IS Auto Europe 2018

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Image Sensors Auto Europe 2018 program has been published and and has a number of image sensing papers, primarily about LiDARs:

  • LiDAR past, present, and future: seeing through the noise
    Anand Gopalan | CTO of Velodyne LiDAR, Inc.
    The presenter will trace the evolution of LiDAR from beginning to the current state of the art.
    He will then discuss the multitude of LiDAR approaches in the public domain and attempt to separate the hype from the reality.
    Finally, he will explore future trends toward greater integration and “edge compute.”
  • Improving LIDAR sensitivity by using a DMD to mask ambient light
    John Fenske | DLP Automotive Systems Engineer of Texas Instruments
    Why ambient light is a problem in LIDAR systems and how it defines the noise floor
    How to use a DMD to block ambient light and increase LIDAR sensitivity
    How to estimate the performance improvement given by the enhanced sensitivity
  • How to improve robustness of automotive LiDARs
    Celine Canal | Senior Project Leader & ADAS Application Engineer of Quantel Laser
    Trends in solid-state LiDARs
    Challenges and design considerations of novel beam steering approaches and flash LiDARs
    Short-pulse edge-emitter diode arrays
  • Moving from legacy LiDAR to next-generation iDAR (Intelligent Detection and Ranging)
    John Stockton | VP of Product of AEye
    Traditional LiDAR systems, because of siloed sensors and rigid data collection methods, tend to oversample or undersample information. This then requires significant processing power and time to extract critical objects, leading to latency. Second-generation systems are emerging that fuse intelligence with the data collection process - enabling the system to dynamically track targets and objects of interest, with almost no computational penalty.
    In this session, AEye will delve into this new form of intelligent data collection, called iDAR”, or “Intelligent Detection and Ranging”. The session will help industry players understand iDAR’s role in optimising data collection, reducing bandwidth, improving vision perception and intelligence, and speeding up motion planning for autonomous vehicles.

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