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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.

A giant planet nearly 700 light-years away has a bizarre daily weather cycle where mineral clouds appear every morning and vanish by nightfall。 Using the James Webb Space Telescope, astronomers discovered that WASP-94A b’s mornings are filled with clouds made of rock-like minerals, while its evenings are surprisingly clear。 The finding gave scienti

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.

NASA’s PExT terminal has shown that spacecraft can seamlessly communicate through multiple government and commercial networks, a major step beyond traditional single-network systems。 The mission is now expanding to test new capabilities that could help create a more flexible, reliable communications infrastructure for future space missions

Scientists have uncovered unexpected quantum complexity inside cobalt, a metal long thought to be fully understood。 Advanced measurements revealed a dense network of topological electronic states that remain robust at room temperature。 These states enable extremely fast electron behavior and can be switched or controlled using magnetism

Researchers at EPFL have developed a chip-scale ultrafast laser that performs on par with traditional tabletop femtosecond lasers。 The innovation could make advanced laser technologies far smaller, cheaper, and more accessible for applications ranging from medical diagnostics to atomic clocks

NASA’s futuristic X-59 jet is about to face its biggest challenge yet: breaking the sound barrier for the first time。 After a successful series of test flights that pushed the aircraft to near-supersonic speeds, engineers are preparing to fly it faster than Mach 1 and eventually up to Mach 1。6 at 60,000 feet

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

The mysterious Amaterasu particle may not be a proton at all。 New research suggests that some of the most extreme cosmic rays could be ultraheavy atomic nuclei, heavier than iron, which are better able to retain their energy while traveling through space。 This idea could help explain how these rare particles reach Earth and provide new clues about

NASA's James Webb Space Telescope has uncovered unusual chemistry in interstellar comet 3I/ATLAS, including the first direct detection of methane on a visitor from another star system。 The comet also contains exceptionally high levels of carbon dioxide, making it unlike most comets born in our solar system。 Scientists believe the methane was hidden

By stacking custom-designed silver nanoparticles like nanoscale LEGO bricks, scientists stabilized a mysterious crystal phase that had never been observed before。 The material not only solves a longstanding puzzle in materials science but also exhibits promising quantum properties at room temperature

Scientists have proposed a new method for finding tightly bound supermassive black hole pairs by searching for stars that flash repeatedly as their light is magnified by the black holes’ gravity。 The timing and brightness of these bursts could provide a unique fingerprint of black holes slowly spiraling toward a future collision

Starlink, SpaceX's top moneymaker, also raised service prices by $5 to $10