论文标题
机器学习基于智能手机的驾驶员行为分类的感应
A Machine Learning Smartphone-based Sensing for Driver Behavior Classification
论文作者
论文摘要
驾驶员行为分析是保险行业和车队管理中的主要问题之一，因此能够通过低成本移动应用程序对驾驶员行为进行分类，这仍然是自主驾驶的焦点。但是，使用移动传感器可能会面临安全，隐私和信任问题的挑战。为了克服这些挑战，我们建议使用智能手机（加速度计，陀螺仪，GPS，GPS）提供的CARLA模拟器收集数据传感器，以便使用速度，加速度，方向，3轴旋转角度（YAW，俯仰，滚动）来考虑当前道路和天气条件的速度限制以更好地确定风险行为的速度限制。其次，将多个传感器的轴间数据融合到一个文件中后，我们探索了时间序列分类的不同机器学习算法，以评估哪种算法会导致最高性能。
Driver behavior profiling is one of the main issues in the insurance industries and fleet management, thus being able to classify the driver behavior with low-cost mobile applications remains in the spotlight of autonomous driving. However, using mobile sensors may face the challenge of security, privacy, and trust issues. To overcome those challenges, we propose to collect data sensors using Carla Simulator available in smartphones (Accelerometer, Gyroscope, GPS) in order to classify the driver behavior using speed, acceleration, direction, the 3-axis rotation angles (Yaw, Pitch, Roll) taking into account the speed limit of the current road and weather conditions to better identify the risky behavior. Secondly, after fusing inter-axial data from multiple sensors into a single file, we explore different machine learning algorithms for time series classification to evaluate which algorithm results in the highest performance.
