Head-On Vehicle Collision Prevention With Machine Learning and a Fully Centralized Radio Sensing Approach

Head-on vehicle collision prevention remains a critical challenge in autonomous and manual driving, particularly for complex vehicular scenarios where conventional sensors face line-of-sight limitations. In this work, we propose a novel fully centralized warning system platform using continuous waveform (CW) signals and Doppler signature analysis. We use a propagation model to analyze Doppler effects in vehicular communication systems, validated empirically across two distinct driving environments (high-speed highway and medium-speed rural road). Our platform was developed using general-purpose equipment to generate a data set of spectrograms computed with the received radio-frequency (RF) CW signals. Furthermore, machine learning models (SVM/KNN/Boosted Trees) applied to spectrogram features reduced via Principal Component Analysis are used to classify five different vehicular events related to head-on collision. Our system achieves up to 99% classification accuracy while demonstrating that Doppler signatures in communication signals can be used to extract high-quality information for safety-critical sensing. Our results show robust performance in both test scenarios, with high-precision for oncoming vehicles at different speeds. The system’s success in using CW RF signals for sensing, establishes a foundation for Integrated Sensing and Communication (ISAC) implementations.