EyeDAS: Securing Perception of Autonomous Cars Against the Stereoblindness Syndrome

TL;DR

EyeDAS is a novel few-shot learning method that significantly improves the accuracy of object detectors in autonomous cars by reliably distinguishing between 2D and 3D objects in real-time, enhancing safety.

Contribution

The paper introduces EyeDAS, a new few-shot learning approach that secures object detection against stereoblindness syndrome in autonomous driving scenarios.

Findings

Reduces 2D misclassification rate from up to 100% to 2.4%.

Achieves an AUC of over 0.999 and TPR of 1.0 with low FPR.

Outperforms baseline methods in real-time object classification.

Abstract

The ability to detect whether an object is a 2D or 3D object is extremely important in autonomous driving, since a detection error can have life-threatening consequences, endangering the safety of the driver, passengers, pedestrians, and others on the road. Methods proposed to distinguish between 2 and 3D objects (e.g., liveness detection methods) are not suitable for autonomous driving, because they are object dependent or do not consider the constraints associated with autonomous driving (e.g., the need for real-time decision-making while the vehicle is moving). In this paper, we present EyeDAS, a novel few-shot learning-based method aimed at securing an object detector (OD) against the threat posed by the stereoblindness syndrome (i.e., the inability to distinguish between 2D and 3D objects). We evaluate EyeDAS's real-time performance using 2,000 objects extracted from seven YouTube video recordings of street views taken by a dash cam from the driver's seat perspective. When applying EyeDAS to seven state-of-the-art ODs as a countermeasure, EyeDAS was able to reduce the 2D misclassification rate from 71.42-100% to 2.4% with a 3D misclassification rate of 0% (TPR of 1.0). We also show that EyeDAS outperforms the baseline method and achieves an AUC of over 0.999 and a TPR of 1.0 with an FPR of 0.024.