arXiv:2606. 01325v1 Announce Type: cross Abstract: Unmanned Aerial Vehicles (UAVs) equipped with radar sensors are deployed for target search missions in diverse environments, where targets exhibit characteristic signatures (e.
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:2609.22868v1 Announce Type: new Abstract: End-to-end driving requires planning-relevant bird's-eye-view (BEV) representations, but existing pretraining approaches often rely on task annotations...
arXiv:2609.37125v1 Announce Type: new Abstract: Prospective memory allows an agent to retain an intention tied to a future condition, but the stored intention does not reveal whether that condition c...
arXiv:2608.29567v1 Announce Type: new Abstract: Three-dimensional box-and-track annotation is the cost bottleneck in autonomous-driving data engines, and the offline systems built to relieve it repla...
arXiv:2609.22762v1 Announce Type: new Abstract: Generative world-action models (WAMs) jointly generate future video and vehicle actions, while their action branches remain primarily optimized by expe...
Decision‑FLIP is a budget‑free pruning framework for Gaussian occupancy models that scores each persistent Gaussian row by how many voxel decisions change when the row is removed. It uses a guarded Otsu split on the log‑score distribution to select survivors without a target, then compiles the survivors into a smaller checkpoint, reducing bank size and latency. Across multiple model‑dataset settings, the method removes 12–79% of rows, cuts latency by 7–45%, and saves up to 8.0% of whole‑model compute while keeping semantic mIoU within 0.18 points of the original.
arXiv:2610.19201v1 Announce Type: new Abstract: We present LidarVerse, a real-time unified LiDAR world model for sensor simulation across tasks and sensors. Existing LiDAR simulators largely follow o...
3D object detection is the backbone of perception for automated vehicles (AV) and broader intelligent transportation systems applications. Long-range detection is challenging because sensing evidence is sparse; yet this ``long-range'' scenario is routine in traffic.
RoadOcc is a new method for roadside occupancy prediction that learns to route information among three memory sources: Persist (fixed-coordinate history), Transport (velocity-addressed history), and Refresh (current evidence). It employs dynamic-aware cross‑attention, multi‑scale voxel velocity estimation, and velocity‑guided dynamic sparse fusion to combine these sources efficiently. On the InfraOcc dataset, RoadOcc achieves 65.29 mIoU and 32.37 dynamic mIoU, outperforming the previous STCOcc baseline by significant margins.
arXiv:2608. 07579v1 Announce Type: cross Abstract: The AI City Challenge 2026 Track 1 evaluates multi-camera 3D perception in large indoor warehouses under a synthetic-to-real (Sim2Real) setting; depth is available only for training and validation, so inference is RGB-only.
MC-DeTra is a reimplementation of the DeTra model that jointly performs object detection and socially-aware trajectory forecasting in bird's-eye-view images. It introduces motion-consistency mechanisms that add supervision from each actor’s past motion, surrounding traffic occupancy, and a consistency constraint aligning predicted heading with motion direction. The added losses are train‑only and inference‑safe, improving dynamic trajectory forecasting on the Waymo Open Dataset while maintaining or enhancing detection accuracy.