Traffic accidents on community roads with occlusions are one of the unsolved social issues in Japan because advanced driver assistance systems and autonomous driving systems only using onboard sensors cannot behave adequately due to the occlusions caused by house walls. Although connected safety system is promising to prevent traffic accidents in such situations, placing roadside sensors at every intersection costs too much. Motivated by above backgrounds, this study proposes a connected collision avoidance system via a stochastic localization method that combines observation information from roadside sensors and onboard/portable devices of traffic participants for realizing the safety systems as well as reducing the necessary number of roadside sensors. To evaluate the proposed system from the viewpoints of uncertainty of the observation due to the layout and the specification of roadside sensors, this study conducts simulations in the course that modeled community roads in the actual town. As a result, this study confirms that the proposed method realizes the collision avoidance between an automobile and a bicycle even on a condition of a sparse layout of the roadside sensors owing to the concept of the stochastic localization based on the extended Kalman filter. The findings of this study will contribute to realization of the connected safety system on community roads with occlusions under the reasonable implementation conditions that roadside sensors will not be fully installed at every intersection.

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This study analyzed the effects of road alignment on visibility delay for curves with experiments using a driving simulator. The results showed that the visibility behavior for curves was the first recognition of the existence of the curve, followed by the recognition of the curvature of the curve. Furthermore, it was found that the curve visibility delay in those phases was influenced by the left-right difference and the difference in curvature. In particular, there is a tendency for drivers to be late in noticing sharp left turns. It would be useful to warn the driver of the existence of a sharp curve at least 150 m before the curve and to encourage the driver to decelerate to prevent accidents. In addition, a left curve with an additional left lane can improve curve recognition, and expanding the shoulder or other space on the left side of the lane would be advantageous for curve recognition.

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In parallel with government policies being implemented internationally to reduce noise pollution along roads, improve automobile fuel efficiency to curb global warming, and reduce traffic accidents caused by slippage, there is a need to improve tire performance, which involves a trade-off between each of these factors: radiated noise, rolling resistance, and wet grip. To efficiently advance these research projects, it is necessary to utilize information on the latest research in this field from around the world. For this reason, the author conducted a literature survey.

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This study focuses on reducing noise generated by heating, ventilation, and air conditioning (HVAC) systems in electric vehicles to enhance in-cabin comfort. To achieve this, we employed feedback active noise control (ANC) with a real-time filter update in the frequency domain. A novel control algorithm was developed using a sliding discrete Fourier transform for each sample, along with a gradient descent method for noise adaptation in the frequency domain. Numerical analysis first confirmed the effectiveness of the feedback control (FB) approach, and subsequent experiments were conducted on a commercial electric vehicle. A microphone installed on the map lamp was used to reference the noise generated by the air conditioner. The experimental results demonstrated that feedback control reduced noise by approximately 3.0 dB (O.A. [A]) in the 20–200 Hz frequency band, showing significant potential for improving vehicle quietness.

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