不确定性意识的多模式轨迹预测通过一个单一的推理从一个单一的模型.
1Seamless Trans-X Lab (STL), School of Integrated Technology, Yonsei University, Incheon 21983, Republic of Korea.
Sensors (Basel, Switzerland)
|January 11, 2025
概括
本研究引入了一种针对自动驾驶的不确定性意识多式联运轨迹预测 (UAMTP) 模型. 在复杂的驾驶场景中,UAMTP模型有效量化不确定性,提高安全性和反应时间.
科学领域:
- 自动驾驶自动驾驶的自动驾驶
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
背景情况:
- 轨迹预测对于自动驾驶安全至关重要.
- 现有的方法往往忽略不确定性量化,依赖于计算上昂贵的深度合奏.
- 解决不确定性对于复杂环境中的可靠导航至关重要.
研究的目的:
- 开发一种新的不确定性意识多式联运轨迹预测 (UAMTP) 模型.
- 在单个前推断中量化 aleatoric 和 epistemic 的不确定性.
- 为了提高自主系统的计算效率和预测准确性.
主要方法:
- 该UAMTP模型将轨迹预测分解为速度和曲率组件.
- 它量化了两个组件中的不确定性,以产生多式联运预测.
- 该方法使用确定性单向前传方法来提高效率.
主要成果:
- 在minFDE和失误率等准确度指标上,UAMTP模型的表现优于Deep Ensembles.
- 它为避免碰撞提供了更长的反应时间.
- 该模型有效地解释了环境的随机性和意图模糊性.
结论:
- 这项研究提出了一种有效的不确定性量化方法,用于多式联运轨迹预测.
- 该UAMTP模型提高了自动驾驶的安全性和可靠性.
- 这些发现对资源有限的自动驾驶平台具有重要意义.
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