Temporal Early Exiting With Confidence Calibration for Driver Identification Based on Driving Sensing Data

Driver identification systems that use deep-neural-network-based sequential models have been studied for personalized intelligent vehicles. After a vehicle starts moving for a trip, the system identifies the driver at each time step using accumulated driving sensing data. We propose a novel driver identification system with temporal early exiting to identify a driver as early as possible while maintaining accuracy. Existing systems require entire-trip data or fixed-length partial trip data, regardless of driver identification difficulty. The proposed system automatically identifies the driver with less driving data for easy-to-identify trips and more driving data for hard-to-identify trips. To adaptively exit the identification by considering the difficulty of a trip, we propose a temporal early-exiting method by thresholding the confidence score. Sequential models output an identified driver and confidence score at each time step. However, the confidence score of deep neural networks is unreliable owing to the overconfidence problem. To overcome this problem, we propose three temporal confidence calibration methods that adjust the calibration strength according to the driving time and trip difficulty. Thus, the system can determine the best time to exit the identification, considering the trade-off between latency and accuracy. Our experiments on a naturalistic driving dataset show that the proposed system achieved 90.06% accuracy with early exiting at an average of 6.7 min, yielding the same accuracy with 74.2% latency reduction compared with driver identification with 26 min of fixed-length data for each trip.