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<p>Moving object detection is crucial for autonomous vehicles, particularly in complex urban environments filled with dynamic and static obstacles. Accurate detection helps prevent collisions and ensures road safety, traditionally requiring multiple sensors like cameras, LiDAR, radar, and IMUs. This study proposes the Adaptive Motion-Aware Ensemble Object Detection System (AMEODS), which relies solely on camera data to identify and analyze moving objects. By extracting behavioural motion cues, AMEODS enhances scene understanding. It successfully detects cars, pedestrians, traffic lights, and trucks with confidence scores up to 98%, proving its reliability and practicality for real-world urban driving scenarios.</p>

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Adaptive Motion-Aware Ensemble Object Detection System (AMEODS) for Urban Driving Scenarios

  • 期刊论文

  • 发表日期：

    2025年11月8日

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  • Sarita Gautam,
  • Anuj Kumar

摘要

Moving object detection is crucial for autonomous vehicles, particularly in complex urban environments filled with dynamic and static obstacles. Accurate detection helps prevent collisions and ensures road safety, traditionally requiring multiple sensors like cameras, LiDAR, radar, and IMUs. This study proposes the Adaptive Motion-Aware Ensemble Object Detection System (AMEODS), which relies solely on camera data to identify and analyze moving objects. By extracting behavioural motion cues, AMEODS enhances scene understanding. It successfully detects cars, pedestrians, traffic lights, and trucks with confidence scores up to 98%, proving its reliability and practicality for real-world urban driving scenarios.