DPNet: Doppler LiDAR Motion Planning for Highly-Dynamic Environments

TL;DR

DPNet integrates Doppler LiDAR data with neural network-based tracking and model predictive control to enable fast, accurate motion planning in highly dynamic environments, outperforming existing methods.

Contribution

Introduction of DPNet, a novel framework combining Doppler LiDAR, neural tracking, and auto-tuned MPC for real-time obstacle avoidance in dynamic settings.

Findings

DPNet achieves high-frequency, accurate obstacle tracking.

DPNet outperforms benchmark schemes in simulations and real-world tests.

The framework effectively adapts to rapid environmental changes.

Abstract

Existing motion planning methods often struggle with rapid-motion obstacles due to an insufficient understanding of environmental changes. To address this, we propose integrating motion planners with Doppler LiDARs, which provide not only ranging measurements but also instantaneous point velocities. However, this integration is nontrivial due to the requirements of high accuracy and high frequency. To this end, we introduce Doppler Planning Network (DPNet), which tracks and reacts to rapid obstacles via Doppler model-based learning. We first propose a Doppler Kalman neural network (D-KalmanNet) to track obstacle states under a partially observable Gaussian state space model. We then leverage the predicted motions of obstacles to construct a Doppler-tuned model predictive control (DT-MPC) framework for ego-motion planning, enabling runtime auto-tuning of controller parameters. These two modules allow DPNet to learn fast environmental changes from minimal data while remaining lightweight, achieving high frequency and high accuracy in both tracking and planning. Experiments on high-fidelity simulator and real-world datasets demonstrate the superiority of DPNet over extensive benchmark schemes.