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针对自动驾驶汽车的新型混合XAI解决方案:通过LIME-SHAP集成实现实时可解释性.

H Ahmed Tahir1, Walaa Alayed2, Waqar Ul Hassan3

  • 1School of Computing, Engineering and Mathematics, Western Sydney University, Penrith, NSW 2751, Australia.

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概括

本研究介绍了用于自动驾驶汽车 (AV) 的混合可解释AI (XAI) 框架,将LIME和SHAP结合起来,以实现透明的AI决策. 这种新的方法提高了实时AV应用程序的模型解释性和效率.

关键词:
人工智能/MLML在这里,我们可以看到AV AV AV AV AV.在XAI,XAI就是XAI.无人驾驶汽车可以自动驾驶.无人驾驶的AV无人驾驶飞行器

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科学领域:

  • 人工智能的人工智能
  • 计算机视觉 计算机视觉
  • 机器人技术 机器人技术 机器人技术

背景情况:

  • 自动驾驶汽车 (AV) 和人工智能 (AI) 的进步需要透明的决策过程.
  • 现有的可解释的人工智能 (XAI) 方法在精度,全球理解和计算效率之间存在权衡.
  • 迫切需要强大的XAI解决方案,适合在安全关键的AV系统中部署.

研究的目的:

  • 为自动驾驶汽车 (AV) 提出和评估一种新的混合可解释AI (XAI) 框架.
  • 结合局部可解释模型不可知解释 (LIME) 和沙普利增量解释 (SHAP) 的优势,以提高透明度和效率.
  • 在安全关键的AV应用中提供平衡的机载部署方法.

主要方法:

  • 开发了一个混合的XAI框架,集成LIME和SHAP.
  • 该框架的评估使用了最先进的模型:ResNet-18,ResNet-50和SegNet-50.
  • 使用KITTI数据集评估性能,重点关注忠实性,可解释性和一致性指标.

主要成果:

  • 混合XAI框架实现了超过85%的保真率,超过80%的解释性因素和超过70%的一致性.
  • 推断时间记录为0.28s (ResNet-18),0.571s (ResNet-50) 和3.889s (SegNet),证明其适用于内部计算.
  • 提出的方法在关键绩效指标方面始终优于传统的XAI方法.

结论:

  • 混合LIME-SHAP框架为AV中的XAI提供了一个平衡的解决方案,优化透明度和计算性能.
  • 这项研究为在安全关键的自动驾驶系统中部署可解释的AI提供了坚实的基础.
  • 开发的框架通过解决模型精度和可解释性之间的权衡来促进实时决策.