Identifying Effective Trajectory Predictions Under the Guidance of Trajectory Anomaly Detection Model
Trajectory Prediction (TP) is an important research topic in computer vision and robotics ﬁelds. Recently, many stochastic TP models have been proposed to deal with this problem and have achieved better performance than the traditional models with deterministic trajectory outputs. However, these stochastic models can generate a number of future trajectories with different qualities. They are lack of self-evaluation ability, that is, to examine the rationality of their prediction results, thus failing to guide users to identify high-quality ones from their candidate results. This hinders them from playing their best in real applications. In this paper, we make up for this defect and propose TPAD, a novel TP evaluation method based on the trajectory Anomaly Detection (AD) technique. In TPAD, we ﬁrstly combine the Automated Machine Learning (AutoML) technique and the experience in the AD and TP ﬁeld to automatically design an effective trajectory AD model. Then, we utilize the learned trajectory AD model to examine the rationality of the predicted trajectories, and screen out good TP results for users. Extensive experimental results demonstrate that TPAD can effectively identify near-optimal prediction results, improving stochastic TP models’ practical application effect.
Email Address of Submitting Author20s003052@stu.hit.edu.cn
Submitting Author's InstitutionHarbin Institute of Technology
Submitting Author's Country