Browse Topic: Automation

Items (3,018)
With the growing diversification of modern urban transportation options, such as delivery robots, patrol robots, service robots, E-bikes, and E-scooters, sidewalks have gained newfound importance as critical features of High-Definition (HD) Maps. Since these emerging modes of transportation are designed to operate on sidewalks to ensure public safety, there is an urgent need for efficient and optimal sidewalk routing plans for autonomous driving systems. This paper proposed a sidewalk route planning method using a cost-based A* algorithm and a mini-max-based objective function for optimal routes. The proposed cost-based A* route planning algorithm can generate different routes based on the costs of different terrains (sidewalks and crosswalks), and the objective function can produce an efficient route for different routing scenarios or preferences while considering both travelling distance and safety levels. This paper’s work is meant to fill the gap in efficient route planning for
Bao, ZhibinLang, HaoxiangLin, Xianke
A significant challenge to the scalability of automated driving systems is the potential unavailability of GPS information for localization. To address this issue, a methodology using a static 2D map of road and lane geometry and vehicle on board sensors data is proposed to ensure reliable localization and navigation for automated vehicles in GPS-denied situations. In this study, a dead reckoning system based on vehicle kinematics is implemented by using onboard sensor data from the vehicle's Controller Area Network (CAN). However, the kinematic dead reckoning estimate has error accumulation, the drift in the dead reckoning position estimate is eliminated by using an arc-length based map matching approach. This innovative approach was tested and validated at various safety-critical intersection scenarios, including four-way intersection, roundabout, slip-lane intersection, and curved road. This approach ensures the continuous and reliable localization of automated vehicles, thereby
Javed, Nur UddinSingh, YuvrajTan, ShengzheAhmed, Qadeer
The recent advancements in fields such as sensors, AI, and IoT are majorly impacting the automotive industry. Automated Driving Systems (ADS) are developing rapidly, meaning that SAE J3016 Level 3 and above vehicles are quickly becoming a reality. As a result, maintenance of such systems becomes essential to ensure their safe and efficient operation. Prognostic techniques in particular are crucial to monitor the state of health and predicting the end of life for components. Prognostics engineering is being applied in many industries and for conventional automotive applications, but ADS is new technology, and the prognostics for these systems are still being developed and adapted. In this paper, we first present a review of the most used prognostic techniques across different safety-critical domains such as aerospace, power, and manufacturing. Then, we summarize the main challenges that must be faced to successfully develop novel approaches for prognostics of ADS components and provide
Merola, FrancescoHanif, AtharLami, GiuseppeAhmed, QadeerMonohon, Mark
The unicycle self-balancing mobility system offers superior maneuverability and flexibility due to its unique single-wheel grounding feature, which allows it to autonomously perform exploration and delivery tasks in narrow and rough terrains. In this paper, a unicycle self-balancing robot traveling on the lunar terrain is proposed for autonomous exploration on the lunar surface. First, a multi-body dynamics model of the robot is derived based on quasi-Hamilton equations. A three-dimensional terramechancis model is used to describe the interaction between the robot wheels and the lunar soil. To achieve stable control of the robot's attitude, series PID controllers are used for pitch and roll attitude self-balancing control as well as velocity control. The whole robot model and control strategy were built in MATLAB and the robot's traveling stability was analyzed on the lunar terrain.
Shi, JunweiZhang, KaidiDuan, YupengWu, JinglaiZhang, Yunqing
Reproducing driving scenarios involving near-collisions and collisions in a simulator can be useful in the development and testing of autonomous vehicles, as it provides a safe environment to explore detailed vehicular behavior during these critical events. CARLA, an open-source driving simulator, has been widely used for reproducing driving scenarios. CARLA allows for both manual control and traffic manager control (the module that controls vehicles in autopilot manner in the simulation). However, current versions of CARLA are limited to setting the start and destination points for vehicles that are controlled by traffic manager, and are unable to replay precise waypoint paths that are collected from real-world collision and near-collision scenarios, due to the fact that the collision-free pathfinding modules are built into the system. This paper presents an extension to CARLA’s source code, enabling the replay of exact vehicle trajectories, irrespective of safety implications
Ai, YanAdhikari, BikramPark, Chung-KyuKan, Cing-DaoWijesekera, Duminda
As the autonomy of ADAS features are moving from SAE level 0 autonomy to SAE level 5 autonomy of operation, reliance on AI/ML based algorithms in ADAS critical functions like perception, fusion and path planning are increasing predominantly. AI/ML based algorithms offer exceptional performance of the ADAS features, at the same time these advanced algorithms also bring in safety challenges as well. This paper explores the functional safety aspects of AI/ML based systems in ADAS functions like perception, object fusion and path planning, by discussing the safety requirements development for AI/ML systems, dataset safety life cycle, verification and validation of AI systems, and safety analysis used for AI systems. Among all the safety aspects listed above, emphasis is put on dataset safety lifecycle as that is not only the most important element for training ML based algorithms for ADAS usage, but also the most cumbersome and expensive. The safety characteristics associated with dataset
Mudunuri, Venkateswara RajuAlmasri, HossamFan, Hsing-HuaChandrasekaran, Mukund
Safety Management Systems (SMSs) have been used in many safety-critical industries and are now being developed and deployed in the automated driving system (ADS)-equipped vehicle (AV) sector. Industries with decades of SMS deployment have established frameworks tailored to their specific context. Several frameworks for an AV industry SMS have been proposed or are currently under development. These frameworks borrow heavily from the aviation industry although the AV and aviation industries differ in many significant ways. In this context, there is a need to review the approach to develop an SMS that is tailored to the AV industry, building on generalized lessons learned from other safety-sensitive industries. A harmonized AV-industry SMS framework would establish a single set of SMS practices to address management of broad safety risks in an integrated manner and advance the establishment of a more mature regulatory framework. This paper outlines a proposed SMS framework for the AV
Wichner, DavidWishart, JeffreySergent, JasonSwaminathan, Sunder
A key challenge for manufacturers of automotive systems, hardware components and software products with no contribution to driving automation is the stringent requirements imposed on elements while being integrated into vehicles with driving automation. The result is increased development cost and low reusability. For such elements or components with no contribution to driving automation, their functions and failure modes remain unchanged when comparing vehicle integration with and without driving automation. The influence of driving automation is not accounted for in the current approach of classifying risk while conducting a Hazard Analysis and Risk Assessment (HARA). Functional safety standards for on-road vehicles rely on human intervention as a parameter to classify risk. Since current safety standards for on-road vehicles are not inclusive of driving automation concepts, classification of risk, based on existing definitions of parameters such as controllability, leads to
Shah, MihirIbarra, Ireri
One of the major issues facing the automated driving system (ADS)-equipped vehicle (AV) industry is how to evaluate the performance of an AV as it navigates a given scenario. The development and validation of a sound, consistent, and transparent dynamic driving task (DDT) assessment (DA) methodology is a key component of the safety case framework (SCF) of the Automated Vehicle – Test and Evaluation Process (AV-TEP) Mission, a collaboration between Science Foundation Arizona and Arizona State University. The DA methodology was presented in earlier work and includes the DA metrics from the recently published SAE J3237 Recommended Practice. This work extends and implements the methodology with an AV developed by OEM May Mobility in four diverse, real-world scenarios: (1) an oncoming vehicle entering the AV’s lane, (2) vulnerable road user (VRU) crossing in front of the AV’s path, (3) a vehicle executing a three-point turn encroaches into the AV’s path, and (4) the AV exhibiting aggressive
Wishart, JeffreyRahimi, ShujauddinSwaminathan, SunderZhao, JunfengFrantz, MattSingh, SatvirComo, Steven Gerard
High-efficiency manufacturing involves the transmission of copious amounts of data, exemplified both by trends in the automotive industry and advances in technology. In the automotive industry, products have been growing increasingly complex, owing to multiple SKUs, global supply chains and the involvement of many tier 2 / Just-In Time (JIT) suppliers. On top of that, recalls and incidents in recent years have made it important for OEMs to be able to track down affected vehicles based on their components. All of this has increased the need for OEMs to be able to collect and analyze component data. The advent of Industry 4.0 and IoT has provided manufacturing with the ability to efficiently collect and store large amounts of data, lining up with the needs of manufacturing-based industries. However, while the needs to collect data have been met, corporations now find themselves facing the need to make sense of the data to provide the insights they need, and the data is often unstructured
Jan, JonathanPreston, JoshuaJuncker, John
Towards the goal of real-time navigation of autonomous robots, the Iterative Closest Point (ICP) based LiDAR odometry methods are a favorable class of Simultaneous Localization and Mapping (SLAM) algorithms for their robustness under any light conditions. However, even with the recent methods, the traditional SLAM challenges persist, where odometry drifts under adversarial conditions such as featureless or dynamic environments, as well as high motion of the robots. In this paper, we present a motion-aware continuous-time LiDAR-inertial SLAM framework. We introduce an efficient EKF-ICP sensor fusion solution by loosely coupling poses from the continuous time ICP and IMU data, designed to improve convergence speed and robustness over existing methods while incorporating a sophisticated motion constraint to maintain accurate localization during rapid motion changes. Our framework is evaluated on the KITTI datasets and artificially motion-induced dataset sequences, demonstrating
Kokenoz, CigdemShaik, ToukheerSharma, AbhishekPisu, PierluigiLi, Bing
Lane-keeping is critical for SAE Level 3+ autonomous vehicles, requiring rigorous validation and end-to-end interpretability. All recently U.S.-approved level 3 vehicles are equipped with lidar, likely for accelerating active safety. Lidar offers direct distance measurements, allowing rule-based algorithms compared to camera-based methods, which rely on statistical methods for perception. Furthermore, lidar can support a more comprehensive and detailed approach to studying lane-keeping. This paper proposes a module perceiving oncoming vehicle behavior, as part of a larger behavior-tree structure for adaptive lane-keeping using data from a lidar sensor. The complete behavior tree would include road curvature, speed limits, road types (rural, urban, interstate), and the proximity of objects or humans to lane markings. It also accounts for the lane-keeping behavior, type of adjacent and opposing vehicles, lane occlusion, and weather conditions. The algorithm was evaluated using
Soloiu, ValentinMehrzed, ShaenKroeger, LukePierce, KodySutton, TimothyLange, Robin
The rapid development of autonomous vehicles necessitates rigorous testing under diverse environmental conditions to ensure their reliability and safety. One of the most challenging scenarios for both human and machine vision is navigating through rain. This study introduces the Digitrans Rain Testbed, an innovative outdoor rain facility specifically designed to test and evaluate automotive sensors under realistic and controlled rain conditions. The rain plant features a wetted area of 600 square meters and a sprinkled rain volume of 600 cubic meters, providing a comprehensive environment to rigorously assess the performance of autonomous vehicle sensors. Rain poses a significant challenge due to the complex interaction of light with raindrops, leading to phenomena such as scattering, absorption, and reflection, which can severely impair sensor performance. Our facility replicates various rain intensities and conditions, enabling comprehensive testing of Radar, Lidar, and Camera
Feichtinger, Christoph Simon
With the continuous advancement of artificial intelligence technology, the automation level of electric vehicles (EVs) is rapidly increasing. Despite the improvements in travel efficiency, safety, and convenience brought about by automation, cutting-edge intelligent technologies also pose the potential of increased energy consumption, such as the computational power required by advanced algorithms and the energy usage of high-precision equipment, leading to higher overall energy consumption for connected or autonomous electric vehicles (CAEVs). To assess the impact of intelligent technologies on AEVs, this study innovatively provides a comprehensive evaluation of the impact of intelligent technologies on CAEV energy consumption from both positive and negative perspectives. After reviewing 59 relevant studies, the findings highlight energy savings achieved through Vehicle-to-Infrastructure and Vehicle-to-Vehicle cooperation as positive effects, while increased energy consumption from
Liu, TianyiQi, HaoOu, Shiqi (Shawn)
Several challenges remain in deploying Machine Learning (ML) into safety critical applications. We introduce a safe machine learning approach tailored for safety-critical industries including automotive, autonomous vehicles, defense and security, healthcare, pharmaceuticals, manufacturing and industrial robotics, warehouse distribution, and aerospace. Aiming to fill a perceived gap within Artificial Intelligence and ML standards, the described approach integrates ML best practices with the proven Process Failure Mode & Effects Analysis (PFMEA) approach to create a robust ML pipeline. The solution views ML development holistically as a value-add, feedback process rather than the resulting model itself. By applying PFMEA, the approach systematically identifies, prioritizes, and mitigates risks throughout the ML development pipeline. The paper outlines each step of a typical pipeline, highlighting potential failure points and tailoring known best practices to minimize identified risks. As
Schmitt, PaulSeifert, Heinz BodoBijelic, MarioPennar, KrzysztofLopez, JerryHeide, Felix
Over the decades, robotics deployments have been driven by the rapid in-parallel research advances in sensing, actuation, simulation, algorithmic control, communication, and high-performance computing among others. Collectively, their integration within a cyber-physical-systems framework has supercharged the increasingly complex realization of the real-time ‘sense-think-act’ robotics paradigm. Successful functioning of modern-day robots relies on seamless integration of increasingly complex systems (coming together at the component-, subsystem-, system- and system-of-system levels) as well as their systematic treatment throughout the life-cycle (from cradle to grave). As a consequence, ‘dependency management’ between the physical/algorithmic inter-dependencies of the multiple system elements is crucial for enabling synergistic (or managing adversarial) outcomes. Furthermore, the steep learning curve for customizing the technology for platform specific deployment discourages domain
Varpe, Harshal BabsahebColeman, JohnSalvi, AmeyaSmereka, JonathonBrudnak, MarkGorsich, DavidKrovi, Venkat N
The rapid development of open-source Automated Driving System (ADS) stacks has created a pressing need for clear guidance on their evaluation and selection for specific use cases. This paper introduces a scenario-based evaluation framework combined with a modular simulation framework, offering a scalable methodology for assessing and benchmarking ADS solutions, including but not limited to off-the-shelf designs. The study highlights the lack of clear Operational Design Domain (ODD) descriptions in such systems. Without a common understanding, users must rely on subjective assumptions, which hinders the process of accurate system selection. To address this gap, the study proposes adopting a standardised ISO 34503 ODD description format within the ADS stacks. The application of the proposed framework is showcased through a case study evaluating two open-source systems, Autoware and Apollo. By first defining the assumed system’s ODD, then selecting a relevant scenario, and establishing
Chodowiec, EmilZhang, XizheMitchell, JoeBaker, PeterKhastgir, SiddarthaJennings, Paul
Vehicles with SAE J3016TM Level 3 systems are exposed to road infrastructure, Vulnerable Road Users (VRUs), traffic and other actors on roadways. Hence safe deployment of Level 3 systems is of paramount importance. One aspect of safe deployment of SAE Level 3 systems is the application of functional safety (ISO 26262) to their design, development, integration, and testing. This ensures freedom from unreasonable risk, in the event of a system failure and sufficient provisions to maintain Dynamic Driving Task (DDT) and to initiate Minimum Risk Maneuver (MRM), in the presence of random hardware and systematic failures. This paper explores leveraging ISO 26262 standard to develop architectural requirements for enabling SAE Level 3 systems to maintain DDT and MRM during fault conditions and outlines the importance of fail-operability for Level 3 systems, from a functional safety perspective. At a high-level, UN Regulation No. 157 – Automated Lane Keeping Systems (ALKS) is used as a baseline
Mudunuri, Venkateswara RajuJayakumar, Namitha
The Automated Mobility Partnership (AMP) is a consortium of industry and academic stakeholders dedicated to advancing Automated Driving Systems (ADS) through a comprehensive suite of tools, datasets, and methodologies. The AMP portal integrates events from over 35 million miles of naturalistic driving data including thousands of annotated crashes and near-crashes and a decade of U.S. police-reported crash data curated by the Virginia Tech Transportation Institute. The portal enables data discovery, visualization, processing, and analysis through secured web access. This paper briefly describes the AMP portal and examines its utility in developing and evaluating the safety of ADS using standardized processes. For the examination, we provide examples based on generic automated driving functions, guided by the Safety of the Intended Functionality (SOTIF) framework. The results show that AMP is instrumental in identifying recorded real-world cases in which the hazardous behavior of a
Antona-Makoshi, JacoboWilliams, VickiAli, GibranSullivan, KayeTerranova, PaoloKefauver, KevinHatchett, Alex
As longitudinal Automated Driving System (ADS) technologies, such as Adaptive Cruise Control (ACC), become more prevalent, robust testing frameworks that encompass both simulation and vehicle-in-the-loop (VIL) methodologies are essential to ensure system reliability, safety, and performance refinement. Although significant research has focused on ACC algorithm development and simulation testing, existing VIL dynamometer testing frameworks are typically tailored to specific vehicle models and sensor simulation tools. These highly customized approaches often fail to account for broader interoperability while overlooking energy consumption as a key performance metric. This paper presents a novel modular framework for ACC dynamometer testing, designed to enhance interoperability across a diverse range of vehicle platforms, simulation tools, and dynamometer facilities with a focus on evaluating impacts of automated longitudinal control on the overall energy consumption of the vehicle. The
Goberville, NicholasHamilton, KaylaDi Russo, MiriamJeong, JongryeolDas, DebashisOrd, DavidMisra, PriyashrabaCrain, Trevor
The current research landscape in path tracking control predominantly focuses on enhancing tracking accuracy, often overlooking the critical aspect of passenger comfort. To address this gap, we propose a novel path tracking control method that integrates vehicle stability indicators and road curvature variations to elevate passenger comfort. The core contributions are threefold: firstly, we conduct comprehensive vehicle dynamics modeling and analysis to identify key parameters that significantly impact ride comfort. By integrating human comfort metrics with vehicle maneuverability indices, we determine the optimal range of dynamics parameters for maximizing passenger comfort during driving. Secondly, inspired by human driving behavior, we design a path tracking controller that incorporates an anti-saturation algorithm to stabilize tracking errors and a curvature optimization algorithm to mimic human driving patterns, thereby enhancing comfort. Lastly, comparative simulations with two
Lu, JunZeng, DequanHu, YimingWang, XiaoliangLiu, DengchengJiang, Zhiqiang
Autonomous Vehicles (AVs) have transformed transportation by reducing human error and enhancing traffic efficiency, driven by deep neural network (DNN) models that power image classification and object detection. However, to maintain optimal performance, these models require periodic re-training; failure to do so can result in malfunctions that may lead to accidents. Recently, Vision-Language Models (VLMs), such as LLaVA-7B and MoE-LLaVA, have emerged as powerful alternatives, capable of correlating visual and textual data with a high degree of accuracy. These models’ robustness and ability to generalize across diverse environments make them especially suited to analyzing complex driving scenarios like crashes. To evaluate the decision-making capabilities of these models across common crash scenarios, a set of real-world crash incident videos was collected. By decomposing these videos into frame-by-frame images, we task the VLMs to determine the appropriate driving action at each frame
Fernandez, DavidMohajerAnsari, PedramSalarpour, AmirPesé, Mert D.
When vehicle accidents occur, investigators rely on event data recorders for accident investigations. However current event data recorders do not support accident investigation involving automated or self-driving vehicles when there is state information that needs to be recorded, for example ADS modes, changes in the ODD that the vehicle operates under, and the various states of vehicle features such as intelligent cruise control, automated lane changes, autonomous emergency braking, and others. In this paper, we propose a model to design new types of event data recorders that supports accident investigations involving automated vehicles when there is state information to be recorded. The model is generic enough to be adapted to any automation level and any set of automated vehicle functional features. The model has been instantiated to a specific ADAS system.
Pimentel, Juan
Machine learning has witnessed widespread adoption across various domains, bringing about transformative changes in decision-making, trend prediction, task automation, and personalized experiences. Despite the remarkable predictive capabilities of machine learning models, the associated uncertainty in their predictions remains a critical concern. Uncertainty estimation plays a pivotal role in ensuring robust decision-making, going beyond mere outcome prediction to quantify the model's confidence and potential error. This paper first presents a review of existing uncertainty quantification techniques in machine learning, including Monte Carlo dropout and ensemble methods, highlighting their advantages in addressing uncertainty as well as their limitations. Then, it presents an efficient and fast novel technique for uncertainty quantification using a combination of the ensemble technique and Gaussian process regression providing an accurate estimation of uncertainty bounds. Due to its
Chavare, SudeepMourelatos, Zissimos P.
With the development of automated vehicle (AV), it is essential to ensure their safety even in the presence of system faults or function inefficiency. Safety controllability refers to the ability to manage and control the vehicle, ensuring that it remains safe even in the presence of faults with unexpected conditions. This study proposed a data driven method to evaluate quantitatively safety controllability for AVs. Safety analysis is conducted to identify the potential hazard events. Taking system function and architecture into consideration, the failure modes of the vehicle hazards are identified with hazardous driving situation. Based on the identified failure modes, fault injection tests are conducted with critical scenarios. According to the vehicle dynamic performance, the improved analytic hierarchy process (AHP) can be explored to quantitatively evaluate the safety controllability based on fault injection test results. In particular, this study focuses on the case study to
Ye, XiaomingYang, YandingLi, LingyangZhang, YaguoWang, Yongliang
Trajectory tracking control is a critical component of the autopilot system, essential for achieving high-performance autonomous driving. This paper presents the design of a stable, reliable, accurate, fast, and robust trajectory tracking controller. Specifically, a lateral and longitudinal trajectory tracking controller based on a linear parameter time-varying model predictive control (LPV-MPC) framework is designed. Firstly, a three-degree-of-freedom vehicle dynamics model and a tracking error model are established. Secondly, a multi-objective function and constraints considering tracking accuracy and lateral stability are formulated, and the quadratic programming (QP) method is employed to solve the optimization problem. Finally, PID speed tracking control is introduced in the longitudinal control scheme for comparison with the proposed MPC longitudinal speed control. A step velocity tracking test validates the effectiveness of the MPC speed tracking controller. In the lateral
Pan, ShicongLu, JunYu, YinquanZeng, DequanYang, JinwenHu, YimingJiang, ZhiqiangLiu, Dengcheng
Advanced driver assistance systems (ADAS) and automated driving systems (ADS) continue to expand into the market at a rapid pace. As improved (i.e., next generation) versions of these systems become available, they will continue to face many challenges in their implementation and benefits for safety and driving operations. The solution will involve many parties, including road safety professionals and researchers who see the potential in these systems but may have difficulties keeping up with them, and safety advocates who are calling for these systems to achieve higher levels of safety now. The Challenges of Next-gen ADAS and ADS and Related Vehicle Safety Topics explores these challenges that will fall on the National Highway Traffic Safety Administration (NHTSA) and automakers as they balance costs and benefits; establish reasonable regulations and standards; and determine how to improve, test, deliver, and use these systems successfully. Perhaps the most formidable challenge will
Chalmers, Seth
This SAE Edge Research Report explores advancements in next-generation mobility, focusing on digitalized and smart cockpits and cabins. It offers literature review, examining current customer experiences with traditional vehicles and future mobility expectations. Key topics include integrating smart cockpit and cabin technologies, addressing challenges in customer and user experience (UX) in digital environments, and discussing strategies for transitioning from traditional vehicles to electric ones while educating customers. User Experience for Digitalized and Smart Cockpits and Cabins of Next-gen Mobility covers both on- and off-vehicle experiences, analyzing complexities in developing and deploying digital products and services with effective user interfaces. Emphasis is placed on meeting UX requirements, gaining user acceptance, and avoiding trust issues due to poor UX. Additionally, the report concludes with suggestions for improving UX in digital products and services for future
Abdul Hamid, Umar Zakir
Developing safe and reliable autonomous vehicles is crucial for addressing contemporary mobility challenges. While the goal of autonomous vehicle development is full autonomy, up to SAE Level 4 and beyond, human intervention remains necessary in critical or unfamiliar driving scenarios. This article introduces a method for gracefully degrading system functionality and seamlessly transferring decision-making and control between the autonomous system and a remote safety operator when needed. This transfer is enabled by an onboard dependability cage, which continuously monitors the vehicle’s performance during its operation. The cage communicates with a remote command control center, allowing for remote supervision and intervention by a safety driver. We assess this methodology in both lab and test field settings in a case study of last-mile parcel delivery logistics and discuss the insights and results obtained from these evaluations.
Aniculaesei, AdinaAslam, IqraZhang, MengBuragohain, AbhishekVorwald, AndreasRausch, Andreas
Systematic testing of Automated Driving Systems (ADS) requires finding relevant test cases. The extraction of critical cases, also called edge or corner cases, from naturalistic driving data is a complex task and often prone to multiple errors. Large Language Models (LLMs) have been employed for virtual testing of ADS in recent years; however, quantitatively benchmarking LLMs’ performance in this task has been barely investigated. In this paper, based on the characteristics of different LLMs, six LLMs were selected for benchmarking the LLMs’ ability to understand ADS functional scenarios on motorways. A novel scenario classification model was introduced to enhance the granularity of data categorization for motorway driving scenarios. Different driving scenarios, described in natural language, were defined for testing the capability of these LLMs to understand various scenarios and convert them into standardized structured data. To perform the benchmarking in a standardized manner, the
Zhou, JiZhao, YongqiYang, AixiEichberger, Arno
Scenario-based testing has become a central approach of safety verification and validation (V&V) of automated driving. The standard ISO 21448: Safety of the intended functionality (SOTIF) [1] proposes triggering conditions (e.g., an occluded traffic sign) as a new aspect to be considered to organize scenario-based testing. In this contribution, we discuss the requirements and the strategy of testing triggering conditions in an iterative, SOTIF-oriented V&V process. Accordingly, we illustrate a method for generating test scenarios for evaluating potential triggering conditions. We apply the proposed method in a two-fold case study: We demonstrate how to derive test scenarios and test these with a virtual automated driving system in simulation. We provide an analysis of the testing result to show how triggering condition-based testing facilitates spotting the weakness of the system. Besides, we exhibit the applicability of the method based on multiple triggering conditions and nominal
Zhu, ZhijingPhilipp, RobinHowar, Falk
Path-tracking control occupies a critical role within autonomous driving systems, directly reflecting vehicle motion and impacting both safety and user experience. However, the ever-changing vehicle states, road conditions, and delay characteristics of control systems present new challenges to the path tracking of autonomous vehicles, thereby limiting further enhancements in performance. This article introduces a path-tracking controller, time-varying gain-scheduled path-tracking controller with delay compensation (TGDC), which utilizes a linear parameter-varying system and optimal control theory to account for time-varying vehicle states, road conditions, and steering control system delays. Subsequently, a polytopic-based path-tracking model is applied to design the control law, reducing the computational complexity of TGDC. To evaluate the effectiveness and real-time capability of TGDC, it was tested under a series of complex conditions using a hardware-in-the-loop platform. The
Hu, XuePengZhang, YuHu, YuxuanWang, ZhenfengQin, Yechen
Soft-bending actuators have garnered significant interest in robotics and biomedical engineering due to their ability to mimic the bending motions of natural organisms. Using either positive or negative pressure, most soft pneumatic actuators for bending actuation have modified their design accordingly. In this study, we propose a novel soft bending actuator that utilizes combined positive and negative pressures to achieve enhanced performance and control. The actuator consists of a flexible elastomeric chamber divided into two compartments: a positive pressure chamber and a negative pressure chamber. Controlled bending motion can be achieved by selectively applying positive and negative pressures to the respective chambers. The combined positive and negative pressure allowed for faster response times and increased flexibility compared to traditional soft actuators. Because of its adaptability, controllability, and improved performance can be used for various jobs that call for careful
Lalson, AbiramiSadique, Anwar
Autonomous vehicles utilise sensors, control systems and machine learning to independently navigate and operate through their surroundings, offering improved road safety, traffic management and enhanced mobility. This paper details the development, software architecture and simulation of control algorithms for key functionalities in a model that approaches Level 2 autonomy, utilising MATLAB Simulink and IPG CarMaker. The focus is on four critical areas: Autonomous Emergency Braking (AEB), Adaptive Cruise Control (ACC), Lane Detection (LD) and Traffic Object Detection. Also, the integration of low-level PID controllers for precise steering, braking and throttle actuation, ensures smooth and responsive vehicle behaviour. The hardware architecture is built around the Nvidia Jetson Nano and multiple Arduino Nano microcontrollers, each responsible for controlling specific actuators within the drive-by-wire system, which includes the steering, brake and throttle actuators. Communication
Ann Josy, TessaSadique, AnwarThomas, MerlinManaf T M, AshikVr, Sreeraj
Soft-bending actuators are gaining considerable attention in robotics for handling delicate objects and adapting to complex shapes, making them ideal for biomimetic robots. Soft pneumatic actuators (SPAs) are preferred in soft robotics because to their safety and compliance characteristics. Using negative pressure for actuation, it enhances stability by reducing the risk of sudden or unintended movements, crucial for delicate handling and consistent performance. Negative pressure actuation is more energy-efficient, safe and are less prone to leakage, increasing reliability and durability. This paper involves development of a new soft pneumatic actuator design by comparing various designs and to determine its performance parameters. This paper depicts on designing, and fabricating flexible soft pneumatic actuators working under negative pressure for soft robotic applications. The material used for fabrication was liquid silicone rubber and uniaxial tensile tests were conducted to
Warriar J S, SreejithSadique, AnwarGeorge, Boby
The growing ubiquity of autonomous vehicles (AVs) has introduced a new attack surface for malicious actors: the embedded systems that govern a vehicle's critical operations. Security breaches in these systems could have catastrophic consequences, potentially leading to loss of control, manipulation of sensor data, or even physical harm. To mitigate these risks, robust cybersecurity measures are paramount. This research delves into a specific threat – side-channel attacks – where attackers exploit data leakage through unintentional physical emanations, like power consumption or electromagnetic waves, to steal cryptographic keys or sensitive information. While various software and hardware countermeasures have been proposed, this study focuses on the implementation of masking techniques within the realm of embedded security. Masking techniques aim to obfuscate sensitive data during cryptographic operations, making it significantly harder for attackers to exploit side-channel
Deepan Kumar, SadhasivamR, Vishnu Ramesh KumarM, BoopathiManojkumar, RR, GobinathM, Vignesh
This article addresses the machines and automated guided vehicles (AGVs) concurrent scheduling with alternative machines in a multi-machine flexible manufacturing system (FMS) in order to provide the best optimum sequences for the minimization of makespan (MKSN).The assignment of AGVs and related trips, such as the dead headed trip and loaded trip times of AGVs to jb-ons, as well as the decision to select machines for job-operations (jb-ons) and the sequencing of jb-ons on the machines, make this problem extremely difficult to solve. This paper offers a mixed integer nonlinear programming (MINLP) formulation for modeling the problem, as well as the crow search algorithm (CSA) to solve the problem. For verification, a manufacturing company's industrial problem is employed. The findings indicate that CSA performs better than the existing techniques, and that the utilization of alternative machines for the operations can bring the MKSN and cost down.
Mareddy, Padma LalithaReddy K, AjayaKatta, Lakshmi NarasimhamuSiva Rami Reddy, Narapureddy
This SAE Recommended Practice establishes a uniform, powered vehicle test procedure and minimum performance requirement for lane departure warning systems used in highway trucks and buses greater than 4546 kg (10000 pounds) gross vehicle weight (GVW). Systems similar in function but different in scope and complexity, including lane keeping/lane assist and merge assist, are not included in this document. This document does not apply to trailers, dollies, etc. This document does not intend to exclude any particular system or sensor technology. This document will test the functionality of the lane departure warning system (LDWS) (e.g., ability to detect lane presence and ability to detect an unintended lane departure), its ability to indicate LDWS engagement, its ability to indicate LDWS disengagement, and its ability to determine the point at which the LDWS notifies the human machine interface (HMI) or vehicle control system that a lane departure event is detected. Moreover, this
Truck and Bus Automation Safety Committee
Intelligent vehicles can utilize a variety of sensors, computing, and control technologies to autonomously perceive the environment and make decisions to achieve safe, efficient, and automated driving. If the speed planning of intelligent vehicles ignores the vehicle dynamics state, it leads to unreasonable planning speed and is not conducive to improving the accuracy of trajectory tracking control. Meanwhile, trajectory tracking usually does not consider the road and speed information beyond the prediction horizon, resulting in poor tracking precision that is not conducive to improving driving comfort. To solve these problems, this study proposes a new longitudinal speed planning method based on variable universe fuzzy rules and designs the piecewise preview model predictive control (PPMPC) to realize the vehicle trajectory tracking. First, the three-degrees-of-freedom vehicle dynamics model and trajectory tracking model are established and verified. Then, the variable universe fuzzy
Zhang, JieTeng, ShipengGao, JianjieZhou, XingxingZhou, Junchao
To tackle the challenge of accurately predicting collision times for autonomous vehicles navigating complex dynamic obstacles, this paper proposes an innovative Angle-Weighted Time-to-Collision (AW-TTC) algorithm. Traditional TTC algorithms are known for their computational simplicity and strong real-time performance, making them widely applicable across various driving scenarios. However, they often struggle with predictive accuracy when encountering obstacles moving at angles, which can delay vehicle response and compromise overall safety. To address this limitation, this study introduces a modification to the traditional TTC algorithm by incorporating an angle-based weighting factor, improving collision time prediction accuracy. A Hardware-in-the-Loop (HIL) experimental setup was developed, utilizing a Vehicle Control Unit (VCU) and the SCANeR simulation platform to simulate dynamic obstacles in complex traffic scenarios. The AW-TTC algorithm’s performance was then evaluated
Zhu, ShaopengTang, YuanningLi, BingWang, XiaoliangChen, Huipeng
The application of millimeter-wave radar technology in autonomous driving has become increasingly widespread with the rapid development of intelligent transportation systems. However, millimeter-wave radar is easily affected by environmental noise, multipath reflections, and electromagnetic interference, resulting in a large number of invalid target signals that reduce the system’s detection accuracy and safety. We proposes a method for filtering invalid targets based on interference signal characteristics and an Adaptive Interactive Multiple Model Kalman Filter (IMM-KF) target tracking algorithm. First, we effectively filter out empty targets, ghost targets, and false targets through a threshold method and lifecycle assessment, achieving a filtering rate exceeding 99.8%. Second, the improved Adaptive IMM-KF algorithm, combined with the Hungarian algorithm, associates and tracks multiple targets. The root mean square error (RMSE) of our methods is reduced by 7.07% and 8.05% compared to
Liu, QiSong, KangXie, HuiMeng, Chunyang
The permanent magnet synchronous motor (PMSM) has become the preferred driving technology in robotic control engineering due to its high-power density and excellent dynamic response capability. However, traditional vector control strategies, while widely used, reveal certain limitations due to their reliance on high-precision sensors and the complex coordinate transformation calculations. These limitations affect the performance of robots in high-speed environments. This paper proposes a decoupling design for the PMSM current loop based on Internal model control (IMC), aiming to improve control accuracy and response speed by simplifying the control algorithm. This new strategy not only maintains the basic framework of vector control but also enhances the dynamic performance of the system through effective decoupling. Simulations conducted using Simulink demonstrate that this strategy significantly improves system stability and dynamic response speed, achieving more precise and rapid
Chen, HaoHuan, DiGong, ChaoLiu, Chenliang
During the operation of autonomous mining trucks in the process of crushing stones, the GPS signal is lost due to signal blockage by the crushing workshop. Simultaneous Localization and Mapping (SLAM) becomes critical for ensuring accurate vehicle positioning and smooth operation. However, the bumpy road conditions and the scarcity of plane and corner feature points in mining environments pose challenges to SLAM algorithms in practical applications, such as pose jumps and insufficient positioning accuracy. To address this, this paper proposes a high-precision positioning algorithm based on inertial navigation 3D signals, incorporating point cloud motion distortion correction, a vehicle roll model, and an Adaptive Kalman Filter (AKF). The goal is to improve the positioning accuracy and stability of autonomous mining trucks in complex scenarios. This paper utilizes real-world operational data from mining vehicles and adopts a 3D point cloud motion distortion correction algorithm to
Meng, ChunyangSong, KangXie, HuiXing, Wanyong
Adaptive cruise control (ACC) systems have increasingly become more robust in adapting to the motion of the preceding vehicle and providing safety and comfort to the driver. But conventional ACC hangs with a concern for rear-end safety in the presence of traffic or aggressive car maneuvers. It often leads to getting dangerously close to the vehicle behind in scenarios where there is less space and time for the rear vehicle to adjust. This research article develops an ACC approach that considers the rear vehicle in addition to the front vehicle, thereby ensuring safety with the rear vehicle without compromising the safety of the front vehicle. Two novel methodologies are devised to enhance the ACC system. The first approach involves utilizing fuzzy logic to associate the inputs with the throttle and brake based on the inference rules within a fuzzy logic controller overseeing both vehicles. The other utilizes a cascaded model predictive control (MPC) system framework that integrates a
Sharma, VishrutSengupta, SomnathGhosh, Susenjit
Nowadays, there are many technologies emerging like firefighting robots, quadcopters, and drones which are capable of operating in hazardous disaster scenarios. In recent years, fire emergencies have become an increasingly serious problem, leading to hundreds of deaths, thousands of injuries, and the destruction of property worth millions of dollars. According to the National Crime Records Bureau (NCRB), India recorded approximately 1,218 fire incidents resulting in 1,694 deaths in 2020 alone. Globally, the World Health Organization (WHO) estimates that fires account for around 265,000 deaths each year, with the majority occurring in low- and middle-income countries. The existing fire-extinguishing systems are often inefficient and lack proper testing, causing significant delays in firefighting efforts. These delays become even more critical in situations involving high-rise buildings or bushfires, where reaching the affected areas is particularly challenging. The leading causes of
Karthikeyan, S.Nithish, U.Sanjay, S.Sibiraj, T.Vishnu, J.
Connected and autonomous vehicles (CAVs) rely on communication channels to improve safety and efficiency. However, this connectivity leaves them vulnerable to potential cyberattacks, such as false data injection (FDI) attacks. We can mitigate the effect of FDI attacks by designing secure control techniques. However, tuning control parameters is essential for the safety and security of such techniques, and there is no systematic approach to achieving that. In this article, our primary focus is on cooperative adaptive cruise control (CACC), a key component of CAVs. We develop a secure CACC by integrating model-based and learning-based approaches to detect and mitigate FDI attacks in real-time. We analyze the stability of the proposed resilient controller through Lyapunov stability analysis, identifying sufficient conditions for its effectiveness. We use these sufficient conditions and develop a reinforcement learning (RL)-based tuning algorithm to adjust the parameter gains of the
Javidi-Niroumand, FarahnazSargolzaei, Arman
This article provides a comprehensive review of existing literature on AI-based functions and verification methods within vehicular systems. Initially, the introduction of these AI-based functions in these systems is outlined. Subsequently, the focus shifts to synthetic environments and their pivotal role in the verification process of AI-based vehicle functions. The algorithms used within the AI-based functions focus primarily on the paradigm of deep learning. We investigate the constituent components of these synthetic environments and the intricate relationships with vehicle systems in the verification and validation domain of the system. In the following, alternative approaches are discussed, serving as complementary methods for verification without direct involvement in synthetic environment development. These approaches include data-oriented methodologies employing statistical techniques and AI-centric strategies focusing solely on the core deep learning algorithm.
Aslandere, TurgayDurak, Umut
This report explores the move from traditional industry practices to emerging technologies, specifically the integration of artificial intelligence (AI) solutions in engineering service sectors. It highlights the increasing problem of “technology washing,” when organizations overstate (sometimes deceivingly) their technology abilities and ethics, posing challenges to accountability, transparency, and trust in various fields. The rise of AI-based solutions in sectors like autonomous mobility, manufacturing, and aerospace has exposed a contrast between ambitious future aspirations and current technological barriers. With this, the role of human knowledge in guaranteeing ethical, efficient, and clear technology incorporation becomes essential. Starting with an examination of today’s technological scene, this report tackles topics such as the buzz around autonomous systems and the difficulties of standardizing fresh innovations. It also points out the problem of organizations exaggerating
Khan, Samir
Items per page:
1 – 50 of 3018