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arxiv: 2604.12656 · v2 · submitted 2026-04-14 · 💻 cs.RO · cs.LG

Recognition: unknown

FeaXDrive: Feasibility-aware Trajectory-Centric Diffusion Planning for End-to-End Autonomous Driving

Baoyun Wang , Zhuoren Li , Ran Yu , Yu Che , Xinrui Zhang , Ming Liu , Jia Hu , Chen Lv
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Bo Leng
Authors on Pith no claims yet

Pith reviewed 2026-05-10 15:03 UTC · model grok-4.3

classification 💻 cs.RO cs.LG
keywords autonomous drivingdiffusion planningtrajectory feasibilityend-to-end planningclosed-loop performancefeasibility-aware modeling
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The pith

Treating clean trajectories as the central object in diffusion planning improves physical feasibility for end-to-end autonomous driving.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper claims that end-to-end diffusion planning for autonomous driving suffers from insufficient physical feasibility because it uses a noise-centric formulation that does not align well with the trajectory space. FeaXDrive instead uses a trajectory-centric formulation where the clean trajectory serves as the unified object for feasibility-aware modeling across the diffusion process. It adds adaptive curvature-constrained training for better geometric and kinematic properties, drivable-area guidance during sampling, and feasibility-aware GRPO post-training. On the NAVSIM benchmark, this yields strong closed-loop performance with substantially fewer infeasible trajectories. A reader cares because feasible trajectories are essential for safe real-world deployment of self-driving vehicles.

Core claim

By shifting from noise-centric to trajectory-centric diffusion, where feasibility is modeled directly on clean trajectories, and incorporating curvature constraints, area guidance, and post-training, the method generates driving trajectories that better respect geometric regularity, kinematic limits, and drivable areas.

What carries the argument

The trajectory-centric formulation that treats the clean trajectory as the unified object for feasibility-aware modeling throughout the diffusion process.

If this is right

  • Adaptive curvature-constrained training enhances intrinsic geometric and kinematic feasibility of trajectories.
  • Drivable-area guidance in reverse diffusion sampling increases consistency with the drivable area.
  • Feasibility-aware GRPO post-training further boosts planning performance while maintaining trajectory feasibility.
  • This leads to improved closed-loop planning performance on the NAVSIM benchmark.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar trajectory-centric approaches might apply to other generative planning problems where constraints are trajectory-level rather than noise-level.
  • Explicit feasibility modeling could decrease reliance on rule-based safety filters in autonomous systems.
  • Future work might test this on real-world vehicle data beyond simulation benchmarks.

Load-bearing premise

The assumption that adding curvature-constrained training, drivable-area guidance, and GRPO post-training to a trajectory-centric diffusion model will sufficiently fix local geometric irregularities, kinematic constraint violations, and drivable-area deviations.

What would settle it

Demonstrating that FeaXDrive trajectories on NAVSIM or a similar driving benchmark show no significant reduction in rates of geometric irregularities, kinematic violations, or area deviations compared to standard diffusion planners would falsify the improvement claim.

Figures

Figures reproduced from arXiv: 2604.12656 by Baoyun Wang, Bo Leng, Chen Lv, Jia Hu, Ming Liu, Ran Yu, Xinrui Zhang, Yu Che, Zhuoren Li.

Figure 1
Figure 1. Figure 1: Overview of the proposed FeaXDrive. Compared with noise-centric di [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overall architecture of FeaXDrive. Under a trajectory-centric formulation, the predicted clean trajectory serves as the shared object for [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Comparison of curvature violation counts under di [PITH_FULL_IMAGE:figures/full_fig_p015_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Comparison of drivable-area violation counts under di [PITH_FULL_IMAGE:figures/full_fig_p015_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Latency breakdown of FeaXDrive inference. [PITH_FULL_IMAGE:figures/full_fig_p016_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Qualitative comparison between the noise-centric baseline and FeaXDrive on representative planning scenes. From top to bottom: [PITH_FULL_IMAGE:figures/full_fig_p017_6.png] view at source ↗
read the original abstract

End-to-end diffusion planning has shown strong potential for autonomous driving, but the physical feasibility of generated trajectories remains insufficiently addressed. In particular, generated trajectories may exhibit local geometric irregularities, violate trajectory-level kinematic constraints, or deviate from the drivable area, indicating that the commonly used noise-centric formulation in diffusion planning is not yet well aligned with the trajectory space where feasibility is more naturally characterized. To address this issue, we propose FeaXDrive, a feasibility-aware trajectory-centric diffusion planning method for end-to-end autonomous driving. The core idea is to treat the clean trajectory as the unified object for feasibility-aware modeling throughout the diffusion process. Built on this trajectory-centric formulation, FeaXDrive integrates adaptive curvature-constrained training to improve intrinsic geometric and kinematic feasibility, drivable-area guidance within reverse diffusion sampling to enhance consistency with the drivable area, and feasibility-aware GRPO post-training to further improve planning performance while balancing trajectory-space feasibility. Experiments on the NAVSIM benchmark show that FeaXDrive achieves strong closed-loop planning performance while substantially improving trajectory-space feasibility. These findings highlight the importance of explicitly modeling trajectory-space feasibility in end-to-end diffusion planning and provide a step toward more reliable and physically grounded autonomous driving planners.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper proposes FeaXDrive, a feasibility-aware trajectory-centric diffusion planning method for end-to-end autonomous driving. It reparameterizes the diffusion process to treat the clean trajectory as the unified object throughout, rather than using the standard noise-centric formulation. Built on this, it adds adaptive curvature-constrained training to address geometric and kinematic issues, drivable-area guidance during reverse sampling, and feasibility-aware GRPO post-training. Experiments on the NAVSIM benchmark are claimed to show strong closed-loop planning performance alongside substantially improved trajectory-space feasibility.

Significance. If the empirical results and attribution to the trajectory-centric formulation hold, the work could meaningfully advance diffusion-based planners by better aligning them with physical feasibility constraints in autonomous driving. The combination of curvature constraints, area guidance, and GRPO on a unified trajectory object is a coherent direction. However, the absence of isolating ablations limits the ability to credit the core reparameterization versus the auxiliary techniques.

major comments (2)
  1. [Experiments / §4] The central claim that the trajectory-centric formulation (as opposed to noise-centric) is what enables the feasibility improvements is not supported by evidence. No ablation compares FeaXDrive against a noise-centric diffusion baseline equipped with identical adaptive curvature-constrained loss, drivable-area guidance during sampling, and feasibility-aware GRPO post-training. Without this control, gains could be attributed to the added components rather than the reparameterization itself. This directly affects the load-bearing motivation in the abstract and §1.
  2. [§4] §4 (NAVSIM results): the abstract and reader's summary indicate no quantitative tables, baselines, or error analysis are presented in sufficient detail to evaluate the claimed improvements in closed-loop performance and feasibility metrics. Standard metrics (e.g., collision rate, progress, feasibility violation rates) and statistical significance are needed to substantiate 'strong' and 'substantially improving' claims.
minor comments (2)
  1. [§3] Notation for the trajectory-centric diffusion process (e.g., how the forward/reverse steps are redefined around the clean trajectory) should be introduced with explicit equations early in §3 to avoid ambiguity.
  2. [§3.3] The GRPO post-training description would benefit from a precise statement of the feasibility reward formulation and how it differs from standard RLHF-style objectives.

Simulated Author's Rebuttal

2 responses · 0 unresolved

Thank you for the constructive feedback on our manuscript. We address each major comment below and describe the revisions we will implement to strengthen the paper.

read point-by-point responses

  1. Referee: [Experiments / §4] The central claim that the trajectory-centric formulation (as opposed to noise-centric) is what enables the feasibility improvements is not supported by evidence. No ablation compares FeaXDrive against a noise-centric diffusion baseline equipped with identical adaptive curvature-constrained loss, drivable-area guidance during sampling, and feasibility-aware GRPO post-training. Without this control, gains could be attributed to the added components rather than the reparameterization itself. This directly affects the load-bearing motivation in the abstract and §1.

    Authors: We agree that isolating the contribution of the trajectory-centric reparameterization is crucial for supporting our central claim. The current experiments compare FeaXDrive to prior noise-centric methods but do not include a noise-centric variant augmented with the exact same auxiliary techniques. In the revised version, we will conduct and report this ablation study. This will allow us to more rigorously attribute the feasibility improvements to the unified trajectory-centric formulation. revision: yes

  2. Referee: [§4] §4 (NAVSIM results): the abstract and reader's summary indicate no quantitative tables, baselines, or error analysis are presented in sufficient detail to evaluate the claimed improvements in closed-loop performance and feasibility metrics. Standard metrics (e.g., collision rate, progress, feasibility violation rates) and statistical significance are needed to substantiate 'strong' and 'substantially improving' claims.

    Authors: We acknowledge that the results section requires more detailed presentation to fully substantiate the claims. Although the manuscript includes experimental results on NAVSIM with baseline comparisons, we will expand §4 with comprehensive quantitative tables incorporating standard metrics including collision rate, progress, and feasibility violation rates. Additionally, we will include error bars or standard deviations to demonstrate statistical significance and provide a more in-depth analysis of the performance gains. revision: yes

Circularity Check

0 steps flagged

No significant circularity; proposal relies on empirical validation rather than self-referential derivation

full rationale

The paper presents FeaXDrive as a modeling choice (trajectory-centric formulation) that enables integration of adaptive curvature-constrained training, drivable-area guidance, and feasibility-aware GRPO post-training. No equations, derivations, or first-principles predictions are described that reduce by construction to fitted inputs or self-citations. The central claims rest on NAVSIM benchmark experiments showing improved feasibility, which is externally falsifiable. GRPO post-training is referenced but without visible reward-fitting mechanics that would create circularity. Absence of uniqueness theorems, self-citation load-bearing premises, or renamed known results keeps the derivation chain self-contained and non-circular.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no explicit free parameters, axioms, or invented entities; the method description implies standard diffusion and RL components without detailing new postulates.

pith-pipeline@v0.9.0 · 5540 in / 1060 out tokens · 74786 ms · 2026-05-10T15:03:23.681865+00:00 · methodology

discussion (0)

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