A Safety-Guaranteed Framework for Neural-Network-Based Planners in Connected Vehicles under Communication Disturbance
A Safety-Guaranteed Framework for Neural-Network-Based Planners in Connected Vehicles under Communication Disturbance
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DOI：
10.23919/date56975.2023.10137184
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发表时间：
2023-04
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影响因子：
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通讯作者：
K. Chang;Xiangguo Liu;Chung-Wei Lin;Chao Huang;Qi Zhu
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作者：
K. Chang;Xiangguo Liu;Chung-Wei Lin;Chao Huang;Qi Zhu
基于神经网络（NN - based）的规划器已越来越多地用于提高自动驾驶车辆的规划性能。然而，基于神经网络的规划器在复杂场景中往往难以平衡效率和安全，特别是在现实世界的通信干扰下。为了应对这一挑战，我们提出了一个在存在通信干扰的网联车辆环境中基于神经网络的规划器的安全保障框架。对于任何没有安全保障的基于神经网络的规划器，该框架生成一个嵌入基于神经网络规划器的稳健复合规划器，以确保整个系统的安全。此外，借助针对不完善通信的信息滤波器以及对不安全集合估计的积极方法，复合规划器能够实现与给定的基于神经网络的规划器相似或更好的效率。对无保护左转的综合案例研究和大量模拟证明了我们框架的有效性。
Neural-network-based (NN-based) planners have been increasingly used to enhance the performance of planning for autonomous vehicles. However, it is often difficult for NN-based planners to balance efficiency and safety in complicated scenarios, especially under real-world communication disturbance. To tackle this challenge, we present a safety-guaranteed framework for NN-based planners in connected vehicle environments with communication disturbance. Given any NN-based planner with no safety-guarantee, the framework generates a robust compound planner embedding the NN-based planner to ensure overall system safety. Moreover, with the aid of an information filter for imperfect communication and an aggressive approach for the estimation of the unsafe set, the compound planner could achieve similar or better efficiency than the given NN-based planner. A comprehensive case study of unprotected left turn and extensive simulations demonstrate the effectiveness of our framework.