DRIFT: Drift and Aggregation for Motion Planning

• URL: http://arxiv.org/abs/2607.14507v1
• Date: Thu, 16 Jul 2026 02:43:13 GMT
• Title: DRIFT: Drift and Aggregation for Motion Planning
• Abstract summary: DRIFT is a fixed-depth trajectory planner that combines one-step drifting in a compact trajectory latent space with scene-aware proposal aggregation.<n>A lightweight Aggregation Head integrates these features with scene, navigation, and ego-state information and directly predicts the final trajectory.<n>Its output is trained with expert-trajectory imitation and a map-derived boundary regularizer that penalizes waypoints outside the drivable polygon.
• Score: 20.309189828710362
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: End-to-end trajectory planners need to represent multiple plausible driving behaviors while producing a single executable trajectory under real-time constraints. Proposal-based approaches address this ambiguity by generating multiple candidates, but converting the proposal set into a final plan remains a key design problem. We present DRIFT, a fixed-depth planner that combines one-step drifting in a compact trajectory latent space with scene-aware proposal aggregation. Conditioned on features from a pretrained visual encoder, the DRIFT Decoder generates 48 proposal features in a single batched pass, with 32 samples at alpha=0.5 and 16 samples at alpha=0.9. A lightweight Aggregation Head integrates these features with scene, navigation, and ego-state information and directly predicts the final trajectory without requiring trajectory-level quality labels for aggregation. Its output is trained with expert-trajectory imitation and a map-derived boundary regularizer that penalizes waypoints outside the drivable polygon and inside waypoints near its boundary. On NAVSIM navtest, DRIFT achieves 89.6 PDMS and 90.4 EPDMS, with strong drivable-area compliance and ego progress among the methods compared. The proposal-generation and aggregation module runs in 10.82 ms on an NVIDIA RTX 4090, while full-model inference including the visual backbone takes 66.43 ms. These results show that one-step latent proposal generation and direct aggregation provide an efficient design for multi-hypothesis motion planning.

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