Towards Accurate and Efficient 3D Object Detection for Autonomous Driving: A Mixture of Experts Computing System on Edge
Journal:
arXiv
Published Date:
Jul 5, 2025
Abstract
This paper presents Edge-based Mixture of Experts (MoE) Collaborative
Computing (EMC2), an optimal computing system designed for autonomous vehicles
(AVs) that simultaneously achieves low-latency and high-accuracy 3D object
detection. Unlike conventional approaches, EMC2 incorporates a scenario-aware
MoE architecture specifically optimized for edge platforms. By effectively
fusing LiDAR and camera data, the system leverages the complementary strengths
of sparse 3D point clouds and dense 2D images to generate robust multimodal
representations. To enable this, EMC2 employs an adaptive multimodal data
bridge that performs multi-scale preprocessing on sensor inputs, followed by a
scenario-aware routing mechanism that dynamically dispatches features to
dedicated expert models based on object visibility and distance. In addition,
EMC2 integrates joint hardware-software optimizations, including hardware
resource utilization optimization and computational graph simplification, to
ensure efficient and real-time inference on resource-constrained edge devices.
Experiments on open-source benchmarks clearly show the EMC2 advancements as a
end-to-end system. On the KITTI dataset, it achieves an average accuracy
improvement of 3.58% and a 159.06% inference speedup compared to 15 baseline
methods on Jetson platforms, with similar performance gains on the nuScenes
dataset, highlighting its capability to advance reliable, real-time 3D object
detection tasks for AVs.
