Human Liability in Semi-Autonomous Vehicles

The promise of fully autonomous vehicles has faced repeated delays, with widespread availability now projected to be limited to specific routes or geographic areas in the near future. Instead, conditional or semi-autonomous systems (SAE Level 3) are being introduced, where the vehicle can drive autonomously under limited conditions but still requires a human driver to take control when necessary. This paper examines the regulatory and liability challenges posed by these semi-autonomous vehicles. Existing laws have gaps in addressing the ambiguous responsibility of the human driver in the shared control environment of Level 3 autonomy. Factors like fatigue, lack of situational information, and over-reliance on the autonomous system diminish a human driver’s ability to intervene effectively. Current negligence and product liability tort doctrines struggle to allocate liability in semi-autonomous scenarios due to issues of foreseeability, the unpredictability of AI decision-making, and shared human-machine control. No-fault insurance schemes also face hurdles in determining coverage and premiums for semi-autonomous risks. To address these gaps, this paper proposes defining a new legal “supervisor” role for human drivers of semi-autonomous vehicles, with liability dependent on providing adequate alerts and road/system status data from the autonomous driving system. Delineating human and machine responsibilities through regulation can align legal frameworks with the realities of human-AI interaction, while simultaneously promoting the benefits of vehicle autonomy technology.

Publication Details

Journal
eYLS (Yale Law School)
Published
2025-01-01
Primary Topic
Human-Automation Interaction and Safety
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Human Liability in Semi-Autonomous Vehicles

eYLS (Yale Law School)
Human-Automation Interaction and Safety
article

Human Liability in Semi-Autonomous Vehicles

article en

Abstract

The promise of fully autonomous vehicles has faced repeated delays, with widespread availability now projected to be limited to specific routes or geographic areas in the near future. Instead, conditional or semi-autonomous systems (SAE Level 3) are being introduced, where the vehicle can drive autonomously under limited conditions but still requires a human driver to take control when necessary. This paper examines the regulatory and liability challenges posed by these semi-autonomous vehicles. Existing laws have gaps in addressing the ambiguous responsibility of the human driver in the shared control environment of Level 3 autonomy. Factors like fatigue, lack of situational information, and over-reliance on the autonomous system diminish a human driver’s ability to intervene effectively. Current negligence and product liability tort doctrines struggle to allocate liability in semi-autonomous scenarios due to issues of foreseeability, the unpredictability of AI decision-making, and shared human-machine control. No-fault insurance schemes also face hurdles in determining coverage and premiums for semi-autonomous risks. To address these gaps, this paper proposes defining a new legal “supervisor” role for human drivers of semi-autonomous vehicles, with liability dependent on providing adequate alerts and road/system status data from the autonomous driving system. Delineating human and machine responsibilities through regulation can align legal frameworks with the realities of human-AI interaction, while simultaneously promoting the benefits of vehicle autonomy technology.

eYLS (Yale Law School)
Peace, Justice and strong institutions
Openalex Percentile: Top 43%
Human-Automation Interaction and Safety
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.

Human Liability in Semi-Autonomous Vehicles · eYLS (Yale Law School) (2025) | TGRS Research Map | TGRS