# ProDrive: Proactive Planning for Autonomous Driving via Ego-Environment Co-Evolution

arXiv: https://arxiv.org/abs/2604.25329 · HTML: https://mlsanity.com/paper/2604.25329/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in World models for autonomous driving, one of 11 directions in World models.
- Builds on 15 of its 41 references that are in the map.
- First posted on arXiv 28 Apr 2026.

## Placed in

- World models / [World models for autonomous driving](https://mlsanity.com/wm/world-models-for-autonomous-driving/)
- End-to-end autonomous driving / [Generative simulation for driving](https://mlsanity.com/driving/generative-simulation-for-driving/)

## Builds on (most cited first, 15 of 15)

- [GAIA-1: A Generative World Model for Autonomous Driving](https://mlsanity.com/paper/2309.17080/) (2023)
- [NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles](https://mlsanity.com/paper/2106.11810/) (2021)
- [VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning](https://mlsanity.com/paper/2402.13243/) (2024)
- [Hydra-MDP: End-to-end Multimodal Planning with Multi-target Hydra-Distillation](https://mlsanity.com/paper/2406.06978/) (2024)
- [Rethinking the Open-Loop Evaluation of End-to-End Autonomous Driving in nuScenes](https://mlsanity.com/paper/2305.10430/) (2023)
- [Enhancing End-to-End Autonomous Driving with Latent World Model](https://mlsanity.com/paper/2406.08481/) (2024)
- [FutureSightDrive: Thinking Visually with Spatio-Temporal CoT for Autonomous Driving](https://mlsanity.com/paper/2505.17685/) (2025)
- [ADriver-I: A General World Model for Autonomous Driving](https://mlsanity.com/paper/2311.13549/) (2023)
- [DRAMA: An Efficient End-to-end Motion Planner for Autonomous Driving with Mamba](https://mlsanity.com/paper/2408.03601/) (2024)
- [Unleashing Generalization of End-to-End Autonomous Driving with Controllable Long Video Generation](https://mlsanity.com/paper/2406.01349/) (2024)
- [iPad: Iterative Proposal-centric End-to-End Autonomous Driving](https://mlsanity.com/paper/2505.15111/) (2025)
- [From Forecasting to Planning: Policy World Model for Collaborative State-Action Prediction](https://mlsanity.com/paper/2510.19654/) (2025)
- [ImagiDrive: A Unified Imagination-and-Planning Framework for Autonomous Driving](https://mlsanity.com/paper/2508.11428/) (2025)
- [Future-Aware End-to-End Driving: Bidirectional Modeling of Trajectory Planning and Scene Evolution](https://mlsanity.com/paper/2510.11092/) (2025)
- [DiffE2E: Rethinking End-to-End Driving with a Hybrid Action Diffusion and Supervised Policy](https://mlsanity.com/paper/2505.19516/) (2025)

## Built on by (most cited first, 4 of 4)

- [TPS-Drive: Task-Guided Representation Purification for VLM-based Autonomous Driving](https://mlsanity.com/paper/2605.27038/) (2026)
- [Not All History Helps: Velocity-Aware Selective Memory for Long-Horizon End-to-End Autonomous Driving](https://mlsanity.com/paper/2608.15573/) (2026)
- [From Proxy Learning to Driving Decisions: A Transfer-Based Framework for Evaluating Future-Aware Autonomous Driving Planners](https://mlsanity.com/paper/2609.02688/) (2026)
- [FeasibleFlow: One-Step Joint Transport of Configuration Feasibility and Trajectories for End-to-End Driving](https://mlsanity.com/paper/2609.23488/) (2026)

## Abstract (arXiv)

End-to-end autonomous driving planners typically generate trajectories from current observations alone. However, real-world driving is highly dynamic, and such reactive planning cannot anticipate future scene evolution, often leading to myopic decisions and safety-critical failures. We propose ProDrive, a world-model-based proactive planning framework that enables ego-environment co-evolution for autonomous driving. ProDrive jointly trains a query-centric trajectory planner and a bird's-eye-view (BEV) world model end-to-end: the planner generates diverse candidate trajectories and planning-aware ego tokens, while the world model predicts future scene evolution conditioned on them. By injecting planner features into the world model and evaluating all candidates in parallel, ProDrive preserves end-to-end gradient flow and allows future outcome assessment to directly shape planning. This bidirectional coupling enables proactive planning beyond current-observation-driven decision-making. Experiments on NAVSIM v1 show that ProDrive outperforms strong baselines in both safety and planning efficiency, while ablations validate the effectiveness of the proposed ego-environment coupling design.