Knapsack-based Online Sensor Selection for Vehicle State Estimation

TL;DR

This paper presents a real-time sensor selection method for vehicle state estimation that balances accuracy and cost using a knapsack problem formulation and a greedy algorithm, validated through simulations and experiments.

Contribution

It introduces a novel knapsack-based approach for online sensor selection in vehicle state estimation with chance constraints, improving efficiency and cost-effectiveness.

Findings

The method effectively balances sensor cost and estimation accuracy.

Simulation and experimental results demonstrate the approach's efficiency.

The greedy algorithm provides a practical solution to the knapsack problem.

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.