For what purpose was the dataset created? We created this CityRefer dataset to facilitate research toward city-scale 3D visual grounding. Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., Who funded the creation of the dataset? What do the instances that comprise the dataset represent? CityRefer contains descriptions for 3D visual grounding on large-scale point cloud data.

The world's only dark sky airport sits inside a national park Visitors at Jackson Hole Airport can spot the Milky Way from the parking lot. Breakthroughs, discoveries, and DIY tips sent six days a week. Airports aren't typically known for being the best places to view the night sky. But last spring, the Jackson Hole Airport in Wyoming became the first airport in the world to become certified as an International Dark Sky Place, thanks to a community committed to night sky preservation. Here's how they did it, why it matters, and how it's still as safe to fly into as any other airport (because we know you were wondering).

They're the first fossils found near Dinosaur National Monument in over 100 years. Breakthroughs, discoveries, and DIY tips sent six days a week. For a place named Dinosaur, it's been a while since the small Colorado town revealed any fossils . But after a 101 year lull in discoveries, work was paused on a new parking lot near Dinosaur National Monument, after construction crews uncovered a section of unexpected sandstone. Park staff and paleontologists soon examined the find, and identified sauropod bones most likely belonging to --a massive, long-necked dinosaur from the Late Jurassic period .

We propose an optimization model where a parking lot manager (PLM) can aggregate parked EV batteries to provide virtual energy storage services that are provably robust under uncertain EV departures and state-of-charge caps. Our formulation yields a data-driven convex optimization problem where a prosumer community agrees on a contract with the PLM for the provision of storage services over a finite horizon. Leveraging recent results in the scenario approach, we certify out-of-sample constraint safety. Furthermore, we enable a tunable profit-risk trade-off through scenario relaxation and extend our model to account for robustness to adversarial perturbations and distributional shifts over Wasserstein-based ambiguity sets. All the approaches are accompanied by tight finite-sample certificates. Numerical studies demonstrate the out-of-sample and out-of-distribution constraint satisfaction of our proposed model compared to the developed theoretical guarantees, showing their effectiveness and potential in robust and efficient virtual energy services.

Parking is a critical pillar of driving safety. While recent end-to-end (E2E) approaches have achieved promising in-domain results, robustness under domain shifts (e.g., weather and lighting changes) remains a key challenge. Rather than relying on additional data, in this paper, we propose Dino-Diffusion Parking (DDP), a domain-agnostic autonomous parking pipeline that integrates visual foundation models with diffusion-based planning to enable generalized perception and robust motion planning under distribution shifts. We train our pipeline in CARLA at regular setting and transfer it to more adversarial settings in a zero-shot fashion. Our model consistently achieves a parking success rate above 90% across all tested out-of-distribution (OOD) scenarios, with ablation studies confirming that both the network architecture and algorithmic design significantly enhance cross-domain performance over existing baselines. Furthermore, testing in a 3D Gaussian splatting (3DGS) environment reconstructed from a real-world parking lot demonstrates promising sim-to-real transfer.

For what purpose was the dataset created? We created this CityRefer dataset to facilitate research toward city-scale 3D visual grounding. Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., Who funded the creation of the dataset? What do the instances that comprise the dataset represent? CityRefer contains descriptions for 3D visual grounding on large-scale point cloud data.

Autonomous Valet Parking (AVP) requires planning under partial observability, where parking spot availability evolves as dynamic agents enter and exit spots. Existing approaches either rely only on instantaneous spot availability or make static assumptions, thereby limiting foresight and adaptability. We propose an approach that estimates probability of future spot occupancy by distinguishing initially vacant and occupied spots while leveraging nearby dynamic agent motion. We propose a probabilistic estimator that integrates partial, noisy observations from a limited Field-of-View, with the evolving uncertainty of unobserved spots. Coupled with the estimator, we design a strategy planner that balances goal-directed parking maneuvers with exploratory navigation based on information gain, and incorporates wait-and-go behaviors at promising spots. Through randomized simulations emulating large parking lots, we demonstrate that our framework significantly improves parking efficiency and trajectory smoothness over existing approaches, while maintaining safety margins.

The rapid growth of private car ownership has worsened the urban parking predicament, underscoring the need for accurate and effective parking availability prediction to support urban planning and management. To address key limitations in modeling spatio-temporal dependencies and exploiting multi-source data for parking availability prediction, this study proposes a novel approach with SST-iTransformer. The methodology leverages K-means clustering to establish parking cluster zones (PCZs), extracting and integrating traffic demand characteristics from various transportation modes (i.e., metro, bus, online ride-hailing, and taxi) associated with the targeted parking lots. Upgraded on vanilla iTransformer, SST-iTransformer integrates masking-reconstruction-based pretext tasks for self-supervised spatio-temporal representation learning, and features an innovative dual-branch attention mechanism: Series Attention captures long-term temporal dependencies via patching operations, while Channel Attention models cross-variate interactions through inverted dimensions. Extensive experiments using real-world data from Chengdu, China, demonstrate that SST-iTransformer outperforms baseline deep learning models (including Informer, Autoformer, Crossformer, and iTransformer), achieving state-of-the-art performance with the lowest mean squared error (MSE) and competitive mean absolute error (MAE). Comprehensive ablation studies quantitatively reveal the relative importance of different data sources: incorporating ride-hailing data provides the largest performance gains, followed by taxi, whereas fixed-route transit features (bus/metro) contribute marginally. Spatial correlation analysis further confirms that excluding historical data from correlated parking lots within PCZs leads to substantial performance degradation, underscoring the importance of modeling spatial dependencies.

The fast development of technology and artificial intelligence has significantly advanced Autonomous Vehicle (AV) research, emphasizing the need for extensive simulation testing. Accurate and adaptable maps are critical in AV development, serving as the foundation for localization, path planning, and scenario testing. However, creating simulation-ready maps is often difficult and resource-intensive, especially with simulators like CARLA (CAR Learning to Act). Many existing workflows require significant computational resources or rely on specific simulators, limiting flexibility for developers. This paper presents a custom workflow to streamline map creation for AV development, demonstrated through the generation of a 3D map of a parking lot at Ontario Tech University. Future work will focus on incorporating SLAM technologies, optimizing the workflow for broader simulator compatibility, and exploring more flexible handling of latitude and longitude values to enhance map generation accuracy.

--While traditional autonomous driving methods with multi-stage pipelines suffer from lengthy processes, error accumulations and maintenance difficulties, the end-to-end method is designed to map the data of multiple sensors directly into motion control commands, with high flexibility, efficiency and generalization. Therefore, the end-to-end model has shown great potential in autonomous driving. Due to the low speed, low risk, and low complexity characteristics of autonomous parking scenarios, end-to-end methods can be applied to autonomous parking systems earlier . While prior work introduced a visual-based parking model and a pipeline for data generation, training and closed-loop test, the dataset itself was not released. T o bridge this gap, we work on creating large end-to-end autonomous parking datasets in CARLA based on the prior work'E2E Parking'. Keyboard control is replaced by Handle Controller to improve usability, efficiency, and operational precision. During the iterative process of dataset generation, we evaluate the effect of different factors on the parking performance of the controlled vehicle, including diverse scenes generated by multiple random seeds, the position of the roadside object's shadow dependent on weather setting, dataset size, initial learning rate and training epochs. We recommend generating at least 2 scenes for each parking slot with different random seeds, where 8 trajectories with different initial positions are collected for each scene. Weather settings should be modified to make the dataset include scenes with shadow projected on the target slot. Experiments demonstrate that an initial learning rate of 7. 5 10 After several iterations, we are able to open-source a high-quality dataset for end-to-end autonomous parking.