# Online and Adaptive Parking Availability Mapping: An Uncertainty-Aware Active Sensing Approach for Connected Vehicles

arXiv: https://arxiv.org/abs/2105.00246 · HTML: https://mlsanity.com/paper/2105.00246/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 29 references that are in the map.
- First posted on arXiv 1 May 2021.

## Builds on (most cited first, 9 of 9)

- [When Gaussian Process Meets Big Data: A Review of Scalable GPs](https://mlsanity.com/paper/1807.01065/) (2018)
- [Fast methods for training Gaussian processes on large data sets](https://mlsanity.com/paper/1604.01250/) (2016)
- [Multi-agents adaptive estimation and coverage control using Gaussian regression](https://mlsanity.com/paper/1407.5807/) (2014)
- [Infrastructure Enabled Autonomy: A Distributed Intelligence Architecture for Autonomous Vehicles](https://mlsanity.com/paper/1802.04112/) (2018)
- [Probabilistic Radio-Visual Active Sensing for Search and Tracking](https://mlsanity.com/paper/2011.10474/) (2020)
- [Fast Gaussian Process Regression for Big Data](https://mlsanity.com/paper/1509.05142/) (2015)
- [A Queuing Approach to Parking: Modeling, Verification, and Prediction](https://mlsanity.com/paper/1908.11479/) (2019)
- [Probabilistic RF-Assisted Camera Wake-Up through Self-Supervised Gaussian Process Regression](https://mlsanity.com/paper/2102.03350/) (2021)
- [Transmitter Discovery through Radio-Visual Probabilistic Active Sensing](https://mlsanity.com/paper/2103.14965/) (2021)

## Abstract (arXiv)

Research on connected vehicles represents a continuously evolving technological domain, fostered by the emerging Internet of Things (IoT) paradigm and the recent advances in intelligent transportation systems. Nowadays, vehicles are platforms capable of generating, receiving and automatically act based on large amount of data. In the context of assisted driving, connected vehicle technology provides real-time information about the surrounding traffic conditions. Such information is expected to improve drivers' quality of life, for example, by adopting decision making strategies according to the current parking availability status. In this context, we propose an online and adaptive scheme for parking availability mapping. Specifically, we adopt an information-seeking active sensing approach to select the incoming data, thus preserving the onboard storage and processing resources; then, we estimate the parking availability through Gaussian Process Regression. We compare the proposed algorithm with several baselines, which attain inferior performance in terms of mapping convergence speed and adaptivity capabilities; moreover, the proposed approach comes at the cost of a very small computational demand.