Maneuver-based Anchor Trajectory Hypotheses at Roundabouts
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 16 of its 25 references that are in the map. First posted on arXiv 22 Apr 2021.
Builds on
16 of 25 references.
References · 16
- 2014Show and Tell: A Neural Image Caption Generatorcited by 877
- 2018Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networkscited by 687
- 2013Generating Sequences With Recurrent Neural Networkscited by 549
- 2019MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Predictioncited by 417
- 2015A Recurrent Latent Variable Model for Sequential Datacited by 362
- 2019CoverNet: Multimodal Behavior Prediction using Trajectory Setscited by 259
- 2018Convolutional Social Pooling for Vehicle Trajectory Predictioncited by 221
- 2018An LSTM Network for Highway Trajectory Predictioncited by 81
- 2014End-to-end Continuous Speech Recognition using Attention-based Recurrent NN: First Resultscited by 68
- 2017Probabilistic Vehicle Trajectory Prediction over Occupancy Grid Map via Recurrent Neural Networkcited by 56
- 2018Accurate and Diverse Sampling of Sequences based on a "Best of Many" Sample Objectivecited by 56
- 2018Sequence-to-Sequence Prediction of Vehicle Trajectory via LSTM Encoder-Decoder Architecturecited by 55
4 more
- 2013Scalable Object Detection using Deep Neural Networkscited by 30
- 2018Naturalistic Driver Intention and Path Prediction using Recurrent Neural Networkscited by 29
- 2019Multi-modal Probabilistic Prediction of Interactive Behavior via an Interpretable Modelcited by 13
- 2020Motion Prediction using Trajectory Sets and Self-Driving Domain Knowledgecited by 8
Built on by
Citing papers per year: 2021: 1.
1 paper.