ENG09040 2021 Sensor Fusion
This module covers the state of the art theory and algorithms for multi-modal sensor fusion in autonomous vehicles with application to localisation, navigation and tracking problems.
On completion of this module the learner will/should be able to;
Evaluate the strengths and weaknesses of common sensor technologies to the development of effective multimodal sensor architectures.
Apply common sensor fusion algorithms for localisation, navigation and tracking applications in the automotive environment.
Critically evaluate sensor fusion networks and their applications in the automotive environment
Communicate the process of design, testing and evaluation of a Sensor Fusion-based system to an audience of peers
Understand and articulate the key concepts of advanced sensor fusion research presented in recent literature
Teaching and Learning Strategies
The module will be delivered via a mix of online lectures and practical assignments. A mix of project based learning and formative assessments will be used .
Module Assessment Strategies
This module will be 100% continuous assessment.
Repeat project work can be submitted at the repeat exam series each year.
- Overview of Multimodal Systems, ADAS Sensors – principles, characteristics, strengths and weaknesses, potential for fusion (LO1)
- Fundamentals of Sensor Fusion in the static case (Localisation). The principles of Weighted Least Squares and Maximum Likelihood estimation (LO2, LO3, LO4)
- Sensor modelling, Overview of measurement errors and how they can be improved by sensor fusion. (LO2, LO3, LO4)
- Motion Models and their role in filtering problems (Navigation and Tracking) (LO2, LO3)
- Recursive Bayesian Filters for data fusion – Kalman Filter, Extended Kalman Filter, Unscented Kalman Filter, Particle Filter (LO2, LO3, LO4, LO5)
- Introduction to SLAM; EKF-SLAM (LO5)
Coursework & Assessment Breakdown
|Title||Type||Form||Percent||Week||Learning Outcomes Assessed|
|1||Assignment 1||Practical||Assignment||25 %||Week 4||1,2,3|
|2||Assignment 2||Project||Assignment||45 %||Week 12||2,3,4|
|3||Assignment 3||Continuous Assessment||Project||30 %||Week 9||5|
Full Time Mode Workload
|Laboratory Practical||Not Specified||Practical||1||Weekly||1.00|
|Independent Learning||Not Specified||Independent Learning||7||Weekly||7.00|
Online Learning Mode Workload
|Independent Learning||Not Specified||Independent Learning||8.5||Weekly||8.50|
|Laboratory Practical||Not Specified||Practical||0.5||Weekly||0.50|
Required & Recommended Book List
2015-11-28 Handbook of Driver Assistance Systems: Basic Information, Components and Systems for Active Safety and Comfort Springer
ISBN 3319123513 ISBN-13 9783319123516
This fundamental work explains in detail systems for active safety and driver assistance, considering both their structure and their function. These include the well-known standard systems such as Anti-lock braking system (ABS), Electronic Stability Control (ESC) or Adaptive Cruise Control (ACC). But it includes also new systems for protecting collisions protection, for changing the lane, or for convenient parking. The book aims at giving a complete picture focusing on the entire system. First, it describes the components which are necessary for assistance systems, such as sensors, actuators, mechatronic subsystems, and control elements. Then, it explains key features for the user-friendly design of human-machine interfaces between driver and assistance system. Finally, important characteristic features of driver assistance systems for particular vehicles are presented: Systems for commercial vehicles and motorcycles.
2017-05-22 Signal Processing for In-Vehicle Systems: Dps, Driver Behavior, and Safety (Signal Processing for In-Vehicle Systems, Driver Behavior, a) Imprint unknown
ISBN 1501504126 ISBN-13 9781501504129
2018-06-08 Statistical Sensor Fusion
ISBN 9144127243 ISBN-13 9789144127248
Sensor fusion deals with merging information from two or more sensors, where the area of statistical signal processing provides a powerful toolbox to attack both theoretical and practical problems. The objective of this book is to explain state of the art theory and algorithms in statistical sensor fusion, covering estimation, detection and nonlinear filtering theory with applications to localisation, navigation and tracking problems. The book starts with a review of the theory on linear and nonlinear estimation, with a focus on sensor network applications. Then, general nonlinear filter theory is surveyed with a particular attention to different variants of the Kalman filter and the particle filter. Complexity and implementation issues are discussed in detail. Simultaneous localisation and mapping (SLAM) is used as a challenging application area of high-dimensional nonlinear filtering problems. The book spans the whole range from mathematical foundations provided in extensive appendices, to real-world problems covered in a part surveying standard sensors, motion models and applications in this field. All models and algorithms are available as object-oriented Matlab code with an extensive data file library, and the examples, which are richly used to illustrate the theory, are supplemented by fully reproducible Matlab code.
IEEE Transactions on Intelligent Transportation Systems