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Massachusetts Institute of Technology

Safe intention-aware maneuvering of autonomous vehicles

Abstract

dc:description.abstract

In order to improve driving performance, while achieving safety in a dynamic environment, it is crucial for a vehicle motion planner to be aware of the intentions of the surrounding agent vehicles. Many existing approaches that ignore the intentions of surrounding vehicles would produce risky or over-conservative plans. In this thesis, we describe a maneuver motion planning system that achieves both safety and efficiency by estimating the types of surrounding drivers and the vehicle motions being executed by them over a finite horizon in the future. Our claim is that a vehicle is able to efficiently and safely navigate in dynamic traffic situations by estimating the possible types of drivers in its immediate vicinity, such as aggressive or careful, and by predicting their likely maneuvers and motions as probability distributions.

Degree

thesis:*
Name thesis:degree_name
Master
Department dc:contributor.department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Grantor dc:publisher
Massachusetts Institute of Technology
Year dc:date.issued
2019

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Huang, Xin(Xin Cyrus)
Advisor dc:contributor.advisor
  • Brian C. Williams.

Subjects

dc:subject × 1

Rights

dc:rights
Statement dc:rights
  • MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
Language dc:language.iso
eng

Identifiers

dc:identifier.*
Handle dc:identifier.uri
https://hdl.handle.net/1721.1/122514
OAI identifier oai:identifier
oai:dspace.mit.edu:1721.1/122514

Chain of custody

source
Harvested from
MIT
Base URL
dspace.mit.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Huang, Xin(Xin Cyrus). Safe intention-aware maneuvering of autonomous vehicles. Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122514