Pith Number

pith:VCV2SF2G

pith:2026:VCV2SF2GISK7YXLMIVOETZRKON

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Real-time Gaussian Process based Approximate Model Predictive Trajectory Tracking Control for Autonomous Vehicles

Alexander Rose, Lukas Theiner, Rolf Findeisen

Gaussian process approximations of model predictive control enable five times faster computation for vehicle trajectory tracking on embedded systems.

arxiv:2605.13220 v1 · 2026-05-13 · eess.SY · cs.SY

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Claims

C1strongest claim

Compared to a model predictive control implementation using real-time iterations, the Gaussian process based approximation computes control inputs about five times faster while achieving similar closed-loop tracking performance.

C2weakest assumption

The curvilinear coordinate transformation combined with the nominal feedforward component reduces the problem sufficiently that a Gaussian process can generalize across distinct reference trajectories using a manageable amount of training data.

C3one line summary

Gaussian process approximation of MPC in curvilinear coordinates with residual feedforward learning enables 5x faster real-time trajectory tracking on embedded hardware with comparable closed-loop performance.

References

42 extracted · 42 resolved · 0 Pith anchors

[1] Model predictive control with Gaussian-process-supported dynamical constraints for autonomous vehicles, 2023

[2] Model predictive control for autonomous ground vehicles: a review, 2021

[3] Model predictive control for quadcopters with almost global trajectory tracking guarantees, 2024

[4] Model predictive interaction control for industrial robots, 2020

[5] Embedded optimization in control: An introduction, opportunities, and challenges, 2026

Receipt and verification

First computed	2026-05-18T03:08:48.403009Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

a8aba917464495fc5d6c455c49e62a735396b648c85f8579cc9b684cd16e22f6

Aliases

arxiv: 2605.13220 · arxiv_version: 2605.13220v1 · doi: 10.48550/arxiv.2605.13220 · pith_short_12: VCV2SF2GISK7 · pith_short_16: VCV2SF2GISK7YXLM · pith_short_8: VCV2SF2G

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/VCV2SF2GISK7YXLMIVOETZRKON \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: a8aba917464495fc5d6c455c49e62a735396b648c85f8579cc9b684cd16e22f6

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "fdf385febbc19aa6d07f267b28b2d84d41a2ca70082dc0ba079631c34352dd1c",
    "cross_cats_sorted": [
      "cs.SY"
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    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "eess.SY",
    "submitted_at": "2026-05-13T09:13:10Z",
    "title_canon_sha256": "a3a7a623069631ec7f1a4f6409a5980d40aeeccc6ec412768f174ab20f072177"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.13220",
    "kind": "arxiv",
    "version": 1
  }
}