## 1 Introduction  
The use of radars in the automotive industry has gained momentum with time, leading to diverse implementations intended to increase safety and offer assistance in driving [1, 2]. Vehicles made in the past decade most likely contain some use of radar, including vehicle detection, adaptive cruise control, and range-based collision alerts. Radar is one of the critical sensors for automotives, because it operates regardless of weather and light conditions, unlike optical systems. Furthermore, radar is less expensive than lidar systems. Radar technology is advancing to improve self-driving vehicles' safety features through accurate target detection and classification. Radar-based object detection and tracking were addressed in [3, 4], especially for multiple target detection purposes. In [5], the automotive use of radar systems for classification was discussed. In the paper, vehicles, clutter, roadside objects, and humans were classified through time-varying features in the range-Doppler diagram. Data from radar, as well as lidar, are considered together for better object detection through data fusion algorithms [6]. In addition, an approach that employed both radar and a camera was studied to improve target detection accuracy [7, 8]. However, further improvements to classification are needed when self-driving vehicles face unknown and nonvisual objects during a trip, using the material-based classification of sealed objects to decide whether a vehicle should avoid the object for the safety of passengers and other drivers. Knowing the composition of the sealed object can greatly increase safety by allowing the classification of contact with an unknown object as harmful. Generally, a self-driving vehicle will stop under such circumstances, but a sudden stop can endanger other vehicles. Even though the classification of concealed materials is impossible for human drivers or optical systems, electromagnetic waves have the ability to penetrate visually opaque objects such as a cardboard box and provide information pertaining to the materials inside the box. Previous work has been done on material classification by incorporating comb-based millimeter-wave detectors, which focused on material and thickness classification that correlates with our classification for lighter and denser materials [9]. Similarly, FMCW radars have been used to classify materials including carpet, tile, liquid, and laminate based on range cross-range images [10]. There has also been prior work that concludes material classification based on unique power dissipation because of a material's exclusive dielectric properties [11]. Furthermore, radar systems have already been used in specific environments to classify objects that are not visible. Ground-penetrating radars have been used to detect land mines due to their ability to penetrate through the soil and concealed object classification has been used to detect hazardous material for security purposes [12; 13]. Such uses are practical for radar, which can classify materials and substances without relying on visual indications. However, to the best of the authors' knowledge, radar has not been attempted for the classification of sealed objects in automotive applications. In this paper, we investigate the feasibility of classifying a sealed paper box through range profiles measured using an FMCW radar operating at 77 GHz. We use a range profile to differentiate between different materials by analyzing attenuation over distance as an incident wave propagates through the material. Similarly, this concept of range profile has been used in [14] to determine a pipe wall's thickness. This experiment is conducted using a 56 cm \(\times\) 40.6 cm \(\times\) 38.1 cm packaging box filled with five different quantities of paper, corresponding to the five stages. We use crumpled papers because it is convenient to control the paper density of the box. The range profile of each stage is unique in its dissipation of the incident wave.