LIDAR for ADAS and Autonomous Sensing C1935

Topics: Advanced Technologies

Advanced Driver Assist System (ADAS) and autonomous vehicle technologies have disrupted the traditional automotive industry with potential to increase safety and optimize the cost of car ownership. Light detection and ranging (LIDAR) sensing, a sensing method that detects objects and maps their distances, is seeing rapid growth and adoption in the industry. However, the sensor requirements and system architecture options continue to evolve. This course will provide the foundation on which to build LIDAR technologies in automotive applications.

The seminar will begin with a review of infrared basics: electromagnetic spectrum, spectral irradiance, night vision and eye safety. The instructor will dive into LIDAR – flash, scanning, wavelengths, lasers, detectors, scanners, range and resolution calculations, optics, thermal design, challenges to automotive qualification, and sensor fusion. The course will conclude with a short discussion on trends and challenges facing optical sensing in autonomous vehicles. 

Learning Objectives

By participating in this course, you will be able to:

  • Recognize market forces, regulation and technology in the ecosystem
  • Comprehend electromagnetic spectrum, spectral irradiance, night vision and eye safety
  • Describe various LIDAR architectures based on key design parameters
  • Formulate LIDAR requirements based on an understanding of system edge use cases
  • Calculate Laser power requirements for ToF LIDAR technologies to meet system needs
  • Gain an overview of challenges and opportunities for sensing trends in ADAS and AV

Who Should Attend

Mechanical, lead, application, and electrical engineers, along with head of innovation and BOM family owner will benefit from this course. Those involved in active safety, LIDAR and automated driving also will benefit.


An undergraduate engineering degree or a strong technical background is highly recommended. A basic knowledge of college algebra, college physics, and a basic awareness of LIDAR applications in ADAS and autonomous vehicles is beneficial.

You must complete all course contact hours and successfully pass the learning assessment to obtain CEUs.

  • ADAS and Autonomous Sensing
    • Market forces shaping industry
    • Regulation landscape
    • From guidelines to regulation – typical path
    • ADAS – NCAP & IIHS rating
    • NHTSA Autonomous Driving Guideline
    • NHTSA Autonomous Levels
    • Human vs Autonomous Driver
    • LIDAR, Camera, RADAR - overview
    • Sensor fusion – need for holistic view
    • The importance of HMI for Level 3
  • Infrared Basics
    • Electromagnetic spectrum
    • Infrared energy, wavelength and temperature
    • Photometric and radiometric units
    • Eye sensitivity to light – visible and infrared
    • Photopic, scotopic and night vision
    • Eye safety basics
    • Near Infrared classifications
    • Spectral irradiance on earth – day and night (ambient noise)
    • Photon energy calculation
  • LIDAR Topics
    • LIDAR basics – laser, detector, range calculation
    • Laser basics
    • Laser types – Edge Emitter, VCSEL, Multimode, Single Mode, Stacked, Fiber, Single Channel, Arrays
    • Detector basics
    • Detector options – PIN Photodiode, APD, SIPM, Arrays, NIR Enhanced CMOS
    • Wavelength selection – 905nm, 1550nm, others
    • Laser & Detector considerations
      • Laser Packages - Bare die, Through Hole, Surface Mount
      • Detector Packages – Through Hole, Arrays
      • Matched Sensitivity of Laser and Detector
      • Wavelength Shifts
      • Temperature and efficiency
      • Heat dissipation
      • EMC and ESD considerations
      • Reduction in Performance due to rain, fog, snow
    • Types of LIDAR – Flash, Scanning, Time-of-Flight, FMCW
    • Scanner Types – Mechanical Mirrors, MEMS Mirror, Optical phase arrays, Liquid Crystal scanning
    • Developing requirements for LIDAR
    • Signal to Noise ratio – ambient noise considerations
    • Eye safety
    • Optical challenges for LIDAR
    • Laser drivers – need, challenges for high current and low pulse width
    • Cost reduction – opportunities and challenges
    • Automotive Qualification and ASIL D standard
  • Autonomous Vehicles and Optical Sensors
    • Trends and challenges
    • Artificial Intelligence
    • New sensing technologies
    • Working with startups, Tier1, Tier2, OEM and other ecosystem partners

Registration for the web seminar (live, online) is available on a per-person basis, similar to purchasing a seat in a classroom. The fee includes one connection to WebEx training center, using a PC with internet access and VoIP or a telephone,* and access to a secure course in the SAE Learning Center for presentations, supplemental materials, assignments, and learning assessment. To enjoy a more personalized experience, use of a webcam is encouraged.

*Global toll-free telephone numbers are provided for many countries outside the U.S., but are limited to those on the WebEx call-in toll-free number list. Check here to see if your country has a global call-in toll free telephone number for this web seminar. If your country is not listed, you may still connect using the US/Canada Call-in toll number or VoIP. 

Although WebEx will automatically launch when you join the web seminar, you are encouraged to test your setup in advance of the course start date. Click here, then follow the onscreen instructions.

Rajeev Thakur

Rajeev Thakur

Rajeev Thakur is currently Director Automotive Programs at Velodyne Lidar - responsible for building Velodyne's LIDAR business in the automotive market. In this role he supports OEM customers to select, design-in and launch Velodyne's wide LIDAR portfolio for autonomous vehicles and ADAS functions. Prior to this, he was at OSRAM Opto Semiconductors as Regional Marketing Manager for infrared product management and business development in the NAFTA automotive market. His focus was on LIDAR, driver monitoring, night vision, blind spot detection and other ADAS applications. He has been in the Detroit automotive industry since 1990 – working for companies such as Bosch, Johnson Controls and Chrysler. He has concept-to-launch experience in occupant sensing, seating and power train sensors. He holds a masters degree in Manufacturing engineering from the University of Massachusetts, Amherst and a Bachelors degree in Mechanical engineering from Guindy engineering college in Chennai, India. He is a licensed professional engineer and holds a number of patents on occupant sensing. He is also a member of the SAE Active Safety Standards development committee and a reviewer for IEEE Intelligent Transportation Systems Transactions.

Duration: 0 Day
CEUs: .7

Fees: $599.00

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