级联驱动:一个统一的深度学习框架,用于在非结构化道路上自动驾驶电动汽车的多功能检测和控制
Kushal Kumar Raju1, B Prahal Bhagavath2, M K Nallakaruppan3
1Amherst Masters in Electrical and comp Engineering, University of Massachusetts, Amherst, MA, 01003, USA.
Scientific reports
|July 2, 2025
概括
本研究介绍了智能城市的自动驾驶电动汽车 (AEV) 系统,利用深度学习来驾驶具有挑战性的道路. 该AEV系统在车道和物体检测方面实现了高精度,使自动驾驶成为发展中国家经济的可行性.
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 工业5.0强调以客户为中心的发展和可持续性.
- 智能城市正在迅速增长,为自动化解决方案创造了机会.
- 自动化对于可持续的智慧城市发展至关重要.
研究的目的:
- 为智慧城市的自动驾驶电动汽车 (AEV) 提供可持续的解决方案.
- 用AEV解决发展中国家非结构化道路的挑战.
- 开发一个全面的系统,在各种道路条件下无人驾驶汽车运行.
主要方法:
- 在级联架构中集成多个深度学习模型.
- 开发一个统一的框架,同时检测车道边界和物体.
- 在资源有限的硬件上实施,并优先考虑安全功能.
主要成果:
- 在车道检测方面获得了97.26%的准确性 (DeepLabv3+).
- 在一般物体检测 (YOLOv5) 中获得了0.92 mAP.
- 在坑洞检测方面达到0.83 mAP (YOLOv7).
结论:
- 拟议的AEV系统在具有挑战性的道路条件下表现出高性能.
- 该系统能够有效地处理多样化和非结构化的道路.
- 在定制电动汽车平台上成功实施显示了发展中国家经济体的商业可行性.
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