用于预测性机动规划的高效深度时空上下文感知决策网络（DST-CAN）
An efficient Deep Spatio-Temporal Context Aware decision Network (DST-CAN) for Predictive Manoeuvre Planning
AI总结：
本文提出DST-CAN模型，通过记忆神经元网络预测周围车辆未来轨迹并构建上下文感知网格，结合CNN与高速公路人类驾驶行为实现自动驾驶汽车的安全高效机动规划。
摘要：
为确保其机动的安全性和高效性，自动驾驶汽车（Autonomous Vehicle，AV）应利用其传感器信息预判周围车辆的未来意图。如果AV能够预测周围车辆的未来轨迹，它就可以做出安全且高效的机动决策。本文提出了这样一种用于AV预测性机动规划的深度时空上下文感知决策网络（Deep Spatio-Temporal Context-Aware decision Network，DST-CAN）模型。该模型采用记忆神经元网络预测周围车辆的未来轨迹。驾驶环境的时空信息（过去、当前以及预测的未来轨迹）被嵌入到一个上下文感知网格中。所提出的DST-CAN模型将这些上下文感知网格作为卷积神经网络的输入，以理解车辆之间的空间关系，并确定安全高效的机动决策。DST-CAN模型还使用了高速公路上的人类驾驶行为信息。研究使用两个公开的NGSIM US-101和I-80数据集对DST-CAN进行了性能评估。此外，还将基于规则的真值决策与DST-CAN生成的决策进行了比较。结果清楚表明，与当前不使用此类预测的现有方法相比，DST-CAN借助相邻车辆3秒的预测轨迹能够做出明显更好的决策。
英文摘要：
To ensure the safety and efficiency of its maneuvers, an Autonomous Vehicle (AV) should anticipate the future intentions of surrounding vehicles using its sensor information. If an AV can predict its surrounding vehicles' future trajectories, it can make safe and efficient manoeuvre decisions. In this paper, we present such a Deep Spatio-Temporal Context-Aware decision Network (DST-CAN) model for predictive manoeuvre planning of AVs. A memory neuron network is used to predict future trajectories of its surrounding vehicles. The driving environment's spatio-temporal information (past, present, and predicted future trajectories) are embedded into a context-aware grid. The proposed DST-CAN model employs these context-aware grids as inputs to a convolutional neural network to understand the spatial relationships between the vehicles and determine a safe and efficient manoeuvre decision. The DST-CAN model also uses information of human driving behavior on a highway. Performance evaluation of DST-CAN has been carried out using two publicly available NGSIM US-101 and I-80 datasets. Also, rule-based ground truth decisions have been compared with those generated by DST-CAN. The results clearly show that DST-CAN can make much better decisions with 3-sec of predicted trajectories of neighboring vehicles compared to currently existing methods that do not use this prediction.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
