An Interactive Prediction and Planning Method for Lane Change Trajectories

When an autonomous vehicle attempts to change lanes, multiple factors must be considered, such as road conditions, and dynamic interactions with other traffic participants. This paper introduces a novel lane-changing method that interactively combines prediction and planning to cope with the complex traffic scenarios. Firstly, a new target vehicle trajectory prediction network based on the hierarchical attention modules is proposed. The initial predictions are fed into a combined sampling and optimization method for selecting an ego vehicle lane changing maneuver. Unlike previous unidirectional frameworks, the selected maneuver is re-entered into the prediction network so that extra ego vehicle planning information can be incorporated to reduce prediction uncertainties. Finally, based on the planning-informed predictions and the selected maneuver, we design a nonlinear model predictive controller to achieve a safe, efficient and comfortable lane change trajectory in the Frenet coordinate. The root mean square errors of the proposed prediction network at the fifth second on the NGSIM and HighD test sets are 3.54 m and 1.18 m, respectively, which both achieve the state-of-the-art performance. Moreover, the results of real traffic data based simulations and real-vehicle experiments highlight the effectiveness of the lane-changing framework.