CausalDriveBench: Evaluating Causal Reasoning in Vision-Language-Action Models for Autonomous Driving

Narendiran Chembu; Navvrat Rao; Shreedhar Shreeshail Kodate; Gayatri Srujana Banda; Arko Sarkar; Abhinav Khanna; Rajarshee Das; Umesh Kanala

Category	Examples
EGO	The ego vehicle itself (unique, exactly one per scene)
OTHER_AGENT	Cars, trucks, buses, pedestrians, cyclists, active emergency vehicles - any entity whose future state can change by its own volition
TRAFFIC_CONTROL	Signals, signs, stop lines, crosswalk markings - entities that impose a codified traffic regulation
ROAD_OBSTACLE	Construction cones, temporary barriers, debris - inert, localised entities requiring only spatial avoidance

Appendix

Table 4: Node categories in the causal scene graph, derived from the decision tree in Figure 4 a.

Effect type	Description	$\mathcal{D}(e)$
GRANTS_PROCEED	Signal or sign permitting ego to enter a region (discrete, permits)	$s,a$
INHIBITS_PROCEED	Entity blocking or prohibiting ego from entering a region: yielding, obstruction, gap waiting (discrete, prohibits)	$s,a$
CONSTRAINS_LONGITUDINAL	Entity modulating ego’s speed or acceleration within its current trajectory: lead-vehicle following, speed limiting (continuous, speed axis)	$s,a$
CONSTRAINS_LATERAL	Entity modulating ego’s heading or lane position: lateral bounding, evasive steering (continuous, heading axis)	$h$

Appendix

Table 5: Ego edge effect types, derived from the decision tree in Figure 4 b.

Figure 4: Both taxonomies are derived from binary decision trees over objectively testable properties. Node types split on agency and rule imposition (after the unique ego split). Edge types split on discrete vs. continuous control and axis or polarity. Each tree is exhaustive and produces mutually exclusive leaves, ensuring both taxonomies are closed by construction , not by enumeration of driving scenarios.

Type	Rung	Format	Description
Active-element types: CaLM causal scenarios over active subgraphs
CaI	0	M	Causal Identification. Identify which visible element is currently constraining ego.
CA	0	M+B	Causal Attribution. Trace a chain of causal influence from a distal element through a mediator to ego.
CB	1	B	Collider Bias. Apply explaining-away: when two independent causes share ego as a collider, learning one reduces the necessity of the other.
CoI	2	M	Confounder Identification. Identify a shared cause that links multiple active influences on ego.
BAS	2	M	Backdoor Adjustment Set. Identify the complete set of variables to control for when isolating one element’s effect on ego.

Appendix

Table 6: Question types in CausalDriveBench. Types marked

\dagger

are novel contributions introduced in this work; the remaining 12 instantiate established CaLM causal scenarios in the embodied driving setting. Format codes: B (binary), M (multiple-choice), M+B (both formats supported).

Structure	Discovery (R0)	Association (R1)	Intervention (R2)	Counterfactual (R3)
Direct ( $A\rightarrow$ ego)	CaI	–	–	CR, SC
Chain ( $A\rightarrow B\rightarrow$ ego)	CA	–	CDE	NIE, NDE
Confounding ( $A\rightarrow$ { $B\rightarrow$ ego})	CaI	–	CoI, BAS, CDE	CR, NIE, NDE, SC
Collider ( $A\rightarrow$ ego $\leftarrow B$ )	–	CB	–	NC

Appendix

Table 7: Active question type taxonomy in CausalDriveBench mapped to causal graph structures and Pearl’s rungs of inference. Empty cells indicate the graph structure does not produce a non-trivial question at that rung. Example, a direct graph has no mediator for Rung-2 mediation

Figure 5: Distribution of QA pairs in CausalDriveBench across the full unfiltered set of 3,189 verified scenes (22,952 QA pairs), shown by (a) question type, (b) PCH layer, (c) difficulty, and (d) entity status. In (a), warm hues mark active-element types, sages mark dormant types, and blues/purples mark distractor types; (d) gives the per-family aggregate using each family’s anchor hue. Compared to the diversity-sampled subset ( fig. 2 ), the per-category and per-rung shares are nearly identical, indicating that the proportional sampling at rate 0.5 within Stage 2 clusters preserves the question-type composition of the source pool while reducing it to 1,887 representative scenes.

Figure 6: Effect of Stage 2 diversity sampling on benchmark composition.

Graph Structure	QA pairs	% of total
Direct ( $A\rightarrow$ ego)	1,646	22.6
Collider ( $A\rightarrow$ ego $\leftarrow B$ )	359	4.9
Confounding ( $C\rightarrow$ ego, $C\rightarrow B\rightarrow$ ego)	286	3.9
Chain ( $A\rightarrow B\rightarrow$ ego)	127	1.7
No active structure (dormant + distractor)	4,867	66.8
Total	7,285	100.0

Appendix

Table 8: Distribution of QA pairs by underlying active-element graph structure. Dormant and distractor questions are aggregated as “no active structure” since they are generated outside the active-edge enumeration.

Format
Property	Count	%
Binary	6,072	83.3
Multiple choice	1,213	16.7
Difficulty
Easy	1,145	15.7
Medium	3,146	43.2

Appendix

Table 9: Format and difficulty distribution.

Figure 7: Level-1 clustering kernels visualised on the joint embedding. Two-dimensional t-SNE of the combined hierarchical-clustering kernel

K_{\text{combined}}=0.5,K_{1}+0.5,K_{2}

over

n{=}3{,}189

verified scene samples, recoloured by each of the four Level-1 categorical axes that the

K_{1}

kernels hash on. (top-left) dominant active causal-source category (signal / vehicle / vulnerable road user / etc.); (top-right) dormant-edge link status – whether the sample has any dormant ego-edge and, if so, whether it connects to an active cause via an inter-node edge ( linked ) or not ( isolated ); (bottom-left) dominant distractor category from the graph’s rejected-cause list; (bottom-right) the dominant active-subgraph topology (empty / direct / chain / collider / confounding). The visible structure within and across panels indicates that the Level-1 axes capture complementary aspects of causal-graph diversity rather than redundant ones.

Figure 8: Level-2 clustering kernels visualised on the joint embedding. Two-dimensional t-SNE of the combined hierarchical-clustering kernel

K_{\text{combined}}=0.5\,K_{1}+0.5\,K_{2}

over

n{=}3{,}189

verified scene samples, recoloured by each of the four Level-2 categorical axes that the

K_{2}

kernels embed. (top-left) ego speed-trajectory class derived from the per-sample state.json future trajectory (stopped / decelerating / cruising / accelerating); (top-right) weather condition parsed from the nuScenes scene.description field; (bottom-left) the ego’s navigation intent at

T{=}0

( go straight , turn left , turn right ); (bottom-right) road-geometry context with junction temporal phase as the primary axis (approaching / at / past) and lane structure as the secondary axis. Together with Figure 7 , the panels show that scene-context diversity is largely orthogonal to the causal-graph structural diversity captured by the Level-1 kernels.

Edit operation	Count
Node $\to$ distractor demotion	1,739
Ego-edge removed	2,054
Inter-node edge removed	1,446
Ego-edge added	527
Ego-edge causal-status string changed	338
Ego-edge active flag toggled	327

Appendix

Table 10: Per-operation edit counts across the 3,189 reviewed samples. Operations are not mutually exclusive: a single review session may apply multiple edits to one graph.

Decision	Metric	Value
Causal status (3-class, overall)	$\kappa$	0.81
Active vs rest	$\kappa$	0.87
Dormant vs rest	$\kappa$	0.63
Distractor vs rest	$\kappa$	0.91
Ego edge effect type (4-class)	$\kappa$	0.85
Inter-node edge type (4-class)	$\kappa$	0.75

Appendix

Table 11: Inter-annotator agreement on causal labels over 50 random samples. Cohen’s

\kappa

reported per decision type. Landis-Koch interpretation: 0.41–0.60 moderate, 0.61–0.80 substantial, 0.81–1.00 almost perfect.

Driving-specific VLAs
Model	CaI	CA	CB	CoI	BAS	CDE	CR	SC	NC	NDE	NIE	DQ	EL	AB	DR	CI	NI	WC
ImpromptuVLA-7B	87.5	71.4	26.9	91.5	90.9	69.4	67.6	59.0	70.9	78.3	52.0	51.4	72.4	69.8	21.7	58.6	93.0	70.1
OpenREAD	87.9	87.8	45.6	93.2	90.9	67.7	61.7	64.7	71.4	52.2	58.0	47.9	72.9	63.2	76.3	41.0	86.6	56.7
Orion	0.0	0.0	12.1	0.0	0.0	43.6	65.1	14.7	55.9	54.4	36.0	59.9	46.3	52.4	0.0	94.1	79.9	63.0
UniDrive-VLA	82.4	89.8	58.4	98.3	95.5	61.3	49.4	66.5	65.7	58.7	60.0	37.1	60.6	65.8	83.5	15.4	68.0	63.2
RecogDrive	87.6	100.0	44.0	88.7	90.5	62.7	55.2	78.1	58.6	65.9	66.7	24.1	61.7	63.2	48.4	29.1	54.7	57.8

Appendix

Table 12: Per-question-type accuracy (%) across the 18 canonical CausalDriveBench types. Bold = best per column. Driving VLAs upper block, general VLMs lower block.

Driving-specialist VLAs
Model	Active overall	Direct	Chain	Confounding	Collider
RecogDrive	69.9	73.2	74.0	70.9	52.5
OpenREAD	69.8	71.0	68.5	74.8	60.7
ImpromptuVLA-7B	69.1	71.1	72.4	76.2	52.7
UniDrive-VLA	65.9	65.0	68.5	74.5	62.1
SafeAuto	56.6	55.9	68.5	69.9	45.4

Appendix

Table 13: Active-element accuracy (%) by graph topology. Dormant and distractor questions have empty graph structure by construction. Bold = best per column.

Driving-specialist VLAs
Model	Composite	Spatial	Mechanism	Conditionality	Consistency
RecogDrive	1.25	1.36	1.40	0.75	1.48
OpenREAD	1.18	1.14	1.30	0.83	1.44
UniDrive-VLA	1.12	1.21	1.24	0.74	1.31
ImpromptuVLA-7B	1.06	1.08	1.12	0.63	1.39
SimLingo	0.71	0.91	0.70	0.30	0.93

Appendix

Table 14: LLM-judge reasoning scores (0–2 per dimension; higher is better), averaged over Claude Sonnet 4.6 and ChatGPT 4o. Composite is the mean of the four dimensions. Bold = best per column. Driving VLAs upper block, general VLMs lower block.

Dimension	Pearson $r$	Spearman $\rho$
Composite	0.97	0.92
Spatial grounding	0.96	0.93
Mechanism identification	0.99	0.97
Conditionality awareness	0.94	0.94
Answer consistency	0.83	0.81

Appendix

Table 15: Inter-judge agreement on per-model reasoning scores between Claude Sonnet 4.6 and ChatGPT 4o across the 13 evaluated models, computed per dimension over the 13 model-level scores.

Driving-specialist VLAs
Model	CB (Active)	EL (Dormant)	CI (Distractor)	Overall
ImpromptuVLA-7B	26.85%	72.38%	58.64%	69.27%
OpenREAD	45.64%	72.93%	40.95%	63.76%
Orion	12.08%	46.33%	94.08%	57.25%
UniDrive-VLA	58.39%	60.58%	15.39%	54.87%
RecogDrive	43.97%	61.69%	29.14%	54.25%

Appendix

Table 16: Rung 1 accuracy decomposed by question source (%). CB targets active-graph collider structures; EL targets dormant entities and probes structural decoupling, shielding, or shared cause; CI targets perceptually salient distractors. Driving VLAs upper block, general VLMs lower block; each block sorted by overall accuracy.

Driving-specialist VLAs
Model	Total CTE	GOOD_CF (%)	INCONCLUSIVE (%)	FLAGGED (%)	TRAJ_SAME_BTE (%)
WiseAD	972	48.66	33.85	17.49	17.49
UniDrive-VLA	1000	20.10	37.90	42.00	42.00
Alpamayo-1.5-10B	1000	16.80	2.50	63.50	34.00
ImpromptuVLA-7B	984	15.04	45.53	42.28	22.97
Omnidrive	999	10.71	42.64	46.65	46.65

Appendix

Table 17: CTE grade and flag distribution. GOOD_CF fires when the counterfactual trajectory differs meaningfully from BTE in the prescribed direction. TRAJ_SAME_BTE fires when the counterfactual is essentially unchanged from BTE. Percentages are relative to total CTE count. Flags can co-occur, so columns need not sum to 100%. Driving VLAs upper block, general VLMs lower block; each block sorted by GOOD_CF . Bold = best per column ( GOOD_CF higher is better, others lower). SafeAuto’s grade and flag fields were not retained in its trajectory report. Alpamayo-1.5-10B’s high FLAGGED share is dominated by an ANSWER_MISMATCH flag (57.40%). Cosmos-Reason-2’s rerun pipeline emits a GOOD / EXCELLENT grade taxonomy rather than INCONCLUSIVE / FLAGGED , so the four columns shown read 0%; the full grade breakdown for Cosmos is GOOD =6.60%, EXCELLENT =8.10%.

Driving-specialist VLAs
Model	BTE (m)	CTE-R2 (m)	CTE-R3 (m)	CTE-R2/BTE	CTE-R3/BTE	Regime
WiseAD	2.91	1.97	1.57	0.68	0.54	C (positive)
UniDrive-VLA	0.68	2.90	1.96	4.26	2.88	B
Alpamayo-1.5-10B	1.31	2.70	1.70	2.06	1.30	B+C
ImpromptuVLA-7B	1.27	5.69	2.97	4.48	2.34	B
Omnidrive	5.39	4.51	2.48	0.84	0.46	C

Appendix

Table 18: CTE failure-mode regime classification. A = frozen, B = wild-swerving, C = default-to-stop. Some models exhibit characteristics of more than one regime (B+C: wild on R2 and stop-collapsing on R3; A+C: frozen on R2 and stop-collapsing on R3). WiseAD is the single positive outlier with proportionate counterfactual differentiation in the prescribed direction.

Model	Language backbone	Source
Driving-specific VLAs
ImpromptuVLA-7B	Qwen2.5-VL-7B	[ 11 ] §2
OpenREAD	Qwen3-VL-8B ∗	[ 43 ] §3.1
UniDrive-VLA	Qwen3-VL-8B ∗	[ 24 ] Tab. 3
RecogDrive	InternVL3-8B ∗	[ 23 ] App. E
Alpamayo-1.5-10B	Cosmos-Reason ∗	[ 36 ] §3.1

Appendix

Table 19: Language backbone of each evaluated model. Driving VLAs in the upper block, general VLMs in the lower block. Models marked ∗ appear as the backbone of one or more driving VLAs in the upper block.

Backbone	Vanilla VLM (%)	Driving VLA	Driving VLA (%)	$\Delta$ (pp)
Qwen3-VL-8B	66.04	OpenREAD	63.76	$-2.3$
Qwen3-VL-8B	66.04	UniDrive-VLA	54.87	$-11.2$
InternVL3-8B	62.91	RecogDrive	54.25	$-8.7$
Cosmos-Reason	70.56	Alpamayo-1.5-10B	36.86	$-33.7$

Appendix

Table 20: Overall causal QA accuracy when the same language backbone is post-trained for general-purpose use (left) versus driving (right).

\Delta

measures the cost of driving-specific post-training in percentage points.

Model	RFT reward composition	Policy updated	$\Delta$ QA (pp)
Alpamayo-1.5-10B	Reasoning quality + reasoning–action consistency + trajectory L2/collision/jerk	Whole VLA (GRPO)	$-33.7$
RecogDrive	NAVSIM PDMS (collisions, drivable area, comfort) only	Diffusion planner only (DiffGRPO; VLM frozen)	$-8.7$
OpenREAD	LLM-as-critic on QA semantic match + embedding similarity + trajectory ADE	Whole VLA (GRPO)	$-2.3$

Appendix

Table 21: RFT reward design across the three RFT-trained driving VLAs in our roster, alongside the resulting same-backbone causal-QA degradation. The reward composition column lists the components of the RFT reward signal; the policy column indicates which model parameters are updated during RFT.

Figure 9: Scene used for the attention-masking probe. The ego vehicle is accelerating straight through a green signal at an open intersection, with the multi-view camera frames and ego-history tokens encoding this factual configuration. This scene is used as the substrate for both the reasoning-on injection sweep ( fig. 10 ) and the reasoning-off ablation sweep ( fig. 11 ).

Reasoning on with injected $R^{\prime}$ : spread of $T^{\prime}$ across counterfactual rationales
Inputs			Driving VLAs		General-purpose VLMs
$V$	$E$	$R$	OpenREAD	AlpaMayo-1.5	Qwen3-VL-8B-Thinking	Cosmos-Reason2-8B
✓	✓	✓	No effect	No effect	No effect	No effect
✓	✗	✓	No effect	No effect	No effect	No effect
✗	✓	✓	Some effect	No effect	Hallucinates	Hallucinates
✗	✗	✓	Good effect	No effect	Hallucinates	Hallucinates

Appendix

Table 22: Qualitative summary of the attention-masking outcomes across the eight

(V,E,R)

configurations for the two driving VLAs and the two general-purpose VLMs. Reasoning-on rows report whether the five injected counterfactual rationales

R^{\prime}

cause the predicted trajectory

T^{\prime}

to fan out into qualitatively distinct curves (more

\Rightarrow

greater rationale influence). Reasoning-off rows report how far the predicted

T^{\prime}

deviates from ground-truth

T

when the rationale is ablated (less

\Rightarrow

closer to

T

). Hallucinates marks configurations where the trajectory collapses to degenerate, near-zero, or unstructured output.

Figure 10: Reasoning-on regime with rationale injection. Predicted trajectories for the un-masked baseline (teal) and the five injected counterfactual rationales cautious , emergency , aggressive , turn-left , turn-right (coloured) versus nuScenes ground truth (dashed black) under the four

(V,E)

mask configurations across the four evaluated models. A faithfully-conditioned trajectory head should produce a visible fan of five distinct curves with emergency decelerating and turn-left and turn-right bending laterally. For AlpaMayo-1.5 the curves remain bunched in every configuration, including

V

: off ,

E

: off ; for OpenREAD a visible fan emerges only once vision is masked, and grows further once ego history is also removed. Qwen3-VL and Cosmos-Reason collapse to degenerate outputs rather than fanning, indicating that the rationale never structurally drives their trajectory head either.

Figure 11: Reasoning-off regime with the rationale ablated. Predicted trajectories (teal) versus nuScenes ground truth (dashed black) under the four

(V,E)

mask configurations across the four evaluated models. Removing the rationale entirely while preserving vision and ego history leaves the prediction nearly aligned with ground truth for both driving VLAs, confirming that the rationale is not a load-bearing input for trajectory generation. Deviations escalate only when vision is also removed, and degenerate when all three modalities are masked.

	$R_{0}$			$R_{1}$			$R_{2}$			$R_{3}$
Model	ER	$\widetilde{\mathcal{L}}$	$D_{\mathrm{CKA}}$	ER	$\widetilde{\mathcal{L}}$	$D_{\mathrm{CKA}}$	ER	$\widetilde{\mathcal{L}}$	$D_{\mathrm{CKA}}$	ER	$\widetilde{\mathcal{L}}$	$D_{\mathrm{CKA}}$
Cosmos-Reason-2 (VLM)	$55.8$	$0.45$	—	$43.4$	$0.51$	—	$57.7$	$0.48$	—	$80.4$	$0.48$	—
Alpamayo-1.5-10B (VLA)	$1.41$	$0.029$	$0.124$	$2.25$	$0.026$	$0.047$	$3.75$	$0.026$	$0.113$	$78.1$	$0.026$	$0.411$
Qwen3-VL-8B (VLM)	$37.3$	$0.30$	—	$14.0$	$0.16$	—	$16.4$	$0.15$	—	$31.5$	$0.13$	—
OpenREAD (VLA)	$47.4$	$0.46$	$0.014$	$27.8$	$0.58$	$0.014$	$33.2$	$0.57$	$0.036$	$62.3$	$0.61$	$0.265$

Appendix

Table 23: Terminal-block (layers

27

–

36

) summary statistics for the two parent–child pairs across the four rungs of the benchmark. Each cell reports the mean over the ten terminal layers. ER is effective rank (higher

=

more directions used);

\widetilde{\mathcal{L}}

is Fisher–Rao length between consecutive layers (higher

=

more change in next-token prediction);

D_{\mathrm{CKA}}

is cross-model CKA distance (higher

=

more divergence from the parent). Alpamayo’s terminal block sits at the floor on

R_{0}

–

R_{2}

and recovers on

R_{3}

; OpenREAD’s terminal block exceeds the parent on every metric, on every rung.

Figure 12: Layer-wise representational metrics across

R_{0}

–

R_{3}

for the two parent–child pairs. Rows in each

3\times 4

panel: per-layer Fisher–Rao length between consecutive layers, effective rank, cross-model

D_{\mathrm{CKA}}

. Columns:

R_{0}

(association),

R_{1}

,

R_{2}

(intervention),

R_{3}

(counterfactual). Shaded: terminal block, layers

27

–

36

. (a) On

R_{0}

–

R_{2}

Alpamayo’s terminal block is collapsed: ER and Fisher–Rao length sit at the floor, and

D_{\mathrm{CKA}}

is small. The terminal layers have stopped contributing to the next-token prediction and lie on a near-

1

D subspace inside the parent’s dominant directions. On

R_{3}

ER climbs back to parent levels and

D_{\mathrm{CKA}}

rises sharply, but Fisher–Rao length stays low: the terminal block has high-rank activations that no longer translate into changes in the predicted output. (b) OpenREAD’s terminal effective rank and Fisher–Rao length are at or above the parent on every rung, and

D_{\mathrm{CKA}}

grows monotonically with depth, with the magnitude of divergence increasing from

R_{0}

to

R_{3}

. The contrast between (a) and (b) shows that the collapse is recipe-specific.

Figure 13: Dataset-wide review dashboard. The page lists all 850 nuScenes scenes alongside their per-sample review status. Each scene row carries four sample chips (S0–S3), with chip colour encoding review state (green for Good, amber for Needs Review or in-progress, grey for filtered). The header surfaces aggregate stat cards (scenes, samples, QA pairs, counterfactual trajectories, alternative-cause trajectories) and a one-click bulk download of the entire reviewed dataset.

Figure 14: Per-sample editing interface in Edit Mode. Reviewers can change a node’s Type, Category, Label, State, Position, and per-camera visibility, or remove it entirely; node-level edits are tracked through an undo / redo stack and committed as a versioned graph.json . The Original / Pruned toggle compares the extractor’s raw output against the post-edit graph, the Active/Dormant and Distractors sub-tabs mirror the causal-status partition of section A.2 , and the Needs Review / Good buttons in the top bar set the sample’s dashboard-level status.

Figure 15: Per-sample read-only inspection view. The header lists the six JSON artefacts ( meta , frames , calib , state , graph , qa ) and node, ego-edge, inter-edge, and timestep counts for the sample. The middle panel renders the four-timestep BEV stack (top row) and the four ego-centric camera views (FRONT, FRONT_LEFT, FRONT_RIGHT, BACK) with all node bounding boxes overlaid. The bottom panel renders the extracted causal scene graph: solid arrows are directed ego edges colour-coded by effect type, and dashed bidirected arrows are CONFOUNDED_BY links between non-ego nodes encoding shared unobserved causes.

Driving-specific VLAs
			PCH layer				Entity type			LLM-judge axis
Model	$n$	Overall	R0	R1	R2	R3	Active	Dormant	Distractor	Comp.	Spat.	Mech.	Cond.	Cons.
ImpromptuVLA-7B [ 11 ]	7285	69.27%	66.43%	59.41%	85.10%	66.93%	69.07%	63.92%	71.69%	1.06	1.08	1.12	0.63	1.39
OpenREAD [ 43 ]	7274	63.76%	70.10%	48.95%	78.89%	61.03%	69.81%	60.62%	60.80%	1.18	1.14	1.30	0.83	1.44
Orion [ 14 ]	7285	57.25%	25.44%	76.03%	66.40%	52.58%	30.27%	53.26%	78.08%	0.19	0.14	0.07	0.06	0.51
UniDrive-VLA [ 24 ]	7285	54.87%	63.69%	29.71%	68.24%	60.08%	65.88%	53.75%	47.54%	1.12	1.21	1.24	0.74	1.31

	BTE (m)					CTE-R2 (m)					CTE-R3 (m)
Model	$n$	Mean	Std	Min	Max	$n$	Mean	Std	Min	Max	$n$	Mean	Std	Min	Max
Driving-specific VLAs
UniDrive-VLA [ 24 ]	815	0.68	0.60	0.02	4.51	209	2.90	1.74	0.14	8.06	791	1.96	1.60	0.04	10.49
RecogDrive [ 23 ]	815	1.19	0.65	0.14	6.63	209	4.30	2.21	0.44	11.66	791	1.97	1.41	0.22	9.77
ImpromptuVLA-7B [ 11 ]	814	1.27	1.58	0.00	18.45	203	5.69	4.58	0.00	19.63	781	2.97	2.49	0.00	15.40
Alpamayo-1.5-10B [ 36 ]	815	1.31	1.25	0.00	6.79	209	2.70	2.16	0.01	11.90	791	1.70	1.50	0.00	10.25

CausalDriveBench: Evaluating Causal Reasoning in Vision-Language-Action Models for Autonomous Driving

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Appendix

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