AI system
- System
-
SAE Level 4 automated driving systems and Level 2 driver-assistance systems reported in California DMV and NHTSA SGO crash records (makers not disaggregated)
- Model
-
Not assessed
- Release
-
Not assessed
- Application developer
-
Multiple automated-vehicle developers (not disaggregated in the study)
- Role
-
Not assessed
- Configuration
-
Not assessed
- Inputs and outputs
-
Not assessed
- Autonomy
-
Not assessed
- Workflow
-
Mandatory crash reporting for automated vehicles to the California DMV and under NHTSA's Standing General Order. A state police-reported crash database (SWITRS) with matching variables for road, environment and pre-crash movement. Matched case-control logistic regression across road, environment and crash type.
- Human oversight
-
Not assessed
- Relation to AI
-
The study compares the conditions under which ADS and human-driven crashes occurred. It does not observe interventions and cannot separate exposure differences from system performance. Independent university authors; regulator databases as sources.
- AI contribution
-
The study compares the conditions under which ADS and human-driven crashes occurred. It does not observe interventions and cannot separate exposure differences from system performance. Independent university authors; regulator databases as sources.
Benefits
- Outcome identifiers
-
Not assessed
- Benefit
-
Compared with human-driven vehicles, the odds of an ADS crash in rain were 0.335 times
- Category
-
Increased Product Safety
- Status
-
Not assessed
- Beneficiary groups
-
Society. Organizations. People.
- Beneficiaries
-
Not assessed
- Reach
-
Not assessed
- Distribution and access
-
Not assessed
- Duration
-
Not assessed
- Expected?
-
Yes
- Costs and harms
-
Not assessed
Measurement
- Studies
-
Not assessed
- Study design
-
Not assessed
- Measurement
-
Not assessed
- Setting and sample
-
Not assessed
- Measures
-
Compared with human-driven vehicles, the odds of an ADS crash in rain were 0.335 times
- Comparison
-
human-driven vehicle crashes from California SWITRS in matched scenarios
- Results
-
Reference: Human-Driven Vehicles (reference odds). Result: the odds of an ADS accident occurring in rainy weather are 0.335 times; 5.25 times higher at dawn/dusk and 1.98 times higher when turning.
- Additional value from AI
-
Not assessed
- Limitations
-
Not assessed
- Denominators
-
Not assessed
- Materials for replication
-
Not assessed
Money
- Kind of value
-
Not assessed
- Amount
-
Not assessed
- Currency and prices
-
Not assessed
- Period and unit
-
Not assessed
- Realization
-
Not assessed
- Recipients and payers
-
Not assessed
- Gross or net
-
Not assessed
- Included costs
-
Not assessed
- Comparison
-
Not assessed
- Method
-
Not assessed
- Return and payback
-
Not assessed
People and places
- Participants
-
Developer: Multiple automated-vehicle developers (not disaggregated in the study). Operator: Multiple operators testing and deploying automated vehicles, mainly in California.
- Jurisdictions
-
Not assessed
- Ownership and control
-
Not assessed
- Dependencies
-
Not assessed
- Cross-border connections
-
Not assessed
- Geopolitical implications
-
Not assessed
Follow-up
- Conditions for replication
-
Continued regulator collection of ADS and ADAS crash reports. Comparable variable definitions between ADS reports and human crash records. Enough ADS crashes in each condition to estimate odds ratios.
- Replication attempts
-
2 follow-up entries. Benefit recurred: 2.
- Later findings
-
No development after the 18 June 2024 publication was found in the cited source.
- Availability
-
Not assessed
- Institutional decision
-
Not assessed
- Grounds
-
Not assessed
- Action and result
-
Not assessed
- Success criterion
-
Not assessed
Evidence
- Source numbers
-
AIBD-0242-source-0001–0002
- Citations
-
Numbered references below.
- Language
-
Not assessed
- Source relationships
-
Independent
- Original evidence
-
Not assessed
- Access and versions
-
Not assessed
- Supported findings
-
Not assessed
- Evidence links
-
Not assessed
- Evidence strength
-
Not assessed
- Missing evidence
-
Not assessed
Grades
- Assessed benefits
-
Society. Organizations. People.
- Depth
-
Not assessed
- Reasons
-
Society: Aggregate statistical comparison with no attributable outcome. Organizations: No organizational outcome is documented. People: No individual beneficiary is documented.
- Observed or intended
-
Not assessed
- Rubric
-
Not assessed
- Assessor and date
-
Not assessed
- Alternative assessments
-
Not assessed
- Fill
-
Independently studied
Entry details
- Kind
-
Not assessed
- Related entries
-
Not assessed
- Field
-
Road vehicle crash avoidance. Automated driving systems.
- Purpose
-
Automated driving systems are built to drive without human intervention. The study measures their crash occurrence against human-driven vehicles.
- Dates
-
Date basis unassessed: 2024 (precision unassessed). Updated: 21 October 2026.
- Stage
-
Not assessed
- Version
-
SHA-256: 892e08bd7336…
References
- Other identifiers
-
Not assessed
- Category mappings
-
Not assessed
- Brief
-
Not assessed
- Revisions
-
6 revisions. Latest: 21 October 2026.
Sources
-
Peer-reviewed study Independent author
Abdel-Aty M, Ding S (2024). A matched case-control analysis of autonomous vs human-driven vehicle accidents. Nature Communications 15, 4931. doi:10.1038/s41467-024-48526-4.
The analysis suggests that accidents of vehicles equipped with Advanced Driving Systems generally have a lower chance of occurring than Human-Driven Vehicles in most of the similar accident scenarios.
-
Peer-reviewed study Independent author
Abdel-Aty M, Ding S (2024). A matched case-control analysis of autonomous vs human-driven vehicle accidents. Nature Communications; doi 10.1038/s41467-024-48526-4 (PMC11189485, full text via Europe PMC).