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

TL;DR

DRAMA is an efficient, end-to-end motion planner for autonomous vehicles that fuses camera and LiDAR data using a novel Mamba-based transformer architecture, achieving higher accuracy with less computation.

Contribution

It introduces the first Mamba-based end-to-end motion planner, combining multi-modal data fusion with a scalable transformer architecture for autonomous driving.

Findings

Higher accuracy on NAVSIM dataset

Fewer parameters and lower computational costs

Enhanced robustness through feature state dropout

Abstract

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.