用低复杂度方法精准预测行人过街意图，适合自动驾驶部署
A low complexity contextual stacked ensemble-learning approach for pedestrian intent prediction
- 用骨架压缩图像+上下文信息构建集成学习模型
- 性能接近顶尖方法，计算量减少99.7%
- 轻量设计适合边缘设备，适合智能交通系统
步行作为可持续出行方式至关重要，准确预测行人过街意图可避免与自动驾驶及高级辅助驾驶车辆发生碰撞。现有研究依赖计算机视觉与机器学习技术预测潜在碰撞，但通常需高算力支持。本文提出一种低复杂度的上下文堆叠集成学习方法，通过行人检测后对图像进行骨架化压缩，并融合上下文信息输入集成模型。在多个数据集上的实验表明，该方法性能接近当前最优水平，同时计算复杂度降低99.7%。论文接受后将开源代码与训练模型。
原文摘要 · Abstract (English)
Walking as a form of active travel is essential in promoting sustainable transport. It is thus crucial to accurately predict pedestrian crossing intention and avoid collisions, especially with the advent of autonomous and advanced driver-assisted vehicles. Current research leverages computer vision and machine learning advances to predict near-misses; however, this often requires high computation power to yield reliable results. In contrast, this work proposes a low-complexity ensemble-learning approach that employs contextual data for predicting the pedestrian's intent for crossing. The pedestrian is first detected, and their image is then compressed using skeleton-ization, and contextual information is added into a stacked ensemble-learning approach. Our experiments on different datasets achieve similar pedestrian intent prediction performance as the state-of-the-art approaches with 99.7% reduction in computational complexity. Our source code and trained models will be released upon paper acceptance
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。