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Understanding traffic participants' behaviour is crucial for predicting theirfuture trajectories, aiding in developing safe and reliable planning systemsfor autonomous vehicles. Integrating cognitive processes and machine learningmodels has shown promise in other domains but is lacking in the trajectoryforecasting of multiple traffic agents in large-scale autonomous drivingdatasets. This work investigates the state-of-the-art trajectory forecastingmodel Trajectron++ which we enhance by incorporating a smoothing term in itsattention module. This attention mechanism mimics human attention inspired bycognitive science research indicating limits to attention switching. Weevaluate the performance of the resulting Smooth-Trajectron++ model and compareit to the original model on various benchmarks, revealing the potential ofincorporating insights from human cognition into trajectory prediction models.

Smooth-Trajectron++: Augmenting the Trajectron++ behaviour prediction model with smooth attention