SemRD-V2X: Closure-Guided Communication with Bounded Inference for Cooperative Perception

Hu Xu; Chun Li; Siyuan Qiu; Zeyan Li; Jianfeng Xu

cs.AISep 28, 2026

SemRD-V2X: Closure-Guided Communication with Bounded Inference for Cooperative Perception

Authors: Hu Xu, Chun Li, Siyuan Qiu, Zeyan Li, Jianfeng Xu

Organizations: Shanghai Jiao Tong University, Shanghai, China

Abstract

Vehicle-to-Everything (V2X) cooperative perception improves 3-D detection by sharing intermediate features, but dense remote features may repeat context that the ego agent can infer locally. Most communication-efficient designs optimize masks or codes empirically, leaving a more basic question open: which remote evidence is indispensable given the receiver's own observation? We introduce a closure-fidelity perspective on ego conditioned remote perception. Under a finite deductive abstraction and explicit conditions, its rate--distortion function decomposes over an irredundant core, and the exact zero-distortion rate becomes $P_A H(π_A)$ . This analysis suggests a concrete design principle: transmit compact evidence and recover derivable context with bounded receiver-side inference. Guided by this principle, SemRD-V2X is an operational neural proxy that combines exact-budget BEV support selection, pointwise channel compression, and masked shared-weight reconstruction before standard fusion. Experiments on simulated V2XSet and real-world DAIR-V2X validate the resulting design. In a controlled five-run V2XSet comparison against a locally reproduced V2X-ViT-v1 baseline on one Tesla V100, SemRD-V2X reduces the analytical feature payload by $26.6\times$ while improving AP@0.5/AP@0.7 by 4.13/8.57 points, with 3.81% additional mean compute latency. These results position closure fidelity as both an analytical lens and an actionable design principle for communication-efficient cooperative perception.

Figures & tables

Figure 1: SemRD-V2X encodes selected remote BEV positions and a spatial bitmap. The receiver reconstructs omitted positions through bounded refinement before V2X-ViT fusion. Analytical payload includes latents and the bitmap but excludes the ego feature and network overhead.

	V2XSet				DAIR-V2X
	Perfect		Noisy		Mean(P,N)
Method	AP@0.5 $\uparrow$	AP@0.7 $\uparrow$	AP@0.5 $\uparrow$	AP@0.7 $\uparrow$	AP@0.5 $\uparrow$	AP@0.7 $\uparrow$
Late Fusion	0.727	0.620	0.549	0.307	0.520	0.422
Early Fusion	0.819	0.710	0.720	0.384	0.525	0.366
F-Cooper	0.840	0.680	0.715	0.469	0.550	0.415
OPV2V	0.807	0.664	0.709	0.487	0.548	0.420

Table 1: 3-D detection on V2XSet and DAIR-V2X. V2XSet reports AP separately under Perfect (P) and Noisy (N), whereas DAIR-V2X reports Mean(P,N).

Method	AP@0.5 $\uparrow$	AP@0.7 $\uparrow$	MiB/frame $\downarrow$	Measured compute (mean $\pm$ SD) $\downarrow$	Analytical comm. (150 Mbit/s) $\downarrow$	Illustrative total (mean $\pm$ SD) $\downarrow$
V2X-ViT-v1	0.8033	0.5606	13.6416	144.348 $\pm$ 0.465	762.895	907.243 $\pm$ 0.465
SemRD-V2X (Full)	0.8445	0.6463	0.5131	149.845 $\pm$ 3.589	28.697	178.542 $\pm$ 3.589

Table 2: Controlled V100 comparison under the V2XSet Noisy protocol using epoch-30 checkpoints. All latencies are in ms/frame. Measured compute and the illustrative sequential total report mean

\pm

sample SD across five paired runs; communication is analytical serialization at 150 Mbit/s. AP is remeasured on the V100.

Variant	MiB/frame $\downarrow$	AP@0.5 $\uparrow$	AP@0.7 $\uparrow$	PCF@0.5 $\uparrow$	SA@K $\uparrow$	$E_{\mathrm{sup}}\downarrow$	$E_{\mathrm{omit}}\downarrow$
Full	0.5131	0.8445	0.6465	0.9357	0.4917	18.1582	7.2013
Random selector	0.5131	0.6528	0.5032	0.9178	0.2998	8.9167	11.1486
w/o rate regularization	0.5131	0.8148	0.5974	0.9301	0.4650	17.4119	9.5521
w/o reconstruction supervision	0.5131	0.8272	0.5951	0.9115	0.4710	21.9847	9.3176
w/o codec	4.0935	0.7976	0.5996	0.9254	0.4714	0.0000	11.5870

Table 3: Matched component ablations under the V2XSet Noisy protocol. Each variant is trained for 30 epochs and evaluated at

\rho=0.3

and

\delta=2

. Payload is analytical; PCF, SA@K,

E_{\mathrm{sup}}

, and

E_{\mathrm{omit}}

are post-hoc diagnostics.

Setting	$\sigma_{xyz}$	Payload $\downarrow$	AP@0.5 $\uparrow$	AP@0.7 $\uparrow$	Miss. S- $L_{1}$ $\downarrow$
Decoder depth (Noisy protocol, $\rho=0.3$ )
$\delta=0$	0.2	0.5131	0.8344	0.6451	0.002193
$\delta=1$	0.2	0.5131	0.8382	0.6440	0.002035
$\delta=2$	0.2	0.5131	0.8445	0.6465	0.001979
$\delta=3$	0.2	0.5131	0.8399	0.6399	0.002356
$\delta=4$	0.2	0.5131	0.8187	0.6213	0.003886

Table 4: Fixed-budget decoder-depth and localization-noise diagnostics. The upper block varies

\delta

\rho=0.3

and

\sigma_{xyz}=0.2

m and Miss. S-

L_{1}

denotes the missing-feature reconstruction proxy. The lower block varies

\sigma_{xyz}

(m) at

\rho=0.3

and

\delta=2

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Collaborative Perception V2X Bird'S-Eye View Voxel Perception Dense+2

Open paper AskPDF arXiv

Collaborative perception improves autonomous perception by sharing intermediate Bird's-Eye-View (BEV) features across connected agents, but dense feature exchange is difficult to deploy under strict Vehicle-to-Everything (V2X) bandwidth limits. Existing efficient methods typically either compress the full feature map uniformly, spending bits on low-value background, or sparsify communication, risking the loss of useful context. We propose a coverage-refinement design for byte-constrained cooperative perception: each agent transmits a highly compressed coarse layer over the full BEV map and allocates the remaining budget to selected high-resolution patches. A Task-Aware Benefit Selector ranks cells by estimated downstream utility, enabling deterministic budgeted refinement and zero-retraining adaptation to changing bandwidth. The receiver reconstructs a dense BEV tensor compatible with standard fusion modules. Experiments on DAIR-V2X and OPV2V show strong accuracy-payload trade-offs at kilobyte-scale budgets. On DAIR-V2X, our method reaches 0.60 AP@0.7 at only 1.87 KB per non-ego agent, compared with 0.52 at 4.61 KB for uniform SimVQ compression. Controlled diagnostics further show that the gain arises from coverage-refinement allocation rather than quantization alone. Code will be published.

Abstract

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