EyeDAS: Securing Perception of Autonomous Cars Against the Stereoblindness Syndrome

• URL: http://arxiv.org/abs/2205.06765v1
• Date: Fri, 13 May 2022 16:53:24 GMT
• Title: EyeDAS: Securing Perception of Autonomous Cars Against the Stereoblindness Syndrome
• Abstract summary: We present EyeDAS, a novel few-shot learning-based method aimed at securing an object detector (OD) for autonomous driving. 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.
• Score: 36.39622328877822
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Illusion of Depth: Revealing Hidden Stereo Vision Vulnerabilities in Depth Estimation [5.959942963741855]
  We reveal an intrinsic vulnerability in stereo cameras that stems from their pixel sampling and calibration processes.<n>Attackers can achieve fine-grained control over the estimated depth of real obstacles using simple repeating patterns.<n>Our work highlights vulnerabilities hidden in stereo matching and deep learning depth estimation models, addressing critical limitations in autonomous system deployments.
  arXiv  Detail & Related papers  (2026-09-14T20:51:49Z)
• Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective [76.02772556944073]
  Environmental illusions are naturally existing yet overlooked phenomena in real-world driving environments.<n>They can disturb visual perception, leading to misinterpretation of the scene and posing serious safety risks to autonomous driving systems.<n>We introduce the first benchmark, LanEvil++, for evaluating the robustness of lane perception under environmental illusions.
  arXiv  Detail & Related papers  (2026-07-07T03:15:00Z)
• Vision-based Lifting of 2D Object Detections for Automated Driving [8.321333802704446]
  We propose a pipeline which lifts the results of existing vision-based 2D algorithms to 3D detections using only cameras.<n>To the best of our knowledge, we are the first using a 2D CNN to process the point cloud for each 2D detection to keep the computational effort as low as possible.
  arXiv  Detail & Related papers  (2025-06-13T14:40:12Z)
• Uncertainty Estimation for 3D Object Detection via Evidential Learning [63.61283174146648]
  We introduce a framework for quantifying uncertainty in 3D object detection by leveraging an evidential learning loss on Bird's Eye View representations in the 3D detector. We demonstrate both the efficacy and importance of these uncertainty estimates on identifying out-of-distribution scenes, poorly localized objects, and missing (false negative) detections.
  arXiv  Detail & Related papers  (2024-10-31T13:13:32Z)
• Discovering New Shadow Patterns for Black-Box Attacks on Lane Detection of Autonomous Vehicles [2.5539742994571037]
  This paper introduces a novel approach to generate physical-world adversarial examples (AEs) negative shadows: deceptive patterns of light on the road created by strategically blocking sunlight, which then cast artificial lane-like patterns. Using a 20-meter negative shadow, it can direct a vehicle off-road with a 100% violation rate at speeds over 10 mph. Other attack scenarios, such as causing collisions, can be performed with at least 30 meters of negative shadow, achieving a 60-100% success rate.
  arXiv  Detail & Related papers  (2024-09-26T19:43:52Z)
• LanEvil: Benchmarking the Robustness of Lane Detection to Environmental Illusions [61.87108000328186]
  Lane detection (LD) is an essential component of autonomous driving systems, providing fundamental functionalities like adaptive cruise control and automated lane centering. Existing LD benchmarks primarily focus on evaluating common cases, neglecting the robustness of LD models against environmental illusions. This paper studies the potential threats caused by these environmental illusions to LD and establishes the first comprehensive benchmark LanEvil.
  arXiv  Detail & Related papers  (2024-06-03T02:12:27Z)
• UniPAD: A Universal Pre-training Paradigm for Autonomous Driving [74.34701012543968]
  We present UniPAD, a novel self-supervised learning paradigm applying 3D differentiable rendering. UniPAD implicitly encodes 3D space, facilitating the reconstruction of continuous 3D shape structures. Our method significantly improves lidar-, camera-, and lidar-camera-based baseline by 9.1, 7.7, and 6.9 NDS, respectively.
  arXiv  Detail & Related papers  (2023-10-12T14:39:58Z)
• Threat Detection In Self-Driving Vehicles Using Computer Vision [0.0]
  We propose a threat detection mechanism for autonomous self-driving cars using dashcam videos. There are four major components, namely, YOLO to identify the objects, advanced lane detection algorithm, multi regression model to measure the distance of the object from the camera. The final accuracy of our proposed Threat Detection Model (TDM) is 82.65%.
  arXiv  Detail & Related papers  (2022-09-06T12:01:07Z)
• BlindSpotNet: Seeing Where We Cannot See [20.689816289568718]
  We introduce 2D blind spot estimation as a critical visual task for road scene understanding. By automatically detecting road regions that are occluded from the vehicle's vantage point, we can proactively alert a manual driver or a self-driving system to potential causes of accidents.
  arXiv  Detail & Related papers  (2022-07-08T12:54:18Z)
• Physically Realizable Adversarial Examples for LiDAR Object Detection [72.0017682322147]
  We present a method to generate universal 3D adversarial objects to fool LiDAR detectors. In particular, we demonstrate that placing an adversarial object on the rooftop of any target vehicle to hide the vehicle entirely from LiDAR detectors with a success rate of 80%. This is one step closer towards safer self-driving under unseen conditions from limited training data.
  arXiv  Detail & Related papers  (2020-04-01T16:11:04Z)
• Road Curb Detection and Localization with Monocular Forward-view Vehicle Camera [74.45649274085447]
  We propose a robust method for estimating road curb 3D parameters using a calibrated monocular camera equipped with a fisheye lens. Our approach is able to estimate the vehicle to curb distance in real time with mean accuracy of more than 90%.
  arXiv  Detail & Related papers  (2020-02-28T00:24:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.