ParallelNet: Multi-mode Trajectory Prediction by Multi-mode Trajectory Fusion
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Abstract (arXiv)
Level 5 Autonomous Driving, a technology that a fully automated vehicle (AV) requires no human intervention, has raised serious concerns on safety and stability before widespread use. The capability of understanding and predicting future motion trajectory of road objects can help AV plan a path that is safe and easy to control. In this paper, we propose a network architecture that parallelizes multiple convolutional neural network backbones and fuses features to make multi-mode trajectory prediction. In the 2020 ICRA Nuscene Prediction challenge, our model ranks 15th on the leaderboard across all teams.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 12 of its 14 references that are in the map. First posted on arXiv 20 Dec 2022.
Builds on
12 of 14 references.
References · 12
- 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
- 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,795
- 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,523
- 2020VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representationcited by 530
- 2020Learning Lane Graph Representations for Motion Forecastingcited by 382
- 2020TNT: Target-driveN Trajectory Predictioncited by 347
- 2018Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networkscited by 289
- 2019CoverNet: Multimodal Behavior Prediction using Trajectory Setscited by 259
- 2021HOME: Heatmap Output for future Motion Estimationcited by 122
- 2021Multimodal Trajectory Prediction Conditioned on Lane-Graph Traversalscited by 113
- 2021LookOut: Diverse Multi-Future Prediction and Planning for Self-Drivingcited by 96
- 2020Trajectory Forecasts in Unknown Environments Conditioned on Grid-Based Planscited by 72
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Citing papers per year: 2023: 1.
1 paper.