Towards In-Cabin Monitoring: A Preliminary Study on Sensors Data Collection and Analysis

TL;DR

This paper explores the integration of wearable sensors, computer vision, and radar technologies for in-cabin driver monitoring to detect drowsiness and distraction, emphasizing data fusion for improved accuracy and reliability.

Contribution

It provides a preliminary analysis of sensor data collection and fusion methods to enhance driver state detection systems in automotive safety.

Findings

Sensor data fusion improves detection accuracy.

Combining physiological and visual data increases reliability.

Preliminary framework for in-cabin monitoring established.

Abstract

The last decade's market has been characterized by wearable devices, mainly smartwatches, edge, and cloud computing. A possible application of these technologies is to improve the safety of dangerous activities, especially driving motor vehicles. Common enabling technologies, such as system-on-chip, ultra-low-power computational platforms, and wide-band wireless connectivity, push all these trends. On the one hand, wearable devices, thanks to the continuous contact with the user's body, can measure physiological parameters. On the other hand, edge computing and machine learning techniques, alongside cameras, allow the implementation of contactless computer vision systems capable of providing information about the user's current behavior. Another trend is the usage of RADARs in automotive applications, both for collision avoidance and monitoring driver behavior. These technologies can be combined to develop systems designed to aid the driver. For the sake of this paper, we are focusing on warning drivers, allowing them to know whenever they are drowsy and hence risking a sleep onset or are not paying attention to the road. Developing such systems poses many challenges, such as automatic classification of physiological signal patterns, facial expression recognition, head movements and eye gaze detection. These challenges have been individually addressed in the literature. Anyway, we noticed a need for more description on implementing data fusion. Two main reasons for adopting the fusion approach are to improve the quality of the overall representation (increasing accuracy and specificity against drowsy) and make a more reliable system due to redundancy.