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<p>This research proposes an adaptive Q-learning framework for precision self-parking and reverse maneuvering in dynamic grid-based environments. The vehicle state includes position, orientation, and velocity and the environment model incorporates moving obstacles and dynamically changing parking slot availability. Key technical contributions are: (i) adaptive exploration where the epsilon-greedy parameter decays only when recent reward improves; (ii) look-ahead obstacle prediction using a Markov transition model with directional persistence; and (iii) priority-based Q-value updates that focus on state-action pairs with large temporal-difference errors. The hybrid control architecture combines Rapidly-exploring Random Trees (RRT) for global path planning, Model Predictive Control (MPC) for smooth trajectory tracking and Q-learning for local corrective adjustments. In simulation with a 10 × 10 dynamic grid and moving obstacles, the proposed method achieves a success rate of 92.4% ± 1.2%, collision rate of 1.8% ± 0.5%, and parking alignment error of 6.7 ± 0.8 cm. Ablation studies confirm that each module contributes significantly (<i>p</i> &lt; 0.01). The method proposed in this paper is shown to be more efficient than DDPG, PPO and DQN baselines all evaluated under the same simulation settings. These results confirm the proof-of-concept (POC) viability of light-weight tabular Q-learning with predictive extensions in a simulated dynamic grid-based parking domain.</p>

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SmartGridDrive: an integrated adaptive Q-learning framework for precision self-parking and reverse navigation in dynamic grid environments—a proof-of-concept study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月28日

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  • Revati Raman Dewangan,
  • Deepali Thombre,
  • Vivek Parganiha,
  • Monika Verma,
  • Bhupesh Kumar Dewangan,
  • Amit Pipalkar,
  • Nilesh Shelke

摘要

This research proposes an adaptive Q-learning framework for precision self-parking and reverse maneuvering in dynamic grid-based environments. The vehicle state includes position, orientation, and velocity and the environment model incorporates moving obstacles and dynamically changing parking slot availability. Key technical contributions are: (i) adaptive exploration where the epsilon-greedy parameter decays only when recent reward improves; (ii) look-ahead obstacle prediction using a Markov transition model with directional persistence; and (iii) priority-based Q-value updates that focus on state-action pairs with large temporal-difference errors. The hybrid control architecture combines Rapidly-exploring Random Trees (RRT) for global path planning, Model Predictive Control (MPC) for smooth trajectory tracking and Q-learning for local corrective adjustments. In simulation with a 10 × 10 dynamic grid and moving obstacles, the proposed method achieves a success rate of 92.4% ± 1.2%, collision rate of 1.8% ± 0.5%, and parking alignment error of 6.7 ± 0.8 cm. Ablation studies confirm that each module contributes significantly (p < 0.01). The method proposed in this paper is shown to be more efficient than DDPG, PPO and DQN baselines all evaluated under the same simulation settings. These results confirm the proof-of-concept (POC) viability of light-weight tabular Q-learning with predictive extensions in a simulated dynamic grid-based parking domain.