## 2 Related Work  
One of the essential parts of the subsystems of an advanced driver assistant system (ADAS), used in automotive as collision avoidance and lane assist, is target tracking [7]. The target tracking can be realized by the cooperation of two techniques: data association and a Kalman filters bank. For each track, a separate filter is implemented. These filters compose the filters bank and operate in parallel. Despite its wide use and success, practitioners employing the Kalman smoother still must resort to manually tuning its parameters. Due to the non-stationarity of the signals at the input of Kalman filters, uncertainties appear at their outputs. A way to reduce this uncertainty consists of the adaptive selection of parameters during the filtering process. Many researchers have proposed methods for automatic tuning of the parameters in Kalman smoothers, based on different strategies [2]. Some of these strategies solve convex or non-convex optimization problems. One of the first methods proposed for automatically tuning Kalman's filter parameters, exploiting its ability to work with missing data, is the joint learning of parameters and state/output sequence using the expectation maximization (EM) algorithm [8]. For a given state-space model, a simple recursive procedure can be derived for the parameters' estimation by maximum likelihood, using the EM algorithm and the conventional Kalman filter algorithm in conjunction. Approaches that are more recent employ different optimization methods, including the simplex algorithm [9]. The subject of [10] is a method based on the coordinate descent algorithm for automatically learning the noise parameters of a Kalman filter. The authors of [10] propose machine-learning algorithms for automatically choosing the noise parameters of the Kalman filter or extended Kalman filter (EKF). They start from the fundamental assumption that during the EKF development, it is possible to instrument the system to measure additional variables. These new variables provide accurate estimates of the Kalman filter's state variables. These additional measurements are available only in the initial tuning phase. The authors of [11] proposed an optimal automated procedure for tuning Kalman filters based on genetic algorithm minimization, which uses statistical consistency tests. The paper [12] focuses on applying gradient-based methods to tune filters that estimate the vertical state of aircraft based on quantized altitude measurements only. Significant time and effort is frequently spent in tuning various Kalman filter's model parameters, as process noise covariance, pre-whitening filter models for non-white noise, etc. Conventional optimization techniques for tuning are non-convex optimization problems, producing poor local minima localization. To address these issues, the authors of [13] developed a new Bayesian optimization strategy for automatically tuning Kalman filters. The authors of [13] propose two stochastic objective functions: normalized estimation error squared (NEES), used when ground truth models are available, and the normalized innovation error squared (NIS), used when only sensor data are available for the optimization purpose. Bayesian optimization can efficiently identify multiple local minima and provide uncertainty quantification on its results by intelligently sampling the parameter space to both learn and exploit a \"nonparametric Gaussian process surrogate function\" for the NEES/NIS costs. Tuning Kalman filters is difficult and time consuming. The filter designer frequently achieves it before navigation. This process is often very expensive in terms of time and is in no way guaranteed to result in optimal parameters. Reinforcement learning is an intelligent solution to this problem, which uses a combination of dynamic programming and trial and error exploration to develop a set of optimal parameters [14].