Motion Planning for Autonomous Driving in Unsignalized Intersections Using Combined Multi-Modal GNN Predictor and MPC Planner

This article presents an interaction-aware motion planning framework that integrates a graph neural network (GNN) based multi-modal trajectory predictor with a model predictive control (MPC) based planner. Unlike past studies that predict a single future trajectory per agent, our algorithm outputs three distinct trajectories for each surrounding road user, capturing different interaction scenarios (e.g., yielding, non-yielding, and aggressive driving behaviors). We design a GNN-based predictor with bi-directional gated recurrent unit (Bi-GRU) encoders for agent histories, VectorNet-based lane encoding for map context, an interaction-aware attention mechanism, and multi-head decoders to predict trajectories for each mode. The MPC-based planner employs a bicycle model and solves a constrained optimal control problem using CasADi and IPOPT (Interior Point OPTimizer). All three predicted trajectories per agent are fed to the planner; the primary prediction is thus enforced as a hard safety constraint, while the alternative trajectories are treated as soft constraints via penalty slack variables. The designed motion planning algorithm is examined in real-world intersection scenarios from the INTERACTION dataset. Results show that the multi-modal trajectory predictor covers possible interaction outcomes, and the planner produces smoother and safer trajectories compared to a single-trajectory baseline. In high-conflict situations, the multi-modal trajectory predictor anticipates potential aggressive behaviors of other drivers, reducing harsh braking and maintaining safe distances. The innovative method by integrating the GNN-based multi-modal trajectory predictor with the MPC-based planner is the backbone of the effective motion planning algorithm for robust, safe, and comfortable autonomous driving in complex intersections.