基于CAN总线标量传感器数据的深度多任务学习异常驾驶检测
Deep Multi-Task Learning for Anomalous Driving Detection Using CAN Bus Scalar Sensor Data
摘要：
在将人工智能（AI）系统应用于安全关键型应用时，角落案例是主要的瓶颈。人工智能系统应该足够智能，能够检测到这种情况，以便系统开发人员为后续规划做好准备。在本文中，我们提出了半监督异常检测考虑正常情况的不平衡。特别地，驾驶数据由多个积极/正常情况（例如，右转，直行），其中一些（例如，掉头）可能是罕见的异常情况。现有的基于机器学习的异常检测方法在应用于这种不平衡的数据时效果不够好。在本文中，我们提出了一种新的基于多任务学习的方法，利用领域知识（机动标签）的异常检测驾驶数据。我们评估所提出的方法在150小时的真实驾驶数据的定量和定性，并显示出比基线方法的性能提高。
英文摘要：
Corner cases are the main bottlenecks when applying Artificial Intelligence (AI) systems to safety-critical applications. An AI system should be intelligent enough to detect such situations so that system developers can prepare for subsequent planning. In this paper, we propose semi-supervised anomaly detection considering the imbalance of normal situations. In particular, driving data consists of multiple positive/normal situations (e.g., right turn, going straight), some of which (e.g., U-turn) could be as rare as anomalous situations. Existing machine learning based anomaly detection approaches do not fare sufficiently well when applied to such imbalanced data. In this paper, we present a novel multi-task learning based approach that leverages domain-knowledge (maneuver labels) for anomaly detection in driving data. We evaluate the proposed approach both quantitatively and qualitatively on 150 hours of real-world driving data and show improved performance over baseline approaches.
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。
