Smart Parking Systems Research

Últimos artículos

• Dynamic Object Masks as Goal Representations for Visual Goal-Conditioned Reinforcement Learning

  Fahim Shahriar, Cheryl Wang, Alireza Azimi, Gautham Vasan, Hany Hamed, Abhishek Naik, A. Rupam Mahmood, Colin Bellinger · 7 de agosto de 2026

  Goal-conditioned reinforcement learning (GCRL) offers a unified way to pursue diverse tasks, yet most existing methods rely on state- or position-based goal representations that are unavailable in real-world robotics. Robots operating in warehouses, agriculture, or laboratory environments rarely hav…

• Mobile-R1: Towards Interactive Capability for VLM-Based Mobile Agent via Systematic Training

  Jihao Gu, Qihang Ai, Yingyao Wang, Pi Bu, Jingxuan Xing, Zekun Zhu, Wei Jiang, Ziming Wang, Yingxiu Zhao, Ming-Liang Zhang, Jun Song, Yuning Jiang, Bo Zheng · 29 de abril de 2026

  Vision-language model-based mobile agents have gained the ability to understand complex instructions and mobile screenshots, benefiting from reinforcement learning paradigms like Group Relative Policy Optimization (GRPO). However, existing approaches centers on offline training or local action-level…

• Bayesian-Symbolic Integration for Uncertainty-Aware Parking Prediction

  Alireza Nezhadettehad, Arkady Zaslavsky, Abdur Rakib, Seng W. Loke · 31 de marzo de 2026

  Accurate parking availability prediction is critical for intelligent transportation systems, but real-world deployments often face data sparsity, noise, and unpredictable changes. Addressing these challenges requires models that are not only accurate but also uncertainty-aware. In this work, we prop…

• Learning Optimal Search Strategies

  Stefan Ankirchner, Maximilian Philipp Thiel · 4 de marzo de 2026

  We explore the question of how to learn an optimal search strategy within the example of a parking problem where parking opportunities arrive according to an unknown inhomogeneous Poisson process. The optimal policy is a threshold-type stopping rule characterized by an indifference position. We prop…

• On the Transition to an Auction-based Intelligent Parking Assignment System

  Levente Alekszejenk\'o, Dobrowiecki Tadeusz · 12 de enero de 2026

  Finding a free parking space in a city has become a challenging task over the past decades. A recently proposed auction-based parking assignment can alleviate cruising for parking and also set a market-driven, demand-responsive parking price. However, the wide acceptance of such a system is far from…

• Probability-Aware Parking Selection

  Cameron Hickert, Sirui Li, Zhengbing He, Cathy Wu · 5 de enero de 2026

  Current parking navigation systems often underestimate total travel time by failing to account for the time spent searching for a parking space, which significantly affects user experience, mode choice, congestion, and emissions. To address this issue, this paper introduces the probability-aware par…

• Dynamic Configuration of On-Street Parking Spaces using Multi Agent Reinforcement Learning

  Oshada Jayasinghe, Farhana Choudhury, Egemen Tanin, Shanika Karunasekera · 3 de diciembre de 2025

  With increased travelling needs more than ever, traffic congestion has become a major concern in most urban areas. Allocating spaces for on-street parking, further hinders traffic flow, by limiting the effective road width available for driving. With the advancement of vehicle-to-infrastructure conn…

• Convergent Reinforcement Learning Algorithms for Stochastic Shortest Path Problem

  Soumyajit Guin, Shalabh Bhatnagar · 3 de diciembre de 2025

  In this paper we propose two algorithms in the tabular setting and an algorithm for the function approximation setting for the Stochastic Shortest Path (SSP) problem. SSP problems form an important class of problems in Reinforcement Learning (RL), as other types of cost-criteria in RL can be formula…

• Single- vs. Dual-Policy Reinforcement Learning for Dynamic Bike Rebalancing

  Jiaqi Liang, Defeng Liu, Sanjay Dominik Jena, Andrea Lodi, Thibaut Vidal · 27 de noviembre de 2025

  Bike-sharing systems (BSS) provide a sustainable urban mobility solution, but ensuring their reliability requires effective rebalancing strategies to address stochastic demand and prevent station imbalances. This paper proposes reinforcement learning (RL) algorithms for dynamic rebalancing problem w…