Towards Accurate and Efficient 3D Object Detection for Autonomous Driving: A Mixture of Experts Computing System on Edge

Linshen Liu, Boyan Su, Junyue Jiang, Guanlin Wu, Cong Guo, Ceyu Xu, Hao Frank Yang

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 existing works, the EMC2 introduces
a novel scenario-aware MoE architecture optimized for fusing complementary sparse 3D point clouds and dense 2D
images to achieve robust multimodal representations for detection. Furthermore, EMC2 integrates an adaptive multimodal data bridge with multi-scale region proposing and
scenario-aware routing, dynamically dispatching features
to complementary experts based on object visibility and
distance. In addition, EMC2 integrates joint hardwaresoftware optimizations, including hardware resource utilization optimization and computational graph simplification, to ensure efficient and real-time inference on resourceconstrained edge devices. Experiments on open-source
benchmarks clearly show the EMC2 advancements as an
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, realtime 3D object detection tasks for AVs.