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End-to-End Driving with Online Trajectory Evaluation via BEV World Model

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arxiv 2504.01941 v2 pith:Z3GPTP4U submitted 2025-04-02 cs.CV

Yingyan Li , Yuqi Wang , Yang Liu , Jiawei He , Lue Fan , Zhaoxiang Zhang This is my paper · ORCID
classification cs.CV
keywords trajectoryworldevaluationmodeldrivingend-to-endframeworkfuture
verification ladder T0 review T1 audit T2 compute T3 formal
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End-to-end autonomous driving has achieved remarkable progress by integrating perception, prediction, and planning into a fully differentiable framework. Yet, to fully realize its potential, an effective online trajectory evaluation is indispensable to ensure safety. By forecasting the future outcomes of a given trajectory, trajectory evaluation becomes much more effective. This goal can be achieved by employing a world model to capture environmental dynamics and predict future states. Therefore, we propose an end-to-end driving framework WoTE, which leverages a BEV World model to predict future BEV states for Trajectory Evaluation. The proposed BEV world model is latency-efficient compared to image-level world models and can be seamlessly supervised using off-the-shelf BEV-space traffic simulators. We validate our framework on both the NAVSIM benchmark and the closed-loop Bench2Drive benchmark based on the CARLA simulator, achieving state-of-the-art performance. Code is released at https://github.com/liyingyanUCAS/WoTE.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. GeoWorldAD: Geometry World Action Model for Autonomous Driving

    cs.RO 2026-07 conditional novelty 6.0 of 10

    Grounding an autonomous-driving action model in ego-aligned multi-scale 3D geometry and latent future-geometry tokens improves NAVSIM closed-loop PDMS/EPDMS over prior geometry- and world-model-based planners.

  2. SpaceDrive: Infusing Spatial Awareness into VLM-based Autonomous Driving

    cs.CV 2025-12 conditional novelty 6.0 of 10

    SpaceDrive replaces textual coordinate tokens with shared 3D positional encodings in a VLM driving planner, achieving state-of-the-art open-loop planning on nuScenes and 78.02 Driving Score on Bench2Drive.

  3. IRL-VLA: Training an Vision-Language-Action Policy via Reward World Model

    cs.AI 2025-08 conditional novelty 6.0 of 10

    IRL-VLA fine-tunes a vision-language-action driving policy with PPO against a learned reward world model trained on NAVSIM's EPDMS metrics, reaching 74.9 EPDMS on navhard-real.

  4. UNIVERSE: Unified Video Action Models for Autonomous Driving with Flexible Mask-Modulated Modality Generation

    cs.CV 2026-07 conditional novelty 5.0 of 10

    A single mask-modulated DiT that co-trains future video and trajectories yields stronger autonomous-driving action generalization and 4.3× faster trajectory-only inference than dual-DiT designs.

  5. DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving

    cs.CV 2025-07 unverdicted novelty 5.0 of 10

    DIVER uses RL-guided diffusion to produce diverse feasible trajectories from one ground-truth path, addressing mode collapse in imitation learning for autonomous driving.

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