## 1 Introduction  
The problem of obtaining essential information about remote objects and using it for detection and computer-assisted classification of objects into predefined groups arises in different contexts of utmost importance [1, 2]. In particular, driving in complex traffic environments or hazardous conditions (night or heavy rain, for instance) would benefit from the computational assistance tools to reduce the risk and help the driver to take the right decision [3]. Other vital applications are detection of forest fires [4] and classification of atmospheric aerosols and clouds [5]. One of the critical issues concerns the detection of surrounding objects and their characteristics' determination. The detection using electromagnetic waves scattered by the object, which can be loosely termed as optical sensing, is one of the most powerful approaches because, in addition to the detection of the object's presence and measuring the distance to it and its relative velocity, this can bring some additional information, which can be used for its classification. This information is encoded in the variation of the optical signal intensity with the radiation wavelength (spectroscopy) or in the polarization states of the detected light (polarimetry). Unfortunately, the interaction of light with real-world objects is usually too complex to be described by solvable models and phenomenological approaches are the only feasible alternative. In this article, we analyze a number of machine learning (ML) techniques for object detection and classification, which can be relevant in the context of autonomous driving. We aim at classifying real-world objects (in particular, in the context of autonomous driving) using polarimetric optical sensing data. The physical information about a remote object is provided by the polarimetry of the optical signal caused by the diffuse scattering of light from the object, a powerful method of remote sensing [6]. The polarization state of an optical beam interacting with matter may be changed by varying amplitudes, relative phase of orthogonal field components, or by its degree of polarization [7; 8; 9]. The approach we adopt is based on the Stokes-Mueller formalism [10]. The Stokes vector describes the state of polarization of any light beam, whereas the Mueller matrix (MM), consisting of \(4\times 4\) real elements, provides information related to the samples. The advantage of this approach with respect to the Jones calculus is that it can describe partially polarized or incoherent light. Polarimetric imaging and sensing methods have been applied to the detection of pollution in the atmosphere [5] or land slides in forested areas [11], and to the classification of materials [7; 8; 9]. Using extensive laboratory polarimetry measurements, described in our previous work [9], a database of several thousands of samples was built, wherein each sample (event) is characterized by 15 Mueller matrix elements (MMEs) normalized by the zeroth element, \(M_{00}\), plus the angle of incidence (AoI) of the testing light beam onto the object's surface. Some examples of the MME images are shown in Figure 1. Each sample in the DB was labeled according to one of seven different object classes (see Section 2). Machine learning (ML) and, in particular, deep learning methods are widely used for the classification of objects and living beings [12; 13; 1, 2; 14]. Within a supervised ML approach, a reference database is used to build a classifier with the objective to associate  
Figure 1: Normalized Mueller matrix images of some of the studied materials taken with 1550 nm radiation and AoI of 20\({}^{\circ}\). new events with one of previously defined groups or clusters using information derived from the experiment. A wide range of machine learning techniques have already been employed in the context of autonomous driving [12]. These techniques include K-nearest neighbors (KNN), first introduced in Ref. [15]; support vector machines (SVMs), introduced in Ref.