## 2 Review of Related Works and Technological Trends
### Review of Literature  
#### 2.3.1 Driver Behavior Analysis Using a Driving Simulator  
The most effective and ideal approach to analyzing driver behavior characteristics is to conduct experiments in actual road environments. However, actual road experiments are limited by difficulties in control, accident risk, budget, and challenges in consistently reproducing various scenarios. Therefore, this study reviewed previous studies that used simulators, considering safety and cost-effectiveness.  
[PERSON] et al. [12] analyzed the differences in driver responses to conventional and autonomous vehicles using a driving simulator. The study recruited experienced and inexperienced drivers operating conventional and autonomous vehicles in various driving environments. The results showed that experienced drivers preferred manual vehicles for driving enjoyment and flexibility, whereas inexperienced drivers preferred autonomous vehicles for safety and convenience.  
[PERSON] et al. [13] developed a methodology for recognizing driver intentions and behaviors using PanoSim and a Gaussian mixture model. The driving style of participants was classified into cautious, normal, and aggressive. Forward collision avoidance (FCA) strategies tailored to each style were designed. The proposed system improved driver comfort, acceptability, and safety compared to existing FCA systems that did not consider driving behavior.  
[PERSON] et al. [14] analyzed the effectiveness of diagrammatic guide signs (DGSs) at urban expressway interchanges using a driving simulator. Twelve scenarios were developed by combining four DGS configurations and three ramp types, and driver perceptions and reactions to these DGSs were quantified. The results showed that DGSs effectively reduced the lane-change frequency and driving speed.  
[PERSON] et al. [15] anticipated that the mixed operation of autonomous and manual vehicles in mixed traffic environments could cause safety issues within the traffic system. Therefore, they analyzed the lane-changing and gazing behaviors of manual vehicle drivers using a driving simulator. In a cut-in experiment with 11 drivers under different platooning headways (0.8, 1.1, and 1.4 s), drivers tended to delay lane change when the headway was 0.8 s, even though autonomous vehicles were expected to yield. This tendency diminished as the headway increased. The study emphasized that in initial mixed traffic scenarios, human drivers should avoid decision-making delays caused by underestimating the headways.  
[PERSON] et al. [16] argued that, in Level 3 autonomous driving, not only efficiency and safety but also driver acceptance must be considered, especially when drivers are required to take command of the vehicle upon system request. Accordingly, they proposed the Tsallis actor-critic (ATAC) algorithm for the longitudinal control of Level 3 autonomous vehicles. Using a next-generation simulation program, they established reward functions related to safety, efficiency, and comfort and trained car-following strategies using six reinforcement learning algorithms. The ATAC algorithm outperformed the other models by simultaneously improving driver comfort and road capacity compared to the conventional full velocity difference model.  
[PERSON] et al. [17] utilized a driving simulator to estimate the duration of reduced driver performance during the takeover process in autonomous driving. To this end, vehicle trajectory and driver behavior data were collected under both critical and non-critical takeover scenarios. The results revealed that the duration of reduced driver performance was 27.25 s in critical situations and 17.48 s in non-critical situations. This duration tended to increase when the lead vehicle's speed was higher and when the preceding automated driving period was longer.