ENG09022 2017 Multi-Modal Sensor Systems
Introduces robust detection methods for the automotive environment and typical approaches for sensor fusion in Advanced Driver Assistance Systems.
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.
Analyse sensor fusion architectures, common problems and solutions.
Apply common sensor fusion algorithms for detection and tracking applications in the automotive environment.
Critically analyse sensor fusion networks and their applications in object detection in the automotive environment
Assess calibration challenges and strategies for fusing multiple sensors in the automotive vehicle
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, LO3, LO4)
- Fundamentals of Sensor Fusion, Fusion Architectures (LO2, LO3)
- Sensor Calibration, Intrinsic and extrinsic calibration, Impact of calibration on sensor perception, Sensor modelling, Overview of measurement errors and how they can be improved by sensor fusion. (LO3, LO5)
- Correlated and uncorrelated errors, Ego motion compensation and impact of odometry (LO2, LO5)
- Recursive Bayesian Filters for data fusion – Kalman Filter, Extended Kalman Filter, Unscented Kalman Filter, Particle Filter (LO2, LO3, LO4, LO5)
- Compatibility and latency issues (LO2, LO3, LO4)
Coursework & Assessment Breakdown
|Title||Type||Form||Percent||Week||Learning Outcomes Assessed|
|1||Assignment 1||Practical||Assignment||30 %||Week 4||1,2|
|2||Assignment 2||Practical||Assignment||30 %||Week 8||3,4,5|
|3||Assignment 3||Project||Project||40 %||OnGoing||1,2,3,4,5|
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.
2016-08-11 Multisensor Attitude Estimation: Fundamental Concepts and Applications (Devices, Circuits, and Systems) CRC Press
ISBN 1498745717 ISBN-13 9781498745710
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
IEEE Transactions on Intelligent Transportation Systems