arXiv:2307. 06647v4 Announce Type: replace-cross Abstract: We propose DeepIPCv2, an end-to-end autonomous driving framework that integrates LiDAR-based environmental perception with command-specific control learning.
By Oskar Natan, Jun Miura
arXiv:2609.09881v1 Announce Type: new
Abstract: Semantic segmentation for autonomous driving requires reliable detection of vulnerable road users (VRUs) despite heavy class imbalance. We introduce CL...
By Toomas Tahves, Mauro Bellone, Raivo Sell
arXiv:2607. 04541v1 Announce Type: cross Abstract: Camera-radar (CR) fusion is a practical sensing configuration for autonomous driving, but existing models are typically trained with task-specific supervision, limiting reusable representation learning.
By Jingyu Song, Yi Liu, Katherine A. Skinner
The paper introduces a multi‑modal traffic sign detection framework that fuses camera and LiDAR data using an Intensity‑Aware Deformable Fusion module to align retro‑reflective LiDAR cues with visual features. It also presents a dual motion‑model tracker to handle non‑linear perspective changes and a semantic attribute classification pipeline that estimates occlusion, readability, sign embeddedness, and road relevance. Evaluated on a dataset covering more than 60 countries and 2,500 hours of driving, the system achieves an Object Miss Ratio of 0.49% across 221,068 sequences, indicating strong global generalization for autonomous driving.
By Meda Lazar, Sourab Sridhar, Shashwata Gupta, Alexandra Tripcea, Varun Ravi, Senthil Yogamani
arXiv:2601. 20720v2 Announce Type: replace-cross Abstract: End-to-end perception and trajectory prediction from raw sensor data is one of the key capabilities for autonomous driving.
By Matej Halinkovic, Nina Masarykova, Alexey Vinel, Marek Galinski
arXiv:2609.06195v1 Announce Type: new
Abstract: This paper proposes a transferable Map of Dynamics (MoD) framework that generalizes to unknown environments using only egocentric 3D LiDAR point clouds...
By Azusa Sawada, Allan Wang, Hideo Saito, Aaron Steinfeld