# DRAMA: An Efficient End-to-end Motion Planner for Autonomous Driving with Mamba

arXiv: https://arxiv.org/abs/2408.03601 · HTML: https://mlsanity.com/paper/2408.03601/

## Key facts

- Cited by 87 papers in the map (data through 14 Oct 2026).
- Placed in End-to-end system architecture, one of 9 directions in End-to-end autonomous driving.
- Builds on 27 of its 41 references that are in the map.
- First posted on arXiv 7 Aug 2024.

## Placed in

- End-to-end autonomous driving / [End-to-end system architecture](https://mlsanity.com/driving/end-to-end-system-architecture/)
- State-space sequence models (S4, Mamba) / [Video, motion and generative Mamba models](https://mlsanity.com/ssm/video-motion-and-generative-mamba-models/)

## Builds on (most cited first, 27 of 27)

- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Efficiently Modeling Long Sequences with Structured State Spaces](https://mlsanity.com/paper/2111.00396/) (2021)
- [Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality](https://mlsanity.com/paper/2405.21060/) (2024)
- [Planning-oriented Autonomous Driving](https://mlsanity.com/paper/2212.10156/) (2022)
- [Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model](https://mlsanity.com/paper/2401.09417/) (2024)
- [NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles](https://mlsanity.com/paper/2106.11810/) (2021)
- [TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving](https://mlsanity.com/paper/2205.15997/) (2022)
- [Jamba: A Hybrid Transformer-Mamba Language Model](https://mlsanity.com/paper/2403.19887/) (2024)
- [NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking](https://mlsanity.com/paper/2406.15349/) (2024)
- [Motion Transformer with Global Intention Localization and Local Movement Refinement](https://mlsanity.com/paper/2209.13508/) (2022)
- [A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles](https://mlsanity.com/paper/1604.07446/) (2016)
- [Hydra-MDP: End-to-end Multimodal Planning with Multi-target Hydra-Distillation](https://mlsanity.com/paper/2406.06978/) (2024)
- [Parting with Misconceptions about Learning-based Vehicle Motion Planning](https://mlsanity.com/paper/2306.07962/) (2023)
- [Multimodal Motion Prediction with Stacked Transformers](https://mlsanity.com/paper/2103.11624/) (2021)
- [GameFormer: Game-theoretic Modeling and Learning of Transformer-based Interactive Prediction and Planning for Autonomous Driving](https://mlsanity.com/paper/2303.05760/) (2023)
- [MTR++: Multi-Agent Motion Prediction with Symmetric Scene Modeling and Guided Intention Querying](https://mlsanity.com/paper/2306.17770/) (2023)
- [Motion Mamba: Efficient and Long Sequence Motion Generation](https://mlsanity.com/paper/2403.07487/) (2024)
- [Baidu Apollo EM Motion Planner](https://mlsanity.com/paper/1807.08048/) (2018)
- [PLUTO: Pushing the Limit of Imitation Learning-based Planning for Autonomous Driving](https://mlsanity.com/paper/2404.14327/) (2024)
- [MoE-Mamba: Efficient Selective State Space Models with Mixture of Experts](https://mlsanity.com/paper/2401.04081/) (2024)
- [DTPP: Differentiable Joint Conditional Prediction and Cost Evaluation for Tree Policy Planning in Autonomous Driving](https://mlsanity.com/paper/2310.05885/) (2023)
- [Rethinking Imitation-based Planner for Autonomous Driving](https://mlsanity.com/paper/2309.10443/) (2023)
- [DenseMamba: State Space Models with Dense Hidden Connection for Efficient Large Language Models](https://mlsanity.com/paper/2403.00818/) (2024)
- [Imitation with Spatial-Temporal Heatmap: 2nd Place Solution for NuPlan Challenge](https://mlsanity.com/paper/2306.15700/) (2023)
- [ControlMTR: Control-Guided Motion Transformer with Scene-Compliant Intention Points for Feasible Motion Prediction](https://mlsanity.com/paper/2404.10295/) (2024)
- [GET-DIPP: Graph-Embedded Transformer for Differentiable Integrated Prediction and Planning](https://mlsanity.com/paper/2211.06031/) (2022)

## Built on by (most cited first, 30 of 87)

- [DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous Driving](https://mlsanity.com/paper/2411.15139/) (2024)
- [AutoVLA: A Vision-Language-Action Model for End-to-End Autonomous Driving with Adaptive Reasoning and Reinforcement Fine-Tuning](https://mlsanity.com/paper/2506.13757/) (2025)
- [ReCogDrive: A Reinforced Cognitive Framework for End-to-End Autonomous Driving](https://mlsanity.com/paper/2506.08052/) (2025)
- [End-to-End Driving with Online Trajectory Evaluation via BEV World Model](https://mlsanity.com/paper/2504.01941/) (2025)
- [Epona: Autoregressive Diffusion World Model for Autonomous Driving](https://mlsanity.com/paper/2506.24113/) (2025)
- [DriveSuprim: Towards Precise Trajectory Selection for End-to-End Planning](https://mlsanity.com/paper/2506.06659/) (2025)
- [ARTEMIS: Autoregressive End-to-End Trajectory Planning with Mixture of Experts for Autonomous Driving](https://mlsanity.com/paper/2504.19580/) (2025)
- [AdaThinkDrive: Adaptive Thinking via Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/2509.13769/) (2025)
- [DiffusionDriveV2: Reinforcement Learning-Constrained Truncated Diffusion Modeling in End-to-End Autonomous Driving](https://mlsanity.com/paper/2512.07745/) (2025)
- [V2X-VLM: End-to-End V2X Cooperative Autonomous Driving Through Large Vision-Language Models](https://mlsanity.com/paper/2408.09251/) (2024)
- [Driving on Registers](https://mlsanity.com/paper/2601.05083/) (2026)
- [iPad: Iterative Proposal-centric End-to-End Autonomous Driving](https://mlsanity.com/paper/2505.15111/) (2025)
- [Hydra-NeXt: Robust Closed-Loop Driving with Open-Loop Training](https://mlsanity.com/paper/2503.12030/) (2025)
- [DriveDPO: Policy Learning via Safety DPO For End-to-End Autonomous Driving](https://mlsanity.com/paper/2509.17940/) (2025)
- [SGDrive: Scene-to-Goal Hierarchical World Cognition for Autonomous Driving](https://mlsanity.com/paper/2601.05640/) (2026)
- [From Forecasting to Planning: Policy World Model for Collaborative State-Action Prediction](https://mlsanity.com/paper/2510.19654/) (2025)
- [Vision-Language-Action Models for Autonomous Driving: Past, Present, and Future](https://mlsanity.com/paper/2512.16760/) (2025)
- [BEVDriver: Leveraging BEV Maps in LLMs for Robust Closed-Loop Driving](https://mlsanity.com/paper/2503.03074/) (2025)
- [SparseDriveV2: Scoring is All You Need for End-to-End Autonomous Driving](https://mlsanity.com/paper/2603.29163/) (2026)
- [ImagiDrive: A Unified Imagination-and-Planning Framework for Autonomous Driving](https://mlsanity.com/paper/2508.11428/) (2025)
- [VGGDrive: Empowering Vision-Language Models with Cross-View Geometric Grounding for Autonomous Driving](https://mlsanity.com/paper/2602.20794/) (2026)
- [Reinforced Refinement with Self-Aware Expansion for End-to-End Autonomous Driving](https://mlsanity.com/paper/2506.09800/) (2025)
- [SpanVLA: Efficient Action Bridging and Learning from Negative-Recovery Samples for Vision-Language-Action Model](https://mlsanity.com/paper/2604.19710/) (2026)
- [DriveFuture: Future-Aware Latent World Models for Autonomous Driving](https://mlsanity.com/paper/2605.09701/) (2026)
- [PRIX: Learning to Plan from Raw Pixels for End-to-End Autonomous Driving](https://mlsanity.com/paper/2507.17596/) (2025)
- [ResWorld: Temporal Residual World Model for End-to-End Autonomous Driving](https://mlsanity.com/paper/2602.10884/) (2026)
- [DreamerAD: Efficient Reinforcement Learning via Latent World Model for Autonomous Driving](https://mlsanity.com/paper/2603.24587/) (2026)
- [InsightDrive: Insight Scene Representation for End-to-End Autonomous Driving](https://mlsanity.com/paper/2503.13047/) (2025)
- [ResAD: Normalized Residual Trajectory Modeling for End-to-End Autonomous Driving](https://mlsanity.com/paper/2510.08562/) (2025)
- [AutoDrive-P3: Unified Chain of Perception-Prediction-Planning Thought via Reinforcement Fine-Tuning](https://mlsanity.com/paper/2603.28116/) (2026)

## Abstract (arXiv)

Motion planning is a challenging task to generate safe and feasible trajectories in highly dynamic and complex environments, forming a core capability for autonomous vehicles. In this paper, we propose DRAMA, the first Mamba-based end-to-end motion planner for autonomous vehicles. DRAMA fuses camera, LiDAR Bird's Eye View images in the feature space, as well as ego status information, to generate a series of future ego trajectories. Unlike traditional transformer-based methods with quadratic attention complexity for sequence length, DRAMA is able to achieve a less computationally intensive attention complexity, demonstrating potential to deal with increasingly complex scenarios. Leveraging our Mamba fusion module, DRAMA efficiently and effectively fuses the features of the camera and LiDAR modalities. In addition, we introduce a Mamba-Transformer decoder that enhances the overall planning performance. This module is universally adaptable to any Transformer-based model, especially for tasks with long sequence inputs. We further introduce a novel feature state dropout which improves the planner's robustness without increasing training and inference times. Extensive experimental results show that DRAMA achieves higher accuracy on the NAVSIM dataset compared to the baseline Transfuser, with fewer parameters and lower computational costs.