Chebyshev Transform-Based Robust Trajectory Prediction Using Recurrent Neural Network

Abstract

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.

Keywords

• Autonomous driving
• Chebyshev polynomial
• Chebyshev transform
• long short-term memory
• recurrent neural network
• trajectory prediction