Course description
Connected and Autonomous Vehicles: Sensors and Sensor Fusion
The first part of the course covers the operational principles of the state-of-the-art sensors used in CAVs, presenting useful case studies and real-world applications. The second part of the course deals with estimation and tracking, presenting the main types of sensor fusion architectures and the principal algorithms used for sensor fusion, such as the Kalman Filter, the Extended Kalman Filter and the Unscented Kalman Filter.
A generical tech savvy audience should be able to follow the first 6 units easily. The last 2 units require more advanced statistical knowledge.
Level: Technical
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Upcoming start dates
Suitability - Who should attend?
The course is aimed at graduates and professional engineers wanting to acquire state-of the art knowledge on CAV technologies. Specifically, this course is aimed at Senior Engineers who are non-specialists of the subject, Project Managers or Junior Engineers.
Outcome / Qualification etc.
By the end of this course you will be able to:
- Understand and classify the types of sensors used in autonomous cars
- Understand the main types of sensor fusion architectures
- Understand real problems and be able to apply filter algorithms for linear and non-linear system
Training Course Content
Units included in this course
- Unit 1 - Random Processes and Estimation
- Unit 2 - Sensors 1
- Unit 3 - Sensors 2
- Unit 4 - Sensors 3
- Unit 5 - Multi-Sensor Fusion and Architectures
- Unit 6 - Kalman Filter
- Unit 7 - Kalman Filter and Extended Kalman Filter
- Unit 8 - Unscented Kalman Filter
- Final Assessment
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