Ning Ding

The leading factor behind most vehicular accidents is the driver’s inattentiveness. To accurately determine driver’s drowsiness, Electroencephalography (EEG) has been proven to be a reliable and effective method. Even though previous studies have developed accurate driver’s drowsiness detection algorithms, certain challenges still persist, such as (a) limited training sample sizes, (b) detecting anomalous signals, and (c) achieving subject-independent classification. In this paper we propose a novel solution, names as EEG-Fest, which is a generalized few-shot model aimed at addressing the aforementioned limitations. The EEG-Fest has the ability to (a) classify a query sample’s level of drowsiness with only a few support sample inputs (b) identify whether a query sample is anomalous signals or not, and (c) perform subject-independent classification. During the evaluation, our proposed EEG-Fest algorithm demonstrates better performance compared to other two conventional EEG algorithms in cross-subject validation.

Object detection (OD) is crucial to autonomous driving. On the other hand, unknown objects, which have not been seen in training sample set, are one of the reasons that hinder autonomous vehicles from driving beyond the operational domain. To addresss this issue, we propose a saliency-based OD algorithm (SalienDet) to detect unknown objects. Our SalienDet utilizes a saliency-based algorithm to enhance image features for object proposal generation. Moreover, we design a dataset relabeling approach to differentiate the unknown objects from all objects in training sample set to achieve Open-World Detection. To validate the performance of SalienDet, we evaluate SalienDet on KITTI, nuScenes, and BDD datasets, and the result indicates that it outperforms existing algorithms for unknown object detection. Notably, SalienDet can be easily adapted for incremental learning in open-world detection tasks. The project page is https://github.com/dingmike001/SalienDet-Open-Detection.git .

A lack of driver's vigilance is the main cause of most vehicle crashes. Electroencephalography(EEG) has been reliable and efficient tool for drivers' drowsiness estimation. Even though previous studies have developed accurate and robust driver's vigilance detection algorithms, these methods are still facing challenges on following areas: (a) small sample size training, (b) anomaly signal detection, and (c) subject-independent classification. In this paper, we propose a generalized few-shot model, namely EEG-Fest, to improve aforementioned drawbacks. The EEG-Fest model can (a) classify the query sample's drowsiness with a few samples, (b) identify whether a query sample is anomaly signals or not, and (c) achieve subject independent classification. The proposed algorithm achieves state-of-the-art results on the SEED-VIG dataset and the SADT dataset. The accuracy of the drowsy class achieves 92% and 94% for 1-shot and 5-shot support samples in the SEED-VIG dataset, and 62% and 78% for 1-shot and 5-shot support samples in the SADT dataset.