基础模型在推进自动驾驶汽车的未来作用
Jianhua Wu1, Bingzhao Gao1,2, Jincheng Gao1
1School of Automotive Studies, Tongji University, Shanghai 201804, China.
Research (Washington, D.C.)
|July 17, 2024
概括
基础模型 (FMs) 通过改善场景理解和生成罕见场景来增强自动驾驶. 这项研究综合了FM应用程序,以解决长尾问题并提高驾驶安全.
科学领域:
- 人工智能的人工智能
- 自主驾驶系统 自主驾驶系统
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 基础模型 (FMs) 展示了人工智能的重大进展,特别是在自然语言处理和计算机视觉方面.
- 将FM集成到自动驾驶系统中,有可能提高场景理解和决策.
研究的目的:
- 探索自动驾驶基础模型的应用和未来趋势.
- 为了利用FM能力来应对自动驾驶场景中长尾分布带来的挑战.
- 提高自动驾驶系统的整体安全性和可靠性.
主要方法:
- 利用在各种语言和视觉数据上预先训练的大规模基础模型.
- 在世界模型中采用自我监督学习范式,以生成可信的驾驶环境.
- 综合现有关于FM应用在自动驾驶中的研究.
主要成果:
- FM可以改善自动驾驶汽车的场景理解和推理.
- FMs为罕见的驾驶场景提供了数据增强,提高了系统的稳定性.
- 由FM驱动的世界模型可以预测道路使用者的行为,并有助于离线战略培训.
结论:
- 基础模型对推进自动驾驶技术具有重大前景.
- 通过FM解决长尾分布对于提高安全性和可靠性至关重要.
- 对FM应用的持续研究将推动自动驾驶领域的创新.
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