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Visual images usually contain the informative context of the environment,thereby helping to predict agents' behaviors. However, they hardly impose thedynamic effects on agents' actual behaviors due to the respectively fixedsemantics. To solve this problem, we propose a deterministic model named BGM toconstruct a guidance map to represent the dynamic semantics, which circumventsto use visual images for each agent to reflect the difference of activities indifferent periods. We first record all agents' activities in the scene within aperiod close to the current to construct a guidance map and then feed it to aContext CNN to obtain their context features. We adopt a Historical TrajectoryEncoder to extract the trajectory features and then combine them with thecontext feature as the input of the social energy based trajectory decoder,thus obtaining the prediction that meets the social rules. Experimentsdemonstrate that BGM achieves state-of-the-art prediction accuracy on the twowidely used ETH and UCY datasets and handles more complex scenarios.

BGM: Building a Dynamic Guidance Map without Visual Images for Trajectory Prediction