LAMP: Lane-Aligned Motion Primitives for Feasible Trajectory Prediction
Motion forecasting is essential for autonomous driving systems to enable safe decision-making and planning in complex driving scenarios. While existing predictors excel at minimizing standard displacement errors, they often overlook the adherence to lane topology of multimodal predictions, particularly for lower-probability modes. Consequently, predicted trajectories may violate physical and logical constraints, making the prediction set unreliable for safety-critical planning. In this paper, we propose LAMP (Lane-Aligned Motion Primitives), a topology-aware forecasting framework that anchors multimodal prediction to structured motion primitives aligned with lane topology. Specifically, w...
| Authors | Sangjin Han, Hoseong Jung, Jeongtae Her et al. |
|---|---|
| Primary category | cs.RO |
| Published | 25 June 2026 |
| Last updated at source | 25 June 2026 |
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