Knapsack-based Online Sensor Selection for Vehicle State Estimation
For connected and autonomous vehicles, this work addresses the practical bottleneck of managing sensor data streams with limited resources, but the solution is incremental as it applies a known knapsack formulation with a greedy algorithm.
The paper tackles the problem of selecting a low-cost subset of external sensors for vehicle state estimation in real time, achieving chance-constrained error bounds while reducing communication and computational costs. The proposed knapsack-based greedy algorithm is validated through simulations and testbed experiments.
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.