Lateral collisions whether during lane changes or straight-line driving are a major safety concern on roads. These crashes often happen due to blind spots, sudden lane shifts, or even simple drifting, leading to dangerous and sometimes fatal accidents. This study uses the Simulation of Urban MObility (SUMO) software to model high-risk scenarios where vehicles collide sideways, whether during intentional lane changes or unexpected deviations while driving straight. By simulating interactions between an autonomous Ego vehicle and human-driven cars, this study analyzes what makes these lateral collisions so common and how they might be prevented. To capture the subtle movements that lead to sideswipe crashes, SUMO’s Sub Lane model is used, which allows for precise tracking of how vehicles shift sideways not just during lane changes but also when they drift out of position.