Steiner, Marlon (Karlsruhe Institute of Technology), Wagner, Royden (KIT), Tas, Omer Sahin (FZI Research Center for Information Technology), Stiller, Christoph (Karlsruhe Institute of Technology)

Prediction-Driven Motion Planning: Route Integration Strategies in Attention-Based Prediction Models

Scheduled for presentation during the Regular Session "S41b-Motion Planning, Trajectory Optimization, and Control for Autonomous Vehicles" (FR-EA-T41), Friday, November 21, 2025, 14:30−14:50, Broadbeach 1&2

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Abstract

Combining motion prediction and motion planning offers a promising framework for enhancing interactions between traffic participants. This introduces challenges in conditioning predictions on navigation goals and ensuring stable, kinematically feasible trajectories. Addressing the former challenge, this paper investigates the extension of attention-based motion prediction models with navigation information. By integrating the ego vehicle’s intended route and goal pose into the model architecture, we bridge the gap between multi-agent motion prediction and goal-based motion planning. We propose and evaluate several architectural navigation integration strategies to our model on the nuPlan dataset. Our results demonstrate the potential of prediction-driven motion planning,highlighting how navigation information can enhance both prediction and planning tasks. Our implementation is available at: https://github.com/KIT-MRT/future-motion.