Deep Crash Detection From Vehicular Sensor Data With Multimodal Self-Supervision

The ability to detect vehicle accidents from on-board sensor data is of the utmost importance to provide prompt assistance to prevent injuries and fatalities. In this article, we present a novel deep learning method capable of analyzing time series recorded from Inertial Measurement Units (IMU) and GPS devices to recognize the presence of an accident along with its severity. We propose a neural architecture capable of exploiting the different sensor streams (i.e., acceleration, gyroscope, and GPS speed), a multimodal contrastive self-supervised training procedure, and an ad-hoc stack of data augmentation techniques, specifically designed to counteract the extreme class imbalance and to improve the generalization capabilities of the whole pipeline. The proposed method has been validated against several state-of-the-art methods on a large and highly imbalanced dataset, composed of more than 200 thousand time series collected from US vehicles, with different vehicle sizes and traveling on different types of road. Our method achieves an average-precision score (AP) of 0.9 in the detection of crashes and 0.76 in the detection of severe crashes, significantly outperforming all the other approaches, and has small footprint and latency, so that it can easily be deployed on embedded devices.

Automated summary

The article introduces a novel deep learning method for detecting vehicle accidents from on-board sensor data, which outperforms other approaches in terms of accuracy and can be easily deployed on embedded devices. The method utilizes neural architecture, multimodal self-supervised training, and data augmentation techniques to improve generalization capabilities and counteract extreme class imbalance.