  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.
