The safety of automated vehicles (AVs) relies on the representation of their environment. Consequently, state-of-the-art AVs employ potent sensor systems to achieve the best possible environment representation at all times. Although these high-performing systems achieve impressive results, they induce significant requirements for the processing capabilities of an AV's computational hardware components and their energy consumption. To enable a dynamic adaptation of such perception systems based on the situational perception requirements, we introduce a model-agnostic method for the scalable employment of single-frame object detection models using frame-dropping in tracking-by-detection systems. We evaluate our approach on the KITTI 3D Tracking Benchmark, showing that significant energy savings can be achieved at acceptable performance degradation, reaching up to 28% reduction of energy consumption at a performance decline of 6.6% in HOTA score.
@article{arxiv.2304.08152,
title = {The Impact of Frame-Dropping on Performance and Energy Consumption for Multi-Object Tracking},
author = {Matti Henning and Michael Buchholz and Klaus Dietmayer},
journal= {arXiv preprint arXiv:2304.08152},
year = {2023}
}
Comments
V1: Accepted for publication at the IEEE Intelligent Vehicle Symposium, 2023 V2: Added DOI 10.1109/IV55152.2023.10186653