Maneuver-based Anchor Trajectory Hypotheses at Roundabouts
This work addresses a critical problem for self-driving cars in unsignalized intersections, but it is incremental as it builds on existing models with domain-specific adaptations.
The paper tackles vehicle motion prediction at roundabouts by extending recurrent encoder-decoder models with maneuver-specific anchor trajectories, reducing average RMSE by 28% compared to the best baseline on the RounD dataset.
Predicting future behavior of the surrounding vehicles is crucial for self-driving platforms to safely navigate through other traffic. This is critical when making decisions like crossing an unsignalized intersection. We address the problem of vehicle motion prediction in a challenging roundabout environment by learning from human driver data. We extend existing recurrent encoder-decoder models to be advantageously combined with anchor trajectories to predict vehicle behaviors on a roundabout. Drivers' intentions are encoded by a set of maneuvers that correspond to semantic driving concepts. Accordingly, our model employs a set of maneuver-specific anchor trajectories that cover the space of possible outcomes at the roundabout. The proposed model can output a multi-modal distribution over the predicted future trajectories based on the maneuver-specific anchors. We evaluate our model using the public RounD dataset and the experiment results show the effectiveness of the proposed maneuver-based anchor regression in improving prediction accuracy, reducing the average RMSE to 28% less than the best baseline. Our code is available at https://github.com/m-hasan-n/roundabout.