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DriveSuprim: Towards Precise Trajectory Selection for End-to-End Planning

Conference: AAAI 2026 arXiv: 2506.06659 Code: Available Area: Autonomous Driving Keywords: End-to-End Planning, Trajectory Selection, Coarse-to-Fine, Data Augmentation, Self-Distillation

TL;DR

This paper proposes DriveSuprim, which addresses three key bottlenecks in selection-based end-to-end planning — difficulty distinguishing similar trajectories, directional bias, and hard-label instability — through a coarse-to-fine trajectory selection paradigm, rotation-based data augmentation, and a self-distillation soft-label framework, achieving state-of-the-art performance on NAVSIM v1/v2 and Bench2Drive.

Background & Motivation

End-to-end autonomous driving planning methods fall into two main categories:

  • Regression-based methods: Directly predict a single expert trajectory without explicit safety evaluation of candidates.
  • Selection-based methods: Generate and score multiple trajectory candidates, then select the optimal one. Oracle experiments show that selection-based methods can theoretically surpass human demonstrations (Top-256 PDMS = 98.7 vs. human 94.8).

However, selection-based methods face three critical bottlenecks:

Difficulty distinguishing hard negatives: During training, the vast majority of candidates are obviously unsafe "easy negatives," providing insufficient fine-grained discriminative supervision. The model struggles to make optimal choices among trajectories that appear plausible but differ subtly.

Directional bias: In NAVSIM, only 18% of GT trajectories involve turns exceeding 30°. The straight-driving-dominated data distribution leads to poor model performance in turning scenarios.

Hard-label instability: Threshold-based binary safety labels make the model overly sensitive to minor score changes, where small differences can flip a safe/unsafe judgment.

Method

Overall Architecture

DriveSuprim adopts a selection-based planning paradigm with three core innovative components:

  1. Coarse-to-Fine Trajectory Selection: Coarse filtering followed by fine re-ranking.
  2. Rotation-based Data Augmentation: Synthesizing turning scenarios to mitigate directional bias.
  3. Self-Distillation Framework: Using a teacher model to generate soft labels for stable training.

Architecture pipeline: Image encoder extracts BEV features → Trajectory encoder encodes candidate vocabulary → Trajectory Decoder (coarse filtering) → Refinement Decoder (fine re-ranking) → Output optimal trajectory.

Key Designs

1. Coarse-to-Fine Trajectory Selection

Coarse filtering stage: Similar to Hydra-MDP, trajectory features and image features interact via Transformer Decoder cross-attention. Multiple prediction heads regress L2 distance and rule-based metric scores to select Top-K candidates:

\[g_j = \text{TransDec}(\mathcal{E}_{img}, f_j), \quad s_j^{(m)} = \text{Sigmoid}(\text{head}^{(m)}(g_j))\]

Fine re-ranking stage: For the filtered candidates (which contain a high proportion of hard negatives), a Refinement Decoder performs multi-layer fine-grained scoring:

\[\{h_{j,l}\}_{l=1}^{n_{ref}} = \text{RefineDec}(\mathcal{E}_{img}, g_j)\]

Each layer output is supervised by a loss, and the highest-scoring candidate at the final layer is taken as the prediction. Since only a small number of candidates are re-ranked, computational overhead remains manageable.

2. Rotation-based Augmentation

To address the straight-driving data bias, an end-to-end rotation augmentation pipeline is designed:

  • Randomly sample a rotation angle \(\theta \sim U[-\Theta, \Theta]\)
  • Concatenate the original FOV and laterally extended view images into a "pseudo-panoramic" image
  • Crop the input image via a sliding window according to \(\theta\), simulating ego-vehicle rotation
  • Apply a corresponding 2D rotation transformation (angle \(-\theta\), rotated about the initial position) to the GT trajectory, preserving world coordinates

This approach synthesizes more challenging turning scenarios, enabling the model to accurately select trajectories regardless of vehicle heading.

3. Self-Distillation Soft-Label Framework

An EMA-updated teacher model generates soft labels to replace hard binary labels:

\[\hat{y}_i^{(m)} = y_i^{(m)} + \text{clip}(s_{i,\text{teacher}}^{(m)} - y_i^{(m)}, -\delta_m, \delta_m)\]
  • The teacher receives only original (unaugmented) data to generate scores as soft labels.
  • The student receives augmented data with noise.
  • The clipping threshold \(\delta_m\) controls the allowed deviation of teacher output from GT.
  • At inference, the teacher model is used to output the planned trajectory.

Loss & Training

The total loss consists of three components:

\[L = L_{ori} + L_{aug} + L_{soft}\]
  • \(L_{ori} = L_{coarse} + L_{refine}\): Coarse filtering and fine re-ranking losses on original data.
  • \(L_{aug}\): Loss on augmented data (isomorphic to \(L_{ori}\)).
  • \(L_{soft}\): Distillation loss based on teacher soft labels.

The coarse filtering loss includes an imitation loss and a BCE classification loss.

Key Experimental Results

Main Results

Table 1: NAVSIM v1 Evaluation

Method Backbone NC↑ DAC↑ EP↑ TTC↑ C↑ PDMS↑
Hydra-MDP ResNet34 98.3 96.0 78.7 94.6 100 86.5
DiffusionDrive ResNet34 98.2 96.2 82.2 94.7 100 88.1
DriveSuprim ResNet34 97.8 97.3 86.7 93.6 100 89.9 (+1.8)
Hydra-MDP ViT-L 98.4 97.7 85.0 94.5 100 89.9
DriveSuprim ViT-L 98.6 98.6 91.3 95.5 100 93.5 (+3.6)

Table 2: Bench2Drive Evaluation

Method DS↑ SR↑ Eff.↑ Comf.↑
DriveAdapter 64.22 33.08 70.22 16.01
AutoVLA 78.84 57.73 146.93 39.33
DriveSuprim 83.02 60.00 238.78 20.89

Ablation Study

  • Removing the Refinement Decoder leads to a ~1–2% PDMS drop, confirming its importance for discriminating hard negatives.
  • Removing rotation augmentation causes significant performance degradation in turning scenarios, particularly on the EP (Ego Progress) metric.
  • Removing self-distillation results in unstable training; soft labels contribute substantially to mitigating hard-boundary sensitivity.
  • Increasing Top-K (number of re-ranking candidates) yields diminishing and eventually negative returns, as more candidates introduce additional noise.

Key Findings

  1. Oracle experiments demonstrate that the theoretical upper bound of selection-based methods far exceeds human performance (Top-256 PDMS 98.7 vs. human 94.8); the key challenge is how to approach this upper bound.
  2. With a ViT-L backbone on NAVSIM v1, DriveSuprim achieves a PDMS of 93.5, leaving approximately 5% room for improvement relative to the Oracle upper bound of 98.7.
  3. Rotation augmentation not only improves turning-scenario performance but also enhances overall robustness, as the model must learn to handle more diverse visual inputs.
  4. The clipping mechanism \(\delta_m\) in self-distillation is critical — it prevents excessive propagation of teacher errors.

Highlights & Insights

  • The coarse-to-fine paradigm is a classic concept in computer vision, but its application to trajectory selection is refreshing: by narrowing the search space, the model can focus on hard negatives.
  • The rotation augmentation design is elegant: simulating ego-vehicle rotation directly at the image level is simpler and more efficient than performing transformations in 3D space.
  • The Oracle analysis clearly demonstrates the substantial potential of selection-based methods and provides a well-defined direction for future research.
  • State-of-the-art performance (93.5 PDMS) is achieved without additional training data, demonstrating the value of methodological innovation.

Limitations & Future Work

  • The trajectory vocabulary is a predefined fixed set, limiting the diversity and granularity of selectable trajectories.
  • Rotation augmentation only simulates yaw rotation, without accounting for more complex pose changes such as pitch and roll.
  • The self-distillation hyperparameter \(\delta_m\) requires separate tuning for different metrics.
  • The method lacks targeted designs for extreme scenarios (e.g., emergency braking, anomalous traffic participants).
  • Hydra-MDP is a representative selection-based method; DriveSuprim builds upon it by introducing the coarse-to-fine mechanism and augmentation strategies.
  • The coarse-to-fine idea draws inspiration from iterative refinement methods in optical flow and detection (e.g., Deformable DETR, RAFT).
  • Self-distillation adapts the well-established EMA teacher approach from semi-supervised learning, following the same lineage as Mean Teacher.

Rating

Dimension Score (1–5)
Novelty 4
Technical Depth 4
Experimental Thoroughness 5
Writing Quality 4
Value 5
Overall 4.4