Selzer, Constantin (University of applied science Munich), Flohr, Fabian (Munich University of Applied Sciences)

DeepUrban: Interaction-Aware Trajectory Prediction and Planning for Automated Driving by Aerial Imagery

Scheduled for presentation during the Regular Session "Traffic prediction and estimation I" (WeAT6), Wednesday, September 25, 2024, 11:50−12:10, Salon 14

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on December 26, 2024

Abstract

The efficacy of autonomous driving systems hinges critically on robust prediction and planning capabilities. However, current benchmarks are impeded by a notable scarcity of scenarios featuring dense traffic, which is essential for understanding and modeling complex interactions among road users. To address this gap, we collaborated with our industrial partner, DeepScenario, to develop ”DeepUrban”—a new drone dataset designed to enhance trajectory prediction and planning benchmarks focusing on dense urban settings. DeepUrban provides a rich collection of 3D traffic objects, extracted from high-resolution images captured over urban intersections at approximately 100 meters altitude. The dataset is further enriched with comprehensive map and scene information to support advanced modeling and simulation tasks. We evaluate state-of-the-art (SOTA) prediction and planning methods, and conducted experiments on generalization capabilities. Our findings demonstrate that adding DeepUrban to nuScenes can boost the accuracy of vehicle predictions and planning, achieving improvements up to 44.1% / 44.3% on the ADE / FDE metrics. Website: https://iv.ee.hm.edu/deepurban