一个增强的运输系统,为决心的人
Uma Perumal1, Fathe Jeribi1, Mohammed Hameed Alhameed1
1College of Engineering and Computer Science, Jazan University, Jazan 45142, Saudi Arabia.
Sensors (Basel, Switzerland)
|October 16, 2024
概括
这项研究引入了一种新的指导系统,以帮助视障人士 (VIPs) 识别正确的公共汽车. 该系统使用先进的AI模型来准确检测公共汽车及其目的地,改善公共交通的可访问性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 人与计算机的交互
背景情况:
- 视力障碍者 (VIPs) 在公共交通工具上面临重大挑战,特别是识别正确的公共汽车和公共汽车停靠区.
- 现有的文献缺乏对VIP公共汽车停靠区识别的分析,这突出了可访问的交通解决方案中的关键差距.
研究的目的:
- 开发和评估一个人工智能驱动的指导系统,帮助视障人士 (VIPs) 识别他们的指定公共汽车和公共汽车.
- 增强视力障碍者公共交通的独立性和可访问性.
主要方法:
- 语音数据处理和文本转换用于目标输入.
- 使用Arctan梯度激活的循环神经网络 (ArcGRNN) 与GPS进行海湾检测.
- 采用经验丰富的扰乱细菌食三角优化算法 (EPBFTOA) 进行最佳海湾选择.
- 通过You Only Look Once (YOLO) 模型进行总线识别,并使用Sub-pixel Shuffling Convoluted Encoder-ArcGRNN Decoder (SSCEAD) 进行文本提取.
- 基于多特征非线性S曲线模糊规则 (MNC-FR) 进行决策,以准确通知公交车站.
主要成果:
- 拟议的系统成功地确定了最佳的公共汽车,并促进了公共汽车目的地识别.
- 实现了海湾选择 (247,891 ms) 和模糊化 (34,197 ms) 的高效处理时间.
- 与现有方法相比,在引导视力受损乘客方面表现出卓越的性能.
结论:
- 开发的指导系统显著改善了公共汽车的识别和视力受损者可访问性.
- 多种人工智能模型的整合为VIPs面临的公共交通导航挑战提供了强大的和有效的解决方案.
- 这项研究有助于通过技术创新创造更具包容性和可访问性的运输系统.
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