利用 CAN 数据预测卡车超车
Predicting Overtakes in Trucks Using CAN Data
AI总结：
本文研究基于 CAN 数据预测卡车超车，比较 ANN、Random Forest 与 SVM，并融合 Random Forest 和线性 SVM，在触发时刻附近实现 TPR 超 91%、TNR 超 92% 的均衡性能。
摘要：
卡车的安全超车对于预防事故、减少拥堵并确保高效交通流至关重要，因此早期预测对于及时且知情的驾驶决策必不可少。为此，我们研究如何从 CAN 数据中检测卡车超车。该任务采用三种分类器：人工神经网络（ANN）、Random Forest 和支持向量机（SVM）。我们的分析覆盖超车事件发生前最长 10 秒，并使用 1 秒的重叠滑动窗口提取 CAN 特征。我们观察到，随着接近超车触发时刻，超车类别的预测分数往往会上升，而非超车类别则保持稳定或随分类器不同而振荡。因此，最佳准确率出现在接近触发时刻时，这使得早期超车预测具有挑战性。各分类器在对超车进行分类时表现出良好的准确率（Recall/TPR > 93%），但在对非超车进行分类时准确率不够理想（TNR 通常为 80–90%，其中一个 SVM 变体低于 60%）。我们进一步通过对输出分数取平均，融合两种分类器（Random Forest 和线性 SVM）。观察到该融合改善了非超车分类（TNR > 92%），代价是降低了超车准确率（TPR）。不过，在超车触发时刻附近，后者仍保持在 91% 以上。因此，该融合平衡了 TPR 与 TNR，相比单个分类器提供了更一致的性能。
英文摘要：
Safe overtakes in trucks are crucial to prevent accidents, reduce congestion, and ensure efficient traffic flow, making early prediction essential for timely and informed driving decisions. Accordingly, we investigate the detection of truck overtakes from CAN data. Three classifiers, Artificial Neural Networks (ANN), Random Forest, and Support Vector Machines (SVM), are employed for the task. Our analysis covers up to 10 seconds before the overtaking event, using an overlapping sliding window of 1 second to extract CAN features. We observe that the prediction scores of the overtake class tend to increase as we approach the overtake trigger, while the no-overtake class remain stable or oscillates depending on the classifier. Thus, the best accuracy is achieved when approaching the trigger, making early overtaking prediction challenging. The classifiers show good accuracy in classifying overtakes (Recall/TPR > 93%), but accuracy is suboptimal in classifying no-overtakes (TNR typically 80-90% and below 60% for one SVM variant). We further combine two classifiers (Random Forest and linear SVM) by averaging their output scores. The fusion is observed to improve no-overtake classification (TNR > 92%) at the expense of reducing overtake accuracy (TPR). However, the latter is kept above 91% near the overtake trigger. Therefore, the fusion balances TPR and TNR, providing more consistent performance than individual classifiers.
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。
