IT 312: 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.

Learning Outcomes

  • Explain the importance of navigation systems.
  • Evaluate the levels of automated vehicles.
  • Recognize and apply laboratory safety procedures.
  • Explain the principles of a navigation system.
  • Explain the fundamentals of basic navigational mathematics.
  • Determine the detailed kinematic relationships between the 4 major frames of interest
  • Explain the fundamental basics navigational mathematics.
  • Demonstrate the understanding of the relationship between specific force, inertial acceleration, and gravitational attraction.
  • Demonstrate the understanding of mobile robot positioning.
  • Demonstrate the understanding of mobile robot positioning with respect to Sensors and techniques applications.
  • Describe the basics of performing INS computations
  • Describe the integration of the inertial navigation system (INS) with other sensors.
  • Discuss various examples of practical INS applications.
  • Describe the fundamentals of satellite navigation
  • Explain the fundamentals of GPS
  • Explain how to set up and operate the components of a mapping-grade GPS system
  • Identify levels of GPS accuracy.
  • Explore the integration of GPS with other technologies
  • Identify sources of GPS errors
  • Measure GPS accuracy
  • Demonstrate understanding of the material covered by the learning outcomes in Lessons 1 through 6 on a mid-term exam.
  • 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.
  • Apply KF to estimate the errors introduced into the unaided INS system due to gyros and accelerometers.
  • Discuss the fundamentals of the integrated navigation system (INS).
  • Describe the different INS/GNSS integration architectures.
  • Describe different integration architectures
  • Combine different navigation sensors for different applications.
  • Explain the limitations of incorporating terrestrial radio navigation
  • Differentiate between loosely coupled integration and tightly coupled integration.
  • Explain what a dead-reckoning reference incorporates.
  • Describe feature matching techniques
  • Discus the failure modes that can occur in navigation systems.
  • Describe the certification that an integrity monitoring system fulfills.
  • Demonstrate the understanding of robotic motion planning problems.
  • Discuss collision avoidance methods.
  • Demonstrate the understanding of robotic motion planning problems.
  • Explain the legal outlook for automated (autonomous) and connected cars.
  • Demonstrate basic understanding of the material covered in the course.
  • Basic Skill Requirements


    Dr. James A. Ejiwale
    This course was developed by Dr. James A. Ejiwale at Jackson State University in the department of Industrial Systems and Technology.

    Course Features

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