Can I read LATTE: Lightweight Attention-based Traffic Accident Anticipation Engine on EtoBox?

LATTE: Lightweight Attention-based Traffic Accident Anticipation Engine by Zhang, Jiaxun; Guan, Yanchen; Wang, Chengyue; Liao, Haicheng; Zhang, Guohui; Li, Zhenning is a scholarly article available to read on EtoBox.

What is LATTE: Lightweight Attention-based Traffic Accident Anticipation Engine about?

Accurately predicting traffic accidents in real-time is a critical challenge in autonomous driving, particularly in resource-constrained environments. Existing solutions often suffer from high computational overhead or fail to adequately address the uncertainty of evolving traffic scenarios. This paper introduces LATTE, a Lightweight Attention-based Traffic Accident Anticipation Engine, which integrates computational efficiency with state-of-the-art performance. LATTE employs Efficient Multiscale Spatial Aggregation (EMSA) to capture spatial features across scales, Memory Attention Aggregation (MAA) to enhance temporal modeling, and Auxiliary Self-Attention Aggregation (AAA) to extract latent dependencies over extended sequences. Additionally, LATTE incorporates the Flamingo Alert-Assisted System (FAA), leveraging a vision-language model to provide real-time, cognitively accessible verbal hazard alerts, improving passenger situational awareness. Evaluations on benchmark datasets (DAD, CCD, A3D) demonstrate LATTE's superior predictive capabilities and computational efficiency. LATTE achieves state-of-the-art 89.74% Average Precision (AP) on DAD benchmark, with 5.4% higher mean Time-

Author

Zhang, Jiaxun; Guan, Yanchen; Wang, Chengyue; Liao, Haicheng; Zhang, Guohui; Li, Zhenning

Published

2025

Language

EN