ABSTRACT
This paper presents a longitudinal control algorithm for autonomous vehicles on highways. The focus is on identifying target vehicles in the scene which are relevant from a control perspective. These not only include vehicles in the ego vehicle’s lane, but also those in neighboring lanes that are likely to cut in. A learning-based framework is used to estimate the lane change probability of such vehicles and identify relevant targets. The ability of the proposed approach to detect target vehicle cut-ins early and yield smoother control actions is demonstrated using data collected from a passenger vehicle in real traffic on a highway.
