ITSC 2025 Paper Abstract

Paper TH-LM-T26.5

Yu, Hang (Mercedes-Benz AG, Karlsruhe Institute of Technology), Jordan, Julian (Mercedes-Benz AG), Schmidt, Julian (Mercedes-Benz AG, Ulm University), Lindner, Silvan (Mercedes-Benz AG), Canevaro, Alessandro (Mercedes-Benz AG), Stork, Wilhelm (Karlsruhe Institute of Technology)

HYPE: Hybrid Planning with Ego Proposal-Conditioned Predictions

Scheduled for presentation during the Regular Session "S26a-Motion Planning, Trajectory Optimization, and Control for Autonomous Vehicles" (TH-LM-T26), Thursday, November 20, 2025, 11:50−12:10, 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

Keywords Real-time Motion Planning and Control for Autonomous Vehicles in ITS Networks, Autonomous Vehicle Safety and Performance Testing

Abstract

Safe and interpretable motion planning in complex urban environments needs to reason about bidirectional multi-agent interactions. This reasoning requires to estimate the costs of potential ego driving maneuvers. Many existing planners generate initial trajectories with sampling-based methods and refine them by optimizing on learned predictions of future environment states, which requires a cost function that encodes the desired vehicle behavior. Designing such a cost function can be very challenging, especially if a wide range of complex urban scenarios has to be considered. We propose HYPE: HYbrid Planning with Ego proposal-conditioned predictions, a planner that integrates multimodal trajectory proposals from a learned proposal model as heuristic priors into a Monte Carlo Tree Search (MCTS) refinement. To model bidirectional interactions, we introduce an ego-conditioned occupancy prediction model, enabling consistent, scene-aware reasoning. Our design significantly simplifies cost function design in refinement by considering proposal-driven guidance, requiring only minimalistic grid-based cost terms. Evaluations on large-scale real-world benchmarks nuPlan and DeepUrban show that HYPE effectively achieves state-of-the-art performance, especially in safety and adaptability.