Automated Lane Change Decision Making in Highway using a Hybrid Approach

This study proposes a decision-making model for lane changing and lane keeping decisions in highway autonomous driving. In order to perform a safe and efficient lane change, it is crucial to decide whether a lane change is needed, the desired lane is more suitable, and making a lane change maneuver is safe. In this work, we propose a model that is capable of assessing these considerations and suggest appropriate lane-change maneuvers. The model uses probabilistic utility functions and a deterministic but conservative gap selection method that considers not only the gaps in the target lane but also the vehicles in the driving lane. In addition to simulation tests, we integrated our model into a SUV vehicle that has 360-degree perception and motion control capabilities and performed autonomous highway driving to test real-life performance.