الملخص
تمت ترجمة أجزاء من هذه الصفحة آلياً وقد تحتوي على أخطاء.
Parking is a routine yet safety-critical task for autonomous vehicles operating in urban environments. However, cluttered and weakly structured parking spaces, compounded by the interactive uncertainty from surrounding vehicles, hinder reliable maneuver generation. To address these challenges, we develop a waypoint-level offline reinforcement learning framework for interaction-aware autonomous parking. Specifically, a dedicated parking dataset is constructed from hierarchical expert rollouts with rotational waypoint augmentation, covering both non-interactive scenarios and interactive ones. The policy is then conditioned on a compact state representation, in which LiDAR-based obstacle features are adapted to the target pose via feature-wise linear modulation. A state-conditioned tokenizer further quantizes continuous waypoint sequences into discrete action tokens, over which conservative Q-learning is performed to suppress value overestimation on poorly supported actions. Extensive closed-loop experiments are conducted in the high-fidelity CARLA simulator. The proposed framework attains the highest parking success rate among all baselines and transfers reliably to unseen parking slots.
الكلمات المفتاحية
الموضوع
بيانات النشر
- المجلة
- غير متاح
- وصول مفتوح
- وصول مفتوح أخضر
اقتبس هذه المقالة
APA 7
Yang, Z., Peng, Z., & Ma, J. (2026). تعلم سياسات الركن الموثوقة عبر التعلم المعزز غير المتصل مع تمثيلات الفعل المكممة. https://omanscience.com/ar/articles/learning-reliable-parking-policies-via-offline-reinforcement-learning-with-quantized-action-representations
MLA 9
Yang, Zewei, et al. "تعلم سياسات الركن الموثوقة عبر التعلم المعزز غير المتصل مع تمثيلات الفعل المكممة." https://omanscience.com/ar/articles/learning-reliable-parking-policies-via-offline-reinforcement-learning-with-quantized-action-representations.
شيكاغو (المؤلف–التاريخ)
Yang, Zewei, Zengqi Peng, and Jun Ma. 2026. "تعلم سياسات الركن الموثوقة عبر التعلم المعزز غير المتصل مع تمثيلات الفعل المكممة." https://omanscience.com/ar/articles/learning-reliable-parking-policies-via-offline-reinforcement-learning-with-quantized-action-representations.
هارفارد
Yang, Z., Peng, Z. and Ma, J. (2026) 'تعلم سياسات الركن الموثوقة عبر التعلم المعزز غير المتصل مع تمثيلات الفعل المكممة', Available at: https://omanscience.com/ar/articles/learning-reliable-parking-policies-via-offline-reinforcement-learning-with-quantized-action-representations.
فانكوفر
Yang Z, Peng Z, Ma J. تعلم سياسات الركن الموثوقة عبر التعلم المعزز غير المتصل مع تمثيلات الفعل المكممة. https://omanscience.com/ar/articles/learning-reliable-parking-policies-via-offline-reinforcement-learning-with-quantized-action-representations
IEEE
Z. Yang, Z. Peng, and J. Ma, "تعلم سياسات الركن الموثوقة عبر التعلم المعزز غير المتصل مع تمثيلات الفعل المكممة," https://omanscience.com/ar/articles/learning-reliable-parking-policies-via-offline-reinforcement-learning-with-quantized-action-representations.