用单目深度信息构建3D场景，实时预测交通事故，提升自动驾驶安全性。
Real-time Accident Anticipation for Autonomous Driving Through Monocular Depth-Enhanced 3D Modeling
- 结合单目深度图实现精细化3D场景建模，突破传统2D方法局限。
- 在多个数据集上平均精度（AP）和事故前平均时间（mTTA）均优于当前最佳方法。
- 针对数据分布不均问题设计新型损失函数，提升对事故早期阶段的敏感度。
交通事故预见的核心目标是利用行车记录仪视频实时预判潜在事故，这对提升自动驾驶的安全性与可靠性至关重要。本文提出一种创新框架AccNet，通过引入单目深度线索进行精细3D场景建模，显著超越现有基于2D的方法。针对交通事故数据集中普遍存在的数据分布偏斜问题，我们提出二值自适应早预警损失函数（BA-LEA），结合多任务学习策略，使模型更聚焦于事故发生前的关键时刻。我们在三个基准数据集——Dashcam Accident Dataset（DAD）、Car Crash Dataset（CCD）、AnAn Accident Detection（A3D）以及DADA-2000上进行了严格评估，通过平均精度（AP）和平均事故前时间（mTTA）等关键指标验证了该框架的优越预测性能。
原文摘要 · Abstract (English)
The primary goal of traffic accident anticipation is to foresee potential accidents in real time using dashcam videos, a task that is pivotal for enhancing the safety and reliability of autonomous driving technologies. In this study, we introduce an innovative framework, AccNet, which significantly advances the prediction capabilities beyond the current state-of-the-art (SOTA) 2D-based methods by incorporating monocular depth cues for sophisticated 3D scene modeling. Addressing the prevalent challenge of skewed data distribution in traffic accident datasets, we propose the Binary Adaptive Loss for Early Anticipation (BA-LEA). This novel loss function, together with a multi-task learning strategy, shifts the focus of the predictive model towards the critical moments preceding an accident. {We rigorously evaluate the performance of our framework on three benchmark datasets--Dashcam Accident Dataset (DAD), Car Crash Dataset (CCD), and AnAn Accident Detection (A3D), and DADA-2000 Dataset--demonstrating its superior predictive accuracy through key metrics such as Average Precision (AP) and mean Time-To-Accident (mTTA).
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