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在自动驾驶中预测代理轨迹的Atten-LTC增强的MoE模型
Shangwu Jiang1, Ruochen Wang1, Renkai Ding2
1School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
本研究介绍了Atten-LTC-MoE模型,用于准确预测自动驾驶轨迹. 该模型擅长预测车辆和行人路径,提高自动驾驶系统的安全性和效率.
科学领域:
- 自主驾驶系统 自主驾驶系统
- 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:
- 自动驾驶依赖于对代理轨迹的准确预测.
- 了解车辆和行人的多样化和不可预测的行为至关重要.
- 当前的方法在处理复杂的时空相互作用时面临挑战.
研究的目的:
- 解决单代理轨迹预测 (SATP) 和多代理轨迹预测 (MATP) 的复杂问题.
- 提出一个创新的,可扩展的模型,用于自动驾驶的轨迹预测.
- 为了提高计算效率和预测准确度.
主要方法:
- 开发了Atten-LTC-MoE模型,整合了注意力机制,液态时间常数 (LTC) 网络和专家混合 (MoE).
- 利用时空特征,代理数据融合和矢量化技术.
- 利用车道和代理向量化来改善数据表示.
主要成果:
- 在Argoverse和Interaction数据集上,Atten-LTC-MoE模型表现出卓越的性能.
- 在minADE6和minFDE6指标中取得了最先进的结果.
- 在代理轨迹预测准确度和计算性能方面取得了显著的改进.
结论:
- 拟议的Atten-LTC-MoE模型为SATP和MATP提供了一个通用和可扩展的解决方案.
- 该模型有效地捕捉复杂的时空动态,以可靠地预测轨迹.
- 这一进步有助于自动驾驶技术的实用性和安全性.
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