Multitask Learning and Prediction of Baseline Driving Performance Measures

Driving performance measures (DPMs) are important indices for driving and personal safety in vehicle operation. The DPMs are collected under various controlled driving conditions to demonstrate different driving behaviors so that mitigating technology interventions can be studied and designed. However, significant costs are involved in the DPM acquisition, and there are a very limited number of controlled driving condition data. Thus, the modeling and prediction of the DPMs under unobserved driving conditions are critical, and many methods have been developed. However, existing literature in this area suffer a common limitation: The interactions among different DPMs are not fully considered (each DPM is modeled individually), although the existence of such interactions is widely reported. The researcher developed and reported a novel DPM modeling and prediction method, i.e., multi-output convolutional Gaussian process (MCGP), that incorporates the interactions among different DPMs. The method features the modeling flexibility for different DPMs and the interpretable modeling structure for integrating the DPM interactions. The method is compared with three benchmark methods on the DPM data set under four different settings, and the results demonstrate the superiorities of the method. The proposed method provides flexible and accurate predictions for DPMs at unobserved driving conditions, which can significantly reduce simulation costs and time.