In the 2005 DARPA Grand Challenge, Stanford’s Stanley robot car, navigated by 5 roof-mounted SICK Lidars, won the race against 4 other competitors. Since then, Lidars have gradually become crucial perception sensors for autonomous driving and ADAS, due to their ability to generate real-time point clouds with accurate 3D information of the vehicle’s surroundings. Extensive research efforts have been invested into Lidar technology in both academia and the industry. As a result, a diverse variety of Lidar sensors have been created in the past decade. In this chapter, the authors aim to review the state of the art of Lidar sensors for autonomous driving or ADAS applications. The manuscript discusses the important metrics for Lidar sensor performance: detection range, field of view (FOV), angular resolution, frame rate, and eye safety. Then, different Lidar mapping methods and distance calculation mechanisms are discussed. Current status of mechanical, MEMS, FLASH, optical phased array (OPA), and frequency-modulated continuous wave (FMCW) Lidars is introduced, and their pros and cons and reliability performance are compared.
Lidar Technology
Advanced Driver Assistance Systems and Autonomous Vehicles ; Kapitel : 8 ; 245-263
2022-10-28
19 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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