## 1 Introduction  
Autonomous vehicles are designed to take human error out of driving actions, which should help make self-driving vehicles safer, thus, dramatically reducing the number of road accidents. Nowadays, high-level automation projects such as General Motors' Cruise Automation [1], Waymo from Google [2], and the highly market penetrated Autopilot from Tesla [3] are the main examples of the leading commercial autonomous vehicle industry. Waymo's robot taxis, although there are still some notable complaints about driver acceptance, according to [4] because of shaky driving experience, as well as lack of trust in the autopilot is one of the critical reasons for low acceptance of the system [5]. Recently, Hyundai Motor Group develops the world's first machine learning based smart cruise control (SCC-ML) technology [6]. SCC-ML combines AI and SCC into a system that learns the driver's patterns and habits on its own. Through machine learning, SCC autonomously drives in an identical pattern as that of the driver. [PERSON] et al. [7] proposed a method for personalizing the driving style of an autonomous vehicle. The driving styles are learned from the human driver in order to benefit from the experienced skill sets of different situations. According to [8], a confusion matrix method based on natural driving data sets was used to tune control parameters in the proposed adaptive cruise control (ACC) system. Although conventional learning framework is beneficial for mimicking human driver behavior, however, it consumes a large amount of time and error-prone process due to the fact that it is developed by human experts [9]. As deep learning has scaled up to more challenging tasks, the architectures have become difficult to design by hand [10]. For these reasons, various studies have been carried out to generate the network architecture automatically [11]. Structure learning is a very useful instrument that is able to automatically find an appropriate artificial neural network (ANN) architecture [12]. For this reason, the structure learning algorithm is used to generate the topology of ANN in this study. Nevertheless, NAS based DNN is still questionable due to a lack of sequential data. [PERSON] and [PERSON] [13] use a recurrent neural network (RNN) policy to sequentially sample a string that in turn encodes the neural architecture. The main advantage of RNN over ANN is that RNN can model a sequence of data (i.e., time series) so that each sample can be assumed to be dependent on previous ones. In order to express the sequential data in both acceleration and deceleration situations of human driving, hand-made RNN is implemented in this paper. With a well-trained neural network, the automated driving function can be achieved by guaranteeing the safety at any driving speed. Thus, human-centered risk assessment approach [14] has been incorporated to demonstrate the naturalistic and safe driving performance for a vehicle longitudinal control situation. There are many different risk assessment methods that have been investigated previously through many scholars [15; 16; 17]. The current stages of risk metrics are mainly explained by the predicted time when some known hazardous events are encountered. The typical predicted time based indices include the time to collision (TTC) or time to impact [18]. [PERSON] et al. proposed the predicted time to minimum distance with sensor-fusion method [19]. Time to react which means the last point to decelerate or accelerate or steer to avoid the predicted collision has been proposed and analyzed [17]. [PERSON] et al. presented an algorithm to compute the probability distribution of TTC induced by an uncertain system input and thus allows to use TTC as a more robust and reliable probabilistic activation condition [20]. This paper is organized as follows. The next section accounts for human driving data based threat assessment by investigating manual driving data. In Section 3, a design of deep neural network for learning human driver is demonstrated.