ENG09022 2017 Multi-Modal Sensor Systems

General Details

Full Title
Multi-Modal Sensor Systems
Transcript Title
Multi-Modal Sensor Systems
N/A %
Subject Area
ENG - Engineering
MENG - Mech. and Electronic Eng.
09 - NFQ Level 9
05 - 05 Credits
Start Term
2017 - Full Academic Year 2017-18
End Term
9999 - The End of Time
Shane Gilroy
Programme Membership
SG_EAUTO_E09 202000 Certificate in Automotive Artificial Intelligence SG_ECONN_O09 202000 Postgraduate Diploma in Engineering in Connected and Autonomous Vehicles SG_ECONN_M09 202000 Master of Engineering in Connected and Autonomous Vehicles SG_ECOFT_O09 202000 Postgraduate Diploma in Engineering in Connected and Autonomous Vehicles

Introduces robust detection methods for the automotive environment and typical approaches for sensor fusion in Advanced Driver Assistance Systems.

Learning Outcomes

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 Assessments

Repeat project work can be submitted at the repeat exam series each year.

Indicative Syllabus

  • 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

Coursework & Continuous Assessment
100 %

Coursework Assessment

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

Type Location Description Hours Frequency Avg Workload
Lecture Lecture Theatre Lecture 2 Weekly 2.00
Laboratory Practical Not Specified Practical 1 Weekly 1.00
Independent Learning Not Specified Independent Learning 7 Weekly 7.00
Total Full Time Average Weekly Learner Contact Time 3.00 Hours

Online Learning Mode Workload

Type Location Description Hours Frequency Avg Workload
Lecture Online Lecture 1 Weekly 1.00
Independent Learning Not Specified Independent Learning 8.5 Weekly 8.50
Laboratory Practical Not Specified Practical 0.5 Weekly 0.50
Total Online Learning Average Weekly Learner Contact Time 1.50 Hours

Required & Recommended Book List

Recommended Reading
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.

Recommended Reading
2016-08-11 Multisensor Attitude Estimation: Fundamental Concepts and Applications (Devices, Circuits, and Systems) CRC Press
ISBN 1498745717 ISBN-13 9781498745710
Recommended Reading
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

Module Resources

Journal Resources

IEEE Transactions on Intelligent Transportation Systems

URL Resources


Other Resources


Additional Information