用新模型高效预测交通中物体未来位置的不确定性分布
Machine Learning Architectures for the Estimation of Predicted Occupancy Grids in Road Traffic
- 用双自编码器加随机森林架构，从增强占用网格推断未来状态
- 相比旧架构提升预测准确率，计算速度更快
- 适合自动驾驶与主动安全系统开发人员参考
本文提出一种新型机器学习架构，用于高效估计复杂交通场景的概率时空表示。精确的未来交通场景表征对自动驾驶和主动安全系统至关重要。该方法首先识别交通场景类型，再将当前状态映射为可能的未来状态。输入为增强占用网格（AOG），输出为包含交通参与者行为不确定性的概率时空表示，称为预测占用网格（POG）。新架构由两个堆叠去噪自编码器（SDAs）和一组随机森林组成，与基于SDAs和DeconvNet的两种现有架构进行对比。通过仿真验证，比较了准确率和计算时间。同时简要介绍了POG在主动安全领域的应用前景。
原文摘要 · Abstract (English)
This paper introduces a novel machine learning architecture for an efficient estimation of the probabilistic space-time representation of complex traffic scenarios. A detailed representation of the future traffic scenario is of significant importance for autonomous driving and for all active safety systems. In order to predict the future space-time representation of the traffic scenario, first the type of traffic scenario is identified and then the machine learning algorithm maps the current state of the scenario to possible future states. The input to the machine learning algorithms is the current state representation of a traffic scenario, termed as the Augmented Occupancy Grid (AOG). The output is the probabilistic space-time representation which includes uncertainties regarding the behaviour of the traffic participants and is termed as the Predicted Occupancy Grid (POG). The novel architecture consists of two Stacked Denoising Autoencoders (SDAs) and a set of Random Forests. It is then compared with the other two existing architectures that comprise of SDAs and DeconvNet. The architectures are validated with the help of simulations and the comparisons are made both in terms of accuracy and computational time. Also, a brief overview on the applications of POGs in the field of active safety is presented.
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