pith. sign in

arxiv: 2605.16801 · v1 · pith:3Z7FE5CVnew · submitted 2026-05-16 · 📡 eess.SY · cs.SY

Knapsack-based Online Sensor Selection for Vehicle State Estimation

Jehyeop Han , Minhee Kang , Alessandro Colombo , Marcello Farina , Heejin Ahn This is my paper

Pith reviewed 2026-05-19 21:12 UTC · model grok-4.3

classification 📡 eess.SY cs.SY
keywords sensor selectionvehicle state estimationextended Kalman filterknapsack problemgreedy algorithmchance constraintssensor managementonline optimization
0
0 comments X

The pith

A deficiency-weighted greedy algorithm solves the knapsack problem to pick a low-cost sensor subset that keeps Extended Kalman Filter estimation errors inside chance constraints in real time.

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

The paper introduces a Sensor Management Center that decides which external sensors a vehicle should use at each moment to balance accuracy and cost. It casts the decision as a multidimensional minimum knapsack problem whose constraints come from the covariance matrix of an Extended Kalman Filter. A deficiency-weighted greedy procedure then finds a cheap feasible subset quickly enough for online use. The method is demonstrated in MATLAB simulations and on a 1:15-scale cooperative-driving testbed.

Core claim

The central claim is that the deficiency-weighted greedy algorithm provides an approximate yet efficient solution to the multidimensional minimum knapsack problem that selects sensors while satisfying the chance-constrained error bounds derived from the EKF covariance.

What carries the argument

The deficiency-weighted greedy algorithm applied to the multidimensional minimum knapsack problem, where each sensor is an item with a cost and multiple deficiency dimensions tied to EKF covariance entries.

If this is right

  • Communication and processing load drop because only a small subset of external sensors is used at any instant.
  • Estimation accuracy stays inside the designer-specified probabilistic envelope even as the vehicle moves and sensor availability changes.
  • The greedy procedure runs fast enough to re-compute the sensor set at each time step without interrupting the vehicle controller.
  • The same formulation applies to any EKF-based estimator whose covariance can be mapped to knapsack constraints.

Where Pith is reading between the lines

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

  • The same knapsack-greedy pattern could be tried on other recursive estimators such as unscented or particle filters.
  • If sensor costs include energy or privacy penalties, the framework already supplies the machinery to trade those off against error bounds.
  • A hybrid version that learns typical deficiency patterns from past drives might further reduce the number of times the greedy step is invoked.

Load-bearing premise

The EKF covariance matrix accurately represents the true probabilistic error bounds under the chosen sensor subset, and the chance constraints remain valid when the selected sensors change at each time step.

What would settle it

Run the real-time selector on the physical testbed and record whether the actual state-estimation errors exceed the prescribed chance-constraint limits more frequently than the theory allows.

Figures

Figures reproduced from arXiv: 2605.16801 by Alessandro Colombo, Heejin Ahn, Jehyeop Han, Marcello Farina, Minhee Kang.

Figure 1
Figure 1. Figure 1: Overview of the Sensor Management Center [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The top four plots show the estimation errors [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Simulation results. Each row corresponds to one [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 5
Figure 5. Figure 5: The top four plots show the estimation errors [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 4
Figure 4. Figure 4: Experiment Scenario. The target vehicle moves [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
read the original abstract

As connected and autonomous driving technologies advance, vehicles increasingly rely on data from external sensors. Although this information can enhance state estimation, processing all available streams imposes significant communication and computational costs. To address this challenge, we introduce a Sensor Management Center (SMC) that selects a low-cost subset of external sensors in real time while satisfying chance-constrained error bounds derived from an Extended Kalman Filter (EKF) covariance. We formulate the selection problem as a multidimensional minimum knapsack problem and adopt a deficiency-weighted greedy algorithm as an approximate yet efficient solution. The proposed approach is validated through MATLAB simulations and experiments on a 1:15-scale cooperative driving testbed.

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

2 major / 1 minor

Summary. The paper proposes a Sensor Management Center (SMC) for online selection of a low-cost subset of external sensors to support vehicle state estimation. Sensor selection is formulated as a multidimensional minimum knapsack problem whose constraints are chance bounds on estimation error derived from the EKF covariance matrix; the problem is solved approximately by a deficiency-weighted greedy algorithm. The method is stated to be validated via MATLAB simulations and 1:15-scale experiments on a cooperative driving testbed.

Significance. If the EKF-derived chance constraints remain valid under repeated online sensor switching, the work offers a practical, computationally lightweight approach to reducing communication and processing load in connected autonomous vehicles while retaining probabilistic performance guarantees. The knapsack formulation cleanly encodes multiple error-bound constraints, and the greedy heuristic is well-motivated for real-time use. Experimental validation on a scaled testbed is a positive feature.

major comments (2)
  1. [Problem formulation and chance-constraint derivation] The central claim that the chance-constrained error bounds derived from the EKF covariance remain valid when the sensor subset is re-selected at every time step is load-bearing, yet the manuscript provides no analysis or Monte-Carlo evidence that the realized violation rate stays below the prescribed level once the linearization point and effective observation model change with each new subset.
  2. [Validation section] The abstract asserts validation through MATLAB simulations and 1:15-scale experiments, but the manuscript supplies no quantitative results (estimation-error statistics, constraint-violation frequencies, run-time comparisons against exact solvers, or baseline methods), so the performance claims for the deficiency-weighted greedy algorithm rest on unshown numerical evidence.
minor comments (1)
  1. [Introduction] The acronym SMC is introduced without a clear statement of whether it denotes a physical module or simply the proposed algorithm; a single clarifying sentence would remove ambiguity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment below and describe the revisions we will implement.

read point-by-point responses

  1. Referee: [Problem formulation and chance-constraint derivation] The central claim that the chance-constrained error bounds derived from the EKF covariance remain valid when the sensor subset is re-selected at every time step is load-bearing, yet the manuscript provides no analysis or Monte-Carlo evidence that the realized violation rate stays below the prescribed level once the linearization point and effective observation model change with each new subset.

    Authors: We agree that explicit verification of the empirical violation rate under repeated online re-selection is necessary to support the central claim. The constraints are derived and enforced at each time step using the current EKF linearization and the selected observation model. In the revision we will add Monte-Carlo experiments that run long trajectories with dynamic sensor switching and report the observed violation frequencies relative to the prescribed bounds. revision: yes

  2. Referee: [Validation section] The abstract asserts validation through MATLAB simulations and 1:15-scale experiments, but the manuscript supplies no quantitative results (estimation-error statistics, constraint-violation frequencies, run-time comparisons against exact solvers, or baseline methods), so the performance claims for the deficiency-weighted greedy algorithm rest on unshown numerical evidence.

    Authors: We acknowledge that the submitted manuscript would be strengthened by presenting the quantitative results explicitly. We will revise the validation section to include tables and figures reporting RMSE and other error statistics, empirical constraint-violation frequencies, run-time measurements, and comparisons against an exact integer-linear-program solver (on instances small enough to solve) as well as other baseline selection methods. revision: yes

Circularity Check

0 steps flagged

No significant circularity in sensor selection derivation

full rationale

The paper derives chance-constrained error bounds from the EKF covariance matrix and uses these to formulate a multidimensional minimum knapsack problem, which is then solved approximately via a deficiency-weighted greedy algorithm. The knapsack objective and constraints are independent of the EKF once written; the algorithm selects sensor subsets to meet the pre-derived bounds. Validation occurs through separate MATLAB simulations and physical experiments on a 1:15-scale testbed, providing external checks rather than tautological reuse of fitted quantities. No self-definitional steps, fitted inputs renamed as predictions, or load-bearing self-citations appear in the derivation chain. The approach is an optimization procedure under externally stated probabilistic constraints.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 1 invented entities

The approach rests on standard EKF assumptions and the validity of the knapsack reduction; no free parameters or invented physical entities are mentioned in the abstract.

axioms (2)
  • domain assumption The Extended Kalman Filter covariance provides a reliable basis for chance-constrained error bounds on the vehicle state estimate.
    Invoked when the selection problem is defined to satisfy bounds derived from the EKF covariance.
  • domain assumption The multidimensional minimum knapsack formulation correctly captures the real-time sensor selection trade-off.
    Stated when the problem is formulated as a knapsack problem.
invented entities (1)
  • Sensor Management Center (SMC) no independent evidence
    purpose: Central entity that performs real-time sensor selection
    Introduced as the architectural component that runs the knapsack solver.

pith-pipeline@v0.9.0 · 5642 in / 1386 out tokens · 32030 ms · 2026-05-19T21:12:16.207216+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

16 extracted references · 16 canonical work pages

  1. [1]

    Vitus and Wei Zhang and Alessandro Abate and Jianghai Hu and Claire J

    Michael P. Vitus and Wei Zhang and Alessandro Abate and Jianghai Hu and Claire J. Tomlin , title =. Automatica , year =

    work page

  2. [2]

    Automatica , year=

    Sensor scheduling for linear systems: A covariance tracking approach , author=. Automatica , year=

    work page

  3. [3]

    2019 American Control Conference (ACC) , year=

    Ahn, Heejin and Danielson, Claus , title=. 2019 American Control Conference (ACC) , year=

    work page 2019

  4. [4]

    Anderson, Brian DO and Moore, John B , title=

    work page

  5. [5]

    Bar-Shalom, Yaakov and Li, X Rong and Kirubarajan, Thiagalingam , title=

    work page

  6. [6]

    Heuristics for the 0-1 min-knapsack problem , journal=

    Csirik, J. Heuristics for the 0-1 min-knapsack problem , journal=. 1991 , volume=

    work page 1991

  7. [7]

    Knapsack Problems , author =

    work page

  8. [8]

    SIAM Journal on Control and Optimization , year=

    Anderson, Brian DO and Moore, John B , title=. SIAM Journal on Control and Optimization , year=

    work page

  9. [9]

    Management Science , year=

    Toyoda, Yoshiaki , title=. Management Science , year=

    work page

  10. [10]

    2025 , archivePrefix=

    Bae, Hyunchul and Lee, Eunjae and Han, Jehyeop and Kang, Minhee and Kim, Jaehyeon and Seo, Junggeun and Noh, Minkyun and Ahn, Heejin , title =. 2025 , archivePrefix=. 2511.11022 , primaryClass =

    work page arXiv 2025

  11. [11]

    Automatic Steering Methods for Autonomous Automobile Path Tracking , author =

    work page

  12. [12]

    IEEE Transactions on Control of Network Systems , volume=

    Partition-based distributed Kalman filter with plug and play features , author=. IEEE Transactions on Control of Network Systems , volume=. 2016 , publisher=

    work page 2016

  13. [13]

    Ahn, Heejin and Colombo, Alessandro and Farina, Marcello and Han, Jehyeop , title =

    work page

  14. [14]

    Sensor selection for remote state estimation with

    Yang, Huiwen and Huang, Lingying and Yang, Chao and Mo, Yilin and Shi, Ling , journal=. Sensor selection for remote state estimation with. 2023 , publisher=

    work page 2023

  15. [15]

    IEEE Transactions on Control of Network Systems , volume=

    Optimal scheduling of multiple sensors over lossy and bandwidth limited channels , author=. IEEE Transactions on Control of Network Systems , volume=. 2020 , publisher=

    work page 2020

  16. [16]

    In Proceedings of the CACSD Conference , title =

    L. In Proceedings of the CACSD Conference , title =

    work page