Trajectory Prediction with Observations of Variable-Length for Motion
  Planning in Highway Merging scenarios
Sajjad Mozaffari Mreza Alipour Sormoli Konstantinos Koufos Graham Lee Mehrdad Dianati
Accurate trajectory prediction of nearby vehicles is crucial for the safe
motion planning of automated vehicles in dynamic driving scenarios such as
highway merging. Existing methods cannot initiate prediction for a vehicle
unless observed for a fixed duration of two or more seconds. This prevents a
fast reaction by the ego vehicle to vehicles that enter its perception range,
thus creating safety concerns. Therefore, this paper proposes a novel
transformer-based trajectory prediction approach, specifically trained to
handle any observation length larger than one frame. We perform a comprehensive
evaluation of the proposed method using two large-scale highway trajectory
datasets, namely the highD and exiD. In addition, we study the impact of the
proposed prediction approach on motion planning and control tasks using
extensive merging scenarios from the exiD dataset. To the best of our
knowledge, this marks the first instance where such a large-scale highway
merging dataset has been employed for this purpose. The results demonstrate
that the prediction model achieves state-of-the-art performance on highD
dataset and maintains lower prediction error w.r.t. the constant velocity
across all observation lengths in exiD. Moreover, it significantly enhances
safety, comfort, and efficiency in dense traffic scenarios, as compared to the
constant velocity model.