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Drifting, a very popular challenge in the recent motorsport, is a cornering technique with large angle of sideslip when rear tires encounter with combined slip over the linear region of the slip-tire friction characteristic. Besides, this technique is frequently used by expert rally drivers in order to ensure that the vehicle will not understeer. In this work, steady state cornering of a RWD vehicle on plane, with constant speed, sideslip and radius of curvature, was simplified via the single track vehicle model. In addition, BNP magic formula, along with MNC model, was used to estimate tire frictions. The computer program was developed, based on EOMs derived via body fixed coordinate, in order to calculate a suitable cornering speed and corresponding open-loop driving control inputs, steering angle and rear slip ratio, for a given radius of curvature and vehicle sideslip.

It is common for users of commuting passenger cars in Thailand to use the vehicle’s HVAC (Heating, Ventilating and Air Conditioning) system predominantly in recirculation (REC) mode. This minimizes the compressor work, thereby saving fuel, and reduces dust and odor infiltration into the vehicle cabin. The car windows are rarely opened for ventilation purposes, except for exchanges at service stations such as garage entrances and tollway booths. As such, there are few opportunities for fresh air to enter the cabin with the consequent accumulation of CO2 in vehicle cabins due to occupants’ exhalations being well documented. Field experiments conducted showed that the in-vehicle CO2 concentrations could reach up to 15 times that of the ambient concentration level during typical city commutes. Preliminary experiments were also conducted to quantify the air exchanges between the cabin and the ambient when the doors are opened for occupant egression.

Driver behavior is one of the most important factors in safe mobility. In general, various driver maneuvers can be determined from acceleration of the vehicle. Physically, the acceleration and brake can be detected with longitudinal acceleration while turning and lane change can be detected with lateral acceleration. Normally, IMU (inertia measurement unit) has been designated to get these data. However, the IMU is not convenience to install in the vehicles especially as aftermarket parts. Nowadays, navigation system technologies have been much improved, both on availability and accuracy with combination of multiple navigation satellite systems. Normally, it's called Multi-GNSS (multiple global navigation satellite system). In particular, the satellite navigation systems available in this work are GPS, GLONASS, and QZSS. With decimeter precision and the update rate scale up to 10-Hz, the GNSS can be a viable alternative for driver behavior detection.

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.

According to the recent study, Thailand has the 2nd most dangerous road in the world. Based on many researches, the driver is the main influencers of the traffic fatalities. Since the more dangerous the driver drive, the more chance of accident become. Therefore, driver’s monitoring system become one of the solutions that acceptable and reliable, especially for fleet management and public transportation. This paper’s goal is to find an algorithm that can distinguish driving behaviour based on cars’ acceleration and velocity, calling it as Risk Driving Score (RDS). The algorithm was tested by driving test by volunteers on highways with observers, who were told to rank the drivers in terms of driving risk from the 1-5 point. Meanwhile, the drivers were asked to drive in 3 different styles, normal, safety, and hurry. All drives were recorded by satellite and video data then filtered and used for the algorithm calculation.

The road accident is major concern around the world, so do Thailand. It is caused by three main factors: man, vehicle and infrastructure. The most important part that accounts the safety of vehicle is human. With experiences and careful driver, the accident could be diminished. So that the vehicle monitoring systems are the vital tools to screen out the inexperience or aggressive driver. In this paper, we state the problem about the dangerous driving behavior by monitoring lateral and longitudinal acceleration. For this purpose, the inertial measurement unit should be applied but it is inconvenient to install in the vehicle. Consequently, the vehicle monitoring system were developed based on novel consumer grade multi-satellite navigation receivers and were compared to Racelogic Video V-Box system in controlled condition tested track. The incidents were virtually detected and reviewed. The incident detection algorithm were proposed and tested alongside with receivers.

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.