###### Abstract
Although the algorithm utilized in [14] excelled by its high classification accuracy, it required some complex calculations and was demanding in terms of the necessary memory requirements. In order to reduce the computational complexity and memory demand, we investigated possibilities for the application of machine learning approaches in order to perform the classification. With the results presented in [15], we were able to show that the supervised machine learning (ML) approach of random forest fulfills these requirements and can be used for reducing the computational complexity and memory requirements. The present manuscript aims on advancing the approach introduced in [15] in order to improve the already good classification accuracy and to simplify the experimental setup by overcoming the necessity to place a light barrier for triggering purposes into the infrastructure (alongside the train tracks). In addition, the movement direction of the train was limited to one direction in our previous work. Here, we expand the number of tasks to be performed by adding also the task of movement direction determination. Furthermore, in the following, we also increase the distance between the VLS unit and the moving object (train) and investigate the achievable classification accuracy when additional ambient light is present. Based on the results of this work, we show that backscattered VLS can be an optimal technology to make battery-powered communication components and sensors on a moving object obsolete, at least for some certain tasks, thus realizing a \"green\" approach for performing identification and sensing tasks. Since this manuscript is a follow-up work of [14, 15], we already discussed and compared our solution to other related works in the field there. In ref. [10], it is shown how humans can be detected by means of backscattered visible light sensing, without the necessity of placing markers or distinct materials on the persons. In the presented non-line-of-sight scenarios, the authors investigated not only the effects of different clothing materials and colors, but also showed in a so-called pass-by experiment in a corridor that the task of presence detection can be very well performed by VLS utilizing only a single photodiode as the receiving element. The threshold-based algorithm utilized by the authors is a straightforward solution for performing presence detection with highest accuracy, although different persons performed the experiments. In comparison to this, in our work, in which we place distinct markers on the moving object, we can realize and fulfill the more complex tasks of additional identification, speed estimation, and movement direction determination, while, as described later, by the here-discussed advancements of our system, we can also perform presence detection implicitly. Nevertheless, ref. [10]outlines an interesting future research direction, where the materials and maybe shapes of objects are used to perform in-depth sensing tasks, without the need of distinct markers. Ref. [16] presents an approach to combine the technologies of VLC and VLS in parallel. By utilizing a low frames per second (FPS) camera, the authors show the feasibility of this combination by using light strobes for sensing and LED on-phases for communication. Based on simulations of such an environment, they outline a method for strobing light-based vibration sensing. The work shows that in future, the borders between VLC, VLP and VLS will deteriorate and that visible light technologies can perform two or more tasks at the same time without negatively affecting one another. In another study [17], the current authors also demonstrated how the functionalities of VLC and VLS can be applied in parallel. Concretely, we successfully demonstrated the rotation direction determination of a robotic arm by VLS utilizing the same retroreflective foils of the vendors 3M and Orafol, as in this work.