Review of Learning-Based Longitudinal Motion Planning for Autonomous Vehicles: Research Gaps Between Self-Driving and Traffic Congestion
Review of Learning-Based Longitudinal Motion Planning for Autonomous Vehicles: Research Gaps Between Self-Driving and Traffic Congestion
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DOI：
10.1177/03611981211035764
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发表时间：
2019-10
影响因子：
1.7
通讯作者：
Hao Zhou-;Jorge A. Laval;Anye Zhou;Yu Wang;W. Wu;Zhuo Qing;S. Peeta
中科院分区：
文献类型：
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作者：
Hao Zhou-;Jorge A. Laval;Anye Zhou;Yu Wang;W. Wu;Zhuo Qing;S. Peeta
自动驾驶技术公司和研究界正在加快将机器学习纵向运动规划（mMP）用于自动驾驶汽车（AV）的步伐。本文回顾了目前的最先进的mMP，独家关注其对交通拥堵的影响。本文识别了当前数据集中拥塞场景的可用性，并总结了训练mMP所需的特征。在学习方法方面，对模仿学习和非模仿学习的主要方法进行了综述。一些领先的AV公司（如特斯拉，Waymo和Comma.ai）采用的新兴技术也得到了强调。发现：（i）自动驾驶行业主要关注与安全相关的长尾问题，忽视了对交通拥堵的影响，（ii）当前的公共自动驾驶数据集没有包括足够的拥堵场景，并且大多缺乏必要的输入特征/输出标签来训练mMP，（iii）尽管强化学习方法可以将缓解拥堵整合到学习目标中，工业上采用的主要的mMP方法仍然是行为克隆，其学习减轻故障的mMP的能力还有待观察。在综述的基础上，该研究确定了当前mMP开发中的研究空白。提出了未来mMP研究的建议：（i）丰富数据收集，促进拥塞学习;（ii）采用非模仿学习方法，将交通效率联合收割机纳入安全导向的技术路线;（iii）从传统跟驰理论中整合领域知识，提高mMP的串稳定性。
Self-driving technology companies and the research community are accelerating the pace of use of machine learning longitudinal motion planning (mMP) for autonomous vehicles (AVs). This paper reviews the current state of the art in mMP, with an exclusive focus on its impact on traffic congestion. The paper identifies the availability of congestion scenarios in current datasets, and summarizes the required features for training mMP. For learning methods, the major methods in both imitation learning and non-imitation learning are surveyed. The emerging technologies adopted by some leading AV companies, such as Tesla, Waymo, and Comma.ai, are also highlighted. It is found that: (i) the AV industry has been mostly focusing on the long tail problem related to safety and has overlooked the impact on traffic congestion, (ii) the current public self-driving datasets have not included enough congestion scenarios, and mostly lack the necessary input features/output labels to train mMP, and (iii) although the reinforcement learning approach can integrate congestion mitigation into the learning goal, the major mMP method adopted by industry is still behavior cloning, whose capability to learn a congestion-mitigating mMP remains to be seen. Based on the review, the study identifies the research gaps in current mMP development. Some suggestions for congestion mitigation for future mMP studies are proposed: (i) enrich data collection to facilitate the congestion learning, (ii) incorporate non-imitation learning methods to combine traffic efficiency into a safety-oriented technical route, and (iii) integrate domain knowledge from the traditional car-following theory to improve the string stability of mMP.