Search Results (57)

Autonomous driving systems (ADSs) are reliable only when heterogeneous sensors, estimation algorithms, and safety mechanisms are engineered as a single coherent safety-critical measurement system rather than as loosely coupled modules. Production stacks integrate cameras, LiDAR, automotive radar, and GNSS/IMU, yet deployment remains constrained by modality-specific failure modes, calibration and synchronization drift, and out-of-distribution (OOD) conditions that violate modeling assumptions. These limitations induce overconfidence and downstream decision errors whenever planning assumes certainty sharper than sensing can justify. This survey introduces a sensor-centric framework linking measurement physics, uncertainty propagation, fusion integrity, safety assurance, and risk-aware planning and control. We formalize what each modality physically measures; unify probabilistic, evidential, and conformal uncertainty representations; analyze filtering, factor-graph, BEV, transformer, and state-space fusion architectures with an emphasis on robustness and graceful degradation; and generalize aviation-style integrity concepts (RAIM/ARAIM) to multi-modal autonomy. The distinctive contribution is a single sensor-to-assurance throughline in which every uncertainty representation is tied to its measurement physics, every fusion architecture is evaluated against an explicit integrity-monitoring requirement generalized from RAIM/ARAIM, and every safety-standard clause is mapped to a concrete architectural mechanism. We map these mechanisms onto ISO 26262, ISO 21448 (SOTIF), ISO/PAS 8800, ANSI/UL 4600, and the UNECE framework, and connect perception uncertainty to decision-making through chance-constrained MPC and formal safety filters (RSS, CBF). Industry case studies and emerging V2X and generative-simulation approaches close the loop to deployable safety arguments. Full article

Automotive radar has emerged as a pivotal technology for navigation in GNSS-denied environments, offering superior robustness to adverse weather and fluctuating lighting conditions compared to vision or LiDAR-based sensors. Despite these advantages, the inherent sparsity and noise of radar measurements often lead to degraded estimation accuracy and system reliability. To address these challenges, various radar-based localization frameworks have been explored, ranging from optimization-based and Extended Kalman Filter (EKF) approaches fused with Inertial Measurement Units (IMUs) to point cloud registration techniques like Iterative Closest Point (ICP). While filter-based methods are favored in multi-sensor fusion for their proven stability, ICP is widely utilized for high-precision pose estimation in point-cloud-centric systems. In this study, we propose a novel Radar-Inertial Odometry (RIO) framework that synergistically integrates ICP-based relative pose estimation with model-based sensor fusion. The proposed methodology leverages relative transformations derived from ICP alongside ego-velocity estimations obtained from radar Doppler measurements. To effectively incorporate relative ICP constraints, a stochastic cloning technique is implemented to augment previous states and their associated covariances, ensuring that the uncertainty of historical poses is explicitly accounted for. The performance of the proposed method is validated using public open-source datasets, demonstrating higher localization accuracy and more consistent performance compared to existing algorithms used for comparison. Full article

A Digital Twin is a virtual environment that simulates, predicts, and optimizes the performance of its physical counterpart. Digital Twin models hold great potential in wireless networking testing and development. This paper aims to envision our concept of simulating the operation of different sensors in vehicle test-track conditions. Vehicle parameters are embedded into the edge computing entity, which uses them to generate a test configuration for the Digital Twin. This configuration is then applied in simulated sensor-output prediction, ultimately producing event data for the vehicle entity. The sensor suite—comprising radar, cameras, GPS and LiDAR—is modeled to provide the multi-modal input required for generating simulated perception data in the Digital Twin. To ensure realistic perception behavior, the physical vehicle is represented within a digital environment that reproduces the actual test track. This allows LiDAR occlusions to be attributed to genuine environmental structures (e.g., trees, buildings, other vehicles) rather than simulation artifacts. Within the Digital Twin, the objective is to evaluate how sensor signals—such as radar waves and LiDAR light pulses—propagate through the environment and how real-world obstacles may weaken or distort them. Historical datasets are used to calibrate and validate the Digital Twin, ensuring that the simulated sensor behavior aligns with real-world observations; the data collected during previous test runs can be used for visualization and analysis. Weather conditions are modeled to evaluate how rain, fog and snow impact sensor performance within the Digital Twin environment, to learn about the effects and predict sensor operation in different weather conditions. In this article, we examine the Digital Twin of our test track as a development environment for designing, deploying and testing ITS-enhanced road-weather services and warnings. These services integrate real-world road-weather observations, forecast data, roadside sensors and on-board vehicle measurements to support safe driving and optimize vehicle trajectories for both passenger and autonomous vehicles. This research is expected to benefit stakeholders involved in automotive testing, simulation and road-weather service development. Full article

A trend analysis mentioned that the global automotive Vehicle-to-Everything—also called V2X—market size will be reached at several billions in the near future. This information clearly highlights the importance of developing V2X communication. Nowadays, automobile manufacturers have introduced vehicles equipped with driver assistance and even conditional autonomous driving features. Light detection and ranging (LiDAR) components are used in sensor networks to detect objects around. Also, vehicles take advantage of LiDAR sensors to discover the neighbor cars in cognitive systems for road safety. Carrying on from our previous works, we found that organizing vehicles into groups can enhance the safety of the vehicle networks by LiDAR assistance. However, the success rate and reliability of grouping vehicles is an important issue. Also, enhancing existing Vehicle-to-Vehicle (V2V) communication mechanisms remains a key factor in ensuring that emergency messages can be transmitted both timely and accurately. To address this, in this research, a method is proposed to make vehicles on the road be self-organized well for Intelligent Transportation Systems (ITS). Also, we found that before data in each car is transmitted, the scenario that data is queued for waiting a random time exponentially distributed outperforms data being sent immediately. Full article

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

The automotive industry is advancing toward fully automated driving, where perception systems rely on complementary sensors such as LiDAR and cameras to interpret the vehicle’s surroundings. For Level 4 and higher vehicles, redundancy is vital to prevent safety-critical failures. One way to achieve this is by using data from one sensor type to support another. While much research has focused on reconstructing LiDAR point cloud data using camera images, limited work has been conducted on the reverse process—reconstructing image data from LiDAR. This paper proposes a deep learning model, named LiDAR Generative Camera (LiGenCam), to fill this gap. The model reconstructs camera images by utilizing multimodal LiDAR data, including reflectance, ambient light, and range information. LiGenCam is developed based on the Generative Adversarial Network framework, incorporating pixel-wise loss and semantic segmentation loss to guide reconstruction, ensuring both pixel-level similarity and semantic coherence. Experiments on the DurLAR dataset demonstrate that multimodal LiDAR data enhances the realism and semantic consistency of reconstructed images, and adding segmentation loss further improves semantic consistency. Ablation studies confirm these findings. 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

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

This review paper provides a comprehensive examination of millimeter-wave radar technologies in automotive systems, reviewing their advancements through signal processing innovations. The evolution of radar systems, from conventional platforms to mmWave technologies, has significantly enhanced capabilities such as high-resolution imaging, real-time tracking, and multi-object detection. Signal processing advancements, including constant false alarm rate detection, multiple-input–multiple-output systems, and machine learning-based techniques, are explored for their roles in improving radar performance under dynamic and challenging environments. The integration of mmWave radar with complementary sensing technologies such as LiDAR and cameras facilitates robust environmental perception essential for advanced driver-assistance systems and autonomous vehicles. This review also calls attention to key challenges, including environmental interference, material penetration, and sensor fusion, while addressing innovative solutions such as adaptive signal processing and sensor integration. Emerging applications of joint communication–radar systems further presents the potential of mmWave radar in autonomous driving and vehicle-to-everything communications. By synthesizing recent developments and identifying future directions, this review stresses the critical role of mmWave radar in advancing vehicular safety, efficiency, and autonomy. Full article

The trend in all automotive manufacturers is to commercialize vehicles with an increasing number of sophisticated Advanced Driver-Assistance Systems (ADASs). These systems often require that several sensors, such as Light Detection and Ranging (LIDAR), radio detection and ranging (radar), cameras, etc., work in cooperation, which makes the systems very complex. To perform the electromagnetic compatibility (EMC) validation of these complex ADASs, the stimulation of multiple sensors composing the system is necessary. Furthermore, the synchronization of these stimulations is essential to create realistic outdoor scenarios in the usual EMC facilities (on a roller bench in a semi-anechoic chamber). This synchronization is mandatory as the integrated safety systems will disable any ADAS or autonomous system in case of incoherencies in the data delivered by the sensors, rendering the validation challenging. Moreover, the current methodologies proposed are meant to be performed to validate simple ADASs based on simple sensors. In addition, with the current test facilities, one cannot stimulate, in a realistic and synchronous way, multiple sophisticated sensors (e.g., LIDARs and inertial measurement units). For all these reasons, the radiated immunity tests of future automotive systems will be endlessly difficult following current trends. In addition, the complexity of the systems and their increasing number increase the duration and cost of these immunity tests and make their validations more challenging. In this article, we present a new methodology to validate the radiated immunity of complex automotive autonomous systems to address these challenges. The results we present show that this new methodology can be performed to validate ADASs and autonomous automotive systems independently of their complexity. Full article

Autonomous vehicles (AVs) have revolutionized the automotive industry by leveraging data to perceive and interact with their environment effectively. Data safety is essential for supporting AV decision-making and ensuring reliability in complex environments. AVs continuously collect data from multiple sources like LiDAR, RADAR, cameras, and ultrasonic sensors to monitor road conditions, traffic signals, and pedestrian movements. An effective data classification framework is crucial for managing vast amounts of information and securing AV systems against cyber threats. This paper proposes a comprehensive framework for AV data classification, categorizing data by sensitivity, usage, and source. By integrating a review of the literature, real-world cases, and practical insights, this study introduces a novel data classification model and explores sensitivity criteria. The findings aim to assist industry stakeholders in creating secure, efficient, and sustainable AV ecosystems. Full article

The fusion of multiple sensors’ data in real-time is a crucial process for autonomous and assisted driving, where high-level controllers need classification of objects in the surroundings and estimation of relative positions. This paper presents an open-source framework to estimate the distance between a vehicle equipped with sensors and different road objects on its path using the fusion of data from cameras, radars, and LiDARs. The target application is an Advanced Driving Assistance System (ADAS) that benefits from the integration of the sensors’ attributes to plan the vehicle’s speed according to real-time road occupation and distance from obstacles. Based on geometrical projection, a low-level sensor fusion approach is proposed to map 3D point clouds into 2D camera images. The fusion information is used to estimate the distance of objects detected and labeled by a Yolov7 detector. The open-source pipeline implemented in ROS consists of a sensors’ calibration method, a Yolov7 detector, 3D point cloud downsampling and clustering, and finally a 3D-to-2D transformation between the reference frames. The goal of the pipeline is to perform data association and estimate the distance of the identified road objects. The accuracy and performance are evaluated in real-world urban scenarios with commercial hardware. The pipeline running on an embedded Nvidia Jetson AGX achieves good accuracy on object identification and distance estimation, running at 5 Hz. The proposed framework introduces a flexible and resource-efficient method for data association from common automotive sensors and proves to be a promising solution for enabling effective environment perception ability for assisted driving. Full article

This article presents an alternative approach to detecting and mapping space debris in low Earth orbit by utilizing commercially available automotive LiDAR sensors mounted on CubeSats. The main objective is to leverage the compact size, low weight, and minimal power consumption of these sensors to create a “Large Cosmic LiDAR” (LCL) system. This LCL system would operate similarly to a giant radar circling the Earth, with strategically positioned LiDAR sensors along the target orbit. The article examines the feasibility of this concept by analyzing the relative orbital velocity between the sensor and debris objects, and calculating the time required to scan a complete orbit. Full article