Fidelity Evaluation of Virtual Traffic Based on Anomalous Trajectory Detection

• Published in: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) pp. 8157 - 8164
• Main Authors: Li, Chaoneng, Chao, Qianwen, Feng, Guanwen, Wang, Qiongyan, Liu, Pengfei, Li, Yunan, Miao, Qiguang
• Format: Conference Proceeding
• Language: English
• Published: IEEE 23.10.2022
• Subjects: anomalous trajectory detection; Benchmark testing; deep learning methods; Long short term memory; Measurement; Memory architecture; Robustness; Simulation and animation; Traffic control; Trajectory; user study
• ISSN: 2153-0866
• DOI: 10.1109/IROS47612.2022.9981800

Measuring the fidelity of synthesized virtual traffic has become an important and fundamental concern for evaluating the performance of different traffic simulation techniques and applications of autonomous vehicle testing. In this work, we propose a novel method to evaluate the fidelity of any trajectory data from the perspective of anomalous trajectory detection. First, given the trajectory data to be evaluated as input, the method learns spatio-temporal traffic features and reconstructs the input trajectory through a Long Short-Term Memory (LSTM)-based autoencoder architecture. Then, the anomalous trajectories are detected by comparing the reconstructed trajectories and the input ones using the reconstruction error as the benchmark. Our method can detect eight different kinds of anomalous trajectory in terms of changes in velocity and moving direction. In order to evaluate the fidelity of the input trajectory, we design a perceptual evaluation on virtual traffic fidelity and derive a mapping from the reconstruction error to the evaluation score. We demonstrated the effectiveness and robustness of our metric through many experiments on real-world and synthetic trajectory data containing different types of motion anomalies.