Deep Learning Advances in Vision-Based Traffic Accident Anticipation: A Comprehensive Review of Methods,Datasets,and Future Directions

要約

Traffic accident prediction and detection are critical for enhancing road safety,and vision-based traffic accident anticipation (Vision-TAA) has emerged as a promising approach in the era of deep learning.This paper reviews 147 recent studies,focusing on the application of supervised,unsupervised,and hybrid deep learning models for accident prediction,alongside the use of real-world and synthetic datasets.Current methodologies are categorized into four key approaches: image and video feature-based
予測、時空の特徴に基づいた予測、シーンの理解、およびマルチモーダルデータ融合。これらの方法は、データ不足、複雑なシナリオへの限定的な一般化、リアルタイムのパフォーマンス制約などの重要な潜在的潜在能力を示しています。
このレビューは、マルチモーダルデータ融合の統合、自己監視学習、および予測の精度とスケーラビリティを強化するための変圧器ベースのアーキテクチャなど、将来の研究の機会を強調しています。既存の進歩を統合し、重要なギャップを特定することにより、このペーパーでは、堅牢で適応的なビジョンTAAシステムを開発するための基本的な参照を提供し、道路安全と交通管理に貢献します。

要約(オリジナル)

Traffic accident prediction and detection are critical for enhancing road safety,and vision-based traffic accident anticipation (Vision-TAA) has emerged as a promising approach in the era of deep learning.This paper reviews 147 recent studies,focusing on the application of supervised,unsupervised,and hybrid deep learning models for accident prediction,alongside the use of real-world and synthetic datasets.Current methodologies are categorized into four key approaches: image and video feature-based prediction, spatiotemporal feature-based prediction, scene understanding,and multimodal data fusion.While these methods demonstrate significant potential,challenges such as data scarcity,limited generalization to complex scenarios,and real-time performance constraints remain prevalent. This review highlights opportunities for future research,including the integration of multimodal data fusion, self-supervised learning,and Transformer-based architectures to enhance prediction accuracy and scalability.By synthesizing existing advancements and identifying critical gaps, this paper provides a foundational reference for developing robust and adaptive Vision-TAA systems,contributing to road safety and traffic management.

arxiv情報

提供元, 利用サービス

arxiv.jp, Google