--In Global Navigation Satellite System (GNSS)- denied environments such as indoor parking structures or dense urban canyons, achieving accurate and robust vehicle positioning remains a significant challenge. This paper proposes a cost-effective, vision-based multi-sensor navigation system that integrates monocular depth estimation, semantic filtering, and visual map registration (VMR) with 3-D digital maps. Extensive testing in real-world indoor and outdoor driving scenarios demonstrates the effectiveness of the proposed system, achieving sub-meter accuracy 92% indoors and more than 80% outdoors, with consistent horizontal positioning and heading average root mean-square errors of approximately 0.98 m and 1.25 Compared to the baselines examined, the proposed solution significantly reduced drift and improved robustness under various conditions, achieving positioning accuracy improvements of approximately 88% on average. This work highlights the potential of cost-effective monocular vision systems combined with 3D maps for scalable, GNSS-independent navigation in land vehicles. OSITIONING is a cornerstone of autonomous driving, enabling vehicles to plan, control, and make decisions [1]. While global navigation satellite system (GNSS) technologies provide high accuracy positioning capabilities in open-sky environments [2], they become unreliable or even denied in environments such as dense urban areas, tunnels, and underground parking [3]. To compensate for GNSS limitations, some approaches employ high-resolution light detection and ranging (LiDAR)-based positioning systems [4] or integrate high-grade inertial navigation system (INS) [5]. Although these solutions can provide accurate and reliable positioning, their high cost hinders their practicality for consumer-level deployment. In contrast, cameras offer a cost-effective, lightweight, and widely available sensing modality. This research is supported by grants from the Natural Sciences and Engineering Research Council of Canada (NSERC) under grant numbers: RGPIN-2020-03900 and ALLRP-560898-20. Ola Elmaghraby and Paulo de Araujo are with the Department of Electrical and Computer Engineering, Queen's University, Kingston, ON K7L 3N6, Canada (e-mail: ola.elmaghraby.a@queensu.ca;

Abstract--Autonomous vehicles need to travel over 11 billion miles to ensure their safety. Therefore, the importance of simulation testing before real-world testing is self-evident. In recent years, the release of 3D simulators for autonomous driving, represented by Carla and CarSim, marks the transition of autonomous driving simulation testing environments from simple 2D overhead views to complex 3D models. During simulation testing, experimenters need to build static scenes and dynamic traffic flows, pedestrian flows, and other experimental elements to construct experimental scenarios. When building static scenes in 3D simulators, experimenters often need to manually construct 3D models, set parameters and attributes, which is time-consuming and labor-intensive. This thesis proposes an automated program generation framework. The generated 3D ground scenes are displayed in the Carla simulator, where experimenters can use this scene for navigation algorithm simulation testing. However, experiments have shown that autonomous vehicles need to travel over 11 billion miles to ensure their safety [2]. In practical use and testing, traffic accidents caused by autonomous A. Background The Self-Driving System, also known as the Autonomous As one of the most critical quality assurance technologies, Driving System (ADS), is a comprehensive integration of ADS testing has garnered attention from both academia and hardware and software designed to autonomously manage industry [3]. Nonetheless, due to the numerous components motion control based on its perception and understanding of and high complexity of ADS, testing faces many challenges. Naturalistic Field Operational Testing (N-FOT) to simulationbased Perception, decision-making, and control constitute the three testing [4], also known as simulation testing.

With the rapid advancement of autonomous driving technology, self-driving cars have become a central focus in the development of future transportation systems. Scenario generation technology has emerged as a crucial tool for testing and verifying the safety performance of autonomous driving systems. Current research in scenario generation primarily focuses on open roads such as highways, with relatively limited studies on underground parking garages. The unique structural constraints, insufficient lighting, and high-density obstacles in underground parking garages impose greater demands on the perception systems, which are critical to autonomous driving technology. This study proposes an accelerated generation method for perception failure scenarios tailored to the underground parking garage environment, aimed at testing and improving the safety performance of autonomous vehicle (AV) perception algorithms in such settings. The method presented in this paper generates an intelligent testing environment with a high density of perception failure scenarios by learning the interactions between background vehicles (BVs) and autonomous vehicles (AVs) within perception failure scenarios. Furthermore, this method edits the Markov process within the perception failure scenario data to increase the density of critical information in the training data, thereby optimizing the learning and generation of perception failure scenarios. A simulation environment for an underground parking garage was developed using the Carla and Vissim platforms, with Bevfusion employed as the perception algorithm for testing. The study demonstrates that this method can generate an intelligent testing environment with a high density of perception failure scenarios and enhance the safety performance of perception algorithms within this experimental setup.

Environmental perception in Automated Valet Parking (AVP) has been a challenging task due to severe occlusions in parking garages. Although Collaborative Perception (CP) can be applied to broaden the field of view of connected vehicles, the limited bandwidth of vehicular communications restricts its application. In this work, we propose a BEV feature-based CP network architecture for infrastructure-assisted AVP systems. The model takes the roadside camera and LiDAR as optional inputs and adaptively fuses them with onboard sensors in a unified BEV representation. Autoencoder and downsampling are applied for channel-wise and spatial-wise dimension reduction, while sparsification and quantization further compress the feature map with little loss in data precision. Combining these techniques, the size of a BEV feature map is effectively compressed to fit in the feasible data rate of the NR-V2X network. With the synthetic AVP dataset, we observe that CP can effectively increase perception performance, especially for pedestrians. Moreover, the advantage of infrastructure-assisted CP is demonstrated in two typical safety-critical scenarios in the AVP setting, increasing the maximum safe cruising speed by up to 3m/s in both scenarios.

Rescue officials arrive at the scene to assess the damage of Tuesday afternoon's dramatic collapse. The New York City street where a parking garage collapsed, killing one person and resulting in five others pulled from the structure, remained closed a day later Wednesday, as investigators have yet to disclose the suspected cause behind the building reportedly with four active violations suddenly caving in by Lower Manhattan's Financial District. At a press conference Tuesday, NYC Department of Buildings Acting Commissioner Kazimir Vilenchik described how drone footage showed how the four-story building on Ann Street, between Nassau Street and William Street, "all the way pancaked, collapsed all the way to the cellar floor." He acknowledged that an active violation on the building dated to 2003. The buildings commissioner said an application was filed in 2010 but did not indicate whether the violation was corrected.

What will it take for a car to be able to drive itself anywhere a human can? Ask autonomous vehicle experts this question and the answer invariably includes a discussion of geofencing. In the broadest sense, geofencing is simply a virtual boundary around a physical area. In the world of self-driving cars, it describes a crucial subset of the operational design domain -- the geographic region where the vehicle is functional. Reaching full Level 5 autonomy means removing the "fence" from geofenced autonomous cars. Experts say that will require artificial intelligence that can make abstractions, inferences, and become smarter as it is being used.

Well, the battery won't allow you to drive for a million miles without recharging, but it will last for a million miles before it must be replaced. This is a big step forward considering EV batteries typically last 200,000 miles. With a million-mile battery, the car would fall apart long before the battery goes dead. This also means the owner can sell it or transfer it to a new car, resulting in less pollution and waste. The brains at Huawei are working on a solution.

New York (CNN Business)Talk of a possible Apple car is back. Apple (AAPL) hasn't commented publicly on its plans for the project, nicknamed Titan, so it's not clear exactly what will come of the effort. Some who follow the company think it could release a whole Apple-branded, electric, self-driving car. Others think it's more likely Apple will partner with existing automakers to sell an operating system (iDrive, maybe?), self-driving tools or other technology. There are some clues available, though.

The trial project is being led by U.K.-based Fetch.ai and Munich-based blockchain company Datarella and was just launched at one of the central Munich offices owned by Connex Buildings. The goal is to control the pricing and use of the building's parking spaces dynamically, and to disincentivize people from driving to work by rewarding them with public transit passes for all the time they aren't using the parking garage. "It could say okay if you park closer, you're going to be charged more; if you park farther away, you'll be charged less," says Humayun Sheikh, CEO of Fetch.ai. "We reward you for doing certain actions and we discourage you from doing certain actions." Sheikh says that if the trial program is expanded to parking garages citywide, it could cut car usage by 10% annually, resulting in a reduction of more than 37,000 tons of CO2 emissions, which is equivalent to the emissions from the annual energy use of nearly 4,000 homes.

Artificial Intelligence is probably the greatest technological achievement in history. It's subjective, of course, but I've been obsessed with technology ever since I was a kid, and while I'm sure there are many technical advances I am unaware of, AI, or machine learning, surpasses them all. An exception could perhaps be made for the invention of language or perhaps the wheel, however I doubt few other things will have such a profound effect on the human species. AI has been a stable component of contemporary culture for multiple generations now. From time traveling machines hell-bent on destroying humanity to friendly and lovable robots adventuring through space, we've anthropomorphized machines in preparation for what we all know is coming.