Safe Control Transitions: Machine Vision Based Observable Readiness Index and Data-Driven Takeover Time Prediction
This work addresses safety in autonomous vehicles by improving driver state monitoring, though it is incremental as it builds on prior research by expanding view angles and adding evaluation metrics.
The paper tackled the problem of predicting driver readiness for safe transitions from autonomous to manual control by developing machine learning models for Observable Readiness Index and Takeover Time that are robust to multiple camera views, and introduced post-takeover metrics like maximal lateral and velocity deviations to correlate with pre-takeover predictions.
To make safe transitions from autonomous to manual control, a vehicle must have a representation of the awareness of driver state; two metrics which quantify this state are the Observable Readiness Index and Takeover Time. In this work, we show that machine learning models which predict these two metrics are robust to multiple camera views, expanding from the limited view angles in prior research. Importantly, these models take as input feature vectors corresponding to hand location and activity as well as gaze location, and we explore the tradeoffs of different views in generating these feature vectors. Further, we introduce two metrics to evaluate the quality of control transitions following the takeover event (the maximal lateral deviation and velocity deviation) and compute correlations of these post-takeover metrics to the pre-takeover predictive metrics.