How does the training course work?
Each AutoSens Academy module combines guided and interactive learning, in a flipped classroom approach, bringing together pre-recorded tutorials and self-driven research, live sessions with agile-based learning, homework and group exercises and extensive access to additional reading materials.
AutoSens Academy is a rolling 12-month programme, consisting of 1 module available every 4 weeks, with a Summer and Winter break.
Sign up at any time to gain 18 months access to the course.
ALL CONTENT AVAILABLE NOW
This module will cover the optical scientific principles for all sensor modalities, covering theory and practice, including manufacturing considerations. Understanding the physical principles of how to capture images is the foundation for all ADAS and AV perception technologies.
Instructor: Prof. Dr. Alexander Braun, Professor of Physics, University of Applied Sciences, Duesseldorf
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This module will provide you with a clear understanding of the foundational concepts in deep learning, challenges, applications, and the role that deep learning is playing in the rapid advancement of the field. This module will present, in terms of theory and practice, the fundamental concepts of deep learning, including the foundations of neural network architectures, and learning techniques.
Instructor: Prof. Dr. Abhinav Valada, Assistant Professor & Director of Robot Learning Lab, University of Freiburg
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Providing comprehensive coverage of all the key aspects of image sensor design and operation for automotive applications. You will learn what the building blocks of CMOS image sensors are and how to differentiate between image sensor type, design and function. Sessions include 3T and 4T Pixels, Temporal, Photon Shot and Fixed-Pattern Noise.
Instructor: Prof. Albert Theuwissen, Founder, Harvest Imaging
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This module will cover the various LiDAR technology types, their design and build, their market status, and the technical considerations for using LiDAR in automotive. You will also learn about LiDAR receivers, optical beam steering and understand how to test LiDAR.
Instructor: Dr Paul McManamon, Technical Director, Lidar and Optical Communications Institute (LOCI)
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This module covers the different automotive radar technology, market status and technical considerations, including the latest innovations and future trends. It will cover unique propagation phenomena experienced by typical automotive radar and radar concepts that can address them. Finally, advanced topics, such as interference mitigation, and sensor fusion will be discussed.
Instructor: Dr. Igal Bilik, Department of Electrical and Computer Engineering, Ben-Gurion University
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This module will cover the working principles of cameras and their automotive applications, including an overview of the full range of use cases in automotive, from driver monitoring to AEB to surround view and beyond.
Instructor: Prof. Dr. Alexander Braun, Professor of Physics, University of Applied Sciences, Duesseldorf
MODULE IN PROGRESS
Perception is one of the most fundamental components of autonomous vehicles. This module will introduce you to the main computer vision tasks for autonomous vehicles and the standard deep learning techniques to solve them. This course will also provide critical concepts related to image processing, a review of essential datasets, and tools used in perception for autonomous vehicles.
Instructor: Prof. Dr. Abhinav Valada, Assistant Professor & Director of Robot Learning Lab, University of Freiburg
AVAILABLE FROM: 1 NOVEMBER 2021
Building on the cameras module, this module will explore practical applications of cameras in real-world scenarios. Gain a full understanding of calibration methods, pitfalls to avoid, impacts and considerations up and down the data processing chain.
Instructor: Uwe Artmann, CTO, Image Engineering
AVAILABLE FROM: 29 NOVEMBER 2021
Highly detailed mapping and precise localization have boosted the performance of self-driving cars and have become an important component of L3+ vehicle automation. HD maps not only augment the information acquired by sensors, but lift environment perception to a telematic horizon, which extends far beyond the sensors’ field of view. This module will provide an overview on fundamental concepts and challenges of HD mapping and localization techniques in theory and practice. It will elaborate offline and online approaches to map building and localization-only methodologies. Practical issues like map validation and crowd mapping concepts will be addressed. Throughout the module, practical applications for automated driving will be discussed.
Instructor: Prof. Christoph Stiller, Director of Institute for Measurement and Control Systems, KIT
AVAILABLE FROM: 10 JANUARY 2022
In this module you will gain an understanding of the principles of fusing rich sensor data provided by LiDAR, Camera and RADAR to better observe and analyse the scene around a moving vehicle. You will learn how these principles are applied in real-life applications, for inference purposes and to enhance sensor outputs. An overview of the current state of the art in the area of road user detection and tracking and trends in current and future research will also be provided.
Instructors: Prof. Wilfried Philips, Prof. Jan Aelterman and Dr. David Van Hamme, IPI Research Group, Ghent University
AVAILABLE FROM: 7 FEBRUARY 2022
This module will provide a detailed understanding of the key image quality factors for automotive imaging systems. Understand how to measure and evaluate image quality, how to use KPIs and metrics to improve camera system performance, and the impact of image quality in real-world applications.
Instructor: Dr. Robert Parada, Instructor, Optical Systems Technology Department, Monroe Community College, Rochester, USA
AVAILABLE FROM: 7 MARCH 2022
In this module, we will be going top-down from a high-level overview of simulation technology and its application in the scope of ADAS / Automated Driving to a deep dive into environment simulation for perception use cases and sensor modeling.
You’ll get an overview of types and purposes of simulation and understand the importance and role of simulation in ADAS/AD development. You will also learn to differentiate between simulation applications for various use cases and understand the data flow from real-world data to simulated sensor perception. You will also apply simulation for verification and validation tasks and implement your own closed-loop simulation.
Instructor: Marius Dupuis, Consultant and Owner of SimCert
Bonus Content
Participants will also have access to:
– Introductory material, including market overviews.
– Insights into the challenges of mass production for autonomous driving, with Prof. Dr. Alexander Braun.
