Joint Modeling of Disengagement and Collision Events from Autonomous Driving Study

Music City Center 

As the popularity of artificial intelligence (AI) continues to grow, AI systems have become increasingly embedded into various aspects of daily life, transforming industries and lifestyles. One of the typical applications of AI systems is autonomous vehicles (AVs). In AVs, the relationship between the level of autonomy and safety is an important research question to answer, which can lead to two types of recurrent events data being recorded: disengagement and collision events. This paper proposes a joint modeling approach with multivariate random effects to analyze these two types of recurrent events data. The proposed model captures the intercorrelation between the levels of autonomy and safety in AVs. We apply an expectation-maximization (EM) algorithm to obtain the maximum likelihood estimates for the functional form of the fixed effects, variance-covariance components, and the tuning parameter for the penalty term. This proposed joint modeling approach can be useful for modeling recurrent events data with multiple event types from various applications. We analyze disengagement and collision data from California's AV testing program to demonstrate its application.

AI reliability

AI robustness

Correlated frailty

EM algorithm

Recurrent events data

Survival models 

Quality and Productivity Section