# Assessment of Driver's Head Acceleration during a Possible Car Skidding Effect
## 1 Introduction
[PERSON] [11], in their study, developed a crash modification factor (CMF) for skid number for all crashes, wet-weather crashes, runoff-the-road crashes, and wet-weather runoff-the-road crashes. A horizontal curve database from a southern state in the United States was used to achieve the study's objective. It was found that the skid number variable is statistically significant, along with traditional variables such as traffic volume, curve radius, and cross-sectional widths. In the experimental study with people without any illness, they only underwent caloric stimulation, which showed that their reaction time was better. The researchers soon realized that the simulation's fidelity mainly depended on how realistic the driving environment they intended to simulate [12]. Therefore, one of the main problems when conducting a driving simulation is to correctly emulate the passenger's body's main decelerations and pressure changes. Thus, they include actuators and motors to match the natural environment conduction [13]. This is due to research reports such as [14], which concluded that the driver determines the speed of the car based on acceleration changes sensed by its body. In the same way, ref. [15] concluded that in a curve, the driver controls the car's speed with an inverse relationship to the lateral accelerations and pressure changes that its body experiences. The reason for this is based on the results reported by different institutions around the world. It has been concluded that a driver experiences a series of acceleration and pressure changes when driving on a curved road [16]. Therefore, the driver's driving ability and perception of movement and reaction time decrease, increasing the risk of an accident because driving on a curved road demands more attention and fatigue from the driver [17]. Furthermore, it has a high relation with pressure and acceleration changes by the driver's vestibular organs [18]. [PERSON] [19] highlights the insufficient understanding of the interaction between the driver and the roadway environment, making it challenging to identify appropriate countermeasures. Using data from the S.H.R.P. 2 naturalistic driving study, they establish connections between the driver, roadway, and environmental characteristics and the risk of road departure in rural areas. The results indicate that roadway data, focusing on rural two-lane curves, encompass factors like geometry, shoulder type, and the presence of rumble strips. [PERSON] [20] introduces an analytical approach to predicting driver fatigue based on a biomathematical model and estimating hard-braking events as a function of predicted fatigue. The analysis utilized de-identified data from a previously published naturalistic field study involving 106 U.S. commercial motor vehicle (CMV) drivers. The results demonstrate a proof of concept for a novel approach predicting fatigue based on drivers' sleep patterns and estimating driving performance in terms of an operational metric related to safety. [PERSON] [21] develops an innovative real-time driving fatigue detection methodology using dry electroencephalographic (E.E.G.) signals. The study employs two methods for online detection of mental fatigue: power spectrum density (PSD) and sample entropy (S.E.). The results show the effectiveness of the proposed methods for fatigue detection. The prediction of fatigue aligns with the observed reaction time recorded during simulated driving, which is considered an objective behavioral measure. Driving on curved roads is a routine task for many drivers, yet it has an underlying impact on both driver behavior and vehicle maneuverability. Previous research has highlighted the significance of driving performance (speed and acceleration) and visual performance (gaze and useful field of view (U.F.O.V.)) in curve analysis. However, as road conditions change, there is a growing concern for the safety of the driving environment, particularly in determining the vehicle's maneuverability [22].