In autonomous driving, accurately predicting the movements of other traffic
participants is crucial, as it significantly influences a vehicle’s planning
processes. Modern trajectory prediction models strive to interpret complex
patterns and dependencies from agent and map data. The Motion Transformer (MTR)
architecture and subsequent work define the most accurate methods in common
benchmarks such as the Waymo Open Motion Benchmark. The MTR model employs
pre-generated static intention points as initial goal points for trajectory
prediction. However, the static nature of these points frequently leads to
misalignment with map data in specific traffic scenarios, resulting in
unfeasible or unrealistic goal points. Our research addresses this limitation
by integrating scene-specific dynamic intention points into the MTR model. This
adaptation of the MTR model was trained and evaluated on the Waymo Open Motion
Dataset. Our findings demonstrate that incorporating dynamic intention points
has a significant positive impact on trajectory prediction accuracy, especially
for predictions over long time horizons. Furthermore, we analyze the impact on
ground truth trajectories which are not compliant with the map data or are
illegal maneuvers.

Este artículo explora los viajes en el tiempo y sus implicaciones.

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