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基于深度学习的行人轨迹预测的调查:挑战和解决方案
Jiaming Jiang1, Kai Yan1, Xindong Xia1
1Wang Zheng Institute of Microelectronics, Changzhou University, Changzhou 213000, China.
Sensors (Basel, Switzerland)
|February 13, 2025
概括
这项调查探讨了行人轨迹预测深度学习的挑战,为运动不确定性,交互建模和场景理解提供解决方案,以改善自主系统.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 对于智能交通系统,自动驾驶和社会机器人技术来说,行人轨迹预测至关重要.
- 深度学习方法的性能优于传统方法,但在准确性,效率和可靠性方面面临挑战.
研究的目的:
- 识别和分析基于深度学习的行人轨迹预测的关键挑战.
- 总结现有解决方案,并讨论未来的研究方向.
主要方法:
- 综合文献审查和现有调查和研究论文的分析.
- 挑战的分类,包括运动不确定性,交互建模,场景理解,数据问题和模型可解释性.
主要成果:
- 确定并详细介绍了行人轨迹预测深度学习的主要挑战.
- 总结了解决这些挑战的当前解决方案.
- 审查了主流数据集和最先进的结果.
结论:
- 深度学习方法显示出希望,但需要进一步的进步来克服固有的挑战.
- 未来的研究应该专注于通过新的方法提高预测准确性,效率和可靠性.
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