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Massachusetts Institute of Technology

Safety Assurance for Automated Vehicles Beyond Collision Avoidance

Abstract

dc:description.abstract

Each year, automotive crashes cause thousands of deaths and injuries. Autonomous safety systems have the potential to greatly reduce this tragic loss of life and improve safety, but such systems must meet existing requirements for automotive certification. Particularly, active safety systems must designed to comply with the Automotive Safety Integrity Level risk classification scheme described in the ISO 26262 standard. In this thesis, I design a system using redundant components to independently enforce safety requirements across parallel software supervisors within an autonomous vehicle planning pipeline. I use Hamilton-Bellman-Jacobi reachability analysis to provide new guarantees for safe navigation on public roadways. I create new and extend existing safety modules to independently verify collision avoidance, obedience to traffic rules, and vehicle lane discipline. This project provides theoretical proof of safety and implements control methods within Nvidia’s DriveWorks autonomous vehicle framework.

Degree

thesis:*
Name thesis:degree_name
Master
Department dc:contributor.department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Grantor dc:publisher
Massachusetts Institute of Technology
Year dc:date.issued
2022

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Vorbach, Charles J.
Advisor dc:contributor.advisor
  • Rus, Daniela

Rights

dc:rights
Statement dc:rights
  • In Copyright - Educational Use Permitted
  • Copyright MIT

Identifiers

dc:identifier.*
Handle dc:identifier.uri
https://hdl.handle.net/1721.1/144508
OAI identifier oai:identifier
oai:dspace.mit.edu:1721.1/144508

Chain of custody

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

Vorbach, Charles J.. Safety Assurance for Automated Vehicles Beyond Collision Avoidance. Massachusetts Institute of Technology, 2022. https://hdl.handle.net/1721.1/144508