arXiv:2608. 04453v1 Announce Type: cross Abstract: Online HD map construction is critical to prediction and planning in autonomous driving.
arXiv:2608.28778v1 Announce Type: cross Abstract: Autonomous vehicles (AVs) rely on accurate camera-LiDAR calibration for multimodal sensor fusion. In practice, calibration can drift due to vibration...
arXiv:2609.07511v1 Announce Type: cross Abstract: Autonomous driving relies on High Definition (HD) maps for safe navigation. Traditional HD maps construction is costly in hardware, data and human re...
arXiv:2609.39969v1 Announce Type: cross Abstract: Physical LiDAR attacks are often evaluated using fixed primitives and manually selected parameters, despite their strong dependence on surrounding tr...
FlexMap is a vectorized high‑definition map construction framework that works with flexible camera configurations without needing calibrated rigs or explicit 2D‑to‑BEV transformations. It replaces geometric projection with a geometry foundation model that encodes cross‑view 3D structure, and uses a spatial‑temporal enhancement module and a camera‑aware decoder to separate spatial reasoning from temporal aggregation. Experiments on nuScenes and Argoverse 2 show that FlexMap outperforms pose‑dependent baselines and remains accurate even when camera views are missing or pose estimates are inaccurate.
arXiv:2610. 08339v1 Announce Type: new Abstract: Camera-based 3D perception for autonomous driving relies heavily on large annotated datasets, and deploying such a system to a new target region typically requires data collection and annotation.
The paper presents 3DGAA, a fabrication-first framework that generates view-consistent, geometry-preserving adversarial wraps for vehicles using 3D Gaussian splatting optimization. It ensures consistency across viewpoints, illumination, and occlusion while limiting changes to vehicle geometry, producing realistic print-only textures that significantly reduce detection confidence and average precision in simulations and physical tests. Ablation and efficiency studies analyze the impact of physical filtering, augmentation, and shape-consistency regularization, and the method demonstrates robustness against common preprocessing defenses and cross-detector transferability.
arXiv:2609.21470v1 Announce Type: new Abstract: Sparse representation formulates the environment perception for the end-to-end driving system as a set of discrete elements like objects and lane lines...
The paper introduces WZPlanner, a new dataset and model for safe autonomous driving in work zones. The dataset, WorkZonePlan, contains over 149,000 synthetic and 5,000 real-world samples with 3D annotations for lane and work zone boundaries, plus 228 evaluation routes in CARLA. The proposed BoundaryFormer (BF) and its enhanced BF++ variants jointly predict lane/work‑zone boundaries and driving trajectories, achieving higher Driving Scores while being significantly smaller than competing models.
MapLightning introduces a new approach to online vectorized HD map construction that replaces dense bird’s‑eye‑view grids with a compact set of 1D learnable map tokens. Using a transformer‑based mapper that applies full self‑attention to combined image and map tokens, the method achieves efficient inference, lower memory usage, and improved global context without relying on camera projection parameters. Experiments on nuScenes and Argoverse 2 show state‑of‑the‑art accuracy, with a lightweight variant outperforming MapTRv2 by over 10 mAP and running 1.73× faster at 40+ FPS while using 53 % less memory.
By leveraging data from video-based perception systems, intelligent transportation systems (ITS) support safety-critical applications that improve road safety. However, adversaries may manipulate video frames to compromise downstream perception modules, causing failures in safety-critical functions and increasing risks to vulnerable road users.
The paper studies how Bird's‑Eye‑View (BEV) maps predicted by Cross‑View Transformers (CVT) can be used directly as inputs to a Behavior‑Cloning (BC) driving policy in the CARLA simulator. It introduces a six‑channel BEV representation and a Kernel Density Estimation (KDE) weighting scheme to focus learning on underrepresented maneuvers. Closed‑loop tests show that the KDE‑weighted model is the only predicted‑BEV agent to finish an episode without infractions, highlighting that global segmentation scores are poor proxies for driving performance and that prediction quality at critical geometries, especially the route channel, is key to reliable navigation.
TerrainForge is a framework that generates counterfactual road geometry edits—such as crests, sags, speed humps, and friction changes—by linking a unified road model to four‑wheel vehicle dynamics. It propagates these edits through vehicle motion, camera viewpoint, and inter‑vehicle clearance, and validates the dynamics against CarSim and reconstructed Waymo scenes. The system also simulates thousands of road edits to train a surrogate model that predicts changes in the ego vehicle’s terminal gap, enabling efficient screening of safety‑critical edits.