Autonomous Vehicle Applications Research Articles

Graph neural network based abnormal perception information reconstruction and robust autonomous navigation

PurposeThis paper aims to develop a robust navigation enhancement framework to handle one of the most urgent needs for real applications of autonomous vehicles nowadays, as these corner cases act as the most commonly occurred risks in potential self-driving accidents.Design/methodology/approachIn this paper, the main idea is to fully exploit the consistent features among spatio-temporal data and thus detect the anomalies and build residual channels to reconstruct the abnormal information. The authors first develop an anomaly detection algorithm, then followed by a corresponding disturbed information reconstruction network which has strong robustness to address both the nature disturbances and external attacks. Finally, the authors introduce a fully end-to-end resilient navigation performance enhancement framework to improve the driving performance of existing self-driving models under attacks and disturbances.FindingsComparison results on CARLA platform and real experiments demonstrate strong resilience of the authors’ approach which enhances the navigation performance under disturbances and attacks.Originality/valueReliable and resilient navigation performance under various nature disturbances and even external attacks is one of the most urgent needs for real applications of autonomous vehicles nowadays, as these corner cases act as the most commonly occurred risks in potential self-driving accidents. The information reconstruction approach provides a resilient navigation performance enhancement method for existing self-driving models.

Integration of MoS2 Memtransistor Devices and Analogue Circuits for Sensor Fusion in Autonomous Vehicle Target Localization.

In contemporary autonomous driving systems relying on sensor fusion, traditional digital processors encounter challenges associated with analogue-to-digital conversion and iterative vector-matrix operations, which are encumbered by limitations in terms of response time and energy consumption. In this study, we present an analogue Kalman filter circuit based on molybdenum disulfide (MoS2) memtransistor, designed to accelerate sensor fusion for precise localization in autonomous vehicle applications. The nonvolatile memory characteristics of the memtransistor allow for the storage of a fixed Kalman gain, which eliminates the data convergence and thus accelerates the processing speeds. Additionally, the modulation of multiple conductance states by the gate terminal enables fast adaptability to diverse autonomous driving scenarios by tuning multiple Kalman filter gains. Our proposed analogue Kalman filter circuit accurately estimates the position coordinates of target vehicles by fusing sensor data from light detection and ranging (LiDAR), millimeter-wave radar (Radar), and camera, and it successfully solves real-word problems in a signal-free crossroad intersection. Notably, our system achieves a 1000-fold improvement in energy efficiency compared to that of digital circuits. This work underscores the viability of a memtransistor for achieving fast, energy-efficient real-time sensing, and continuous signal processing in advanced sensor fusion technology.

Visual SLAM for Unmanned Aerial Vehicles: Localization and Perception.

Localization and perception play an important role as the basis of autonomous Unmanned Aerial Vehicle (UAV) applications, providing the internal state of movements and the external understanding of environments. Simultaneous Localization And Mapping (SLAM), one of the critical techniques for localization and perception, is facing technical upgrading, due to the development of embedded hardware, multi-sensor technology, and artificial intelligence. This survey aims at the development of visual SLAM and the basis of UAV applications. The solutions to critical problems for visual SLAM are shown by reviewing state-of-the-art and newly presented algorithms, providing the research progression and direction in three essential aspects: real-time performance, texture-less environments, and dynamic environments. Visual-inertial fusion and learning-based enhancement are discussed for UAV localization and perception to illustrate their role in UAV applications. Subsequently, the trend of UAV localization and perception is shown. The algorithm components, camera configuration, and data processing methods are also introduced to give comprehensive preliminaries. In this paper, we provide coverage of visual SLAM and its related technologies over the past decade, with a specific focus on their applications in autonomous UAV applications. We summarize the current research, reveal potential problems, and outline future trends from academic and engineering perspectives.

Effect of Positive-Negative Image Ratio on the Performance of Pedestrian Detection Model

Pedestrian detection holds significant importance in computer vision, finding applications in video surveillance, human-computer interaction, and autonomous vehicles. Surprisingly, there is a scarcity of research addressing the optimal ratio of positive to negative images for training detection models. This study endeavors to fill this research gap by exploring various detection models and determining the ideal ratio. Two distinct scenarios are investigated, each characterized by an equal total image count and an equivalent number of positive images sourced from CVC-14 night/visible, night/FIR, and INRIA databases. The study leverages the Histogram of Oriented Gradient, utilizing both Support Vector Machines and Medium Neural Networks to construct the detection models. Notably, the experiments reveal that the accuracy of the models remains relatively stable, even with an increase in the ratio of negative images. However, a noteworthy inverse relationship between sensitivity and specificity emerges as the ratio escalates. The findings, guided by the Youden Index, pinpoint the optimal training ratio for pedestrian detection models, falling within the range of 1:0.5 to 1:2In the CVC-14 nighttime database, the Youden index reached 100% when the model was trained with a 1:0.5 ratio using SVM, and a total of 4500 images were employed in the training process. On the other hand, in the INRIA dataset, the Youden index exhibited its highest value at 98.50%. This occurred when both SVM and a Medium neural network were utilized to train the model with a ratio of 1:2, utilizing a total of 3000 images for the training phase. It's worth highlighting that the processing time for SVM models lags behind that of Medium Neural Networks. This disparity arises from the heightened computational complexity inherent to medium-sized neural networks, making them computationally demanding compared to SVMs. This study contributes valuable insights into the nuanced relationship between image ratios and the performance of pedestrian detection models.

Color-Driven Object Recognition: A Novel Approach Combining Color Detection and Machine Learning Techniques

INTRODUCTION: Object recognition is a crucial task in computer vision, with applications in robotics, autonomous vehicles, and security systems. 
 OBJECTIVES: The objective of this paper is to propose a novel approach for object recognition by combining color detection and machine learning techniques.
 METHODS: The research employs YOLO v3, a state-of-the-art object detection algorithm, and k-means optimized clustering to enhance the accuracy and efficiency of object recognition. RESULTS: The main results obtained in this paper showcase the outperformance of the authors’ approach on a standard object recognition dataset compared to state-of-the-art approaches using only color features. Additionally, the effectiveness of this approach is demonstrated in a real-world scenario of detecting and tracking objects in a video stream.
 CONCLUSION: In conclusion, this approach, integrating color and shape features, has the potential to significantly enhance the accuracy and robustness of object recognition systems. This contribution can pave the way for the development of more reliable and efficient object recognition systems across various applications.

Advancements in Uncooled Bolometer Technology: Shortwave Infrared Detection via CuFeSe2 Nanocrystal Colloidal Thin Films

Microbolometers have emerged as a cost‐effective alternative to cooled infrared photon detectors, albeit with certain trade‐offs in terms of responsivity (), detectivity (), and response time (). The research in this field is driven by the potential applications in night vision devices, military surveillance, and autonomous vehicles, leading to a growing interest in exploring new materials to bridge the performance gap between cooled photon detectors and uncooled bolometers. This study focuses on the optoelectronic and bolometric characteristics of nanocrystals (NCs) in a colloidal solution. These NCs exhibit a significant change in resistivity (ρ) when subjected to temperature variations ranging from 170 to 400 K. Specifically, the temperature coefficient of resistance (TCR), α, is 1.9% per Kelvin for a room temperature resistivity of 505 Ω cm. Furthermore, the responsivity of NCs is reported to be 0.101 A W−1, and the Hall mobility of the colloidal solution is determined as . Finally, a comprehensive comparison is conducted between the performance metrics of established bolometer materials, such as VOx and a‐Si, and those of colloidal NCs. Based on the results, colloidal NCs are a promising option for future bolometer technology.

Adversarial Transferability in Embedded Sensor Systems: An Activity Recognition Perspective

Machine learning algorithms are increasingly used for inference and decision-making in embedded systems. Data from sensors are used to train machine learning models for various smart functions of embedded and cyber-physical systems ranging from applications in healthcare, autonomous vehicles, and national security. However, recent studies have shown that machine learning models can be fooled by adding adversarial noise to their inputs. The perturbed inputs are called adversarial examples. Furthermore, adversarial examples designed to fool one machine learning system are also often effective against another system. This property of adversarial examples is called adversarial transferability and has not been explored in wearable systems to date. In this work, we take the first stride in studying adversarial transferability in wearable sensor systems from four viewpoints: (1) transferability between machine learning models; (2) transferability across users/subjects of the embedded system; (3) transferability across sensor body locations; and (4) transferability across datasets used for model training. We present a set of carefully designed experiments to investigate these transferability scenarios. We also propose a threat model describing the interactions of an adversary with the source and target sensor systems in different transferability settings. In most cases, we found high untargeted transferability, whereas targeted transferability success scores varied from 0% to 80%. The transferability of adversarial examples depends on many factors such as the inclusion of data from all subjects, sensor body position, number of samples in the dataset, type of learning algorithm, and the distribution of source and target system dataset. The transferability of adversarial examples decreased sharply when the data distribution of the source and target system became more distinct. We also provide guidelines and suggestions for the community for designing robust sensor systems. Code and dataset used in our analysis is publicly available here. 1

Macroscopic modeling of mixed bi-modal urban networks: A hybrid model of accumulation- and trip-based principles

Network-level traffic flow models either assume steady-state urban flows (i.e. accumulation-based models) or track the movement of all vehicles (i.e. trip-based models). The steady-state assumption present in the accumulation-based models may pose a challenge in light of the multi-modal nature of urban flows. It might be indeed a rough assumption for the flow of some transportation modes like buses, cruising-for-parking vehicles, taxis, and on-demand vehicles. Trip-based models address this concern, however, they need significant parameter calibration effort and are not computationally efficient, which substantially reduces the practicality of these models in real-world applications. Nevertheless, despite the critical importance of developing multi-modal traffic flow models, few attempts have been made to investigate these models in network macroscopic fundamental diagram (NMFD)-related literature. This paper bridges this gap by developing a hybrid network-level traffic flow model for mixed bi-modal (i.e. car and bus) networks. The present hybrid model reproduces the dynamics of car flows via accumulation-based model principles while tracking the movement of buses using the trip-based model. This effort also includes the development of a new FIFO-based entrance function to ensure different modes experience the same delay under saturated traffic conditions. Different numerical experiments are conducted to study the hybrid model performance and to compare it with that of accumulation-based and trip-based models in both steady-state and transition periods under different traffic conditions. Our observations reveal that the hybrid model simulates the dynamics of cars and buses by closely following the behavior of its components under free-flow conditions. The model also outperforms the accumulation-based model under saturated traffic conditions while being considerably less demanding than the trip-based model. A further investigation of the model performance is performed for networks with different bus shares in both free-flow and saturated traffic conditions, confirming the results of the initial numerical experiments. The hybrid model’s computational efficiency is demonstrated. The potential real-world applications of the hybrid model in development of bi-modal network-level simulation models, NMFD-based control strategies along with bus space allocation policies, public transport operation problems, modeling of cruising-for-parking vehicles, taxis, and on-demand vehicles, and modeling and application of autonomous modular vehicles are discussed and future research directions are highlighted.

Development of an Effective Corruption-Related Scenario-Based Testing Approach for Robustness Verification and Enhancement of Perception Systems in Autonomous Driving.

Given that sensor-based perception systems are utilized in autonomous vehicle applications, it is essential to validate such systems to ensure their robustness before they are deployed. In this study, we propose a comprehensive simulation-based process to verify and enhance the robustness of sensor-based perception systems in relation to corruption. Firstly, we introduce a methodology and scenario-based corruption generation tool for creating a variety of simulated test scenarios. These scenarios can effectively mimic real-world traffic environments, with a focus on corruption types that are related to safety concerns. An effective corruption similarity filtering algorithm is then proposed to eliminate corruption types with high similarity and identify representative corruption types that encompass all considered corruption types. As a result, we can create efficient test scenarios for corruption-related robustness with reduced testing time and comprehensive scenario coverage. Subsequently, we conduct vulnerability analysis on object detection models to identify weaknesses and create an effective training dataset for enhancing model vulnerability. This improves the object detection models' tolerance to weather and noise-related corruptions, ultimately enhancing the robustness of the perception system. We use case studies to demonstrate the feasibility and effectiveness of the proposed procedures for verifying and enhancing robustness. Furthermore, we investigate the impact of various "similarity overlap threshold" parameter settings on scenario coverage, effectiveness, scenario complexity (size of training and testing datasets), and time costs.

Material Classification based on Non-contact Ultrasonic Echo Signal Using Deep Learning Approach

Non-contact ultrasonic sensors are generally used for range measurement and object detection. In addition to the shape and size of the target object, the identification of the material types plays a vital role in robotic navigation and autonomous vehicle applications. Ultrasonic echo signals contain a significant amount of information and can be used to recognize and categorize different materials. Echo signals can help robots detect the obstacle's material on the path and comply with its behavior accordingly. The emergence of deep neural networks has shown great promise, offering cutting-edge performance for a wide range of signal-processing tasks. This work uses the non-contact ultrasonic echo signal from a set of materials (glass, wood, metal plate, sponge, and cloth) for classification. The main idea is to classify the materials using the embedded information of reflected echo signals. Hilbert transform is used to get the envelope from the raw echo ultrasonic signal. In this paper, a novel architecture for one-dimensional convolutional neural network (1D-CNN) has been proposed to accomplish the classification task. The CNN model takes raw echo signals as input to detect and classify materials accurately. The performance of the classifier model is evaluated using accuracy, precision, recall, and F1-score. The proposed 1D-CNN deep learning-based multi-class classifier model can classify the different types of materials with an accuracy of 96%, precision of 95%, recall of 95%, and F1-score of 95%.

GRTR: Gradient Rebalanced Traffic Sign Recognition for Autonomous Vehicles

Traffic sign recognition is a crucial aspect of autonomous vehicle research, and deep learning techniques have significantly contributed to its progress. Nevertheless, the distribution of traffic sign information in natural complex road conditions is long-tailed, and traffic sign identification in complex road conditions has become a significant barrier to autonomous vehicle applications. The imbalanced distribution of information on the dataset migrates to the feature space during training, resulting in imbalanced classifier prediction. In this paper, we propose the gradient rebalanced traffic sign recognition (GRTR) method to address this problem for the first time. GRTR first evaluates the prediction and classification bias of the classifier using the fitted deviation between the model’s output probability and the ground-truth distributions. Then, GRTR dynamically adjusts the correction and compensation factors following the classifier’s prediction and classification biases. GRTR rebalances the positive and negative sample gradients for each category based on the synergistic effect of the correction and compensation factors to prevent the transfer of distribution imbalance and to significantly enhance the performance of the traffic sign classifier under difficult road conditions. Experimental results demonstrate that our GRTR achieves state-of-the-art performance on long-tailed traffic sign and multilabel datasets. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Most traffic sign recognition algorithms are still designed based on the assumption of a balanced distribution of traffic signs in the dataset. Real-world autonomous vehicles require traffic sign recognition on datasets with severely imbalanced distributions. This paper proposes a general approach to solving the long-tailed traffic sign recognition problem.

A Comprehensive Survey of Threats in Platooning—A Cloud-Assisted Connected and Autonomous Vehicle Application

Cloud-Assisted Connected and Autonomous Vehicles (CCAV) are set to revolutionise road safety, providing substantial societal and economic advantages. However, with the evolution of CCAV technology, security and privacy threats have increased. Although several studies have been published around the threat and risk estimation aspects of CCAV, limited research exists on the security implications and emerging threat landscapes in the CCAV platooning application. We conducted an extensive review and categorisation of real-world security incidents and created an account of 132 threats from scholarly sources and 64 threats from recorded events in practice. Furthermore, we defined thirty-one (31) trust domains and outlined eight (8) unique attack vectors to supplement existing research efforts for the systematic security analysis of such cyberinfrastructures. Using these findings, we create a detailed attack taxonomy to communicate threat-related information in CCAV and platooning applications and highlight emerging challenges and ways to safeguard the broader CCAV systems. This work acts as a roadmap to existing researchers and practitioners advocating for a ‘security and privacy by design’ framework for a dynamically evolving CCAV threat landscape.

ESTABLISHMENT OF HD MAPS VERIFICATION AND VALIDATION PROCEDURE WITH OPENDRIVE AND AUTOWARE (LANELET2) FORMATS

Abstract. Mobile mapping technologies, for example multi-sensor integration and multi-platform mapping technology, have developed and improved over recent decades, various applications such as conventional mapping scenarios, rapid disaster response, smart city, and autonomous vehicle application arise synchronously. Especially, autonomous driving vehicles have made enormous progress. High-definition (HD) maps are key for autonomous driving because of their high accuracy and rich information of road scenes. However, how to make sure that HD maps are suitable for autonomous vehicle requirement is an important topic. The HD maps guidelines and standards in Taiwan are released since 2018 and mainly focus on point cloud and shape file format. In this paper, a procedure for the verification and validation of HD maps for OpenDRIVE and Autoware (Lanelet2) is proposed. It discusses about the verification strategies, suggestion review item, recommendation tools, and process. As shown by our preliminary results, the proposed process can conform not only in closed area but also public road. These issues can help reducing HD maps production costs. When the foundation of HD maps accomplishes, the autonomous driving techniques can naturally complement. The vision of full automation vehicle will come true rapidly in the future.

Autonomous Vehicles for Healthcare Applications: A Review on Mobile Robotic Systems and Drones in Hospital and Clinical Environments

The development of autonomous vehicles, both ground and airborne, for hospitals and clinical settings is an extremely interesting topic that has developed rapidly in recent years. Given their significant potential to improve operational efficiency and safety protocols, these devices are gradually gaining an important place in the healthcare industry. This is true for both in-hospital and out-of-hospital functions. The integration of autonomous vehicles into these environments will greatly increase operational efficiency and enrich the experience for both medical staff and patients. This document provides an updated and comprehensive overview of the current state-of-the-art in the development of autonomous vehicles for the healthcare sector. Particular emphasis is placed on design, functionality, and level of autonomy. The review is organized on two levels: a prospective review highlights the main trends in the design and application of autonomous vehicles, and an analytical review performs an in-depth analysis of the main aspects of the technical solutions developed and implemented in the scientific research reviewed. The results are presented in a schematic approach.

AMFF-Net: An Effective 3D Object Detector Based on Attention and Multi-Scale Feature Fusion

With the advent of autonomous vehicle applications, the importance of LiDAR point cloud 3D object detection cannot be overstated. Recent studies have demonstrated that methods for aggregating features from voxels can accurately and efficiently detect objects in large, complex 3D detection scenes. Nevertheless, most of these methods do not filter background points well and have inferior detection performance for small objects. To ameliorate this issue, this paper proposes an Attention-based and Multiscale Feature Fusion Network (AMFF-Net), which utilizes a Dual-Attention Voxel Feature Extractor (DA-VFE) and a Multi-scale Feature Fusion (MFF) Module to improve the precision and efficiency of 3D object detection. The DA-VFE considers pointwise and channelwise attention and integrates them into the Voxel Feature Extractor (VFE) to enhance key point cloud information in voxels and refine more-representative voxel features. The MFF Module consists of self-calibrated convolutions, a residual structure, and a coordinate attention mechanism, which acts as a 2D Backbone to expand the receptive domain and capture more contextual information, thus better capturing small object locations, enhancing the feature-extraction capability of the network and reducing the computational overhead. We performed evaluations of the proposed model on the nuScenes dataset with a large number of driving scenarios. The experimental results showed that the AMFF-Net achieved 62.8% in the mAP, which significantly boosted the performance of small object detection compared to the baseline network and significantly reduced the computational overhead, while the inference speed remained essentially the same. AMFF-Net also achieved advanced performance on the KITTI dataset.

Evaluation of autonomous vehicle applications in smart airports using Dombi Bonferroni mean operator based CIVL-BWM-TODIM decision making methodology

Recent advancements on autonomous vehicles (AVs) have revolutionized the aviation industry, providing great potential to enhance sustainability for this fuel-intensive industry. Diverse operation types of AVs at airports have emerged (e.g., for baggage/passenger movement). This study aims to provide a guideline on how to switch from the traditional human-driving vehicle operations to AV operations, to help improve the smartness and sustainability of airport operations. Five alternative operation types are evaluated under ten criteria. We propose a new CIVL-BWM-TODIM with Dombi Bonferroni mean operator (DBM) decision framework. The combination of the Continuous Interval-Valued Linguistic tool (CIVL) and the Best Worst Method (BWM) improves the criterial evaluation consistency, and the integration of CIVL and TODIM increases the evaluation accuracy on human language and psychological feelings. Besides, the DBM operator helps effectively integrate information from different experts. Results suggest that applying AVs for both functional activities and scheduled people delivery is the best alternative to build smart and sustainable airports. Sensitivity analyses are conducted to examine the impacts of various parameters on decision making. Moreover, comparative analysis over existing decision-making methods are carried out to validate the merits of the proposed approach.

Otomatik sürüş için derin öğrenme metodu kullanarak trafik işaretlerinin tespiti

Deep learning practices used in many fields, in particular, in health, military, economy, and production industries, are an important area of artificial intelligence in our age. The object classification and object recognition applications, which play a significant role in the development of autonomous vehicle technologies, constitute the focal point of the deep learning studies. It is clear that the recent studies based on the deep learning models show that they are useful and successful performances for safe driving not only for vehicles but also for pedestrians. It is very crucial and significant that the autonomous systems recognize the traffic signs with high accuracy for a safe driving. Especially, the pedestrian crossing, school district, urban speed limits can be regarded among the most critical traffic signs. In this study, we have used the data set including the traffic signs obtained by our own means to carry out trainings by using faster R-CNN which is regarded as one of the most important recognition architectures. Thanks to the hardware module produced as a result of the operation, we have developed a system that warns the driver of the vehicle with audible warning. The developed hardware module can detect not only the speed limits, traffic signs but also pedestrian crossings and school districts and alert the driver in reel-time. The developed hardware module is based on Arduino and because of the GPS sensor, it can also show the speed of the vehicle. Moreover, we have used Python for the developed software and the dataset trainings have been carried out by using the Tensorflow library. We think that the study will contribute a lot to the recognition of traffic signs for the autonomous vehicle applications.

Automotive Ethernet architecture and security: challenges and technologies

&lt;span lang="EN-US"&gt;Vehicle infrastructure must address the challenges posed by today's advances toward connected and autonomous vehicles. To allow for more flexible architectures, high-bandwidth connections and scalability are needed to connect many sensors and electronic control units (ECUs). At the same time, deterministic and low latency is a critical and significant design requirement to support urgent real-time applications in autonomous vehicles. As a recent solution, the time-sensitive network (TSN) was introduced as Ethernet-based amendments in IEEE 802.1 TSN standards to meet those needs. However, it had hurdle to be overcome before it can be used effectively. This paper discusses the latest studies concerning the automotive Ethernet requirements, including transmission delay studies to improve worst-case end-to-end delay and end-to-end jitter. Also, the paper focuses on the securing Ethernet-based in-vehicle networks (IVNs) by reviewing new encryption and authentication methods and approaches.&lt;/span&gt;

Investigation of Object Detection and Identification at Different Lighting Conditions for Autonomous Vehicle Application

Ensuring the safety of autonomous vehicles requires effective detection and tracking of surrounding objects. This paper proposes the design and development of a driverless transportation system module focused on identifying obstacles around vehicles. By integrating computer vision with deep learning, the system presents a reliable and cost-effective solution for autonomous driving. Utilizing Raspberry Pi 4B and a USB webcam, a compact hardware setup is created for seamless implementation in autonomous vehicles. The algorithm presented in this study enables the detection, classification, and tracking of both moving and stationary objects, including cars, buses, trucks, people, and motorcycles. TensorFlow Lite, a deep-learning network, is employed for efficient object detection and classification. Leveraging Python as the primary programming language, known for its high-level object-oriented features and integrated semantics, the algorithm is tailored for web and application development. Experimental results demonstrate the system’s capability to concurrently detect and identify multiple local objects with an accuracy ranging from 50% to 80% in day and night conditions. These findings underscore the potential of deep learning in advancing autonomous vehicle technology.

Real-Time Pedestrian Detection with YOLOv7 and Intel MiDaS: A Qualitative Study

Abstract: In the realm of Advanced Driving Assistance Systems (ADAS), the accurate assessment of pedestrian proximity is of paramount importance. This paper introduces a qualitative methodology that integrates YOLOv7-pose for object detection and pose estimation with the MiDaS (Monocular Depth Estimation in Real-Time with Deep Learning) model for monocular depth estimation. The main objective is to qualitatively assess pedestrian proximity to the camera within the ADAS framework. This procedure involves classifying pedestrians as "near" or "far" based on an inverse depth threshold that has been predetermined. In addition, the paper performs a qualitative comparative analysis of the results produced by the MiDaS Small, Hybrid, and Large variants to learn more about the performance of depth estimation in these contexts, particularly in relation to the presence of pedestrians. The evaluation emphasises this approach's qualitative potential for achieving situationally appropriate and context-aware pedestrian proximity assessment. The safety and adaptability of ADAS systems can be improved with the help of such insights, which have numerous applications in robotics, surveillance, and autonomous vehicles.