Trajectory prediction is gaining attention as a form of situational awareness because it is an essential component of the support system of autonomous driving, particularly in urban areas. A promising application is cooperative driving automation, where the traffic scene is monitored by roadside sensors with undisrupted views. A critical problem is that these sensors are adversely affected by inclement weather, including drenching rain or large amounts of snow, in which case the reliability of the prediction results can be significantly compromised. To address these problems, this study proposes a framework for robust vehicle-trajectory predictions based on the Chebyshev transform. In the proposed framework, the original trajectory snippets (partial trajectories) are Chebyshev-transformed, and the resulting coefficients form new snippets. The LSTM (long-short term memory) encoder-decoder structure was trained and tested using these new coefficient snippets, which were extracted from a public vehicle trajectory dataset. The performance and robustness of the proposed framework were verified by emulating sensor data that were incomplete as a result of environmental factors. The proposed framework provides stable and accurate long-term trajectory prediction because the Chebyshev transform is robust to incomplete sensor data by virtue of its uniform nature.

中文翻译：

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差分量化梯度法


轨迹预测作为态势感知的一种形式而受到关注，因为它是自动驾驶支持系统的重要组成部分，特别是在城市地区。一个有前途的应用是协作驾驶自动化，其中交通场景由路边传感器监控，视野不受干扰。一个关键问题是这些传感器会受到恶劣天气的不利影响，包括大雨或大雪，在这种情况下，预测结果的可靠性可能会受到严重影响。为了解决这些问题，本研究提出了一个基于切比雪夫变换的稳健车辆轨迹预测框架。在所提出的框架中，原始轨迹片段（部分轨迹）经过切比雪夫变换，所得系数形成新的片段。使用这些从公共车辆轨迹数据集中提取的新系数片段来训练和测试 LSTM（长期短期记忆）编码器-解码器结构。通过模拟由于环境因素而不完整的传感器数据，验证了所提出框架的性能和鲁棒性。所提出的框架提供了稳定且准确的长期轨迹预测，因为切比雪夫变换凭借其统一性质对不完整的传感器数据具有鲁棒性。