arXiv AI
Jun 2

DeepIPCv3: Event-Aware Multi-Modal Sensor Fusion for Sudden Pedestrian Crossing Avoidance

arXiv:2606. 01277v1 Announce Type: cross Abstract: Current end-to-end autonomous driving systems predominantly rely on frame-based sensors, which suffer from inherent perception latency and motion blur during highly dynamic encounters, specifically sudden pedestrian crossings.

By Oskar Natan, Andi Dharmawan, Aufaclav Zatu Kusuma Frisky, Jazi Eko Istiyanto, Jun Miura
arXiv Computer Vision
5d ago

FastBEVFusion: Simple Multi-Modal Fusion for 3D Object Detection at 50 FPS

FastBEVFusion is a lightweight fusion system for real‑time 3D object detection that combines LiDAR and camera data without depth estimation. It projects modality‑specific features into semi‑aligned BEV spaces and fuses them with a dynamic cross‑attention module, achieving over 50 FPS on the nuScenes dataset. The method also demonstrates strong robustness to calibration errors.

By Tom Sanitz, Emre G\"ulsoylu, Simone Frintrop, Christian Wilms
arXiv Machine Learning
Jul 28

SimBEV2X: A Large-Scale Dataset and Data Generation Tool for Multi-Task Vehicle-to-Everything Cooperative Perception

arXiv:2607. 23910v1 Announce Type: cross Abstract: Cooperative perception through vehicle-to-everything (V2X) communication can overcome the inherent physical limitations of individual autonomous vehicles, such as occlusions and limited sensor range.

By Goodarz Mehr, Sepideh Gohari, Montasir Abbas, Azim Eskandarian