Planning and decision-making for autonomous vehicles

Journal article (2018)

Authors

Wilko Schwarting Massachusetts Institute of Technology
J. Alonso-Mora Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering
Daniela Rus Massachusetts Institute of Technology
Research Group
Learning & Autonomous Control (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2018 Wilko Schwarting, J. Alonso-Mora, Daniela Rus
DOI
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https://doi.org/10.1146/annurev-control-060117-105157
Artificial intelligence Autonomous vehicles Verification Intelligent vehicles Decision-making Fleet management Motion planning
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Published Date

2018

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Cognitive Robotics

Research Group

Learning & Autonomous Control

Abstract

In this review, we provide an overview of emerging trends and challenges in the field of intelligent and autonomous, or self-driving, vehicles. Recent advances in the field of perception, planning, and decision-making for autonomous vehicles have led to great improvements in functional capabilities, with several prototypes already driving on our roads and streets. Yet challenges remain regarding guaranteed performance and safety under all driving circumstances. For instance, planning methods that provide safe and system-compliant performance in complex, cluttered environments while modeling the uncertain interaction with other traffic participants are required. Furthermore, new paradigms, such as interactive planning and end-to-end learning, open up questions regarding safety and reliability that need to be addressed. In this survey, we emphasize recent approaches for integrated perception and planning and for behavior-aware planning, many of which rely on machine learning. This raises the question of verification and safety, which we also touch upon. Finally, we discuss the state of the art and remaining challenges for managing fleets of autonomous vehicles.