A random finite set approach to multiple lane detection

Robust lane detection is the precondition for advanced driver assistance systems like lane departure warning and overtaking assistants. While detecting the vehicle's lane is sufficient for lane departure warning, overtaking assistants or autonomous driving functions also need to detect adjacent lanes. In this contribution, a novel approach to multiple lane detection based on multi-object Bayes filtering is presented. This method allows for directly considering the dependencies between multiple lanes without explicit data association in post processing. Furthermore, the proposed lane detection algorithm is applied to a challenging scenario of a rural road.