Traffic microsimulators rely on hand-crafted behavior models that reproduce aggregate flow but miss the heterogeneous interactions between vehicles at signalized intersections. Learned trajectory predictors capture richer interactions but are short-horizon and tend to be unstable when run in closed loop.
arXiv:2611.06007v1 Announce Type: new
Abstract: Motorized Two-Wheelers (MTWs) dominate personal transport across much of the Global South, yet remain largely overlooked in predictive perception resea...
By Varun A. Paturkar, Shankar Gangisetty, C. V. Jawahar
arXiv:2602. 23499v4 Announce Type: replace-cross Abstract: Collecting a high-quality dataset is a critical task that demands meticulous attention to detail, as overlooking certain aspects can render the entire dataset unusable.
By Tugrul Gorgulu, Atakan Dag, M. Esat Kalfaoglu, Halil Ibrahim Kuru, Baris Can Cam, Halil Ibrahim Ozturk, Ozsel Kilinc
arXiv:2606. 18824v1 Announce Type: cross Abstract: Pedestrian trajectory prediction from an ego-centric camera is challenging since it depends on complex interactions with vehicles and scene context, as well as the intention of the pedestrian.
By Yuxuan Xie, Nicolas Pugeault, Chongfeng Wei, Hubert P. H. Shum, Edmond S. L. Ho
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.
By Nishad Sahu (Raj), Changzhong Qian (Raj), Guangzhou Cai (Raj), Shounak Sural (Raj), Ragunathan (Raj), Rajkumar
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.
By Yang Chen, Yicheng Zhu, zhenning Li, Tao Li, Zilin Bian