Egolf, Robin (Technische Hochschule Ingolstadt), Fertig, Alexander (Technische Hochschule Ingolstadt), Botsch, Michael (Technische Hochschule Ingolstadt)

Conditioned Trajectory Generation for Realistic Driving Scenarios Via a Hybrid Machine Learning Architecture

Scheduled for presentation during the Regular Session "S38b-Towards Scalable and Trustworthy AI in Connected Mobility" (FR-EA-T38), Friday, November 21, 2025, 13:30−13:50, Coolangata 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

Simulation is considered a critical component in the validation of autonomous driving systems, particularly for evaluating rare and safety-critical scenarios. While recent trajectory prediction methods have shown strong capabilities in generating realistic future trajectories, these models are typically designed to estimate the most likely outcome and do not support explicit conditioning on scenario parameters. As a result, their use in targeted simulation or scenario design remains limited. To address this, a framework for conditioned trajectory generation is presented, enabling the generation of a set of diverse and physically plausible trajectories from a single initial scenario state, conditioned on interpretable high-level variables such as desired endpoints or average velocity. A hybrid machine learning framework is employed to generate control actions, which are subsequently processed by a kinematic motion model. Expert knowledge is incorporated through soft physical constraints and a smoothness loss, ensuring physical realism and signal continuity. Through experiments, it is demonstrated that realistic and diverse trajectories aligned with scenario context can be produced. The proposed framework can be integrated into simulation environments for validating autonomous vehicles.