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Accurate trajectory prediction of nearby vehicles is crucial for the safemotion planning of automated vehicles in dynamic driving scenarios such ashighway merging. Existing methods cannot initiate prediction for a vehicleunless observed for a fixed duration of two or more seconds. This prevents afast reaction by the ego vehicle to vehicles that enter its perception range,thus creating safety concerns. Therefore, this paper proposes a noveltransformer-based trajectory prediction approach, specifically trained tohandle any observation length larger than one frame. We perform a comprehensiveevaluation of the proposed method using two large-scale highway trajectorydatasets, namely the highD and exiD. In addition, we study the impact of theproposed prediction approach on motion planning and control tasks usingextensive merging scenarios from the exiD dataset. To the best of ourknowledge, this marks the first instance where such a large-scale highwaymerging dataset has been employed for this purpose. The results demonstratethat the prediction model achieves state-of-the-art performance on highDdataset and maintains lower prediction error w.r.t. the constant velocityacross all observation lengths in exiD. Moreover, it significantly enhancessafety, comfort, and efficiency in dense traffic scenarios, as compared to theconstant velocity model.

Trajectory Prediction with Observations of Variable-Length for Motion Planning in Highway Merging scenarios