DeepAI AI Chat
Log In Sign Up

Longitudinal Motion Planning for Autonomous Vehicles and Its Impact on Congestion: A Survey

by   Hao Zhou, et al.

This paper reviews machine learning methods for the motion planning of autonomous vehicles (AVs), with exclusive focus on the longitudinal behaviors and their impact on traffic congestion. An extensive survey of training data, model input/output, and learning methods for machine learning longitudinal motion planning (mMP) is first presented. Each of those major components is discussed and evaluated from the perspective of congestion impact. The emerging technologies adopted by leading AV giants like Waymo and Tesla are highlighted in our review. We find that: i) the AV industry has been focusing on the long tail problem caused by "corner errors" threatening driving safety, ii) none of the existing public datasets provides sufficient data under congestion scenarios, and iii) although alternative and more advanced learning methods are available in literature, the major mMP method adopted by industry is still behavior cloning (BC). The study also surveys the connections between mMP and traditional car-following (CF) models, and it reveals that: i) the model equivalence only exists in simple settings, ii) studies have shown mMP can significantly outperform CF models in long-term speed prediction, and iii) mMP's string stability remains intractable yet, which can only be analyzed by model approximation followed with numerical simulations. Future research needs are also identified in the end.


Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles

Academic research in the field of autonomous vehicles has reached high p...

Task-Motion Planning for Safe and Efficient Urban Driving

Autonomous vehicles need to plan at the task level to compute a sequence...

A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles

In this survey, we systematically summarize the current literature on st...

A Survey on the Integration of Machine Learning with Sampling-based Motion Planning

Sampling-based methods are widely adopted solutions for robot motion pla...

Predicting Vehicles' Longitudinal Trajectories and Lane Changes on Highway On-Ramps

Vehicles on highway on-ramps are one of the leading contributors to cong...

A Review on Longitudinal Car-Following Model

The car-following (CF) model is the core component for traffic simulatio...