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Conference Paper
Predicting Accidents in Conditional Autonomous Driving: A Multimodal Approach Integrating Human Misuse, Biometric Indicators, and Spatial Complexity
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- Authors
- Eun Hye Jang, Mi Chang, Woojin Kim, Daesub Yoon
- Issue Date
- 2025-08
- Citation
- ACM SIGGRAPH 2025, pp.1-3
- Language
- English
- Type
- Conference Paper
- Abstract
- Decreased attention, distraction, and complex environments are major contributors to accidents in Level 2 autonomous driving. This study examines how spatial complexity and human factors affect accident risk using scenario-based simulations. We analyzed subjective factors (workload, situation awareness) and biometric data (eye tracking, HRV). Logistic regression identified age, workload, and situation awareness as significant predictors, with 74.2% accuracy (5-fold cross-validation). High spatial complexity increased cognitive load and visual scanning, elevating accident risk. These results support the need for integrated prediction strategies and adaptive driver support systems to enhance safety.
- KSP Keywords
- 5-fold cross-validation, Accident risk, Cross validation(CV), Driver support, Human Factors, Integrated prediction, Level 2, Multimodal approach, Scenario-based, Situation awareness(SA), Visual scanning