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 to build LIDAR technologies in automotive applications.
The course reviews 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.
The course has been approved by the Accreditation Commission for Traffic Accident Reconstruction (ACTAR) for 7 Continuing Education Units (CEUs). Upon completion of this course, accredited reconstructionists should mail a copy of their course certificate of achievement and the $5 participant CEU fee to ACTAR, PO Box 1493, North Platte, NE 69103.
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, heads of innovation and BOM family owners, and professionals involved inactive safety, LIDAR, and automated driving
Prerequisites
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.
Rajeev Thakur
Rajeev Thakur is currently senior technical sales manager at Aeva – responsible for sales and business development of FMCW lidar technology. He previously was director – sales technical support at Ouster – responsible for providing technical support to North American Sales – across all industries. He also was director automotive programs at Velodyne Lidar. In this role he supported 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 holds a master’s degree in manufacturing engineering from the University of Massachusetts, Amherst, and a bachelor’s degree in mechanical engineering from Guindy Engineering College in Chennai, India. He is a licensed professional engineer and holds several patents on occupant sensing. He is also a member of the SAE Active Safety Standards Development Committee.
