Perspective Transform-Based Lane Detection for Lane Keep Assistance
摘要
Modern cars employ Lane Keep Assist (LKA) as a crucial safety feature, using sensors and cameras to warn drivers when the vehicle veers off the road and correct it. Hardware-in-the-Loop (HIL) simulation is a powerful testing method to model sensor behaviour in controlled environments. Developers can assess LKA systems in diverse conditions, including road geometry, climate, and traffic patterns. HIL validation via dSPACE Scalexio provides a realistic and reliable platform for virtual testing. This paper used image processing techniques to achieve 95% accuracy in lane detection.