  Self-driving technology companies and the research community are accelerating
their pace to use 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. We identify
the availability of congestion scenarios in current datasets, and summarize the
required features for training mMP. For learning methods, we survey the major
methods in both imitation learning and non-imitation learning. We also
highlight the emerging technologies adopted by some leading AV companies, e.g.
Tesla, Waymo, and Comma.ai. We find that: i) the AV industry has been mostly
focusing on the long tail problem related to safety and 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) albeit reinforcement learning
(RL) approach can integrate congestion mitigation into the learning goal, the
major mMP method adopted by industry is still behavior cloning (BC), 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 towards 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 (CF) theory to improve the string stability of mMP.
