This presentation shows how sensors are being distributed in an autonomous vehicle and how we can extract data from multi-sensor collaboration.

1. Vivek Maru
Chemnitz University of Technology
Micro and Nano Systems
2. Semester
Autonomous Driving
Seminar Automotive Sensors
Chair for Measurement and Sensor Technology
Univ.-Prof. Dr.-Ing. O. Kanoun

2. 2
Outline
• Motiviation
• Sensors Distribution
• System Architecture
• Multi-Sensor collaboration
• Sensors setup
• Data extraction and Observation
• References
Seminar Automotive Sensors
Chair for Measurement and Sensor Technology
Univ.-Prof. Dr.-Ing. O. Kanoun

3. 3
Seminar Automotive Sensors
Chair for Measurement and Sensor Technology
Univ.-Prof. Dr.-Ing. O. Kanoun
What is Autonomous Driving? Why?
• An Autonomous car : A vehicle which is capable enough to fulfill human-
transportation capabilities without human inputs.
• Why do we need it?
• Accroding to The Eno Center for Transportations,Washington D.C,
- 90% of road accidents are actually human errors
- If 10 % cars are autonomous,
- 2,11,000 fewer crashes , 1100 lives can be saved p.a.
- If 50% cars are autonomous,
- 1.88 million fewer crashes, 9600 lives can be saved p.a.
BMW Next100, Image :
http://www.bmweducatiom.co.uk.
Google‘s AV, Toyota Prius.Image :
http://www.spectrum.ieee.org

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Seminar Automotive Sensors
Chair for Measurement and Sensor Technology
Univ.-Prof. Dr.-Ing. O. Kanoun
Sensors Distribution
• Sensors distribution, Image by http://www.engineering.com

5. 5
Seminar Automotive Sensors
Chair for Measurement and Sensor Technology
Univ.-Prof. Dr.-Ing. O. Kanoun
System Architecture
System structure, Image :
http://www.makezine.com
• Sensors :
- Raw input data
• ECU (Processor) :
- Decision Making
- Cheking functionalities of all
control drives
- Sending signals to actuators
according situation
- Alerts to Driver using UI.
• Actuators :
- Steering control
-Throttle control
-Differential control
- Motion contorl
- Break control

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Multi-Sensor Collaboration
Seminar Automotive Sensors
Chair for Measurement and Sensor Technology
Univ.-Prof. Dr.-Ing. O. Kanoun
• A self-driving car, to be deployed in real-world driving environments, must be
capable of reliably detecting and effectively tracking of nearby moving objects.
• Sensor fusion for object detection
developed by Carnegie Mallon
University.
• LIDAR – For edge detection
• RADAR – For point detection
• Vision sensor / Camera – For
vision target
6x LIDAR
6x RADAR
3x Vision sensors / Cameras
• Final simulation screenshot, Image : http://www.cmu.edu

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Sensors setup
Seminar Automotive Sensors
Chair for Measurement and Sensor Technology
Univ.-Prof. Dr.-Ing. O. Kanoun
• A radar – LIDAR pair at different
heights – Maximize the reliability and
range of measurements.
• 200 m – Any side of the vehicle
• 60 m – Object detected by at least 2
sensors (i.e. radar and LIDAR or radar
and camera)
• Sensor setup, Image : http://www.cmu.edu
• Coverage of sensors and blind spots, Image : http://www.cmu.edu

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Data extraction and Observation
Seminar Automotive Sensors
Chair for Measurement and Sensor Technology
Univ.-Prof. Dr.-Ing. O. Kanoun
• High definition
screenshots of scenes.
• 3-D point clouds.
• Line segments or
junction of lines (“L”)
shaped.
• 2-D position and velocity
of objects.
• Precise measurements
even after 200m.
• Data from multiple sensors of same type – considered as 1 module.
• Sequential sensor method applied
a) Detection using camera, b) Detection using LIDAR, c) Detection using radar, Images : ICRA 2014, sensor fusion

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Data extraction and Observation
Seminar Automotive Sensors
Chair for Measurement and Sensor Technology
Univ.-Prof. Dr.-Ing. O. Kanoun
a) Urben environment, b) Detection by radar, Image : ICRA 2014, sensor fusion
c) Detection by LIDAR, d) Detection by Vision sensors, Image : ICRA 2014, sensor fusion

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References
• Daniel Fagnant and Kara M. Kockelman "Preparing A Nation For Autonomous Vehicles:
Opportunities, Barriers and Policy Recommandation" , October 2013
• Hyunggi Cho, Young-Woo Seo, B.V.K. Vijaya Kumar, and Ragunathan (Raj) Rajkumar “A Multi-
Sensor Fusion System for Moving Object Detection and Tracking in Urban Driving
Environments”, June 2014
• C. Mertz et al. Moving object detection with laser scanners. Journal of Field Robotics, 30(1), :
17-43 , 2013.
• June 2016 – http://www.cmu.edu
• June 2016 – http://www. http://driving.stanford.edu
• May, 2016 - https://www.enotrans.org/etl-material/preparing-a-nation-for-autonomous-vehicles-
opportunities-barriers-and-policy-recommendations/
• May, 2016 - https://www.engineering.com/electronicsdesign
• May, 2016 - http://www.makezine.com
• May, 2016 - http://www.bmweducation.co.uk
• May, 2016 - http://www.autotrader.ca
Seminar Automotive Sensors
Chair for Measurement and Sensor Technology
Univ.-Prof. Dr.-Ing. O. Kanoun

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Thank you !!
Seminar Automotive Sensors
Chair for Measurement and Sensor Technology
Univ.-Prof. Dr.-Ing. O. Kanoun