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Advanced Driver-Assistance Systems (ADAS) offer significant potential to improve road safety and driving efficiency, but their increasing complexity can overwhelm drivers. This paper presents a conceptual framework for a hybrid ADAS recommendation system that integrates real-time analysis of driver behavior and environmental conditions within a MAPE-K control loop. Currently under development, the framework leverages deep learning and reinforcement learning to provide personalized context-aware recommendations for optimal use of ADAS. Future research includes evaluating the system through simulations and exploring personalization and real-world deployment.

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An Overview of a Hybrid Recommendation System for ADAS Using the MAPE-K Approach

  • 会议论文

  • 发表日期：

    2026年

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  • Sameh Ben-Aoun,
  • Meriem Belguidoum,
  • Ahmed Hadj-Kacem

摘要

Advanced Driver-Assistance Systems (ADAS) offer significant potential to improve road safety and driving efficiency, but their increasing complexity can overwhelm drivers. This paper presents a conceptual framework for a hybrid ADAS recommendation system that integrates real-time analysis of driver behavior and environmental conditions within a MAPE-K control loop. Currently under development, the framework leverages deep learning and reinforcement learning to provide personalized context-aware recommendations for optimal use of ADAS. Future research includes evaluating the system through simulations and exploring personalization and real-world deployment.