MTR++:多代理运动预测与对称场景建模和指导意图查询
IEEE transactions on pattern analysis and machine intelligence
|January 12, 2024
概括
本研究介绍了用于先进的自动驾驶运动预测的运动转换器 (MTR). MTR通过定位代理意图和改进移动来增强多式联运轨迹预测,提高安全性和效率.
科学领域:
- 计算机科学 计算机科学
- 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:
- 自动驾驶系统需要准确的运动预测,以便在复杂的环境中安全导航.
- 预测各种交通参与者的行为和情境互动仍然是一个重大挑战.
研究的目的:
- 提出运动转换器 (MTR) 框架,以实现高效和准确的多式联运运动预测.
- 开发MTR++用于同时进行多代理运动预测,增强场景理解和交互建模.
主要方法:
- 使用变压器编码解码器开发了MTR,用于轨迹预测,具有可学习的意图查询.
- 在MTR框架内实施全球意图本地化和本地移动精细化流程.
- 引入了MTR++与对称上下文建模和相互引导的意图查询,用于多代理预测.
主要成果:
- 在竞争性运动预测基准上,MTR实现了最先进的性能.
- MTR++在预测多种代理的多模式未来轨迹方面表现出卓越的性能和效率.
- 提出的框架有效地减少了对密集目标候选人的依赖,并提高了预测准确性.
结论:
- MTR和MTR++框架在自动驾驶运动预测方面取得了重大进展.
- 这些模型为单个和多个代理提供准确,高效和符合场景的未来轨迹预测.
- 这种方法提高了自动驾驶系统的决策能力.
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