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Although semantic 3D city models are internationally available and becoming increasingly detailed, the incorporation of material information remains largely untapped. However, a structured representation of materials and their physical properties could substantially broaden the application spectrum and analytical capabilities for urban digital twins. At the same time, the growing number of repeated mobile laser scans of cities and their street spaces yields a wealth of observations influenced by the material characteristics of the corresponding surfaces. To leverage this information, we propose radiometric fingerprints of object surfaces by grouping LiDAR observations reflected from the same semantic object under varying distances, incident angles, environmental conditions, sensors, and scanning campaigns. Our study demonstrates how 312.4 million individual beams acquired across four campaigns using five LiDAR sensors on the Audi Autonomous Driving Dataset (A2D2) vehicle can be automatically associated with 6368 individual objects of the semantic 3D city model. The model comprises a comprehensive and semantic representation of four inner-city streets at Level of Detail (LOD) 3 with

UWB ranging systems have been adopted in many critical and security sensitive applications due to its precise positioning and secure ranging capabilities. We present a practical jamming attack, namely UWBAD, against commercial UWB ranging systems, which exploits the vulnerability of the adoption of the normalized cross-correlation process in UWB ranging and can selectively and quickly block ranging sessions without prior knowledge of the configurations of the victim devices, potentially leading to severe consequences such as property loss, unauthorized access, or vehicle theft. UWBAD achieves more effective and less imperceptible jamming due to: (i) it efficiently blocks every ranging session by leveraging the field-level jamming, thereby exerting a tangible impact on commercial UWB ranging systems, and (ii) the compact, reactive, and selective system design based on COTS UWB chips, making it affordable and less imperceptible. We successfully conducted real attacks against commercial UWB ranging systems from the three largest UWB chip vendors on the market, e.g., Apple, NXP, and Qorvo. We reported our findings to Apple, related Original Equipment Manufacturers (OEM), and the Automo

Effective leveraging of real-world driving datasets is crucial for enhancing the training of autonomous driving systems. While Offline Reinforcement Learning enables training autonomous vehicles with such data, most available datasets lack meaningful reward labels. Reward labeling is essential as it provides feedback for the learning algorithm to distinguish between desirable and undesirable behaviors, thereby improving policy performance. This paper presents a novel approach for generating human-aligned reward labels. The proposed approach addresses the challenge of absent reward signals in the real-world datasets by generating labels that reflect human judgment and safety considerations. The reward function incorporates an adaptive safety component that is activated by analyzing semantic segmentation maps, enabling the autonomous vehicle to prioritize safety over efficiency in potential collision scenarios. The proposed method is applied to an occluded pedestrian crossing scenario with varying pedestrian traffic levels, using simulation data. When the generated rewards were used to train various Offline Reinforcement Learning algorithms, each model produced a meaningful policy, d

This study investigates the aerodynamic performance of an Audi A4 sedan using CFD analysis. A 3D model was developed in SolidWorks and validated against DrivAer Notchback wind-tunnel data, showing only a 3.25 percent deviation in drag coefficient (Cd). Ride height varied from 1.336 to 1.536 m and rake angle from 0 to 5 degrees, across four Reynolds numbers. Gradient Boosting emerged as the most accurate predictive model (R square = 0.97 for Cd and 0.96 for lift coefficient, Cl), outperforming Random Forest and LightGBM. Differential Evolution optimization was performed under balanced, drag-focused, and downforce-focused conditions. Reynolds number had minimal impact on optimum location; therefore, detailed results are reported for one Reynolds number, with other Re showing similar trends. The baseline geometry exhibited Cd = 0.313 and Cl = 0.0288. Balanced optimization achieved Cd = 0.287 and Cl = - 0.0826. Minimum drag condition reached Cd = 0.285 with slight positive lift (Cl = 0.0142), while maximum downforce optimization reached Cl = - 0.1084 with a 6.71 percent drag penalty (Cd = 0.334). Near-optimal solutions were found within ride heights of 1.341 to 1.365 m and rakes of 0.1

Autonomous driving (AD) relies heavily on high precision localization as a crucial part of all driving related software components. The precise positioning is necessary for the utilization of high-definition maps, prediction of other road participants and the controlling of the vehicle itself. Due to this reason, the localization is absolutely safety relevant. Typical errors of the localization systems, which are long term drifts, jumps and false localization, that must be detected to enhance safety. An online assessment and evaluation of the current localization performance is a challenging task, which is usually done by Kalman filtering for single localization systems. Current autonomous vehicles cope with these challenges by fusing multiple individual localization methods into an overall state estimation. Such approaches need expert knowledge for a competitive performance in challenging environments. This expert knowledge is based on the trust and the prioritization of distinct localization methods in respect to the current situation and environment. This work presents a novel online performance assessment technique of multiple localization systems by using subjective logic (SL)

Autonomous vehicles are growing rapidly, in well-developed nations like America, Europe, and China. Tech giants like Google, Tesla, Audi, BMW, and Mercedes are building highly efficient self-driving vehicles. However, the technology is still not mainstream for developing nations like India, Thailand, Africa, etc., In this paper, we present a thorough comparison of the existing datasets based on well-developed nations as well as Indian roads. We then developed a new dataset "Indian Roads Dataset" (IRD) having more than 8000 annotations extracted from 3000+ images shot using a 64 (megapixel) camera. All the annotations are manually labelled adhering to the strict rules of annotations. Real-time video sequences have been captured from two different cities in India namely New Delhi and Chandigarh during the day and night-light conditions. Our dataset exceeds previous Indian traffic light datasets in size, annotations, and variance. We prove the amelioration of our dataset by providing an extensive comparison with existing Indian datasets. Various dataset criteria like size, capturing device, a number of cities, and variations of traffic light orientations are considered. The dataset ca

Virtual testing is a crucial task to ensure safety in autonomous driving, and sensor simulation is an important task in this domain. Most current LiDAR simulations are very simplistic and are mainly used to perform initial tests, while the majority of insights are gathered on the road. In this paper, we propose a lightweight approach for more realistic LiDAR simulation that learns a real sensor's behavior from test drive data and transforms this to the virtual domain. The central idea is to cast the simulation into an image-to-image translation problem. We train our pix2pix based architecture on two real world data sets, namely the popular KITTI data set and the Audi Autonomous Driving Dataset which provide both, RGB and LiDAR images. We apply this network on synthetic renderings and show that it generalizes sufficiently from real images to simulated images. This strategy enables to skip the sensor-specific, expensive and complex LiDAR physics simulation in our synthetic world and avoids oversimplification and a large domain-gap through the clean synthetic environment.

In emerging automotive cyber-physical systems (CPS), accurate environmental perception is critical to achieving safety and performance goals. Enabling robust perception for vehicles requires solving multiple complex problems related to sensor selection/ placement, object detection, and sensor fusion. Current methods address these problems in isolation, which leads to inefficient solutions. We present PASTA, a novel framework for global co-optimization of deep learning and sensing for dependable vehicle perception. Experimental results with the Audi-TT and BMW-Minicooper vehicles show how PASTA can find robust, vehicle-specific perception architecture solutions.

Researchers have developed a compact quantum detector that makes terahertz radiation much easier to detect。 A specially designed metasurface funnels incoming energy into tiny active regions, greatly strengthening the electrical signal produced。 The approach boosted efficiency by roughly 20 times compared to earlier designs and could pave the way fo

Mystery of GPS interference across Europe raises questions about Russian motives

Scientists say moons around rogue planets wandering through the galaxy could remain warm enough for life thanks to tidal heating and hydrogen-rich atmospheres。 These dark, starless worlds may have had stable oceans for billions of years — long enough for complex life to potentially emerge

Scientists used nanoscale gold metamaterials to supercharge heat transfer across tiny gaps, achieving up to four times more energy flow than similar conventional systems。 The breakthrough could lead to better chip cooling, more efficient energy technologies, and a new era of precision heat engineering

A team at the University of Minnesota discovered that changing a metal film's thickness by just a few nanometers can dramatically alter how it behaves electronically。 The finding reveals a surprising new way to control metals and could help power future advances in electronics, catalysis, and quantum technology

Scientists have created a tiny chip that can generate, steer, and read light-based information all in one device, marking a major leap toward ultra-fast, energy-efficient computing。 The breakthrough uses atomically thin materials and nanoscale structures to control a unique quantum property of light called the “valley” degree of freedom, allowing i

A new room-temperature quantum device uses twisted light to entangle photons and electrons, overcoming one of the biggest hurdles in quantum technology。 The breakthrough could pave the way for smaller, cheaper quantum systems with applications ranging from secure communications to future AI and computing platforms

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You'll need an M1 or better to run the next release of macOS

June's night sky delivers several must-see events, starting with a close encounter between Venus and Jupiter after sunset。 Mercury joins the pair to form a rare three-planet lineup, while the Moon puts on a special show by passing in front of Venus for viewers in parts of the Americas。 The month also marks the start of astronomical summer and the r

US Navy’s Task Force 59 achieved the drone rescue at sea near Strait of Hormuz