The paper introduces a multi-vehicle dataset that includes camera, LiDAR, and radar sensor data along with scanned 3D models of all vehicles. Each vehicle’s pose and continuous kinematics are provided via RTK‑GNSS, enabling precise knowledge of the dynamic surroundings at any time. The dataset supports single‑ and multi‑object recordings with seven target vehicles, allowing evaluation of measurement effects such as occlusion and reflections thanks to known vehicle surface normals.
arXiv:2608.13147v2 Announce Type: replace Abstract: Camera-based autonomous driving perception requires a shared representation that preserves metric 3D structure across synchronized multi-camera str...
arXiv:2609.32013v2 Announce Type: replace-cross Abstract: We present TriO, a multi-modal unsupervised world model that predicts 4D occupancy, obstacle segmentation, flow and LiDAR. In contrast to pri...
DriveOSS is a new real‑world multimodal dataset and benchmark for 3D anomaly segmentation and open‑set semantic segmentation in autonomous driving. It contains 12,661 synchronized frames from six RGB cameras, a 128‑beam LiDAR, and GPS/IMU, covering 52 driving scenes under various weather conditions. The data are split into in‑distribution (ID) scenes for training and out‑of‑distribution (OOD) scenes for validation and testing, with unknown objects labeled as an outlier class to challenge current 3D perception systems.
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.
arXiv:2511.16949v2 Announce Type: replace-cross Abstract: Dense 3D semantic occupancy perception is critical for mobile robots operating in pedestrian-rich environments, yet it remains underexplored...
AutoExpert proposes a new benchmark for automating 3D LiDAR annotation using expert‑crafted guidelines, redefining evaluation protocols and re‑annotating nuScenes and PandaSet datasets. The pipeline combines 2D detection and segmentation in RGB images, lifts these detections into 3D with known sensor poses, and generates 3D cuboids, improving 3D detection mAP from 12.1 to 25.4 on the AutoExpert‑nuScenes benchmark.
The paper introduces an open‑vocabulary 3D object detection pipeline that uses a promptable segmentation model (SAM3) to generate instance masks from six surround‑view cameras. These masks are converted into metric 3D boxes, achieving up to 0.413 mAP/0.555 NDS without any training when supervised box geometry is borrowed at inference. The approach also improves a supervised LiDAR‑only detector by 0.034 mAP through a camera‑witness rule, demonstrating that measurement precision, not 2D detection, limits performance.
arXiv:2607. 19528v1 Announce Type: cross Abstract: Recent advances in Multimodal Large Language Models (MLLMs) have triggered the development of end-to-end MLLMs for autonomous driving.
MLPerf Automotive is the first standardized public performance benchmark for evaluating machine learning systems used in automotive AI acceleration. Developed by MLCommons, it addresses the unique constraints of automotive workloads—such as sensor suites, safety, and real‑time processing—that existing benchmarks cannot handle. The benchmark covers perception tasks (2D/3D object detection, semantic segmentation), end‑to‑end driving, and infotainment, and includes curated models, methodology, and reference implementations available on GitHub.
arXiv:2606. 02979v1 Announce Type: cross Abstract: We present a novel compact deep multi-task learning model to handle various autonomous driving perception tasks in one forward pass.
arXiv:2610.18417v1 Announce Type: cross Abstract: End-to-end deep learning pipelines for camera-radar 3D object detection are well established in driver assistance systems and autonomous vehicles; ho...
TriSAR-3D is a new trimodal dataset combining high‑resolution LiDAR, RGB, and thermal imagery to capture people in varied indoor and outdoor poses, including lying and partially occluded positions. The authors propose PaintFusion, a lightweight trimodal 3D detection pipeline that fuses PointPainting evidence with an MLP, achieving 65.4 AP@50 and outperforming LiDAR‑only, RGB+LiDAR, and other trimodal baselines while running faster (51.6 ms per frame). Experiments show that removing lying instances from training drastically reduces detection performance, highlighting the importance of dataset diversity for robust 3D person detection in search and rescue scenarios.