Deep Learning for Risk Assessment in Automotive Applications

Within the framework of the assisted systems for automotive applications, considerable research has been employed to monitoring the driver's attention level in order to assess the risk level of the driving scenario. In this context, physiological monitoring of the driver's condition has emerged as a relevant approach to enhance driving assistance without having an invasive approach. According to these premises, the authors have developed a driving assistance system capable to employ a dedicated bio-sensor for capture the driver's photoplethysmo-graphic (PPG) signal, which is closely linked to their level of attention. This PPG signal is then processed by a dedicated deep learning architecture to reconstruct the driver's attention level. Meanwhile a separate automotive-grade intelligent vision-based system has been developed to quantify the risk level of the driving scenario by means of a video saliency analysis technique. The effectiveness of this comprehensive system has been validated through experimental results.