# Research on Transparency Design Based on Shared Situation Awareness in Semi-Automatic Driving
## 1 Introduction  
The taxonomy of automation is essential when conducting automation research. The degree of automation can be defined as the fraction of automated functions from the overall functions of an installation or system. In an automated, or semi-automatic system, some or frequent human intervention is required [1]. There is a taxonomy and standards in robotics, according to the research of [PERSON] [2]. In the field of autonomous driving, according to the degree of vehicle intelligence, the American Society of Automotive Engineers (SAE) defines six levels of driving automation [3]: L0--no driving automation, L1--driver assistance, L2--partial driving automation, L3--conditional driving automation, L4--high driving automation and L5--full driving automation. In L2 automated driving, the intelligent driving system provides driving assistance for steering wheel operation, acceleration and deceleration operation, and other driving actions are performed by human drivers. In this process, drivers need to constantly supervise the information of the interface even if their feet are off the pedals and they are not steering. However, they must steer, brake, or accelerate as needed to maintain safety.  
At present, various smart car brands such as TESLA, NIO and XPENG have launched L2 assisted driving functions, such as Tesla's NOA (navigation on autopilot), Nio's NOP (navigation on pilot) and Xpeng's NGP (navigate guided pilot). In this kind of function, thedriver is the main controller of the car, and the assisted driving function provides certain driving assistance in specific scenes.  
Drivers will not be required to control the trajectory, maneuver the vehicle, or plan the route as we currently do [4]. So, when the driving assistance function is turned on, the driver will easily relax their vigilance and pay attention to other things instead of the driving environment.  
When there is danger in the driving environment, the driver cannot take measures quickly because they do not receive the environmental information in time, which frequently results in traffic accidents. According to the National Highway Traffic Safety Administration (NHTSA), 11 accidents related to Tesla autopilot or TACC have been confirmed since 2018. Therefore, in L2 automated driving, it is very important for drivers to understand the environmental state in real time and maintain high situation awareness.  
In L2 automated driving, the human driver cooperates with the \"virtual driver\" (intelligent driving system) to execute driving tasks together. Through the human-machine interface (HMI), the system can provide the human driver with environmental information. One way to provide drivers with information about system status is system transparency [5].  
[PERSON]'s research indicated that the transparency between the intelligent system and the human is a mechanism to promote the effective interaction between the human and the intelligent system. The intelligent system conveys its understanding of the current task to the driver to promote the shared situation awareness [6]. Moreover, the HMI must also provide a transparent representation of different stages of the machine decision-making process, so that the driver can understand the behavior intention of the intelligent system [7]. To improve the driver's understanding of the intelligent system and its perception level of the surroundings in assisted driving, this paper proposes a continuous change transparency design method according to the task stage of the scenario and compares it with the static transparency design method.  
Based on the task model of transparency and situation awareness theory, we investigated which stage of situation awareness (SA) has a better transparency design effect and the impact of adding the SA stage of transparency design on usability, driver's workload, and situation awareness. Thus, an effective transparency design method is proposed to accurately display the current situation awareness information of the intelligent system.