LATTE: Lightweight Attention-based Traffic Accident Anticipation Engine
Journal:
arXiv
Published Date:
Apr 5, 2025
Abstract
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-To-Accident (mTTA) than the second-best model, and maintains competitive
mTTA at a Recall of 80% (TTA@R80) (4.04s) while demonstrating robust accident
anticipation across diverse driving conditions. Its lightweight design delivers
a 93.14% reduction in floating-point operations (FLOPs) and a 31.58% decrease
in parameter count (Params), enabling real-time operation on resource-limited
hardware without compromising performance. Ablation studies confirm the
effectiveness of LATTE's architectural components, while visualizations and
failure case analyses highlight its practical applicability and areas for
enhancement.
