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This paper presents a Tire-Suspension-Steering Hardware-In-the-Loop Simulator (HILS) system for using in a development of a student formula car suspension. The HILS system was modified from an existing Tire-Suspension-Steering Hardware-In-the-Loop Simulator developed in Chulalongkorn University. The system can be used to reduce the development time and cost when comparing to testing a real prototype car on a test track. Firstly, we designed and built the adjustable support for the student formula car suspension on the HILS such hat wheel parameters such as camber and toe can be adjusted. CATIA V5 was used to design and test the structural strength. On the other hand, the HILS system is used to simulate and test vehicle dynamics by replacing the front wheel of a bicycle model with a real student formula wheel and suspension. The wheel is running on a rotating drum whose speed and orientation can be controlled to simulate the car speed and the tire side slip angle.

The world is aging rapidly. Many countries can already be categorized as aging or aged societies while a few are becoming super-aged societies. In Thailand as well as in other countries, traffic accidents caused by elderly drivers will continue to rise as a significant percentage of elderly people still prefer to drive. Accidents may be prevented with driving tests and screening methods for elderly drivers. However, it is also necessary to understand the effect of aging on driving ability. With this understanding, driver training, driver assistant systems, and improvements on infrastructure may be designed accordingly. Among various physical changes, cognitive ability of the brain is one of the most significant factors affecting driving ability. In this paper, correlation between various cognitive functions of the brain and car following skill of drivers are considered.

Drowsiness has been one of the major reasons of road fatality over a decade because the effect of drowsiness is trying to shut down both mind and body. That means drivers are unable to completely control the car while trapped in an unconscious state. It will lead to a several number of casualties on road. In this research, two sets of people, partial sleep-deprivation and non-sleep-deprivation, are tested on the driving simulator. Two methods were used in this study to identify driver drowsiness. First, driving behavior focusing on steering pattern. And secondly, vehicle offset from the lane center. The tests were carried out on the designed scenarios in the Driving Simulator. The scenarios are such as following straight lane with vehicle along the road, following curve lane without other cars, car following on a single lane and following straight lane without other cars. From the results, there are significant results that can roughly separate drowsy driver from regular driver.