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Trajectory Prediction (TP) is an important research topic in computer visionand robotics fields. Recently, many stochastic TP models have been proposed todeal with this problem and have achieved better performance than thetraditional models with deterministic trajectory outputs. However, thesestochastic models can generate a number of future trajectories with differentqualities. They are lack of self-evaluation ability, that is, to examine therationality of their prediction results, thus failing to guide users toidentify high-quality ones from their candidate results. This hinders them fromplaying their best in real applications. In this paper, we make up for thisdefect and propose TPAD, a novel TP evaluation method based on the trajectoryAnomaly Detection (AD) technique. In TPAD, we firstly combine the AutomatedMachine Learning (AutoML) technique and the experience in the AD and TP fieldto automatically design an effective trajectory AD model. Then, we utilize thelearned trajectory AD model to examine the rationality of the predictedtrajectories, and screen out good TP results for users. Extensive experimentalresults demonstrate that TPAD can effectively identify near-optimal predictionresults, improving stochastic TP models' practical application effect.

TPAD: Identifying Effective Trajectory Predictions Under the Guidance of Trajectory Anomaly Detection Model