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Autonomous Driving- TU Chemnitz
The document outlines a seminar on autonomous driving, focusing on the importance of automotive sensors and their role in enhancing safety by reducing human error-related accidents. It discusses system architecture, multi-sensor collaboration for effective object detection, and data extraction techniques. Additionally, it references various studies and sources to support the necessity of autonomous vehicles in modern transport.
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Autonomous Driving- TU Chemnitz
- 1. Vivek Maru Chemnitz Universityof 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 • SensorsDistribution • 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 Chairfor 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
- 4. 4 Seminar Automotive Sensors Chairfor 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 Chairfor 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
- 6. 6 Multi-Sensor Collaboration Seminar AutomotiveSensors 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
- 7. 7 Sensors setup Seminar AutomotiveSensors 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
- 8. 8 Data extraction andObservation 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
- 9. 9 Data extraction andObservation 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
- 10. 10 References • Daniel Fagnantand 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
- 11. 11 Thank you !! SeminarAutomotive Sensors Chair for Measurement and Sensor Technology Univ.-Prof. Dr.-Ing. O. Kanoun
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