate the understanding of robotic motion planning problems.
  35. Discuss collision avoidance methods.
  36. Demonstrate the understanding of robotic motion planning problems.
  37. Explain the legal outlook for automated (autonomous) and connected cars.
  38. Demonstrate basic understanding of the material covered in the course.

This material is based upon work supported by the National Science Foundation under Grant No. 1400593.

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Connected and Automated Vehicles
Grid Interface (Power and Communications)
Integration, Networking, and Communications
Product Lifecycle:
Pre-production: Research, Design, Development, Testing, and Tooling
Resource Type:
Classroom Activity
Lab Activity
Lesson Plan
Jackson State University
Author & Title:
Dr. James A. Ejiwale, Professor
Date Developed:
Tuesday, May 17, 2016
Navigation techniques,connected and automated vehicles,autonomous navigation,connected vehicles,navigational mathematics,inertial sensors,GPS,fault detection,integrity monitoring
Education Level:
Undergrad Students (15-16)