As technology is moving rapidly toward automation and connectivity, it is of paramount importance to predict vehicle trajectories ahead of time. This not only enhances safety but also ensures mobility in a connected and automated environment. Previous studies have shown that, given the previous trajectory, the future trajectory can be estimated. But this method suffers from considerable drawbacks in the case of intersections as it cannot predict turning movements. It also requires advanced sensors that are not readily available in most vehicles. A smartphone device can also be used in such scenarios, bringing partial automation to vehicles without these sensors. This paper presents an integrated method of estimating vehicle trajectories for both general roadway segments and intersections by using a smartphone. A lane change detection system is taken as an indicator of intersection turning movement estimation and corresponding vehicle trajectories are estimated accordingly. The system can achieve high penetration rates and can be used to replicate onboard units. Sensor readings are taken periodically which are first filtered with a low-pass filter to zero out any high-frequency noise and then fed into a machine learning model to detect lane changes. The model can successfully capture lane changes with smartphone data with high accuracy (95%). Finally, vehicle trajectory is estimated using Chebyshev’s polynomial. This type of estimation system can find applications in collision prediction at intersections between a turning vehicle and a pedestrian on a crosswalk.

随着技术朝着自动化和连接性快速发展，提前预测车辆轨迹至关重要。这不仅增强了安全性，而且确保了在连接的自动化环境中的移动性。先前的研究表明，给定先前的轨迹，可以估算未来的轨迹。但是，这种方法在相交的情况下具有很大的缺点，因为它无法预测转弯运动。它还需要在大多数车辆中不容易获得的高级传感器。智能手机设备也可以在这种情况下使用，从而为没有这些传感器的车辆带来部分自动化。本文提出了一种使用智能手机估算一般道路段和交叉口的车辆轨迹的综合方法。车道变化检测系统被用作交叉路口转弯运动估计的指示，并且相应地估计相应的车辆轨迹。该系统可以获得很高的穿透率，可用于复制机载单元。定期读取传感器读数，然后先通过低通滤波器对其进行过滤以将高频噪声归零，然后将其输入到机器学习模型中以检测车道变化。该模型可以使用智能手机数据成功地准确捕获车道变化（95％）。最后，使用切比雪夫多项式估算车辆的轨迹。这种类型的估算系统可以在人行横道上的转弯车辆和行人之间的交叉点的碰撞预测中找到应用。