焦点层-将注意力吸引到必要的障碍上

Tianyu Wang, Yuhang Ye, Zihan Zhang, Haoran Zhang, Zonghan Yang
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引用次数: 0

摘要

随着自动驾驶技术的发展,需要快速、安全的运动规划算法。然而,传输到运动规划部分的数据可能会有噪声,并且一些障碍物对于后续处理是不必要的。本文提出了一个焦点层和一个DQN代理来选择必要的障碍物并将其提交给运动规划算法。焦点层忽略了一些不太可能影响自我载体的障碍,并将注意力集中在那些关键障碍上。从业者注意:本文的动机是自动驾驶在规划轨迹时需要大量的计算时间。道路上的障碍等限制因素影响规划方法的效率。现有的研究是通过实验来捕捉司机在路上开车时的面部表情或眼神交流。然而,这种研究并不适合自动驾驶算法。因此,我们提出了一种在模拟环境中减少不必要障碍的方法,类似于关注驾驶员的基本要素。我们的过程在轨迹规划算法之前生成一个层,将自我车辆的注意力集中在关键障碍物上,并且可以很容易地适用于所有轨迹规划算法。
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Focus Layer - Drawing Attention to Necessary Obstacles
With the development of automatic driving, fast and safe motion planning algorithms are in need. However, data transferred to the motion planning part may be noisy, and some obstacles are unnecessary for later processing. This paper proposes a focus layer and a DQN agent to select necessary barriers and submit them to the motion planning algorithms. The Focus layer ignores some obstacles that are not likely to impact the ego vehicle and focuses attention on those critical obstacles. Note to Practitioners: This paper is motivated by the heavy computation time in automatic driving when planning a trajectory. Constraints such as obstacles along the road affect the efficiency of the planning methodology. Existing research conducts experiments on capturing drivers' facial expressions or eye contact when driving on the road. However, such research cannot fit into the automatic driving algorithms. Thus, we propose a method to reduce unnecessary obstacles in a simulation environment, which is similar to focusing on the essential elements for drivers. Our process generates a layer to focus ego vehicles' attention on critical obstacles before the trajectory planning algorithm and can easily fit in all trajectory planning algorithms.
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