# Study on the Driver/Steering Wheel Interaction in Emergency Situations
## 1 Introduction  
The driver acts on the steering wheel by applying forces and moments with the hands, activating the muscles with voluntary and involuntary actions. [PERSON] and [PERSON] [1] presented a neuro-muscular system (NMS) model that replicates the driver's actions while driving. They highlighted the involuntary actions as a consequence of an inner control loop that allows the driver to maintain a certain position in the space of the hands or arms, despite external disturbances. Similarly, in [2], [PERSON] et al. developed a NMS model of the driver for the human-vehicle shared control in autonomous vehicles. The knowledge of the driver's actions on the steering wheel allows the study of the interaction between the driver's hands and the advanced driver assistance systems (ADAS) [3] to lead improvements in their activation procedures and make them less intrusive during their action. [PERSON] et al. [4] developed an ADAS control able to intervene only in necessary situations. In [5], the authors highlighted the importance of the steering feeling while driving. The authors studied the influence of the steering geometry on the applied steering torque. [PERSON] et al. [6] presented a new method to classify the driver behaviours by analysing a set of data collected during normal driving. They obtained a driving fingerprint map that characterized the driver's behaviour. By using such a classification, it is possible to train ADAS to customize their intervention. An instrumentedsteering wheel could also improve the performance of haptic guidance steering systems, which are actually ADAS for driver assistance [7]. In order to intervene correctly, ADAS require a deep knowledge of human-vehicle interactions, and the research on driver models is very active. [PERSON] et al. [8] proposed an electric power steering (EPS) logic developed adopting human simulated intelligent control (HSIC). The authors noticed that the information on the steering effort allows an increase in the performance of the proposed EPS control logic. [PERSON] and [PERSON] [9] studied the performance of some data-driven human driver models that are able to predict the torque applied by the driver on the steering wheel. In [10; 11], driver models were developed that imposed the steering wheel angle using a neural network. Similarly, in [12; 13; 14], the driving styles were classified to optimize the ADAS intervention. [PERSON] et al. [15] developed a human-like driver model that was able to capture the steering behaviour for both routine and emergency situations. [PERSON] et al. [16] identified the driver behaviour characteristics of novel, normal, and skilled drivers and applied the model parameters to a driving simulation, obtaining personalized control in order to successfully follow a path. [PERSON] et al. [17] identified driver parameters using Kalman filters, but concluded that the driver parameters were not time constant, but varied during the driving task. [PERSON] et al. [18] presented a method to identify the driver characteristics using inverse dynamic optimal control. In [19], the authors studied a clustering method for driver's steering intention classification, based on certain steering action parameters, such as steering angle and applied torque. In [20; 21], the authors proposed two different methods for driver behaviour analysis, considering the available vehicle operation information. Some authors, such as [22; 23; 24], analysed vehicle dynamic signals to identify the driver behaviours, while other authors preferred to use data measured at the human-machine interface, such as electrocardiography (ECG) sensors [25] or wearable sensors [26]. In [27], the authors proposed the use of physiological data of the driver to analyse his state while driving. None of the presented works studied the forces and the moments applied by the driver's hands, which constitute the direct interaction between the driver and the vehicle. The knowledge of such interactions drives a better comprehension of the phenomena that occur while driving.