Search Results (170)

Modern vehicles are rapidly transforming into interconnected cyber–physical systems that rely on advanced sensor technologies and pervasive connectivity to support autonomous functionality. Yet, despite this evolution, standardized methods for quantifying cybersecurity vulnerabilities across critical automotive components remain scarce. This paper introduces a novel hybrid model that integrates expert-driven insights with generative AI tools to adapt and extend the Common Vulnerability Scoring System (CVSS) specifically for autonomous vehicle sensor systems. Following a three-phase methodology, the study conducted a systematic review of 16 peer-reviewed sources (2018–2024), applied CVSS version 4.0 scoring to 15 representative attack types, and evaluated four free source generative AI models—ChatGPT, DeepSeek, Gemini, and Copilot—on a dataset of 117 annotated automotive-related vulnerabilities. Expert validation from 10 domain professionals reveals that Light Detection and Ranging (LiDAR) sensors are the most vulnerable (9 distinct attack types), followed by Radio Detection And Ranging (radar) (8) and ultrasonic (6). Network-based attacks dominate (104 of 117 cases), with 92.3% of the dataset exhibiting low attack complexity and 82.9% requiring no user interaction. The most severe attack vectors, as scored by experts using CVSS, include eavesdropping (7.19), Sybil attacks (6.76), and replay attacks (6.35). Evaluation of large language models (LLMs) showed that DeepSeek achieved an F1 score of 99.07% on network-based attacks, while all models struggled with minority classes such as high complexity (e.g., ChatGPT F1 = 0%, Gemini F1 = 15.38%). The findings highlight the potential of integrating expert insight with AI efficiency to deliver more scalable and accurate vulnerability assessments for modern vehicular systems.This study offers actionable insights for vehicle manufacturers and cybersecurity practitioners, aiming to inform strategic efforts to fortify sensor integrity, optimize network resilience, and ultimately enhance the cybersecurity posture of next-generation autonomous vehicles. Full article

Traditional 3D millimeter-wave radars lack target height information, leading to identification failures in complex scenarios. Upgrading to 4D millimeter-wave radars enables four-dimensional information perception, enhancing obstacle detection and improving the safety of autonomous driving. Given the high cost-sensitivity of in-vehicle radar systems, single-chip 4D millimeter-wave radar solutions, despite technical challenges in imaging, are of great research value. This study focuses on developing single-chip 4D automotive millimeter-wave radar, covering system architecture design, antenna optimization, signal processing algorithm creation, and performance validation. The maximum measurement error is approximately ±0.2° for azimuth angles within the range of ±30° and around ±0.4° for elevation angles within the range of ±13°. Extensive road testing has demonstrated that the designed radar is capable of reliably measuring dynamic targets such as vehicles, pedestrians, and bicycles, while also accurately detecting static infrastructure like overpasses and traffic signs. Full article

Millimeter-wave frequencies are crucial for meeting the high-capacity, low-latency demands of 5G communication systems, thereby driving the need for compact, high-gain antenna arrays capable of efficient beamforming. This paper presents the design, simulation, fabrication, and experimental validation of a compact, high-efficiency 1 × 6 linear series-fed microstrip patch antenna array for 5G millimeter-wave communication operating at 28 GHz. The proposed antenna is fabricated on a low-loss Rogers RO3003 substrate and incorporates an integrated symmetric two-way microstrip power divider to ensure balanced feeding and phase uniformity across elements. The antenna achieves a simulated peak gain of 11.5 dBi and a broad simulated impedance bandwidth of 30.21%, with measured results confirming strong impedance matching and a return loss better than −20 dB. The far-field radiation patterns demonstrate a narrow, highly directive beam in the E-plane, and the H-plane results reveal beam tilting behavior, validating the antenna’s capability for passive beam steering through feedline geometry and element spacing (~0.5λ). Surface current distribution analysis confirms uniform excitation and efficient radiation, further validating the design’s stability. The fabricated prototype shows excellent agreement with the simulation, with minor discrepancies attributed to fabrication tolerances. These results establish the proposed antenna as a promising candidate for applications requiring compact, high-gain, and beam-steerable solutions, such as 5G mm-wave wireless communication systems, point-to-point wireless backhaul, and automotive radar sensing. Full article

The signal-processing architecture of passive surface acoustic wave (SAW) sensors presents significant implementation challenges due to its radar-like operational principle and the inherent complexity of discrete component-based hardware design. While System-in-Package (SiP) has demonstrated remarkable success in miniaturizing electronic systems for smartphones, automotive electronics, and IoT applications, its potential for revolutionizing SAW sensor interrogator design remains underexplored. This paper presents a novel architecture that synergistically combines time-domain ADC design with SiP-based miniaturization to achieve unprecedented simplification of SAW sensor readout systems. The proposed time-domain ADC incorporates an innovative delay chain calibration methodology that integrates physical unclonable function (PUF) principles during time-to-digital converter (TDC) characterization, enabling the simultaneous generation of unique system IDs. The experimental results demonstrate that the integrated security mechanism provides variable-length bit entropy for device authentication, and has a reliability of 97.56 and uniqueness of 49.43, with 53.28 uniformity, effectively addressing vulnerability concerns in distributed sensor networks. The proposed SiP is especially suitable for space-constrained IoT applications requiring robust physical-layer security. This work advances the state-of-the-art wireless sensor interfaces by demonstrating how time-domain signal processing and advanced packaging technologies can be co-optimized to address performance and security challenges in next-generation sensor systems. Full article

In this work, the mechanical properties of composites with a polymer matrix and reinforced with carbon particles have been studied. It has been established that the obtained engineering materials have increased elastic and plastic characteristics. The thermosetting polymers used are epoxy, polyester, and vinylester resins. The carbon particles are carbon nanotubes and waste carbon from the plasma decomposition of methane in the production of green hydrogen. The carbon particles used are in an amount of 1 wt% and 2 wt% of the weight of the composite, and they are not subjected to pre-treatment (modification). The studied composites are used in shipping, automotive, and aviation technology, and the presence of carbon particles in them is a prerequisite for improving their anti-radar properties. Full article

In the automotive industry, the implementation of Process Failure Mode and Effect Analysis (PFMEA) is conducted by a PFMEA team comprising employees who are connected to the production process or a specific product. Core PFMEA team members are actively engaged in PFMEA execution through meetings, analysis, and the implementation of corrective actions. Although the current handbook provides guidelines on the potential composition of the PFMEA team, it does not strictly define its members, allowing companies the flexibility to determine the team structure independently. This study aims to identify the core PFMEA team members by adhering to criteria based on the recommended knowledge and competencies outlined in the current handbook. By applying the RAnking based on the Distances and Range (RADAR) approach, extended with Interval-Valued Pythagorean Fuzzy Numbers (IVPFNs), a ranking of potential candidates was conducted. A case study was performed in a Tier-1 supplier company within the automotive supply chain. Full article

This study develops an applied optimization method to address practical challenges in Laser Radar station planning for automotive Body-In-White (BIW) manufacturing inspection. Focusing on the spatially constrained industrial environments and complex measurement specifications, the work reformulates Laser Radar inspection planning as a multi-constrained optimization problem challenge. Firstly, a parametric geometric modeling approach is developed to define measurement spaces for individual features, accompanied by an innovative maximal complete subgraph mining algorithm to intelligently identify shared feasible measurement regions among multiple features. Secondly, kinematic equations are formulated using Denavit–Hartenberg (D-H) parameters, while a hierarchical bounding volume collision detection mechanism is integrated to establish a comprehensive constraint. Therefore, unified optimization method synergizing measurement coverage, robotic manipulator reachability, and operational safety requirements are proposed. Through experimental validations utilizing BIW (BIW) component inspection, the research has demonstrated its industrial applicability and has achieved a 92% measurement coverage with robot trajectories free of collisions. Compared with traditional manual planning methods, the proposed approach reduces the number of required inspection stations by 35% and improves the computational efficiency to meet industrial real-time deployment requirements. Experimental validation demonstrates the method’s effectiveness in measurement accuracy, operational safety, and equipment utilization for advanced manufacturing quality control systems. Full article

Mono RGB cameras and automotive radar sensors provide a complementary information set that makes them excellent candidates for sensor data fusion to obtain robust traffic user detection. This has been widely used in the vehicle domain and recently introduced in roadside-mounted smart infrastructure-based road user detection. However, the performance of the most commonly used late fusion methods often degrades when the camera fails to detect road users in adverse environmental conditions. The solution is to fuse the data using deep neural networks at the early stage of the fusion pipeline to use the complete data provided by both sensors. Research has been carried out in this area, but is limited to vehicle-based sensor setups. Hence, this work proposes a novel deep neural network to jointly fuse RGB mono-camera images and 3D automotive radar point cloud data to obtain enhanced traffic user detection for the roadside-mounted smart infrastructure setup. Projected radar points are first used to generate anchors in image regions with a high likelihood of road users, including areas not visible to the camera. These anchors guide the prediction of 2D bounding boxes, object categories, and confidence scores. Valid detections are then used to segment radar points by instance, and the results are post-processed to produce final road user detections in the ground plane. The trained model is evaluated for different light and weather conditions using ground truth data from a lidar sensor. It provides a precision of 92%, recall of 78%, and F1-score of 85%. The proposed deep fusion methodology has 33%, 6%, and 21% absolute improvement in precision, recall, and F1-score, respectively, compared to object-level spatial fusion output. Full article

A virtual model enables the study of reality in a virtual environment using a theoretical model, which is a digital image of a real model. The complexity of the virtual model must correspond to the reality of the evaluated system, becoming as complex as necessary and nevertheless as simple as possible, allowing for computer simulation results to be validated by experimental measurements. The virtual model of the autonomous vehicle was created using the CarMaker software package version 12.0, which was developed by the IPG Automotive company and is extensively used in both the international academic community and the automotive industry. The virtual model simulates the real-time operation of a vehicle’s elementary systems at the system level and provides an open platform for the development of virtual test scenarios in the application areas of autonomous vehicles, ADAS, Powertrain, and vehicle dynamics. This model included the following virtual sensors: slip angle sensor, inertial sensor, object sensor, free space sensor, traffic sign sensor, line sensor, road sensor, object-by-line sensor, camera sensor, global navigation sensor, radar sensor, lidar sensor, and ultrasonic sensor. Virtual sensors can be classified based on how they generate responses: sensors that operate on parameters derived from measurement characteristics, sensors that operate on developed modeling methods, and sensors that operate on applications. Full article

This paper proposes a compact 2 × 2 on-chip microstrip antenna array operating for W-band applications. The design utilizes a low-loss glass substrate to mitigate dielectric losses and integrates an embedded feeding structure with wideband T-junction power dividers, addressing bandwidth limitations and feed network losses in conventional approaches. Experimental results demonstrate a relative bandwidth of 10.1% (76.11–83.87 GHz) with gain exceeding 10 dBi across the bandwidth, closely aligning with simulated predictions. This work provides a cost-effective solution for millimeter-wave and terahertz antenna systems, balancing high-performance requirements with fabrication simplicity for automotive radar and 5G/6G communication applications. Full article

Millimeter-wave antennas have become more important recently due to the diversity of applications in 5G and upcoming 6G technologies, of which automotive systems constitute a significant part. Two crucial indices, detection range and angular resolution, are used to distinguish the performance of the automotive antenna. Strong gains and narrow beamwidths of highly directive radiation beams afford longer detection range and finer spatial selectivity. Although conventionally used, patch antennas suffer from intrinsic path losses that are much higher when compared to the waveguide antenna. Designed at 77 GHz, presented in this article is an 8-element slot array on the narrow side wall of a rectangular waveguide, thus being readily extendable to planar arrays by adding others alongside while maintaining the element spacing requirement for grating lobe avoidance. Comprising tilted Z-shaped slots for higher gain while keeping constrained within the narrow wall, adjacent ones separated by half the guided wavelength are inclined with reversed tilt angles for cross-polar cancelation. An open-ended external waveguide is placed over each slot for polarization purification. Equivalent circuit models of slotted waveguides aid the design. An approach for sidelobe suppression using the Chebyshev distribution is adopted. Four types of arrays are proposed, all of which show potential for different demands and applications in automotive radar. Prototypes based on designs by simulations were fabricated and measured. Full article

In recent years, synthetic aperture radar (SAR) technology has been increasingly explored for automotive applications. However, automotive SAR images generated via matched filter (MF) often exhibit challenges such as noisy backgrounds, sidelobe artifacts, and limited resolution. Sparse regularization methods have the potential to enhance image quality. Nevertheless, conventional unweighted ${\mathcal{l}}_1$ regularization methods struggle to address cases with radar cross section (RCS) distributed over a wide dynamic range, often resulting in insufficient sidelobe suppression, amplitude distortion, and inconsistent super-resolution performance. In this paper, we propose a novel reweighted regularization method, termed multi-segment-reweighted regularization (MSR), for automotive SAR image restoration. By introducing a novel weighting scheme, MSR localizes the global scattering point enhancement problem to the mainlobe scale, effectively mitigating sidelobe interference. This localization ensures consistent enhancement capability independent of RCS variations. Furthermore, MSR employs multi-segment regularization to constrain amplitude within the mainlobes, preserving the characteristics of the original response. Correspondingly, a new thresholding function, named Thinner Response Undistorted THresholding (TRUTH), is introduced. An iterative algorithm for enhancing automotive SAR images using MSR is also presented. Real data experiments validate the feasibility and effectiveness of the proposed method. Full article

Object detection is a critical task in autonomous driving. Currently, 3D object detection methods for autonomous driving primarily rely on stereo cameras and LiDAR, which are susceptible to adverse weather conditions and low lighting, resulting in limited robustness. In contrast, automotive mmWave radar offers advantages such as resilience to complex weather, independence from lighting conditions, and a low cost, making it a widely studied sensor type. Modern 4D millimeter-wave (mmWave) radar can provide spatial dimensions (x, y, z) as well as Doppler information, meeting the requirements for 3D object detection. However, the point cloud density of 4D mmWave radar is significantly lower than that of LiDAR in the case of short distances, and existing point cloud object detection methods struggle to adapt to such sparse data. To address this challenge, we propose a novel 4D mmWave radar point cloud object detection framework. First, we employ moving least squares (MLS) to densify multi-frame fused point clouds, effectively increasing the point cloud density. Next, we construct a 3D object detection network based on point pillar encoding and utilize an SSD detection head for detection on feature maps. Finally, we validate our method on the VoD dataset. Experimental results demonstrate that our proposed framework outperforms comparative methods, and the MLS-based point cloud densification method significantly enhances the object detection performance. Full article

Autonomous driving technologies for environmental perception are mostly based on visual cues obtained from sensors like cameras, RADAR, or LiDAR. They capture the environment as if seen through “human eyes”. If this visual information is complemented with auditory information, thereby also providing “ears”, driverless cars can become more reliable and safer. In this paper, an Acoustic Event Detection model is presented that can detect various acoustic events in an automotive context along with their time of occurrence to create an audio scene description. The proposed detection methodology uses the pre-trained network Bidirectional Encoder representation from Audio Transformers (BEATs) and a single-layer neural network trained on the database of real audio recordings collected from different cars. The performance of the model is evaluated for different parameters and datasets. The segment-based results for a duration of 1 s show that the model performs well for 11 sound classes with a mean accuracy of 0.93 and F1-Score of 0.39 for a confidence threshold of 0.5. The threshold-independent metric mAP has a value of 0.77. The model also performs well for sound mixtures containing two overlapping events with mean accuracy, F1-Score, and mAP equal to 0.89, 0.42, and 0.658, respectively. Full article

Commercial automotive radar systems for advanced driver assistance systems (ADASs) have relied on frequency-modulated continuous wave (FMCW) waveforms for years due to their low-cost hardware, simple signal processing, and established academic and industrial expertise. However, FMCW systems face several challenges, including limited unambiguous velocity, restricted multiplexing of transmit signals, and susceptibility to interference. This work introduces a unified automotive radar signal model and reviews the alternative modulation schemes such as phase-coded frequency-modulated continuous wave (PC-FMCW), phase-modulated continuous wave (PMCW), orthogonal frequency-division multiplexing (OFDM), orthogonal chirp division multiplexing (OCDM), and orthogonal time frequency space (OTFS). These schemes are assessed against key technological and economic criteria and compared with FMCW, highlighting their respective strengths and limitations. Full article