Optimization of Cornering Lights in Adaptive Headlamps

Adaptive headlights with cornering lights can improve nighttime driving visibility and safety. However, current cornering light systems rely solely on steering angle input which causes inefficiencies. This paper explores integrating computer vision with steering-governed algorithms to optimize cornering lights. Our approach showed promising results in correcting headlamp angles at certain curves left unlit by steering algorithms alone. Findings reveal that computer vision integration enables reliable, efficient adaptive headlights, warranting further research for safer automotive lighting.