Intended Audience: The targeted audience are university students, researchers from academia, researchers and developers from the industry and also everyone, who is interested to get to know the different automotive environment perception and fusion systems used in the intelligent vehicles sector.

Description: This tutorial is focussed towards the stringent requirements, foundations, development and testing of sensor fusion algorithms meant for advanced driver assistance functions and driverless applications in automotive vehicle systems. The audience would be provided with the materials used in the tutorial.
The audience can see the different representations of the surrounding environment as perceived by the heterogeneous environment perception sensors e.g. different radars, stereo camera and lidar. The relevant state estimation algorithms, sensor fusion frameworks and the evaluation procedures with reference ground truth are presented in detail. The audience can get a first ever glimpse of the data set obtained from a sensor configuration that would be used in the future Mercedes Benz autonomous vehicles.
The interesting part of the tutorial is covered on the different challenging and important practical aspects such as fusion with incomplete information, data association, etc. related to fusion and target tracking in automotive setting. Fusion and management of the different extended target representations of heterogeneous nature obtained from sensors with different resolution is presented with examples. More than one kind of intelligent vehicular sensor fusion framework dealing with tracked objects i.e. track level fusion and raw sensor measurements i.e. measurement level fusion, with results obtained using several real world data sets that contains various static and dynamic targets would be presented in this tutorial.

Prerequisites: No special prerequisites are expected from the audience. The presenters need a conference room equipped with audio-visual presentation medium and equipments e.g. projector. If possible availability of flip charts or a white board would be helpful but it is not mandatory.

Presenter: Bharanidhar Duraisamy, Ting Yuan, Tilo Schwarz, and Martin Fritzsche

Bharanidhar Duraisamy Bharanidhar Duraisamy has been with Daimler’s department of environment perception for the past five years. His work is in the area of automotive multi-level sensor fusion with active and passive environment perception sensors, state estimation and signal processing designed for automotive intelligent vehicular applications, classification-fusion of relevant objects, multisensor data association, target tracking and detection . He has completed his master studies in robotics and automation from the Dortmund university of technology, Germany and he is at present working towards his doctoral degree.

Ting Yuan Dr. Ting Yuan is currently a Senior Research Scientist at the Mercedes-Benz Research and Development North America, Inc., Sunnyvale, CA within the Autonomous Driving Department, where his fields of endeavor lie in detection, classification and tracking of moving/static objects using information from camera, Radar and Lidar systems, as well as data fusion for the multi-sensor systems. He received his Ph.D. degree from the Electrical and Computer Engineering Department at the University of Connecticut, Storrs, CT in 2013. He is an invited presenter on Automotive Radar System at 2016 IEEE Radar Conference, Philadelphia, PA. His research interests include target tracking, data fusion and multiple-model analysis.

Tilo Schwarz Dr. Tilo Schwarz has received his Diploma in Physics from the University of Stuttgart in 1995 and the Doctorate degree in Physics from the University of Kiel in 2000. He has worked from 1996-1999 as PhD student and from 2000 till today as a senior research scientist in the Environment Perception and Sensor Fusion departments of the Daimler Research and Advanced Engineering in Ulm. His scientific interests are in the domains of computer vision, machine learning, signal processing and sensor fusion with primary applications in the field of machine vision and driver assistance systems.

Martin Fritzsche Dr. Martin Fritzsche has received his diploma in Geophysics and his Doctoral degree in Electrical Engineering both fro the University of Karlsruhe, Germany. He is with Daimler’s research and development department for the past two decades. He has worked on several sensor fusion focussed active and passive safety oriented in-house and public funded projects related to intelligent vehicles. He has held lecture and keynote series in many conferences and events specific to intelligent vehicles, automotive safety research and applications. His core research interests are in the domains of pattern recognition, signal processing, state estimation, sensor fusion, target detection and tracking.