BridgeSim: Unveiling the OL-CL Gap in End-to-End Autonomous Driving

Seth Z. Zhao; Luobin Wang; Hongwei Ruan; Yuxin Bao; Yilan Chen; Ziyang Leng; Abhijit Ravichandran; Honglin He

cs.ROApr 12, 2026

BridgeSim: Unveiling the OL-CL Gap in End-to-End Autonomous Driving

Authors: Seth Z. Zhao, Luobin Wang, Hongwei Ruan, Yuxin Bao, Yilan Chen, Ziyang Leng, Abhijit Ravichandran, Honglin He,

Organizations: UCLA · UCSD · Qualcomm

Abstract

Open-loop (OL) to closed-loop (CL) gap (OL-CL gap) exists when OL-pretrained policies scoring high in OL evaluations fail to transfer effectively in closed-loop (CL) deployment. In this paper, we unveil the root causes of this systemic failure and propose a practical remedy. Specifically, we demonstrate that OL policies suffer from Observational Domain Shift and Objective Mismatch. We show that while the former is largely recoverable with adaptation techniques, the latter creates a structural inability to model complex reactive behaviors, which forms the primary OL-CL gap. We find that a wide range of OL policies learn a biased Q-value estimator that neglects both the reactive nature of CL simulations and the temporal awareness needed to reduce compounding errors. To this end, we propose a Test-Time Adaptation (TTA) framework that calibrates observational shift, reduces state-action biases, and enforces temporal consistency. Extensive experiments show that TTA effectively mitigates planning biases and yields superior scaling dynamics than its baseline counterparts. Furthermore, our analysis highlights the existence of blind spots in standard OL evaluation protocols that fail to capture the realities of closed-loop deployment.

Figures & tables

Figure 1: Motivation. (a) : Open-loop (OL) train-test paradigms do not necessarily align with the objectives and evaluation metrics in closed-loop (CL) deployment; (b) : This mismatch results in significant performance discrepancies, characterized by decaying correlation and diverging scaling dynamics; (c) : We attribute this OL-CL gap to observational domain shift and objective mismatch .

Name	Closed-loop	Long-term Simulation	MV-Consistency	TF-Consistency	Supported Maps	Supported Traffic Modes
NAVSIM [ 3 , 2 ]	$\times$	$\times$	✓	✓	nuPlan	Log-replay
Bench2Drive [ 6 ]	✓	✓	✓	✓	CARLA	IDM
DriveArena [ 27 ]	✓	✓	$\times$	$\times$	nuScenes	Log-replay
HUGSIM [ 28 ]	✓	✓	✓	$\times$	nuScenes, Waymo, KITTI-360, PandaSet	Log-replay, IDM, Adversarial
BridgeSim (ours)	✓	✓	✓	✓	CARLA, Waymo, nuScenes, nuPlan, PG	Log-replay, IDM, Adversarial

Table 1: Comparisons with other E2E open-loop and closed-loop benchmarks. MV-Consistency: multi-view temporal consistency, TF-Consistency: Traffic elements annotations consistency. PG: procedural map and behavior generation. See Appendix B for more details of BridgeSim platform.

Figure 2: Empirical Study on Observational Domain Shift. (a) : Flow-matching procedure for observational calibration; (b) : Calibration effects on OL and CL metrics. See Appendix D.2 for experimental details.

Figure 3: Empirical Study on Objective Mismatch. (a) : Compounding execution error of $J(\pi)$ w.r.t. execution horizon $k$ ; (b) : $J(\pi)$ under CL and OL objectives, where the divergence between these curves constitutes the objective mismatch gap ( $\Delta_{\mathrm{obj}}$ ); (c) : OL-CL Pearson correlation under different traffic modes w.r.t. simulation horizon $T$ . See Appendix D.3 for experimental details.

Figure 4: Test-time Policy Adaptation. (a) : Truncated Q-value estimator selects safe planned trajectories during closed-loop execution; (b) : Adaptive replan preserves previously verified planned trajectories if the newly proposed trajectory doesn’t obtain higher Q-values; (c) : Test-time scaling dynamics between biased (baseline) and truncated Q-value estimators (TTA).

Figure 5: Improvements of proposed TTA framework. (a): Performance gain under different simulation horizons on BridgeSim-NavHard scenarios; (b): Performance gain under different log-replay domains; (c): Component analysis of TTA framework.

Model	IDM							Safety-Critical
Model	DS	EPDMS	RC	NC	DAC	TTC	LK	DS	EPDMS	RC	NC	DAC	TTC	LK
DiffusionDrive	45.30	72.95	59.93	95.41	85.47	89.73	89.83	38.68	68.16	54.77	93.62	83.86	84.97	90.71
DiffusionDriveV2	31.94	65.43	45.85	94.57	82.09	89.10	85.42	28.17	61.45	43.50	93.15	80.78	85.72	85.81
RAP	60.02	76.85	77.20	97.68	88.84	93.07	78.16	51.83	70.88	71.22	95.03	88.16	86.37	78.26
TTA (DiffusionDrive)	62.68 +17.4	84.36	72.28	96.85	95.63	91.57	92.43	55.02 +16.3	78.95	67.92	94.73	95.21	86.64	91.30
TTA (DiffusionDriveV2)	53.96 +22.0	77.47	67.89	96.08	90.53	89.46	89.98	47.64 +19.5	71.66	65.91	93.54	89.16	84.23	90.11

Table 2: Performance comparisons with different reactive traffic modes. Note that TTA improves key closed-loop metrics such as NC , TTC , and RC , resulting in overall higher DS . See Appendix D.4 for complete sub-metric scores.

Appendix figures & tables7 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 6: Illustration of BridgeSim Platform. BridgeSim supports unified closed-loop simulation of policies pretrained from any other simulators, enabling us to perform rigorous decomposition analysis.

Critical Constraints ( $\mathcal{C}$ )
Metric	Description
No At-Fault Collisions ( NC )	Ego polygon $\mathcal{P}_{ego}$ must not intersect any replayed object polygons $\mathcal{P}_{obj}$ .
Drivable Area Compliance ( DAC )	Entire horizon must satisfy $\mathcal{P}_{ego}\subset\mathcal{M}_{drivable}$ within the HD map.
Traffic Light Compliance ( TLC )	Agent cannot cross stop lines associated with red light states.
Driving Direction Compliance ( DDC )	Heading deviation $\|\theta_{ego}-\theta_{lane}\|$ must remain within $\pm\pi/2$ .
Quality Features ( $\mathcal{F}$ )

Appendix

Table 3: Descriptions of subscores of EPDMS .

Model	Pretrained Sim	DS	EPDMS	RC	NC	DAC	DDC	TLC	TTC	LK	HC	EC
Human Expert	N/A	91.14	91.96	98.86	98.18	99.23	99.76	98.62	94.55	90.22	99.10	71.44
UniAD (CVPR’23)	Bench2Drive	31.00	57.55	55.25	93.15	71.75	99.76	99.61	81.13	97.70	74.25	30.04
VAD (ICCV’23)	Bench2Drive	26.25	57.60	46.76	93.29	71.22	99.76	99.68	82.31	98.67	71.13	27.67
DiffusionDrive (CVPR’25)	NAVSIM	43.29	69.70	61.30	95.38	81.48	99.76	98.81	87.47	91.57	91.64	39.02
DiffusionDriveV2 (arXiv)	NAVSIM	29.81	62.01	45.85	93.48	79.75	99.76	98.83	86.93	84.11	71.38	20.62
RAP (ICLR’26)	NAVSIM	52.08	72.41	70.74	95.63	85.24	99.76	99.18	88.14	84.69	92.84	48.91

Appendix

Table 4: Quantitative comparison of E2E driving policies evaluated on BridgeSim-NavHard. All results are reported with a replan rate of 5 and a simulation horizon of 8 seconds. We use

\mathrm{rr}=5

to align with the temporal discretization commonly used for training on NAVSIM/OpenScene, where the dataset is provided at 2 Hz (i.e.,

\Delta t=0.5\,\mathrm{s}

Figure 7: Visualization of observation pairs in source and target domains.

Figure 8: Qualitative analysis of simulation artifacts in DriveArena: multi-view incoherence (left) and temporal instability (right). Red boxes highlight inconsistencies that lead to policy failures or metric distortion.

Figure 9: Qualitative analysis of reconstruction artifacts and ghosting effects in HUGSIM. Red boxes highlight inconsistencies.

Model	IDM											Safety-Critical
Model	DS	EPDMS	RC	NC	DAC	DDC	TL	TTC	LK	HC	EC	DS	EPDM	RC	NC	DAC	DDC	TL	TTC	LK	HC	EC
DiffusionDrive	45.30	72.95	59.93	95.41	85.47	100.00	98.36	89.73	89.83	92.31	40.82	38.68	68.16	54.77	93.62	83.86	100.00	98.36	84.97	90.71	89.56	39.13
DiffusionDriveV2	31.94	65.43	45.85	94.57	82.09	100.00	98.23	89.10	85.42	74.52	22.99	28.17	61.45	43.50	93.15	80.78	100.00	98.30	85.72	85.81	70.99	21.68
RAP	60.02	76.85	77.20	97.68	88.84	100.00	98.49	93.07	78.16	94.49	53.84	51.83	70.88	71.22	95.03	88.16	100.00	98.58	86.37	78.26	90.22	50.53
TTA (DiffusionDrive)	62.68 +17.4	84.36	72.28	96.85	95.63	100.00	98.98	91.57	92.43	87.95	40.13	55.02 +16.3	78.95	67.92	94.73	95.21	100.00	98.76	86.64	91.30	83.65	37.11
TTA (DiffusionDriveV2)	53.96 +22.0	77.47	67.89	96.08	90.53	100.00	99.43	89.46	89.98	85.46	29.90	47.64 +19.5	71.66	65.91	93.54	89.16	100.00	99.07	84.23	90.11	81.73	27.49

Appendix

Table 5: Performance comparisons with different reactive traffic modes with complete sub-metric scores.

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Open-loop imitation learning has advanced modern autonomous driving policy architectures, but closed-loop deployment remains vulnerable to policy-induced distribution shift. Existing post-training paradigms exhibit fundamental trade-offs: closed-loop RL fine-tuning provides grounded feedback from executed actions but is constrained by the sparsity of informative events, whereas counterfactual fine-tuning provides dense supervision over candidate futures but inherits bias from imperfect future estimates. We introduce Counterfactual-to-Interactive Reinforcement Fine-Tuning (CRAFT), an on-policy framework that formulates closed-loop post-training as proxy-residual optimization. CRAFT uses group-normalized counterfactual advantages as a dense proxy for real closed-loop advantages and aligns this proxy with the closed-loop world through grounded residual correction from interaction-critical events. To stabilize adaptation, CRAFT regularizes the online policy toward an EMA teacher via asymmetric KL self-distillation. Theoretically, CRAFT decomposes the real closed-loop policy gradient into proxy and residual terms under the same visited-state distribution, reducing residual variance with an aligned proxy while mitigating proxy bias through grounded residual approximation. Empirically, CRAFT achieves the strongest closed-loop gains on Bench2Drive across hierarchical planning, vision-language-action, and vocabulary-scoring architectures. Ablations, scaling behavior, stability analyses, and transfer results further validate the complementary roles of dense counterfactual proxy and grounded residual correction. Project page: https://currychen77.github.io/CRAFT.

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