Attention-Based Hierarchical Deep Reinforcement Learning for Lane Change Behaviors in Autonomous Driving

机译：基于关注的分层深度加强学习自动驾驶中的车道变革行为

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Performing safe and efficient lane changes is a crucial feature for creating fully autonomous vehicles. Recent advances have demonstrated successful lane following behavior using deep reinforcement learning, yet the interactions with other vehicles on road for lane changes are rarely considered. In this paper, we design a hierarchical Deep Reinforcement Learning (DRL) algorithm to learn lane change behaviors in dense traffic. By breaking down overall behavior to sub-policies, faster and safer lane change actions can be learned. We also apply temporal and spatial attention to the DRL architecture, which helps the vehicle focus more on surrounding vehicles and leads to smoother lane change behavior. We conduct our experiments in the TORCS simulator and the results outperform the state-of-art deep reinforcement learning algorithm in various lane change scenarios.

机译：执行安全高效的车道变化是创建完全自主车辆的重要特征。最近的进展已经证明了使用深度加强学习的行为之后的成功车道，但很少考虑与车道变化道路道路上的其他车辆的相互作用。在本文中，我们设计了一个分层深度加强学习（DRL）算法，以学习密集流量中的车道变革行为。通过将整体行为分解为子策略，可以获得更快，更安全的车道更改动作。我们还应用于DRL架构的时间和空间关注，帮助车辆更多地关注周围的车辆并导致更平滑的车道变革行为。我们在TORCS模拟器中进行我们的实验，结果优于各种车道变化场景中的艺术技术的深增强学习算法。

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《IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops》|2019年|1 v.|共9页
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作者
Yilun Chen; Chiyu Dong; Praveen Palanisamy; Priyantha Mudalige; Katharina Muelling; John M. Dolan;
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中图分类 TP391.41;
关键词
Machine learning; Task analysis; Training; Autonomous vehicles; Neural networks; Roads; Supervised learning;

机译：机器学习;任务分析;培训;自主车;神经网络;道路;监督学习;

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专利

1. Highway Lane Change Decision-Making via Attention-Based Deep Reinforcement Learning [J] . Junjie Wang, Qichao Zhang, Dongbin Zhao 自动化学报（英文版） . 2022,第003期

机译：Highway Lane Change Decision-Making via Attention-Based Deep Reinforcement Learning
2. Highway Lane Change Decision-Making via Attention-Based Deep Reinforcement Learning [J] . Junjie Wang, Qichao Zhang, Dongbin Zhao 自动化学报：英文版 . 2022,第003期

机译：Highway Lane Change Decision-Making via Attention-Based Deep Reinforcement Learning
3. Space-weighted information fusion using deep reinforcement learning: The context of tactical control of lane-changing autonomous vehicles and connectivity range assessment [J] . Dong Jiqian, Chen Sikai, Li Yujie, Transportation research . 2021,第Jula期

机译：使用深度加强学习的空间加权信息融合：车道改变自动车辆的战术控制和连接范围评估的背景
4. Attention-Based Hierarchical Deep Reinforcement Learning for Lane Change Behaviors in Autonomous Driving [C] . Yilun Chen, Chiyu Dong, Praveen Palanisamy, IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops . 2019

机译：基于注意的分层深度强化学习，用于自动驾驶中的换道行为
5. A Human Driver Model for Autonomous Lane Changing in Highways: Predictive Fuzzy Markov Game Driving Strategy [D] . Coskun, Serdar 2018

机译：高速公路自动车道变更的驾驶员模型：预测模糊马尔可夫博弈驾驶策略
6. Decision-Making for the Autonomous Navigation of Maritime Autonomous Surface Ships Based on Scene Division and Deep Reinforcement Learning [O] . Xinyu Zhang, Chengbo Wang, Yuanchang Liu, 2019

机译：基于场景划分和深度强化学习的水面自主舰艇自主航行决策
7. Autonomous Driving using Safe Reinforcement Learning by Incorporating a Regret-based Human Lane-Changing Decision Model [O] . Dong Chen, Longsheng Jiang, Yue Wang, 2020

机译：通过结合遗憾的人道内改变决策模型，使用安全强化学习自动驾驶

Attention-Based Hierarchical Deep Reinforcement Learning for Lane Change Behaviors in Autonomous Driving

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