Trajectory optimization for autonomous overtaking with visibility maximization
Name
17-andersen-ITSC.pdf
Description
Accepted version
Size
2.65 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Andersen, Hans
Schwarting, Wilko
Naser, Felix
Eng, You Hong
Ang, Marcelo H.
Rus, Daniela
Alonso-Mora, Javier
Date Issued
October 2017
Publisher
IEEE
Citation
Andersen, Hans, Schwarting, Wilko, Naser, Felix, Eng, You Hong, Ang, Marcelo H. et al. 2017. "Trajectory optimization for autonomous overtaking with visibility maximization."
Version
Author's final manuscript
Abstract
© 2017 IEEE. In this paper we present a trajectory generation method for autonomous overtaking of static obstacles in a dynamic urban environment. In these settings, blind spots can arise from perception limitations. For example, the autonomous car may have to move slightly into the opposite lane in order to cleanly see in front of a car ahead. Once it has gathered enough information about the road ahead, then the autonomous car can safely overtake. We generate safe trajectories by solving, in real-time, a non-linear constrained optimization, formulated as a Receding Horizon planner. The planner is guided by a high-level state machine, which determines when the overtake maneuver should begin. Our main contribution is a method that can maximize visibility, prioritizes safety and respects the boundaries of the road while executing the maneuver. We present experimental results in simulation with data collected during real driving.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Singapore-MIT Alliance in Research and Technology (SMART)
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/itsc.2017.8317853
