# An Image Processing-Based Method to Analyze Driver Visual Behavior Using Eye-Tracker Data
## 5 Discussion
This effect is much less intense for the portions of images in the center of the scene than when the driver is looking inside the road section. The higher the distance from the roadside, the higher the distortion. The procedure has a further step in the presence of buildings or objects. It must identify where the gaze points; then, the vertical plane (the facade in the case of a building) on which the gaze is located must be identified, and along this plane, its projection on the ground determines the location of the gaze. This procedure is cumbersome, and besides the error introduced by perspective distortion, it can be affected by the difficulty of identifying the ground border. In any case, there is no alternative to this set of data. We decided to use only the projection of the gaze on the ground because the point on the facade is affected by a higher distortion than the corresponding projected point on the ground. It is worth noting that the method can also be applied to images collected by the same devices while driving in real life. [PERSON] et al. (2019) [52] quantified driver mental workload using a combined vestibular-ocular reflex (VOR) and optokinetic reflex (OKR) model, which is essential for maintaining stable vision while driving. However, their method's reliance on head movements presents a significant limitation, particularly as modern vehicle suspensions and improved road conditions reduce the necessary vibrations for accurate modeling. Their approach assumes that discrepancies between predicted and observed eye movements indicate cognitive distraction. Our proposed method addresses this limitation by eliminating the need for head movements. We use an image-processing algorithm to link eye-tracker data with the simulation. While our research focus differs slightly, our method can assess drivers' attention allocation to road geometry, helping to detect distraction and analyze the impact of different road types. Despite differing specific aims, both studies aim to improve understanding of driver distraction and attention. While we acknowledge the benefits of the Canny approach in our study, particularly its robustness in detecting weak edges amidst noise, the novelty of our method lies in its integration and adaptation of various components to analyze gaze positions concerning road borders. Our approach specifically addresses the challenge of analyzing gaze data without 3D information, a prevalent constraint in eye-tracker studies of this nature. We overcame this challenge by carefully considering perspective and introducing innovative adjustments such as the \"ratio\" variable approach and selection window adjustment. Additionally, our method seamlessly connects eye-tracker data with virtual reality simulation, tackling a significant challenge in such simulator environments. While we drew from both older and recent literature, finding recent studies yielding robust results on road geometry analysis via gaze data was challenging. A gap persists between older studies and current driving conditions, despite newer research. Integrating vehicle technology and road infrastructure advancements could enhance driver behavior analysis, but few recent studies have explored this. However, filling this gap remained challenging, as did finding methods for accurately pinpointing driver focus in virtual environments lacking 3D information. To overcome this, we established a novel connection between gaze data and simulation, enabling a more precise examination of driver behavior concerning road geometry. Its versatility allows for application across diverse scenarios, such as assessing advertisement effectiveness along roadways and comparing distraction levels pre- and post-implementation of proposed urban layout changes. Although our method has certain limitations, it addresses many challenges associated with 2D data collection. In the absence of 3D data, this represents the best achievable outcome.