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Embedded systems with low computing resources for artificial intelligence are being a key piece for the deployment of the Internet of Things in different areas as energy efficiency, agriculture or water monitoring, amid others. This paper carries out a study of the computational performance of a smart road detection and signalling system. To this end, the implementation methodology from Matlab® to C++ of a one-class SVM classifier with two pattern analysis strategies based on RADAR signals and RAW data is described. As a result, we found a balance between AUC, RAM consumption, processing time and power consumption for a Teensy 4.1 microcontroller with STFT and the fitcsvm2 algorithm versus other hardware options such as an I7-3770K processor, Raspberry Pi Zero and Teensy 3.6.