FeaXDrive: Feasibility-aware Trajectory-Centric Diffusion Planning for End-to-End Autonomous Driving
Authors: Baoyun Wang, Zhuoren Li, Ran Yu, Yu Che, Xinrui Zhang, Ming Liu, Jia Hu, Chen Lv, Bo Leng
Organizations: College of Automotive and Energy Engineering, Tongji University, Shanghai, 201804, China · College of Transportation Engineering, Tongji University, Shanghai, 201804, China · School of Mechanical and Aerospace Engineering, Nanyang Technological University, 639798, Singapore
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.
Figures & tables
Failure Cause
DiffusionDrive ( Liao et al., 2025 )
ReCogDrive ( Li et al., 2025c )
Drivable-area non-compliance
69.25%
56.59%
At-fault collision
32.42%
45.44%
Both
1.67%
2.03%
Table 1: Failure cause distribution in score-zero planning scenes of reproduced diffusion-based planners. Percentages are computed with respect to the score-zero scenes of each planner.
Figure 1: Overview of the proposed FeaXDrive. Compared with noise-centric diffusion planning, FeaXDrive adopts a trajectory-centric formulation in which the predicted clean trajectory serves as the unified object for feasibility-aware modeling. On this basis, the method combines training-time curvature feasibility regularization and inference-time drivable-area guidance to enhance trajectory-space feasibility throughout the diffusion planning process.
Figure 2: Overall architecture of FeaXDrive. Under a trajectory-centric formulation, the predicted clean trajectory serves as the shared object for feasibility-aware modeling throughout training, inference, and post-training. The method integrates adaptive differentiable curvature-regularized training, drivable-area guidance during reverse diffusion sampling, and feasibility-aware GRPO post-training.
Method type
Model
NC ↑
EP ↑
Comf. ↑
TTC ↑
DAC ↑
PDMS ↑
IL
VADv2 ( Chen et al., 2024a )
97.2
76.0
100
91.6
89.1
80.9
Driving-GPT ( Chen et al., 2025 )
98.9
79.7
95.6
94.9
90.7
82.4
Hydra-MDP ( Li et al., 2024 )
97.9
77.6
100
92.9
91.7
83.0
UniAD ( Hu et al., 2023 )
97.8
78.8
100
92.9
91.9
83.4
PARA-Drive ( Weng et al., 2024 )
97.9
79.3
99.8
93.0
92.4
84.0
TransFuser ( Prakash et al., 2021 )
97.7
79.2
100
92.8
92.8
84.0
Table 2: Main closed-loop planning results on the NAVSIM benchmark. RLFT denotes reinforcement learning fine-tuning; for FeaXDrive, it is instantiated as feasibility-aware GRPO.
Method
DiffusionDrive ( Liao et al., 2025 )
ReCogDrive-IL ( Li et al., 2025c )
ReCogDrive w/GRPO ( Li et al., 2025c )
FeaXDrive-IL (Ours)
FeaXDrive w/FA-GRPO (Ours)
Curvature Viol. Rate ↓
8.59%
8.05%
15.5%
0.88%
2.40%
Table 3: Comparison of curvature violation rates among reproduced diffusion-based planners.
Method
x0-pred
Curvature Regularization
Sampling Guidance
PDMS ↑
EP ↑
DAC ↑
Drivable Area Viol. Rate ↓
Curvature Viol. Rate ↓
Baseline
✗
✗
✗
85.32
79.56
93.84
6.16%
11.36%
Trajectory-centric
✓
✗
✗
86.56
81.26
94.58
5.42%
7.51%
+ Training-time feasibility enhancement
✓
✓
✗
86.57
81.32
94.94
5.06%
0.13%
FeaXDrive-IL
✓
✓
✓
88.75
83.34
97.46
2.54%
0.88%
Table 4: IL-stage ablation study of the proposed method on benchmark performance and trajectory-space feasibility.
Figure 3: Comparison of curvature violation counts under different IL-stage ablation settings.
Figure 4: Comparison of drivable-area violation counts under different IL-stage ablation settings.
Method
PDMS ↑
EP ↑
DAC ↑
Drivable Area Viol. Rate ↓
Curvature Viol. Rate ↓
FeaXDrive-IL
88.75
83.34
97.46
2.54%
0.88%
FeaXDrive w/GRPO
90.56
85.13
98.28
1.72%
5.79%
FeaXDrive w/FA-GRPO
90.00
84.20
98.31
1.69%
2.40%
Table 5: Comparison of post-training fine-tuning strategies for FeaXDrive-IL. FA-GRPO denotes feasibility-aware GRPO.
Figure 5: Latency breakdown of FeaXDrive inference.
Figure 6: Qualitative comparison between the noise-centric baseline and FeaXDrive on representative planning scenes. From top to bottom: trajectory-level kinematic infeasibility, local geometric irregularities, and drivable-area inconsistency. Compared with the baseline, FeaXDrive produces trajectories that are smoother, more kinematically feasible, and better aligned with the drivable area.
Diffusion-based planning generates diverse action sequences or trajectories by progressively denoising candidates conditioned on goals, scenes, and constraints.