# Perfect Prediction or Plenty of Proposals? What Matters Most in Planning for Autonomous Driving arXiv: https://arxiv.org/abs/2510.15505 ยท HTML: https://mlsanity.com/paper/2510.15505/ ## Key facts - Cited by 4 papers in the map (data through 14 Oct 2026). - Placed in Imitation-learning end-to-end planners, one of 9 directions in End-to-end autonomous driving. - Builds on 45 of its 51 references that are in the map. - First posted on arXiv 17 Oct 2025. ## Placed in - End-to-end autonomous driving / [Imitation-learning end-to-end planners](https://mlsanity.com/driving/imitation-learning-end-to-end-planners/) ## Builds on (most cited first, 30 of 45) - [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019) - [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019) - [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017) - [Planning-oriented Autonomous Driving](https://mlsanity.com/paper/2212.10156/) (2022) - [Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting](https://mlsanity.com/paper/2301.00493/) (2023) - [VAD: Vectorized Scene Representation for Efficient Autonomous Driving](https://mlsanity.com/paper/2303.12077/) (2023) - [Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Dataset](https://mlsanity.com/paper/2104.10133/) (2021) - [VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation](https://mlsanity.com/paper/2005.04259/) (2020) - [NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles](https://mlsanity.com/paper/2106.11810/) (2021) - [ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst](https://mlsanity.com/paper/1812.03079/) (2018) - [Learning Lane Graph Representations for Motion Forecasting](https://mlsanity.com/paper/2007.13732/) (2020) - [AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecasting](https://mlsanity.com/paper/2103.14023/) (2021) - [DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents](https://mlsanity.com/paper/1704.04394/) (2017) - [Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks](https://mlsanity.com/paper/1809.10732/) (2018) - [Learning to Drive in a Day](https://mlsanity.com/paper/1807.00412/) (2018) - [End-to-end Interpretable Neural Motion Planner](https://mlsanity.com/paper/2101.06679/) (2021) - [Parting with Misconceptions about Learning-based Vehicle Motion Planning](https://mlsanity.com/paper/2306.07962/) (2023) - [GenAD: Generative End-to-End Autonomous Driving](https://mlsanity.com/paper/2402.11502/) (2024) - [MotionDiffuser: Controllable Multi-Agent Motion Prediction using Diffusion](https://mlsanity.com/paper/2306.03083/) (2023) - [IntentNet: Learning to Predict Intention from Raw Sensor Data](https://mlsanity.com/paper/2101.07907/) (2021) - [MotionLM: Multi-Agent Motion Forecasting as Language Modeling](https://mlsanity.com/paper/2309.16534/) (2023) - [Perceive, Predict, and Plan: Safe Motion Planning Through Interpretable Semantic Representations](https://mlsanity.com/paper/2008.05930/) (2020) - [GameFormer: Game-theoretic Modeling and Learning of Transformer-based Interactive Prediction and Planning for Autonomous Driving](https://mlsanity.com/paper/2303.05760/) (2023) - [Diffusion-Based Planning for Autonomous Driving with Flexible Guidance](https://mlsanity.com/paper/2501.15564/) (2025) - [Urban Driver: Learning to Drive from Real-world Demonstrations Using Policy Gradients](https://mlsanity.com/paper/2109.13333/) (2021) - [Multimodal Trajectory Prediction Conditioned on Lane-Graph Traversals](https://mlsanity.com/paper/2106.15004/) (2021) - [PlanT: Explainable Planning Transformers via Object-Level Representations](https://mlsanity.com/paper/2210.14222/) (2022) - [Towards learning-based planning:The nuPlan benchmark for real-world autonomous driving](https://mlsanity.com/paper/2403.04133/) (2024) - [LookOut: Diverse Multi-Future Prediction and Planning for Self-Driving](https://mlsanity.com/paper/2101.06547/) (2021) - [Differentiable Integrated Motion Prediction and Planning with Learnable Cost Function for Autonomous Driving](https://mlsanity.com/paper/2207.10422/) (2022) ## Built on by (most cited first, 4 of 4) - [Beyond Self-Play and Scale: A Behavior Benchmark for Generalization in Autonomous Driving](https://mlsanity.com/paper/2605.10034/) (2026) - [Driving is a Game: Combining Planning and Prediction with Bayesian Iterative Best Response](https://mlsanity.com/paper/2512.03936/) (2025) - [TerraZero: Procedural Driving Simulation for Zero-Demonstration Self-Play at Scale](https://mlsanity.com/paper/2607.13028/) (2026) - [INTERACT: Interactive Planning for Autonomous Driving via Anchor-Conditioned Prediction and Trust-Region Refinement](https://mlsanity.com/paper/2609.31137/) (2026) ## Abstract (arXiv) Traditionally, prediction and planning in autonomous driving (AD) have been treated as separate, sequential modules. Recently, there has been a growing shift towards tighter integration of these components, known as Integrated Prediction and Planning (IPP), with the aim of enabling more informed and adaptive decision-making. However, it remains unclear to what extent this integration actually improves planning performance. In this work, we investigate the role of prediction in IPP approaches, drawing on the widely adopted Val14 benchmark, which encompasses more common driving scenarios with relatively low interaction complexity, and the interPlan benchmark, which includes highly interactive and out-of-distribution driving situations. Our analysis reveals that even access to perfect future predictions does not lead to better planning outcomes, indicating that current IPP methods often fail to fully exploit future behavior information. Instead, we focus on high-quality proposal generation, while using predictions primarily for collision checks. We find that many imitation learning-based planners struggle to generate realistic and plausible proposals, performing worse than PDM - a simple lane-following approach. Motivated by this observation, we build on PDM with an enhanced proposal generation method, shifting the emphasis towards producing diverse but realistic and high-quality proposals. This proposal-centric approach significantly outperforms existing methods, especially in out-of-distribution and highly interactive settings, where it sets new state-of-the-art results.