Satoshi Suga

Taihei Takahashi, Kanata Takayasu, Satoshi Suga et al.

Accurately extracting patterns that appear frequently only within specific time intervals, together with their dense intervals, is important in many applications such as understanding seasonal demand and detecting anomalous behavior.Frequent itemset mining evaluates support over the entire dataset and therefore cannot detect locally dense patterns. Existing methods for dense pattern mining with interval output estimate dense intervals through occurrence-gap constraints; however, since the gap constraint parameter governs both pattern identification accuracy and interval detection accuracy simultaneously, finding a parameter setting that achieves high accuracy for both objectives is difficult.In this paper, we propose Apriori-window, an exact algorithm that resolves this structural limitation. The proposed method directly evaluates local frequency within a sliding window and thus requires no gap constraint parameter, and it efficiently enumerates dense intervals through anti-monotonicity-based pruning of the search space and stride-skip reduction of the number of window scans. Experiments on three real-world datasets demonstrate that existing methods struggle to simultaneously achieve high accuracy in both pattern identification and dense interval detection, and scalability experiments on synthetic data confirm the practical applicability of the proposed method.

Zheng Wang, Satoshi Suga, Edric John Cruz Nacpil et al.

Driver distraction is a well-known cause for traffic collisions worldwide. Studies have indicated that shared steering control, which actively provides haptic guidance torque on the steering wheel, effectively improves the performance of distracted drivers. Recently, adaptive shared steering control based on the physiological status of the driver has been developed, although its effect on distracted driver behavior remains unclear. To this end, a high-fidelity driving simulator experiment was conducted involving 18 participants performing double lane changes. The experimental conditions comprised two driver states: attentive and distracted. Under each condition, evaluations were performed on three types of haptic guidance: none (manual), fixed authority, and adaptive authority based on feedback from the forearm surface electromyography of the driver. Evaluation results indicated that, for both attentive and distracted drivers, haptic guidance with adaptive authority yielded lower driver workload and reduced lane departure risk than manual driving and fixed authority. Moreover, there was a tendency for distracted drivers to reduce grip strength on the steering wheel to follow the haptic guidance with fixed authority, resulting in a relatively shorter double lane change duration.

Zheng Wang, Satoshi Suga, Edric John Cruz Nacpil et al.

Shared steering control has been developed to reduce driver workload while keeping the driver in the control loop. A driver could integrate visual sensory information from the road ahead and haptic sensory information from the steering wheel to achieve better driving performance. Previous studies suggest that, compared with adaptive automation authority, fixed automation authority is not always appropriate with respect to human factors. This paper focuses on designing an adaptive shared steering control system via sEMG (surface electromyography) measurement from the forearm of the driver, and evaluates the effect of the system on driver behavior during a double lane change task. The shared steering control was achieved through a haptic guidance system which provided active assistance torque on the steering wheel. Ten subjects participated in a high-fidelity driving simulator experiment. Two types of adaptive algorithms were investigated: haptic guidance decreases when driver grip strength increases (HG-Decrease), and haptic guidance increases when driver grip strength increases (HG-Increase). These two algorithms were compared to manual driving and two levels of fixed authority haptic guidance, for a total of five experimental conditions. Evaluation of the driving systems was based on two sets of dependent variables: objective measures of driver behavior and subjective measures of driver workload. The results indicate that the adaptive authority of HG-Decrease yielded lower driver workload and reduced the lane departure risk compared to manual driving and fixed authority haptic guidance.