English(EN) INTENT: An LSTM Framework for Vehicle Intention Prediction in Intersection Scenarios with Comprehensive Ablation Analysis

LSTM框架以99.71%的准确率预测交叉路口的车辆意图

作者 PulseAugur 编辑部 · [2 个来源] · 2026-07-09 09:56

研究人员开发了一个名为INTENT的新框架，该框架利用长短期记忆（LSTM）模型来预测交叉路口的车辆意图。该系统旨在通过提前两秒预测车辆是直行、左转还是右转，来提高自动驾驶汽车的安全性和灵活性。在InD数据集上进行测试，INTENT框架通过各种实验和消融研究，实现了99.71%的准确率。 AI

影响这项研究通过更准确地预测车辆的机动动作，可以提高自动驾驶系统的安全性和效率。

排序理由该集群包含一篇详细介绍新框架及其在特定任务上性能的学术论文。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

报道来源 [2]

arXiv cs.AI TIER_1 English(EN) · Logine M. Zaki, Catherine M. Elias · 2026-07-10 04:00

INTENT: An LSTM Framework for Vehicle Intention Prediction in Intersection Scenarios with Comprehensive Ablation Analysis

arXiv:2607.08316v1 Announce Type: new Abstract: Vehicle intention prediction is a pivotal aspect in the agility and safety of autonomous vehicles in all driving scenarios; if genuine enhancement of autonomous vehicles are required, we need to make them adopt human interpretation …
arXiv cs.AI TIER_1 English(EN) · Catherine M. Elias · 2026-07-09 09:56

INTENT: An LSTM Framework for Vehicle Intention Prediction in Intersection Scenarios with Comprehensive Ablation Analysis

Vehicle intention prediction is a pivotal aspect in the agility and safety of autonomous vehicles in all driving scenarios; if genuine enhancement of autonomous vehicles are required, we need to make them adopt human interpretation of driver's intention especially in cases that r…