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This paper forecasts the future of the advanced driver assistance system (ADAS) and how driving safety can be improved by the usage of critical-lane-learning, posture sensing the best fit, tiredness/− sleepiness recognition. Well-trained intellectual capabilities, such as computer vision algorithms, make this bureaucracy/ the latter government body to correctly identify the road boundaries, thus providing the most detailed advice to drivers. The implementation of yoga and smart wearables in vehicle safety documentation makes sure that drivers get the best position, avoid fatigue and have the best safety driving methods. The machine intelligence models are used to monitor the fatigue levels of the vehicle drivers and, if necessary, they alert the trainer and suggest the rest. Moreover, the reaction system collects driver reviews to continuously polish the system’s efficiency. Further, the abstract outlines a new approach allowing creative ideas define the trainer support technology, finding a new application in the state-of-the-art comprehensive security system for vehicles.

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Driver Assistance System with Lane and Object Detection Based on Driver Attention Monitoring

  • 会议论文

  • 发表日期：

    2026年

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  • R. Kishore Harshan Kumar,
  • S. Roughit,
  • T. Nagulraj,
  • M. Shalini

摘要

This paper forecasts the future of the advanced driver assistance system (ADAS) and how driving safety can be improved by the usage of critical-lane-learning, posture sensing the best fit, tiredness/− sleepiness recognition. Well-trained intellectual capabilities, such as computer vision algorithms, make this bureaucracy/ the latter government body to correctly identify the road boundaries, thus providing the most detailed advice to drivers. The implementation of yoga and smart wearables in vehicle safety documentation makes sure that drivers get the best position, avoid fatigue and have the best safety driving methods. The machine intelligence models are used to monitor the fatigue levels of the vehicle drivers and, if necessary, they alert the trainer and suggest the rest. Moreover, the reaction system collects driver reviews to continuously polish the system’s efficiency. Further, the abstract outlines a new approach allowing creative ideas define the trainer support technology, finding a new application in the state-of-the-art comprehensive security system for vehicles.