Lane Change Scheduling for Autonomous Vehicle: A Prediction-and-Search Framework

Abstract

Automation in road vehicles is an emerging technology that has developed rapidly over the last decade. There have been many inter-disciplinary challenges posed on existing transportation infrastructure by autonomous vehicles (AV). In this paper, we conduct an algorithmic study on when and how an autonomous vehicle should change its lane, which is a fundamental problem in vehicle automation field and root cause of most 'phantom' traffic jams. We propose a prediction-and-search framework, called Cheetah (Change lane smart for autonomous vehicle), which aims to optimize the lane changing maneuvers of autonomous vehicle while minimizing its impact on surrounding vehicles. In the prediction phase, Cheetah learns the spatio-temporal dynamics from historical trajectories of surrounding vehicles with a deep model (GAS-LED) and predict their corresponding actions in the near future. A global attention mechanism and state sharing strategy are also incorporated to achieve higher accuracy and better convergence efficiency. Then in the search phase, Cheetah looks for optimal lane change maneuvers for the autonomous vehicle by taking into account a few factors such as speed, impact on other vehicles and safety issues. A tree-based adaptive beam search algorithm is designed to reduce the search space and improve accuracy. Extensive experiments on real and synthetic data evidence that the proposed framework excels state-of-the-art competitors with respect to both effectiveness and efficiency.