[2604.03325] Safety-Aligned 3D Object Detection: Single-Vehicle, Cooperative, and End-to-End Perspectives

Computer Science > Computer Vision and Pattern Recognition arXiv:2604.03325 (cs) [Submitted on 2 Apr 2026] Title:Safety-Aligned 3D Object Detection: Single-Vehicle, Cooperative, and End-to-End Perspectives Authors:Brian Hsuan-Cheng Liao, Chih-Hong Cheng, Hasan Esen, Alois Knoll View a PDF of the paper titled Safety-Aligned 3D Object Detection: Single-Vehicle, Cooperative, and End-to-End Perspectives, by Brian Hsuan-Cheng Liao and 3 other authors View PDF HTML (experimental) Abstract:Perception plays a central role in connected and autonomous vehicles (CAVs), underpinning not only conventional modular driving stacks, but also cooperative perception systems and recent end-to-end driving models. While deep learning has greatly improved perception performance, its statistical nature makes perfect predictions difficult to attain. Meanwhile, standard training objectives and evaluation benchmarks treat all perception errors equally, even though only a subset is safety-critical. In this paper, we investigate safety-aligned evaluation and optimization for 3D object detection that explicitly characterize high-impact errors. Building on our previously proposed safety-oriented metric, NDS-USC, and safety-aware loss function, EC-IoU, we make three contributions. First, we present an expanded study of single-vehicle 3D object detection models across diverse neural network architectures and sensing modalities, showing that gains under standard metrics such as mAP and NDS may not translat...