Ellipsometry for Autonomous Vehicles

Introduction

Ellipsometry consists of the measurement of the change in polarization state of a beam of light upon reflection from the sample of interest. The exact nature of the polarization change is determined by the sample's properties (thickness and refractive index). The experimental data are usually expressed as two parameters Ψ and Δ. The polarization state of the light incident upon the sample may be decomposed into an s and a p component (the s-component is oscillating parallel to the sample surface, and the p-component is oscillating parallel to the plane of incidence).

The intensity of the s and p component, after reflection, are denoted by R_s and R_p. The fundamental equation of ellipsometry is then written:

Polarimetry and Vehicle Paint

Vehicle Paint consists of multiple layers of thin films.  Please see below.

Schematic of a typical four-layer automotive coating system.

At the first approximation, the quantity ρ in the fundamental equation of ellipsometry above would be a function of refractive index n, extinction coefficient κ and the thickness d of the Clearcoat. Note that n and κ are frequency-dependent parameters.  That means that the quantity ρ will have different values at different frequencies for different sensor types. 

(For multi-layer films, a more detailed treatment may be found in: "Simultaneous measurement of the refractive index and thickness of thin films by S-polarized reflectances" by Tami Kihara and Kiyoshi Yokomori, Applied Optics Vol. 31, Issue 22, pp. 4482-4487 (1992) https://doi.org/10.1364/AO.31.004482).

Application to Autonomous Vehicles

Autonomous vehicles are equipped with multiple sensors that operate in different ranges of the electromagnetic spectrum.  These include LiDAR, RADAR, Cameras, and Infrared sensors.  The basic idea is to characterize the polarization signature of GM vehicles by measuring the quantity ρ at two locations: at the vehicle during test and development of that vehicle make and model and inside the AV vehicle during its operations to infer if an object is a vehicle or not.

The steps of this approach are as follows:

Step 1

•  Camera: Measure ρ in the visible light range: 7.5 exp (14) Hz. To 4.3 exp (14) Hz.
• LiDAR: Measure ρ at 905 nanometer and 1550 nanometer
• RADAR: Measure ρ in the range automotive band: of 76 GHz. to 81 GHz.
• Infrared: Measure ρ in the range of 300 GHz. to 430 THz.

Step 2

• For each sensor type in the vehicle, develop processing logic to measure the quantity ρ from scattered electromagnetic signals that are received.
• For each sensor type (LiDAR, RADAR, Camera, IR) cluster the values that have been computed in the previous sub-step.  This will give a spatial distribution of the quantity ρ for each sensor type.  This spatial distribution is a proxy for the object that is being detected.
• For each sensor type (and the corresponding frequency range), using statistical or other Machine Learning techniques, match the values of ρ against those obtained in Step 1.  Use those values, singly or as part of a voting scheme, and in conjunction with other forms of analysis (i.e., ANN) to infer if the signals detected by the sensors are coming from a vehicle or not.

Other Steps

• Work with other OEMs to get their polarization signatures added to the internal models of AV.
• Test and validate there are a need for a calibration step as well as a need to model sensor degradation over time for this sort of applications.
• Use nanoparticles in the paint to enhance the polarization signal and give a specific GM-signature.