由内而外：从视频中预测驾驶员意图
Looking Inside Out: Anticipating Driver Intent From Videos
AI总结：
本文提出联合利用舱内与外部摄像头数据并显式提取物体级和道路级特征，结合Transformer和LSTM模型，实现更早且更准确的驾驶员意图预测，准确率达87.5%，平均提前4.35秒。
摘要：
在混合且人机自主程度各异的车辆共享道路时，预测驾驶员意图是一项重要任务。驾驶员意图可用于提升道路安全，例如在驾驶员试图执行危险操作时向周围车辆发出警告。在本工作中，我们提出了一种利用舱内和外部摄像头数据的新方法，以提升预测未来驾驶员动作的最先进（SOTA）性能。与现有方法相比，我们的方法显式地从外部摄像头数据中提取物体级和道路级特征，并证明这些特征是预测驾驶员意图的重要特征。通过将我们手工设计的特征同时输入基于Transformer和基于LSTM的架构，我们通过实验表明，与仅使用舱内特征相比，联合利用舱内和外部特征能够提升性能。此外，我们的模型比现有方法更准确且更早地预测驾驶员操作，准确率达到87.5%，平均预测时间为操作发生前4.35秒。我们在https://github.com/ykung83/Driver-Intent-Prediction上发布了模型配置和训练脚本。
英文摘要：
Anticipating driver intention is an important task when vehicles of mixed and varying levels of human/machine autonomy share roadways. Driver intention can be leveraged to improve road safety, such as warning surrounding vehicles in the event the driver is attempting a dangerous maneuver. In this work, we propose a novel method of utilizing in-cabin and external camera data to improve state-of-the-art (SOTA) performance in predicting future driver actions. Compared to existing methods, our approach explicitly extracts object and road-level features from external camera data, which we demonstrate are important features for predicting driver intention. Using our handcrafted features as inputs for both a transformer and an LSTM-based architecture, we empirically show that jointly utilizing in-cabin and external features improves performance compared to using in-cabin features alone. Furthermore, our models predict driver maneuvers more accurately and earlier than existing approaches, with an accuracy of 87.5% and an average prediction time of 4.35 seconds before the maneuver takes place. We release our model configurations and training scripts on https://github.com/ykung83/Driver-Intent-Prediction
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
