# Bridging Past and Future: End-to-End Autonomous Driving with Historical Prediction and Planning arXiv: https://arxiv.org/abs/2503.14182 ยท HTML: https://mlsanity.com/paper/2503.14182/ ## Key facts - Cited by 48 papers in the map (data through 14 Oct 2026). - Placed in Generative simulation for driving, one of 9 directions in End-to-end autonomous driving. - Builds on 0 of its 67 references that are in the map. - First posted on arXiv 18 Mar 2025. ## Placed in - End-to-end autonomous driving / [Generative simulation for driving](https://mlsanity.com/driving/generative-simulation-for-driving/) ## Built on by (most cited first, 30 of 48) - [Impromptu VLA: Open Weights and Open Data for Driving Vision-Language-Action Models](https://mlsanity.com/paper/2505.23757/) (2025) - [SGDrive: Scene-to-Goal Hierarchical World Cognition for Autonomous Driving](https://mlsanity.com/paper/2601.05640/) (2026) - [ImagiDrive: A Unified Imagination-and-Planning Framework for Autonomous Driving](https://mlsanity.com/paper/2508.11428/) (2025) - [DriveFuture: Future-Aware Latent World Models for Autonomous Driving](https://mlsanity.com/paper/2605.09701/) (2026) - [EponaV2: Driving World Model with Comprehensive Future Reasoning](https://mlsanity.com/paper/2605.14696/) (2026) - [ResAD: Normalized Residual Trajectory Modeling for End-to-End Autonomous Driving](https://mlsanity.com/paper/2510.08562/) (2025) - [Reasoning-VLA: A Fast and General Vision-Language-Action Reasoning Model for Autonomous Driving](https://mlsanity.com/paper/2511.19912/) (2025) - [MindDrive: An All-in-One Framework Bridging World Models and Vision-Language Model for End-to-End Autonomous Driving](https://mlsanity.com/paper/2512.04441/) (2025) - [ColaVLA: Leveraging Cognitive Latent Reasoning for Hierarchical Parallel Trajectory Planning in Autonomous Driving](https://mlsanity.com/paper/2512.22939/) (2025) - [DynVLA: Learning World Dynamics for Action Reasoning in Autonomous Driving](https://mlsanity.com/paper/2603.11041/) (2026) - [SafeDrive: Fine-Grained Safety Reasoning for End-to-End Driving in a Sparse World](https://mlsanity.com/paper/2602.18887/) (2026) - [CoReVLA: A Dual-Stage End-to-End Autonomous Driving Framework for Long-Tail Scenarios via Collect-and-Refine](https://mlsanity.com/paper/2509.15968/) (2025) - [FocalAD: Local Motion Planning for End-to-End Autonomous Driving](https://mlsanity.com/paper/2506.11419/) (2025) - [VR-Drive: Viewpoint-Robust End-to-End Driving with Feed-Forward 3D Gaussian Splatting](https://mlsanity.com/paper/2510.23205/) (2025) - [HENet++: Hybrid Encoding and Multi-task Learning for 3D Perception and End-to-end Autonomous Driving](https://mlsanity.com/paper/2511.07106/) (2025) - [Do Open-Loop Metrics Predict Closed-Loop Driving? A Cross-Benchmark Correlation Study of NAVSIM and Bench2Drive](https://mlsanity.com/paper/2605.00066/) (2026) - [KnowVal: A Knowledge-Augmented and Value-Guided Autonomous Driving System](https://mlsanity.com/paper/2512.20299/) (2025) - [Decoupling Scene Perception and Ego Status: A Multi-Context Fusion Approach for Enhanced Generalization in End-to-End Autonomous Driving](https://mlsanity.com/paper/2511.13079/) (2025) - [DAP: A Discrete-token Autoregressive Planner for Autonomous Driving](https://mlsanity.com/paper/2511.13306/) (2025) - [WPT: World-to-Policy Transfer via Online World Model Distillation](https://mlsanity.com/paper/2511.20095/) (2025) - [Echo Planning for Autonomous Driving: From Current Observations to Future Trajectories and Back](https://mlsanity.com/paper/2505.18945/) (2025) - [Foundation Models for Trajectory Planning in Autonomous Driving: A Review of Progress and Open Challenges](https://mlsanity.com/paper/2512.00021/) (2025) - [OpenWorldLib: A Unified Codebase and Definition of Advanced World Models](https://mlsanity.com/paper/2604.04707/) (2026) - [Map-World: Masked Action planning and Path-Integral World Model for Autonomous Driving](https://mlsanity.com/paper/2511.20156/) (2025) - [UniUncer: Unified Dynamic Static Uncertainty for End to End Driving](https://mlsanity.com/paper/2603.07686/) (2026) - [Can Attribution Predict Risk? From Multi-View Attribution to Planning Risk Signals in End-to-End Autonomous Driving](https://mlsanity.com/paper/2605.06264/) (2026) - [A New Perspective On AI Safety Through Control Theory Methodologies](https://mlsanity.com/paper/2506.23703/) (2025) - [SpaRC-AD: A Baseline for Radar-Camera Fusion in End-to-End Autonomous Driving](https://mlsanity.com/paper/2508.10567/) (2025) - [MAP: End-to-End Autonomous Driving with Map-Assisted Planning](https://mlsanity.com/paper/2509.13926/) (2025) - [From Features to Reference Points: Lightweight and Adaptive Fusion for Cooperative Autonomous Driving](https://mlsanity.com/paper/2511.18757/) (2025) ## Abstract (arXiv) End-to-end autonomous driving unifies tasks in a differentiable framework, enabling planning-oriented optimization and attracting growing attention. Current methods aggregate historical information either through dense historical bird's-eye-view (BEV) features or by querying a sparse memory bank, following paradigms inherited from detection. However, we argue that these paradigms either omit historical information in motion planning or fail to align with its multi-step nature, which requires predicting or planning multiple future time steps. In line with the philosophy of future is a continuation of past, we propose BridgeAD, which reformulates motion and planning queries as multi-step queries to differentiate the queries for each future time step. This design enables the effective use of historical prediction and planning by applying them to the appropriate parts of the end-to-end system based on the time steps, which improves both perception and motion planning. Specifically, historical queries for the current frame are combined with perception, while queries for future frames are integrated with motion planning. In this way, we bridge the gap between past and future by aggregating historical insights at every time step, enhancing the overall coherence and accuracy of the end-to-end autonomous driving pipeline. Extensive experiments on the nuScenes dataset in both open-loop and closed-loop settings demonstrate that BridgeAD achieves state-of-the-art performance.