A Root Cause Analysis of a Self-Driving Car Dragging a Pedestrian

Using the Taxonomy for Artificial Intelligence Hazard Analysis to examine the recent accident where a pedestrian was dragged by a self-driving car, major themes include lacking consideration for remote operations issues, significant post-collision assessment deficiencies, and a lack of periodic design and test protocol reviews.

Authors

Institutions

Publication Details

Journal
Computer
Published
2024-10-16
DOI
https://doi.org/10.1109/mc.2024.3429276
Citations
3
Primary Topic
Automotive and Human Injury Biomechanics
Type
article
Field-Weighted Citation Impact
1.26
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Root Cause Analysis of a Self-Driving Car Dragging a Pedestrian

Mary L. Cummings, Benjamin Wheeler, John Kliem
3 citations
Computer
Automotive and Human Injury Biomechanics
1.26
article

A Root Cause Analysis of a Self-Driving Car Dragging a Pedestrian

Mary L. Cummings, Benjamin Wheeler, John Kliem
article en
3 citations

Abstract

Using the Taxonomy for Artificial Intelligence Hazard Analysis to examine the recent accident where a pedestrian was dragged by a self-driving car, major themes include lacking consideration for remote operations issues, significant post-collision assessment deficiencies, and a lack of periodic design and test protocol reviews.

ComputerVol. 57(11)
United States Naval Research Laboratory (US), George Mason University (US), Naval Surface Warfare Center (US), NSWC Dahlgren Division
Openalex Percentile: Top 18%
Automotive and Human Injury Biomechanics
1.26
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.