Conceptual Sensors Testing Framework for Autonomous Vehicles

Jernigan, Michael; Alsweiss, Suleiman; Cathcart, J. Michael; Razdan, Rahul

doi:10.1109/vnc.2018.8628370

Cited by 3 publications

(1 citation statement)

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“…As a result of the growing interest in AVs, several testing and validation procedures have been developed to optimize their performance for maximum safety before being deployed onto public roadways and infrastructures [ 5 ]. AVs can be tested in simulations or in the real world.…”

Section: Introductionmentioning

confidence: 99%

An Overview of Autonomous Vehicles Sensors and Their Vulnerability to Weather Conditions

Vargas

Alsweiss

Toker

et al. 2021

Sensors

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185

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Autonomous vehicles (AVs) rely on various types of sensor technologies to perceive the environment and to make logical decisions based on the gathered information similar to humans. Under ideal operating conditions, the perception systems (sensors onboard AVs) provide enough information to enable autonomous transportation and mobility. In practice, there are still several challenges that can impede the AV sensors’ operability and, in turn, degrade their performance under more realistic conditions that actually occur in the physical world. This paper specifically addresses the effects of different weather conditions (precipitation, fog, lightning, etc.) on the perception systems of AVs. In this work, the most common types of AV sensors and communication modules are included, namely: RADAR, LiDAR, ultrasonic, camera, and global navigation satellite system (GNSS). A comprehensive overview of their physical fundamentals, electromagnetic spectrum, and principle of operation is used to quantify the effects of various weather conditions on the performance of the selected AV sensors. This quantification will lead to several advantages in the simulation world by creating more realistic scenarios and by properly fusing responses from AV sensors in any object identification model used in AVs in the physical world. Moreover, it will assist in selecting the appropriate fading or attenuation models to be used in any X-in-the-loop (XIL, e.g., hardware-in-the-loop, software-in-the-loop, etc.) type of experiments to test and validate the manner AVs perceive the surrounding environment under certain conditions.

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Section: Introductionmentioning

confidence: 99%