The Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, Japan.
Sensors (Basel). 2023 Jan 26;23(3):1405. doi: 10.3390/s23031405.
With the development of automated driving, inferring a driver's behavior can be a key element for designing an Advanced Driver Assistance System (ADAS). Current research is focused on describing and predicting a driver's behaviors as labels, e.g., lane shifting, lane keeping, etc., during driving. In our work, we consider that predicting a driver's behavior can be described as predicting a trajectory the driver may follow in the near future. The target trajectory can be calculated through certain polynomial functions. Via the data set collected by a Driving Simulator experiment covering nine volunteers, we proposed a model based on a deep learning network which is capable of predicting the corresponding coefficients of polynomial functions and then generating the trajectories in the next few seconds. The results also discussed and analyzed some possible factors affecting the prediction error. In conclusion, the model proved to be effective in predicting the target trajectory of a driver.
随着自动驾驶技术的发展，推断驾驶员的行为可以成为设计高级驾驶辅助系统（ADAS）的关键要素。目前的研究集中在描述和预测驾驶员在驾驶过程中的行为，例如变道、保持车道等，并将其作为标签。在我们的工作中，我们认为预测驾驶员的行为可以描述为预测驾驶员在不久的将来可能遵循的轨迹。目标轨迹可以通过某些多项式函数来计算。通过涵盖九名志愿者的驾驶模拟器实验收集的数据，我们提出了一个基于深度学习网络的模型，该模型能够预测多项式函数的相应系数，然后生成接下来几秒钟的轨迹。结果还讨论和分析了一些可能影响预测误差的因素。总之，该模型在预测驾驶员的目标轨迹方面被证明是有效的。
