Summary of Real-time Accident Anticipation For Autonomous Driving Through Monocular Depth-enhanced 3d Modeling, by Haicheng Liao et al.
Real-time Accident Anticipation for Autonomous Driving Through Monocular Depth-Enhanced 3D Modeling
by Haicheng Liao, Yongkang Li, Chengyue Wang, Songning Lai, Zhenning Li, Zilin Bian, Jaeyoung Lee, Zhiyong Cui, Guohui Zhang, Chengzhong Xu
First submitted to arxiv on: 2 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel framework, AccNet, which leverages monocular depth cues for 3D scene modeling to improve traffic accident anticipation. The AccNet framework outperforms existing 2D-based methods by predicting accidents more accurately. To address the issue of skewed data distribution in traffic datasets, the authors propose the Binary Adaptive Loss for Early Anticipation (BA-LEA) and a multi-task learning strategy. These advancements enable the predictive model to focus on critical moments preceding an accident. The framework is evaluated on four benchmark datasets, demonstrating its superior performance through metrics such as Average Precision (AP) and mean Time-To-Accident (mTTA). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper aims to improve traffic accident anticipation using dashcam videos. It introduces a new approach that combines 2D and 3D information to predict accidents more accurately. The method addresses the problem of uneven data distribution in datasets by adjusting its loss function. This helps the model focus on important moments before an accident occurs. The results show that this approach performs better than current methods. |
Keywords
» Artificial intelligence » Loss function » Multi task » Precision
