DAT:基于深度学习的加速意识轨迹预测
Ali Asghar Sharifi1, Ali Zoljodi1, Masoud Daneshtalab1,2
1School of Innovation, Design and Technology (IDT), Mälardalen University, 72123 Västerås, Sweden.
Journal of imaging
|December 27, 2024
概括
本研究介绍了基于深度学习的加速感知轨迹预测 (DAT) 模型,以实现更安全的自动驾驶. 通过纳入加速数据,DAT显著提高了对象轨迹预测的准确性,优于现有的方法.
科学领域:
- 机器人和人工智能 机器人和人工智能
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 自动驾驶 (AD) 系统需要强大的安全功能,对象检测和轨迹预测对于防止碰撞至关重要.
- 当前的AD系统在准确预测周围车辆和行人未来的移动方面面临着挑战.
研究的目的:
- 引入基于深度学习的加速感知轨迹预测 (DAT) 模型,用于在AD系统中增强对象检测和轨迹预测.
- 为了利用原始传感器测量和加速数据来更准确地预测代理运动.
主要方法:
- 开发了一个端到端的深度学习模型 (DAT),该模型处理顺序传感器数据,用于对象检测和轨迹预测.
- 引入了一个新的预测模块,利用加速数据和估计地面真相加速的方法.
- 集成了一个对象探测器,可以预测加速属性和一个新的轨迹预测方法.
主要成果:
- DAT在NuScenes数据集上进行了训练和评估,显示出与最先进的方法相比的显著改进.
- 预测准确度提高了2倍,特别是对于具有复杂线性和非线性运动模式的物体.
- 经验验证证证实了将加速数据纳入预测模型的有效性.
结论:
- 通过提高轨迹预测准确度,DAT模型代表了自动驾驶安全的重大进步.
- 纳入加速数据对于开发更可靠,更安全的自动驾驶系统至关重要.
- 建议的加速估计和轨迹预测方法为现实世界AD应用提供了强大的解决方案.
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