Toshiyuki Sugimachi

Greater Activity in the Frontal Cortex on Left Curves: A Vector-Based fNIRS Study of Left and Right Curve Driving

Objectives In the brain, the mechanisms of attention to the left and the right are known to be different. It is possible that brain activity when driving also differs with different horizontal road alignments (left or right curves), but little is known about this. We found driver brain activity to be different when driving on left and right curves, in an experiment using a large-scale driving simulator and functional near-infrared spectroscopy (fNIRS). Research Design and Methods The participants were fifteen healthy adults. We created a course simulating an expressway, comprising straight line driving and gentle left and right curves, and monitored the participants under driving conditions, in which they drove at a constant speed of 100 km/h, and under non-driving conditions, in which they simply watched the screen (visual task). Changes in hemoglobin concentrations were monitored at 48 channels including the prefrontal cortex, the premotor cortex, the primary motor cortex and the parietal cortex. From orthogonal vectors of changes in deoxyhemoglobin and changes in oxyhemoglobin, we calculated changes in cerebral oxygen exchange, reflecting neural activity, and statistically compared the resulting values from the right and left curve sections. Results Under driving conditions, there were no sites where cerebral oxygen exchange increased significantly more during right curves than during left curves (p > 0.05), but cerebral oxygen exchange increased significantly more during left curves (p < 0.05) in the right premotor cortex, the right frontal eye field and the bilateral prefrontal cortex. Under non-driving conditions, increases were significantly greater during left curves (p < 0.05) only in the right frontal eye field. Conclusions Left curve driving was thus found to require more brain activity at multiple sites, suggesting that left curve driving may require more visual attention than right curve driving. The right frontal eye field was activated under both driving and non-driving conditions.

Evaluation of Fuzzy Inference-based Self-tuning of Steering Control Gains for Heavy-duty Trucks

As the number of automobiles in worldwide increases, the number of associated serious environmental and safety problems also increases. A solution to these problems is an autonomous platooning system for trucks, which is expected to have various effects such as reduction in carbon dioxide emissions and increase in traffic capacity. Although various control laws have been proposed for automated driving, control gains are typically tuned manually. The optimal values change owing to the freight or over a period of several years. Therefore, when the control performance decreases, gain tuning is again required. In this study, as additional evaluations of the proposed self-tuning method, we newly examine if the method can adjust gain to the change of a freight weight by simulation and evaluate the convergence property of the method experimentally.