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Recently, with the active development of automated driving systems (ADSs) corresponding to SAE automated driving level 2, the comfort of ADSs has gained significant attention. In our previous research, it was proposed that the comfort of ADSs is affected by their maneuvers and the degree of information sharing between ADS and drivers. However, even if the drivers are well-informed of the recognized traffic environment and ADS-controlled trajectory, the comfort and trust of drivers in ADSs could be insufficient. This is because each driver has distinct preferred trajectories. Although some researchers have proposed ADSs that learn driver preferences, the maneuvers presented by these systems are not easily modified, because of off-line learning. In addition, a few quantitative investigations on the subjective evaluation of comfort have been conducted. This study proposes an on-demand learning collision avoidance ADS that learns the preferred maneuvers of drivers through driver intervention. In our system, the drivers teach their preferred trajectory to the system only if they are unsatisfied with the maneuver presented by the system. The system updates the parameters and shows the learned maneuver at the next avoidance. To realize the proposed system, we applied modified risk potential functions and gain-tuning method, as well as a cost function and learning method for gradual and stable maneuver learning. Driving simulator experiments demonstrated stable trajectory learning and smooth intervention. Furthermore, the drivers were satisfied with the learned maneuvers of the ADS, and their comfort and trust in the ADS improved when using the proposed ADS. [ABSTRACT FROM AUTHOR]

Superconducting cables have been developed around the world and demonstrated its operation in the grid. One of the important subjects is to study what happens in case of fault events, such as a ground fault, short circuit fault and so on, occurred in the superconducting cable. Therefore, we have conducted experimental and numerical simulations at the fault events. For the short circuit accidents, we conducted experimental simulation with a 66 kV 3-core cable in 40 m length to measure LN2 temperatures and pressures in the cable during and after simulated fault current as 16 kA- 8 sec, which has the same heat generation as the worst fault condition in the Japanese 66 kV network as 31.5 kA for 2 sec. The measured values of temperature and pressure are almost good agreement with calculated values. Using the same calculation code, temperatures and pressures at the fault event are estimated in the superconducting cable with 3 km length for practical use. As the results, in case of 31.5 kA in 2 sec, maximum temperature at superconducting conductor is calculated as 112 K and maximum pressure is as 3 MPa under the base pressure of 1 MPa. In that case, no gasification of LN2 is occurred. So, blowout of cryostat would not be occurred. However, it expects that several hours are necessary to turn back original state. For the ground fault, we conducted experimental simulations using samples with protection sheets and short cable samples. The fault current condition is assumed as 1.5 kA for 2 sec, which is the worst case in the Japanese 66 kV network. In such condition, the cable cryostat can be penetrated by the arc energy of the insulation break down and then LN2 would be blown out to make some damage for surrounding apparatus. So, we conducted LN2 leakage test in the manhole to measure LN2 accommodation level and concrete temperatures. Using experimental results and diffusion equation with thermal conductivity of concrete, inner pressure of manhole was simulated. The calculation results show the pressure rising is not so fast that the manhole lid can be blown off. [ABSTRACT FROM AUTHOR]