Summary of Futurenet-lof: Joint Trajectory Prediction and Lane Occupancy Field Prediction with Future Context Encoding, by Mingkun Wang et al.
FutureNet-LOF: Joint Trajectory Prediction and Lane Occupancy Field Prediction with Future Context Encoding
by Mingkun Wang, Xiaoguang Ren, Ruochun Jin, Minglong Li, Xiaochuan Zhang, Changqian Yu, Mingxu Wang, Wenjing Yang
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes FutureNet, a novel approach to motion prediction in autonomous driving that explicitly integrates initially predicted trajectories into the future scenario. This allows for more accurate capture of diverse agent movements (e.g., vehicles or pedestrians). The authors also introduce Lane Occupancy Field (LOF), a new representation with lane semantics for motion forecasting. LOF enables simultaneous prediction of all agents’ joint probability distribution of future spatial-temporal positions. A novel network is proposed, combining future context encoding with joint prediction of trajectory and lane occupancy field. This approach outperforms existing methods on two large-scale benchmarks: Argoverse 1 and Argoverse 2. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Autonomous driving needs to predict the movements of multiple agents, like vehicles or pedestrians, in a complex environment. Most current approaches don’t capture these diverse movements well enough. To solve this problem, researchers propose FutureNet, which takes into account the predicted trajectories of all agents and their future contexts. They also introduce Lane Occupancy Field (LOF), which represents the space where different agents can move on the road. This allows for more accurate predictions of how all agents will behave in the future. The new approach is tested on two large datasets and outperforms existing methods. |
Keywords
» Artificial intelligence » Probability » Semantics
