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This paper describes a vision-based vehicle detection system for a blind spot warning function. This detection system has been designed to provide ample performance as a driving safety support system, while streamlining the image processing algorithm so that it can be processed using the computational power of an existing ECU. The procedure used by the system to detect a vehicle in a blind spot is as follows. The system consists of four functional components: obstacle detection, velocity estimation, vertical edge detection, and final classification. In obstacle detection, a predicted image is generated under the assumption that the road surface is a perfectly flat plane, and then an object is detected based on a histogram that is created by comparing the predicted image and an actually observed image. The velocity of the object is estimated by tracking the histogram over time, assuming that both the object and the host vehicle are traveling in the same direction.

In the present study, a challenge has been made to quantitatively determine the vapor phase concentration distributions in an evaporating multicomponent fuel spray using the LAS imaging technique. The theoretical considerations were particularly given when applying the LAS imaging technique to the multicomponent fuel spray and reconstructing the vapor concentration distributions from the spray images.

One of the world's largest unsteady turbulence simulations of flow around a formula car was conducted using Large Eddy Simulation (LES) on the Earth Simulator in Japan. The main objective of our study is to investigate the validity of LES for the assessment of vehicle aerodynamics, as an alternative to a conventional wind tunnel measurement or the Reynolds Averaged Navier-Stokes (RANS) simulation. The aerodynamic forces estimated by LES show good agreement with the wind tunnel data (within several percent!) and various unsteady flow features around the car is visualized, which clearly indicate the effectiveness of large-scale LES in the very near future for the computation of flow around vehicles with complex configurations.

A road vehicle’s cornering motion is known to be a compound motion composed mainly of forward, sideslip and yaw motions. But little is known about the aerodynamics of cornering because little study has been conducted in this field. By clarifying and understanding a vehicle’s aerodynamic characteristics during cornering, a vehicle’s maneuvering stability during high-speed driving can be aerodynamically improved. Therefore, in this study, the aerodynamic characteristics of a vehicle’s cornering motion, i.e. the compound motion of forward, sideslip and yaw motions, were investigated. We also considered proposing an aerodynamics evaluation method for vehicles in dynamic maneuvering. Firstly, we decomposed cornering motion into yaw and sideslip motions. Then, we assumed that the aerodynamic side force and yaw moment of a cornering motion could be expressed by superposing linear expressions of yaw motion parameters and those of sideslip motion parameters, respectively.

A numerical method specially designed to predict unsteady aerodynamics of road vehicle was developed based on unstructured Large-Eddy Simulation (LES) technique. The code was intensively optimized for the Earth Simulator in Japan to deal with the excessive computational resources required for LES, and could treat numerical meshes of up to around 120 million elements. Moving boundary methods such as the Arbitrary Lagrangian-Eulerian (ALE) or the sliding method were implemented to handle dynamic motion of a vehicle body during aerodynamic assessment. The method can also model a gusty crosswind condition. The method was applied to three cases in which unsteady aerodynamics are expected to be crucial.