Towards Optimal Mixture of Experts System for 3D Object Detection: A Game of Accuracy, Efficiency and Adaptivity.

Abstract

Autonomous vehicles, open-world robots, and other automated systems rely on accurate, efficient perception modules for real-time object detection. Although high-precision models improve reliability, their processing time and computational overhead can hinder real-time performance and raise safety concerns. This paper introduces an Edge-based Mixture-of-Experts Optimal Sensing (EMOS) System that addresses the challenge of co-achieving accuracy, latency and scene adaptivity, further demonstrated in the open-world autonomous driving scenarios. Algorithmically, EMOS fuses multimodal sensor streams via an Adaptive Multimodal Data Bridge and uses a scenario-aware MoE switch to activate only a complementary set of specialized experts as needed. The proposed hierarchical backpropagation and a multiscale pooling layer let model capacity scale with real-world demand complexity. System-wise, an edge-optimized runtime with accelerator-aware scheduling (e.g., ONNX/TensorRT), zero-copy buffering, and overlapped I/O-compute enforces explicit latency/accuracy budgets across diverse driving conditions. Experimental results establish EMOS as the new state of the art: on KITTI, it increases average AP by 3.17% while running $2.6\times$ faster on Nvidia Jetson. On nuScenes, it improves accuracy by 0.2% mAP and 0.5% NDS, with 34% fewer parameters and a $15.35\times$ Nvidia Jetson speedup. Leveraging multimodal data and intelligent experts cooperation, EMOS delivers accurate, efficient and edge-adaptive perception system for autonomous vehicles, thereby ensuring robust, timely responses in real-world scenarios.