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arxiv: 2512.00375 · v2 · submitted 2025-11-29 · 💻 cs.RO

DPNet: Doppler LiDAR Motion Planning for Highly-Dynamic Environments

Wei Zuo , Zeyi Ren , Chengyang Li , Yikun Wang , Mingle Zhao , Shuai Wang , Wei Sui , Fei Gao
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Yik-Chung Wu Chengzhong Xu
This is my paper

Pith reviewed 2026-05-17 03:40 UTC · model grok-4.3

classification 💻 cs.RO
keywords Doppler LiDARmotion planningdynamic environmentsKalman neural networkmodel predictive controlobstacle trackingvelocity measurements
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The pith

DPNet fuses Doppler LiDAR velocity readings into a neural tracker and adaptive planner to handle fast-moving obstacles at high frequency and accuracy.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Existing motion planners often fail against rapid obstacles because they cannot capture environmental changes fast enough. Doppler LiDAR supplies both distances and instantaneous velocities, yet turning those extra measurements into reliable high-speed planning is difficult. The paper introduces DPNet with two linked modules: D-KalmanNet tracks obstacle states inside a partially observable Gaussian model, and DT-MPC uses those predictions to auto-tune controller parameters during ego-motion planning. Together the modules let the system learn velocity patterns from small amounts of data while staying lightweight enough for high-frequency operation. Tests in simulators and on real datasets show gains over standard benchmarks in both tracking and planning.

Core claim

DPNet tracks obstacle states with a Doppler Kalman neural network under a partially observable Gaussian state space model and uses the predicted motions to construct a Doppler-tuned model predictive control framework that auto-tunes controller parameters for ego-motion planning, enabling high-frequency and high-accuracy responses to rapid environmental changes from limited data.

What carries the argument

D-KalmanNet for Doppler-based state tracking combined with DT-MPC for runtime parameter tuning, which together incorporate point velocities to support responsive planning.

If this is right

  • Motion planners can maintain high update rates while incorporating instantaneous obstacle velocities instead of relying solely on position history.
  • Systems trained on modest datasets can still achieve accurate velocity-aware predictions in changing scenes.
  • Auto-tuning of MPC parameters based on Doppler forecasts reduces manual calibration for new environments.
  • Lightweight neural modules allow deployment on resource-limited platforms without sacrificing planning frequency.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same velocity-aware tracking pattern could apply to radar or camera-based velocity estimation in settings where LiDAR is unavailable.
  • Auto-tuned MPC might transfer to other control tasks that need rapid reaction to predicted motion, such as swarm coordination.
  • Extending the Gaussian state model to include uncertainty from Doppler noise could further improve robustness in cluttered scenes.

Load-bearing premise

Doppler velocity measurements can be integrated into the tracking and planning modules without creating unacceptable latency or instability.

What would settle it

A controlled experiment in which obstacle speeds increase stepwise until DPNet either falls below real-time update rates or shows lower accuracy than a non-Doppler baseline on the same hardware.

Figures

Figures reproduced from arXiv: 2512.00375 by Chengyang Li, Chengzhong Xu, Fei Gao, Mingle Zhao, Shuai Wang, Wei Sui, Wei Zuo, Yik-Chung Wu, Yikun Wang, Zeyi Ren.

Figure 1
Figure 1. Figure 1: Doppler model-based learning enables DPNet to un [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The proposed DPNet system, which consists of D-KalmanNet and DT-MPC modules. [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Doppler velocity rectification. this goal, but two challenges exist. First, according to Doppler LiDAR manufacturers [14], real-world velocity measurements typically involve noises with standard deviation of 0.1 m/s. Second, built upon past trajectories, the transition model may mismatch the actual obstacle motions in the future [5]. Our D-KalmanNet can simultaneously tackle both challenges. 1) Doppler Vel… view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative analysis of robot motions (in ROS-Rviz and Carla [13] views) and corresponding control commands. Static [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Clutter levels. D (S) refers to dynamic (static) obstacles [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 7
Figure 7. Figure 7: Visualization of critical predictions for a rapidly approaching vehicle (at about 25 m/s, highlighted in orange) in [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The manuscript proposes DPNet for motion planning in highly-dynamic environments by integrating Doppler LiDAR data. It introduces D-KalmanNet, a neural network for tracking obstacle states under a partially observable Gaussian state-space model, and DT-MPC, a Doppler-tuned MPC framework that uses predicted obstacle motions for runtime auto-tuning of controller parameters. The central claim is that these modules enable learning fast environmental changes from minimal data while remaining lightweight, achieving high frequency and high accuracy in tracking and planning, with experiments on simulators and real-world datasets demonstrating superiority over benchmarks.

Significance. If the quantitative results, complexity analysis, and stability properties hold, the work could meaningfully advance real-time robotics planning by leveraging instantaneous velocity measurements from Doppler LiDAR to improve prediction in dynamic scenes with low data requirements. The lightweight, high-frequency emphasis addresses a practical gap in existing MPC and Kalman-filter approaches for rapidly moving obstacles.

major comments (3)
  1. [Abstract] Abstract: The assertion of superiority over extensive benchmark schemes supplies no quantitative metrics, error bars, specific accuracy/frequency numbers, or comparison tables, preventing verification of the central performance claims.
  2. [Sections 3–4] D-KalmanNet and DT-MPC descriptions (Sections 3–4): The integration is presented via standard state-space and MPC blocks with runtime auto-tuning, yet no complexity analysis, stability bounds, or measured cycle times are provided to confirm that the neural update and parameter tuning sustain high frequency without unacceptable latency or instability under rapid obstacle motion.
  3. [Experiments] Experiments section: Absence of ablation studies on data efficiency, latency overhead of the Doppler-tuned modules, or failure cases under varying motion speeds leaves the claims of minimal-data learning and high-frequency operation unsubstantiated.
minor comments (2)
  1. [Section 3] Notation for the partially observable Gaussian state-space model should include explicit definitions of all variables and the observation model to improve clarity.
  2. [Figures] Figure captions for any tracking or planning visualizations could more explicitly label Doppler velocity inputs versus standard range-only baselines.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed comments. We appreciate the opportunity to clarify and strengthen our manuscript. Below we respond point by point to the major comments and indicate the revisions we will make.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The assertion of superiority over extensive benchmark schemes supplies no quantitative metrics, error bars, specific accuracy/frequency numbers, or comparison tables, preventing verification of the central performance claims.

    Authors: We agree that the abstract would be strengthened by including concrete quantitative results. The Experiments section already contains tables and figures with specific metrics (tracking RMSE, planning frequency in Hz, and relative improvements with error bars), but these are not summarized numerically in the abstract. In the revised version we will add key performance numbers and direct references to the relevant tables. revision: yes

  2. Referee: [Sections 3–4] D-KalmanNet and DT-MPC descriptions (Sections 3–4): The integration is presented via standard state-space and MPC blocks with runtime auto-tuning, yet no complexity analysis, stability bounds, or measured cycle times are provided to confirm that the neural update and parameter tuning sustain high frequency without unacceptable latency or instability under rapid obstacle motion.

    Authors: This observation is correct. The current manuscript presents the algorithmic integration and relies on empirical timing results in the experiments but does not supply an explicit complexity analysis, formal stability bounds, or tabulated cycle-time measurements. We will add a complexity subsection (FLOPs and runtime scaling for both modules), report measured cycle times from our implementation, and include a brief stability discussion based on the Doppler-tuned MPC formulation and observed behavior under fast obstacle motion. revision: yes

  3. Referee: [Experiments] Experiments section: Absence of ablation studies on data efficiency, latency overhead of the Doppler-tuned modules, or failure cases under varying motion speeds leaves the claims of minimal-data learning and high-frequency operation unsubstantiated.

    Authors: We acknowledge that dedicated ablation studies are missing. The existing experiments focus on end-to-end comparisons but do not isolate data-efficiency curves, per-module latency overhead, or systematic variation of obstacle speeds. In the revision we will add these ablations, including performance versus training-set size, latency breakdowns for the Doppler components, and additional trials across a range of obstacle velocities with any observed failure modes. revision: yes

Circularity Check

0 steps flagged

No significant circularity in DPNet derivation chain

full rationale

The paper introduces D-KalmanNet as a Doppler Kalman neural network for tracking under a partially observable Gaussian state space model and DT-MPC as a Doppler-tuned model predictive control framework with runtime auto-tuning. These are presented as integrations of Doppler LiDAR data into planning, relying on established state-space models and MPC techniques without any equations, fitted parameters, or self-citations that reduce the claimed high-frequency high-accuracy performance to inputs defined by the same performance. No self-definitional, fitted-input-called-prediction, uniqueness-imported, or ansatz-smuggled patterns appear. The central claims rest on the novelty of the integration rather than tautological reductions, making the derivation self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides no explicit free parameters, axioms, or invented entities; the Gaussian state-space model and MPC framework are treated as standard background.

pith-pipeline@v0.9.0 · 5505 in / 1084 out tokens · 79538 ms · 2026-05-17T03:40:23.886467+00:00 · methodology

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