Rethinking Intermediate Module Utilization in V2X End-to-End Autonomous Driving

Yiming Kan, Huilin Yin, Daniel Watzenig

Keywords: Autonomous Driving, End-to-End, Mixture of Experts, V2X

Abstract: End-to-end autonomous driving has progressed rapidly, with vehicle-side models relying on perception or ego status. UniV2X has extended this paradigm to the Vehicle-to-Everything (V2X) domain, where the broader perceptual scope of V2X offers a more revealing context for revisiting the effective utilization of intermediate modules. Prior work has examined the utility of intermediate modules in vehicle-side models, with studies suggesting that historical trajectories or current ego status alone may suffice for achieving competitive performance on open-loop datasets. Our paper aims to revisit this assumption in the V2X setting. Using the UniV2X model as the baseline and the V2X-Seq dataset as the testbed, we examine the contribution of intermediate modules to the final planning output and explore the extent to which their utility is fully realized. Our study reveals that current end-to-end models tend to underutilize the guidance provided by intermediate modules to the planning stage, reflecting a lack of planning-oriented design. To address this issue, we propose Optimized Multi-Experts Guided Autonomous Driving (OMEGA), a functional integration mechanism that explicitly improves the contribution of intermediate modules to the planning process. Experimental results demonstrate that our approach significantly enhances the functional contribution of each intermediate component. Our findings suggest that performance limitations are not due to the lack of new modules but stem from the underutilization of existing ones, urging a reconsideration of current end-to-end design practices.

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Submission Number: 2