ExecDiffuser: Closing the Sample-to-Execution Gap in Diffusion Planning

Shuo Pei; Bosun Liang; Saihui Zha; Chen Sun; Huachun Tan; Yong Wang

cs.ROOct 27, 2026

ExecDiffuser: Closing the Sample-to-Execution Gap in Diffusion Planning

Authors: Shuo Pei, Bosun Liang, Saihui Zha, Chen Sun, Huachun Tan, Yong Wang

Organizations: Shuo Pei, Saihui Zha, Huachun Tan are with the Beijing Institute of Technology, Guangdong, China. · Shuo Pei, Bosun Liang, Chen Sun, and Yong Wang (Corresponding author) are with the Department of Data and Systems Engineering, The University of Hong Kong, Hong Kong, China.

Abstract

Generative planners are often evaluated by the trajectories they can generate, although closed-loop driving ultimately depends on the single candidate committed to control. We identify this mismatch as the sample-to-execution gap and show that improving proposal quality alone can worsen the executed decision. Here, we introduce ExecDiffuser, a recurrent proposal-to-execution framework that couples candidate generation, trajectory selection, and subsequent replanning through shared execution semantics. Intent-Guided Diffusion Proposal (IGD) assigns hypothesis responsibility and shapes trajectories with execution-conditioned supervision. Execution-Grounded Selection (EGS) learns a candidate-conditioned publication utility, commits one trajectory to control, and returns it as context for the next planning cycle. Across nuPlan, InterPlan, and NAVSIM, ExecDiffuser achieves leading closed-loop performance against strong state-of-the-art baselines. Under a unified five-seed protocol, it obtains an average score of 85.62, improving over Diffusion Planner and FlowDrive by 4.02 and 1.31 points, respectively. On NAVSIM, it achieves 92.6 PDMS under the official evaluation pipeline. Matched counterfactual analysis further shows that ExecDiffuser more reliably converts strong candidates into committed decisions, while prefix interventions confirm that this advantage persists after physical execution and subsequent replanning.

Figures & tables

Fig. 1: A strong candidate can already be available yet remain uncommitted to control. Strengthening proposals alone may raise the available-candidate ceiling while widening candidate-publication regret. ExecDiffuser connects proposal learning and trajectory publication through shared execution semantics: IGD improves candidate availability, while recurrent EGS commits one trajectory to control and returns it to the next planning cycle.

Fig. 2: ExecDiffuser couples proposal learning and publication through a shared online execution interface. IGD assigns and shapes latent hypotheses; EGS filters invalid candidates, ranks the remaining candidates, publishes one trajectory, and returns it as recurrent context.

Results reported in the original papers for context
Method	Val14 NR	Val14 R	Hard NR	Hard R	Test14 NR	Test14 R	Avg.	InterPlan
PDM-Closed [ 20 ]	92.84	92.12	65.08	75.19	90.05	91.63	84.49	–
PlannerRFT [ 9 ]	89.96	84.46	77.16	72.21	–	–	–	–
LAP [ 8 ]	89.37	82.23	78.52	70.53	90.64	85.32	82.77	–
G2DP [ 14 ]	92.26	83.91	80.05	73.91	91.58	85.26	84.50	–
G2DP, no history [ 14 ]	91.08	86.67	78.77	77.61	88.54	88.42	85.18	–

TABLE I: Closed-loop results on nuPlan and InterPlan. NR/R denote non-reactive/reactive simulation. Bold compares methods reproduced under the same five-seed protocol; the remaining rows provide reported-paper context.

Proposal training	Val14 NR/R $\uparrow$	Oracle-best $\uparrow$	Feasible ratio (%) $\uparrow$	All-invalid (%) $\downarrow$
IGD + imitation WTA	90.84/83.95	91.36	86.60	2.17
+ resp. + shaping	92.05/86.41	92.63	92.10	0.82
+ specialization	92.37/87.11	92.93	93.40	0.68

TABLE II: Stage-controlled Val14 ablations. Panel (a) fixes the full EGS selector and changes proposal learning; panel (b) freezes the same full proposal generator and changes publication. Rule cost minimizes Eq. ( 5 ), and oracle quantities use the complete candidate set.

Fig. 3: Proposal-only contradiction on Val14. Both proposal generators use the same imitation publication rule. Stronger proposals raise the available-best ceiling, but published quality fails to follow. Values are five-seed means.

Fig. 4: Publication cascade with the proposal generator fixed. Candidate-conditioned scoring, online evidence, hard validity, and recurrent context progressively improve oracle top-1 recall and reduce candidate-publication regret. Values are five-seed means.

Prefix horizon	Unique scenarios / eligible states	$\Delta$ score vs. imitation (paired 95% CI)	$\Delta$ score vs. rule cost (paired 95% CI)	Recovery rate (%) imitation / EGS
0.5 s	1,118 / 111,820	$+0.51\,[0.33,0.69]$	$+0.37\,[0.21,0.53]$	84.7 / 87.8
1.0 s	1,118 / 111,406	$+0.91\,[0.66,1.15]$	$+0.62\,[0.39,0.84]$	82.9 / 88.3
2.0 s	1,117 / 110,736	$+1.42\,[1.08,1.75]$	$+0.95\,[0.66,1.23]$	79.8 / 87.1
4.0 s	1,115 / 109,284	$+1.90\,[1.44,2.34]$	$+1.25\,[0.87,1.62]$	75.4 / 85.9

TABLE III: Resumed replanning after prefix intervention. Score differences compare EGS with imitation and rule-cost prefixes; intervals are paired over scenario clusters. Recovery uses the same degraded-state denominator within each horizon.

Fig. 5: Matched-prefix intervention on nuPlan. Different initial publications create different physical states before the same frozen planner resumes.

Method	NC	DAC	TTC	EP	PDMS
DiffusionDrive [ 6 ]	98.2	96.2	94.7	82.2	88.1
CdDrive [ 15 ]	98.7	97.5	95.4	82.7	89.2
ReCogDrive [ 27 ]	97.9	97.3	94.9	87.3	90.8
DiffusionDriveV2 [ 10 ]	98.3	97.9	94.8	87.5	91.2
ExecDiffuser	99.1	98.2	97.4	87.6	92.6

TABLE IV: NAVSIM v1.1 navtest results (%). Baselines are reported-paper values; ExecDiffuser is the five-seed mean.

Fig. 6: Qualitative NAVSIM examples of an open-space lane change, lane-aligned motion after a turn, and navigation around queued traffic. Orange denotes the logged expert reference and cyan the trajectory selected by ExecDiffuser.

Appendix figures & tables33 assets

Supplementary material from the paper’s appendix.

Appendix

Configuration	Value
Training data	nuPlan v1.1 training split
Calibration split	Scenario-disjoint 10% of training data
Observation / planning horizon	2.0 s / 8.0 s
Planning frequency	10 Hz
Intent queries / budgets	$8$ / $\{2,4,8\}$
Training / inference diffusion steps	1000 / 25

Appendix

TABLE A1: Training, timing, and reporting configuration.

Evidence	Nonnegative raw cost	Direction
$e_{\mathrm{drive}}$	Swept-footprint fraction outside drivable polygons	More outside area $\uparrow$ cost
$e_{\mathrm{route}}$	Distance/area outside the commanded route corridor	More route deviation $\uparrow$ cost
$e_{\mathrm{lane}}$	Lane-boundary overlap and lane-topology inconsistency	More violation $\uparrow$ cost
$e_{\mathrm{risk}}$	Predicted overlap plus clearance and TTC shortfalls	More overlap/shortfall $\uparrow$ cost
$e_{\mathrm{progress}}$	Shortfall from route-aligned progress target	Less progress $\uparrow$ cost
$e_{\mathrm{kin}}$	Speed, acceleration, curvature, and yaw-rate exceedance	More exceedance $\uparrow$ cost

Appendix

TABLE A2: Orientation of the online execution evidence. These normalized quantities enter the training preference, rule-cost control, and learned evidence encoder with the same penalty direction. Coordinate shaping uses aligned differentiable surrogates defined in Eq. ( A21 ).

Group	Parameter	Fixed value
Hard validity	Look-ahead / outside-footprint ratio	3.0 s / $5\%$
Drivable validity	Ego-center tolerance	0.30 m
Collision validity	Swept overlap / ego inflation	0.05 m 2 / 0.15 m per side
TTC validity	Threshold / minimum closing speed	1.0 s / 0.5 m/s
Uncertainty inflation	Radius	$\min(0.80,0.15+0.50\sigma_{t})$ m
Training-preference weights	drive/route/lane/risk/prog/kin/comfort	$1.00/0.80/0.40/1.50/0.30/0.25/0.20$

Appendix

TABLE A3: Fixed online-validity, training, allocation, gate, and MRM parameters.

Method	Params (M)	Added params	FLOPs/cycle (G)	Train h/seed	GPU-h/seed	Five-seed GPU-h	Latency (ms)
Diffusion Planner	43.7	–	111.8	42.6	340.8	1704.0	50.7
FlowDrive	46.2	–	96.4	39.1	312.8	1564.0	47.9
ExecDiffuser	48.9	+5.2	138.6	49.8	398.4	1992.0	59.5

Appendix

TABLE A4: Model size and computation under the same NVIDIA GeForce RTX 4090D environment. FLOPs are measured for batch size one at each method’s default operating point; training cost is reported per seed and over all five seeds.

Seed	Val14 NR	Val14 R	Test14-hard NR	Test14-hard R	Test14 NR	Test14 R	InterPlan
0	92.25	86.82	79.31	74.30	92.32	88.08	38.68
1	92.54	87.32	78.91	74.83	92.05	88.45	37.93
2	92.34	87.17	79.18	74.57	92.21	88.41	38.46
3	92.44	87.00	79.04	74.90	92.02	88.27	38.62
4	92.28	87.24	79.21	74.45	92.20	88.39	38.06
Mean	92.37	87.11	79.13	74.61	92.16	88.32	38.35

Appendix

TABLE A5: Per-seed closed-loop results. The mean row exactly matches the main paper’s closed-loop results table; Std. is the sample standard deviation across five independently trained models.

Fig. A1: Seed-level stability across the seven reported settings. Open circles denote independently trained models, horizontal lines denote arithmetic means, and shaded bands denote one sample standard deviation across the five seeds. Exact values appear in Table A5 .

Comparison	Difference	Paired 95% CI
ExecDiffuser $-$ Diffusion Planner, Val14 NR	+2.50	[1.76, 3.21]
ExecDiffuser $-$ Diffusion Planner, Val14 R	+4.73	[3.68, 5.77]
ExecDiffuser $-$ FlowDrive, Val14 NR	+1.16	[0.44, 1.86]
ExecDiffuser $-$ FlowDrive, Val14 R	+1.74	[0.73, 2.71]
ExecDiffuser $-$ Diffusion Planner, Hard R	+5.39	[4.28, 6.42]
ExecDiffuser $-$ FlowDrive, Hard R	+1.52	[0.57, 2.46]

Appendix

TABLE A6: Scenario-level paired bootstrap with 10,000 resamples and identical scenario seeds.

Seed	Base	IGD	Diff.	Base	IGD	Diff.	Base	IGD	Diff.
	Oracle-best			Published score			Regret
0	90.28	91.29	$+1.01$	89.77	89.56	$-0.21$	0.51	1.73	$+1.22$
1	90.41	91.44	$+1.03$	89.90	89.72	$-0.18$	0.51	1.72	$+1.21$
2	90.24	91.33	$+1.09$	89.75	89.59	$-0.16$	0.49	1.74	$+1.25$
3	90.35	91.39	$+1.04$	89.84	89.66	$-0.18$	0.51	1.73	$+1.22$
4	90.37	91.35	$+0.98$	89.84	89.67	$-0.17$	0.53	1.68	$+1.15$

Appendix

TABLE A7: Five-seed consistency of the proposal-only contradiction. Both variants use the same imitation selector. Diff. is proposal-only IGD minus Base. The available-best ceiling improves for every seed, while published quality slightly decreases and regret increases for every seed.

Comparison	Mean diff.	Paired 95% CI
Oracle-best	$+1.03$	$[0.76,1.29]$
Published score	$-0.18$	$[-0.34,-0.03]$
Regret	$+1.21$	$[0.93,1.48]$
Oracle top-1 recall	$-6.8$	$[-8.9,-4.6]$

Appendix

TABLE A8: Paired scenario-level bootstrap intervals for proposal-only IGD minus Base. The bootstrap uses 10,000 resamples and preserves method and seed pairing. Recall differences are percentage points.

Seed	Published $S_{\mathrm{cf}}$	Oracle-best	$\Delta_{\mathrm{cf}}$	Top-1 recall
0	92.20	92.86	0.66	85.9
1	92.46	93.03	0.57	87.2
2	92.28	92.92	0.64	86.4
3	92.37	92.98	0.61	86.9
4	92.24	92.86	0.62	86.6
Mean	92.31	92.93	0.62	86.6

Appendix

TABLE A9: Per-seed counterfactual candidate diagnostics over

\Omega_{\mathrm{sel}}

. Oracle-best is taken over all generated candidates, including candidates rejected by online hard validity. Published and oracle-best scores use identical candidate rollouts; all-invalid/MRM states are reported separately. Std. is computed across seeds.

Fig. A2: Stage-controlled proposal learning with the full EGS selector fixed. Execution-conditioned responsibility, differentiable geometry shaping, and specialization progressively improve candidate availability and reactive closed-loop performance. Values are five-seed means.

Fig. A3: Execution-grounded publication cascade with the full proposal generator fixed. Learned scoring, online evidence, hard validity, and adaptive recurrent execution progressively improve reactive and TTC scores while reducing candidate-publication regret. Values are five-seed means.

Temporal strategy	Val14 R	TTC	Plan dev. $\downarrow$	Switch $\downarrow$	Hazard delay $\downarrow$	Hazard success $\uparrow$	Avg. speed	Unnec. stop $\downarrow$	Route progress $\uparrow$
No temporal feedback	86.14	92.89	0.313	13.6	0.16	91.9	6.42	3.42	89.82
Fixed temporal feedback	86.49	93.33	0.229	8.8	0.31	90.8	6.38	3.37	89.91
Adaptive Continuity Gate	87.11	94.06	0.205	7.3	0.18	93.4	6.39	3.19	90.05

Appendix

TABLE A11: Recurrent stability, hazard responsiveness, and execution efficiency on Val14. Hazard delay is in seconds; rate columns are percentages.

Commit horizon	Selector	Published score $\uparrow$	Oracle-best $\uparrow$	Regret $\downarrow$	Top-1 recall (%) $\uparrow$
0.5 s	Imitation selector	96.84	97.12	0.28	67.3
0.5 s	Rule-cost selector	96.93	97.12	0.19	74.8
0.5 s	Full EGS selector	97.03	97.12	0.09	88.1
1.0 s	Imitation selector	95.38	95.94	0.56	62.5
1.0 s	Rule-cost selector	95.56	95.94	0.38	72.1
1.0 s	Full EGS selector	95.77	95.94	0.17	87.4

Appendix

TABLE A12: Commit-horizon sensitivity under a fixed candidate set. All selectors operate on the same candidates, so oracle-best is identical across selectors within each horizon. Scores should not be compared directly across horizons because the rollout duration changes. Values follow the same five-seed protocol as the primary evaluation.

Prefix	Initial source	Scenarios	Eligible states	Score $\uparrow$	TTC score $\uparrow$	Collision (%) $\downarrow$	Progress score $\uparrow$
0.5 s	Imitation prefix	1,118	111,820	86.44	92.81	0.31	89.61
0.5 s	Rule-cost prefix	1,118	111,820	86.58	93.02	0.27	89.72
0.5 s	EGS prefix	1,118	111,820	86.95	93.41	0.19	89.91
0.5 s	Oracle prefix	1,118	111,820	87.12	93.56	0.17	90.02
1.0 s	Imitation prefix	1,118	111,406	86.05	92.38	0.39	89.34
1.0 s	Rule-cost prefix	1,118	111,406	86.34	92.76	0.32	89.55

Appendix

TABLE A13: Closed-loop branch evaluation after prefix execution and resumed replanning. Each branch starts from the same cloned state, executes the indicated prefix, and then uses the same frozen full ExecDiffuser planner for the remainder of the fixed 8.0-s window. Counts are unique scenario tokens and matched eligible diagnostic states. Bold marks the strongest online prefix source; the retrospective oracle is shown only as a diagnostic upper bound.

Prefix	Common degraded	Imitation	Rule cost	EGS	Oracle
0.5 s	18,734	84.7	85.5	87.8	88.4
1.0 s	21,968	82.9	84.1	88.3	89.0
2.0 s	27,615	79.8	81.6	87.1	88.7
4.0 s	35,902	75.4	78.3	85.9	87.6

Appendix

TABLE A14: Recovery rates (%) after resumed replanning. Within each horizon, all prefix sources are evaluated over the same common degraded-state denominator. Bold marks the strongest online prefix source; the oracle remains retrospective.

Seed	Prefix	Imitation	Rule cost	EGS	Oracle
0	0.5 s	86.34	86.51	86.86	87.04
1	0.5 s	86.55	86.71	87.08	87.22
2	0.5 s	86.39	86.56	86.88	87.06
3	0.5 s	86.47	86.64	86.99	87.15
4	0.5 s	86.45	86.49	86.95	87.12
0	1.0 s	85.97	86.27	86.89	87.14

Appendix

TABLE A15: Seed-level resumed scores for each prefix horizon and source. Each row is an independently trained model evaluated under the matched branch protocol.

Prefix	Comparison	Mean diff.	Paired 95% CI
0.5 s	EGS $-$ imitation	$+0.51$	$[+0.33,+0.69]$
	EGS $-$ rule cost	$+0.37$	$[+0.21,+0.53]$
	Oracle $-$ EGS	$+0.17$	$[+0.06,+0.29]$
1.0 s	EGS $-$ imitation	$+0.91$	$[+0.66,+1.15]$
	EGS $-$ rule cost	$+0.62$	$[+0.39,+0.84]$
	Oracle $-$ EGS	$+0.24$	$[+0.10,+0.39]$

Appendix

TABLE A16: Scenario-clustered paired bootstrap intervals for resumed score. Scenario tokens are resampled as clusters while retaining their associated method, seed, and diagnostic-state outcomes.

Fig. A4: Scenario-wise resumed-replanning gains. Each cell reports the five-seed EGS-minus-imitation score difference. Columns show 1.0-, 2.0-, and 4.0-s forced-prefix horizons; values should be compared within a column because longer forced prefixes leave less time for the common planner to recover.

Fig. A5: Stability, fallback coverage, and forecast-dependent failure. (a) The Adaptive Continuity Gate reduces plan switching without the hazard-response delay introduced by fixed temporal feedback; marker area encodes hazard success. (b) Learned forecasting with uncertainty increases coverage of counterfactually required MRM events. (c) False-valid, false-reject, and severe low-TTC rates among false-valid decisions increase in high-risk subsets, especially occluded intersections. Rates and scores are pooled or averaged according to the units defined in Appendix Sec. B .

Fig. A6: Scenario dependence, computation, and parameter boundary. (a) Scenario-wise gains over FlowDrive are largest in unprotected turns, pedestrian or cut-in events, dense traffic, and merge or yield interactions. (b) Learned candidate allocation improves the Val14 reactive-score versus latency frontier relative to fixed budgets and heuristic adaptation. (c) Increasing the uncertainty-aware risk margin improves TTC, but excessive margins reduce route progress and overall reactive performance; 0.50 is the fixed default. Values are five-seed means.

Forecast	minADE	minFDE	Val14 R	TTC	False-valid	False-reject
Constant velocity	1.10	2.36	86.02	92.76	4.80	3.20
Learned deterministic	0.82	1.72	86.66	93.51	3.50	3.40
Learned + uncertainty	0.82	1.72	87.11	94.06	2.70	4.00

Appendix

TABLE A17: Forecast accuracy is measured in meters over the 8.0-s horizon on interaction-relevant agents. Validity rates are percentages.

Fig. A7: Forecast and validity diagnostics. (a) Learned forecasting reduces minADE and minFDE relative to constant velocity. (b) Uncertainty reduces false-valid decisions at the cost of a moderate increase in false rejection. (c) The same uncertainty-aware interface raises required-MRM coverage and reduces missed-MRM cycles. Exact values appear in Tables A17 and A21 .

Budget policy	Candidate order	Avg. $K$	Latency	Budget regret	Oracle-best	Feasible ratio	Val14 NR	Val14 R
Random matched	Random	5.5	59.4	0.43	92.38	90.81	91.76	86.19
Random matched	Scene-conditioned priority	5.5	59.1	0.27	92.61	91.75	92.04	86.48
Learned budget	Fixed query order	5.5	59.6	0.22	92.69	92.26	92.11	86.77
Learned budget	Random order	5.5	59.3	0.30	92.55	91.94	91.97	86.59
Joint learned allocation	Scene-conditioned priority	5.5	59.5	0.12	92.93	93.40	92.37	87.11

Appendix

TABLE A18: Matched-compute controls disentangle budget prediction, candidate priority, and nested ordering. Every row has

\bar{K}=5.5

and uses the same 25-step decoder, hardware, and five seeds.

Variant	Entropy	Slots	Intent cons.	Feasible	Oracle	Collapse
Imitation WTA	0.73	4.58	87.94	86.60	91.36	18.2
+ responsibility	0.82	5.47	90.21	89.70	92.07	12.9
+ usage balance	0.88	6.19	92.16	91.84	92.44	8.4
Full specialization	0.91	6.63	94.20	93.40	92.93	5.8

Appendix

TABLE A19: Latent-query specialization diagnostics.

Fig. A8: Normalized latent-intent diagnostics. Used slots are divided by eight; percentage metrics are divided by 100; “no collapse” is one minus the collapse rate. The heatmap complements the exact unnormalized values in Table A19 .

Policy	Avg. $K$	Raw APD	Feasible APD	Feasible ratio	Total ms	Failure/MRM	Val14 NR	Val14 R
$K=1$	1.0	0.00	0.00	78.40	31.6	2.84	88.96	81.46
$K=2$	2.0	0.83	0.63	84.00	39.8	2.02	90.18	83.49
$K=4$	4.0	1.42	1.15	89.20	52.4	1.31	91.25	85.61
$K=8$	8.0	2.07	1.49	88.60	80.0	0.76	91.86	86.52
Heuristic adaptive	5.8	1.94	1.57	91.60	62.2	0.74	92.05	86.76
Learned adaptive	5.5	1.89	1.66	93.40	59.5	0.68	92.37	87.11

Appendix

TABLE A20: Candidate budget, executable diversity, runtime, and complete proposal failure.

Online forecast	Oracle-required MRM	Online MRM	Coverage	Missed MRM	False MRM	Collision among missed	TTC $<1$ s among missed
Constant velocity	0.84	0.61	66.7	0.28	0.05	8.1	27.4
Learned deterministic	0.79	0.65	79.1	0.17	0.03	3.8	16.2
Learned + uncertainty	0.76	0.68	87.6	0.09	0.02	0.0	9.3

Appendix

TABLE A21: Forecast-failure-aware MRM analysis. The first, second, fifth, and sixth columns except coverage are percentages of all planning cycles; the final two columns are percentages within missed-MRM events. Coverage is the fraction of counterfactually required MRM events detected online.

Subset	Events	False-valid	False-reject	Collision among false-valid	TTC $<1$ s among false-valid
All diagnostic states	112,840	2.70	4.00	0.8	4.6
Counterfactual TTC $<3$ s	6,387	3.91	4.54	1.7	8.9
Counterfactual TTC $<2$ s	2,264	4.76	5.11	2.5	13.8
Cut-in	1,193	4.24	4.69	2.0	10.7
Pedestrian conflict	742	4.58	5.29	2.3	12.0
Merge/yield	1,527	3.67	4.38	1.2	8.5

Appendix

TABLE A22: Validity errors stratified by counterfactual risk and scenario type, pooled across five seeds. Errors rise in the hardest subsets, motivating uncertainty inflation and explicit reporting rather than a global average alone.

Fig. A9: Non-conservative execution diagnostics on Val14. (a) TTC improves without reducing average speed; marker size encodes fewer unnecessary stops. (b) The full model reduces stopping, unnecessary stopping, high-jerk events, and emergency braking across planning cycles. Values are five-seed means.

Seed	NC $\uparrow$	DAC $\uparrow$	TTC $\uparrow$	Comf. $\uparrow$	EP $\uparrow$	PDMS $\uparrow$
0	99.1	98.3	97.5	100.0	87.6	92.7
1	99.0	97.9	97.1	100.0	87.4	92.2
2	98.9	98.3	97.2	100.0	87.3	92.4
3	99.1	98.3	97.5	100.0	87.7	92.8
4	99.2	98.2	97.7	100.0	87.8	92.9
Mean	99.1	98.2	97.4	100.0	87.6	92.6

Appendix

TABLE A23: Per-run results on the NAVSIM v1.1 navtest split. NC, DAC, TTC, Comf., EP, and PDMS denote no at-fault collision, drivable-area compliance, time-to-collision, comfort, ego progress, and PDM Score, respectively. All values are percentages.

Fig. A10: Seed-level stability across the six NAVSIM metrics. Open circles denote independently trained models, horizontal lines denote arithmetic means, and shaded bands denote one sample standard deviation across five runs. Exact values are reported in Table A23 .

Fig. A11: Five-frame BEV excerpts from three NAVSIM scenes. Time progresses from left to right at 0.5-s intervals, covering 2.0 s from the first to the last displayed frame. (a) Open-space selection in a left-lane-change context. (b) Lane-aligned motion after a left turn. (c) A rightward maneuver around queued traffic. Orange and cyan denote the logged expert and ExecDiffuser trajectories, respectively. The sequences illustrate replanning consistency across consecutive logged states rather than closed-loop execution of the predicted trajectories.

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Diffusion-Based Planning Diffusion Model Guidance Autonomous Driving Scenario Generation Diffusion Transformer Autonomous Driving Safety Evaluation Adversarial Scenario Generation

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Diffusion probabilistic models can capture the multi-modal, interaction-rich distribution of joint future trajectories in driving scenes. We show that a single pretrained diffusion traffic model can serve two complementary roles in the autonomous driving development loop: as an ego motion planner, and as a controllable generator of safety-critical scenarios for stress-testing the planners. On the planning side, we introduce a Single-Stream Dual-Stream (SSDS) diffusion-transformer decoder that fuses scene context via joint attention rather than late cross-attention, improving closed-loop performance on nuPlan. We further propose Decoupled Annealing Posterior Sampling with Energy (DAPSE), a training-free guidance scheme that injects arbitrary energy functions at the clean-sample level, avoiding the first-order approximation errors while requiring no auxiliary networks. Beyond planning, we leverage the same diffusion model as a controllable scenario generator to create realistic long-tail driving interactions for closed-loop evaluation. Through inference-time guidance, selected agents are steered toward safety-critical behaviors, including aggressive cut-ins, lead-vehicle braking, and combined longitudinal-lateral interactions, while preserving realistic traffic behaviors. Evaluated in closed-loop nuPlan simulations with independent black-box planners, the generated scenarios expose failure modes that remain hidden under standard benchmarks. Although the SSDS-based planner achieves stronger nominal performance, it experiences larger degradation under these challenging scenarios, demonstrating that benchmark superiority does not necessarily translate to robustness. These results demonstrate that a single learned traffic prior can simultaneously improve motion planning and provide a realistic framework for systematic planner robustness evaluation.

Abstract

Autonomous Driving Planning

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