An Automotive LiDAR Performance Test Method in Dynamic Driving Conditions

Author:

Park Jewoo1,Cho Jihyuk2,Lee Seungjoo2,Bak Seokhwan1,Kim Yonghwi2

Affiliation:

1. Durability Technology Team, Hyundai Motor Company, Hwaseong 18280, Republic of Korea

2. IT Convergence Components Research Center, Korea Electronics Technology Institute (KETI), Gwangju 61005, Republic of Korea

Abstract

The Light Detection and Ranging (LiDAR) sensor has become essential to achieving a high level of autonomous driving functions, as well as a standard Advanced Driver Assistance System (ADAS). LiDAR capabilities and signal repeatabilities under extreme weather conditions are of utmost concern in terms of the redundancy design of automotive sensor systems. In this paper, we demonstrate a performance test method for automotive LiDAR sensors that can be utilized in dynamic test scenarios. In order to measure the performance of a LiDAR sensor in a dynamic test scenario, we propose a spatio-temporal point segmentation algorithm that can separate a LiDAR signal of moving reference targets (car, square target, etc.), using an unsupervised clustering method. An automotive-graded LiDAR sensor is evaluated in four harsh environmental simulations, based on time-series environmental data of real road fleets in the USA, and four vehicle-level tests with dynamic test cases are conducted. Our test results showed that the performance of LiDAR sensors may be degraded, due to several environmental factors, such as sunlight, reflectivity of an object, cover contamination, and so on.

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

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5. Heinzler, R., Schindler, P., Seekircher, J., Ritter, W., and Stork, W. (2019, January 9–12). Weather influence and classification with automotive lidar sensors. Proceedings of the 2019 IEEE Intelligent Vehicles Symposium (IV), Paris, France.

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