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1、1Autonomous Vehicles:Evolution of Artifi cialIntelligence and Learning AlgorithmsDivya Garikapati,Senior Member,IEEE,Sneha Sudhir Shetiya,Senior Member,IEEEAbstractThe advent of autonomous vehicles has heralded atransformative era in transportation,reshaping the landscape ofmobility through cutting-

2、edge technologies.Central to this evolu-tion is the integration of Artificial Intelligence(AI)and learningalgorithms,propelling vehicles into realms of unprecedentedautonomy.This paper provides a comprehensive exploration ofthe evolutionary trajectory of AI within autonomous vehicles,tracing the jou

3、rney from foundational principles to the mostrecent advancements.Commencing with a current landscape overview,the paperdelves into the fundamental role of AI in shaping the autonomousdecision-making capabilities of vehicles.It elucidates the stepsinvolved in the AI-powered development life cycle in

4、vehicles,addressing ethical considerations and bias in AI-driven softwaredevelopment for autonomous vehicles.The study presents statis-tical insights into the usage and types of AI/learning algorithmsover the years,showcasing the evolving research landscape withinthe automotive industry.Furthermore,

5、the paper highlights thepivotal role of parameters in refining algorithms for both trucksand cars,facilitating vehicles to adapt,learn,and improveperformance over time.It concludes by outlining different levelsof autonomy,elucidating the nuanced usage of AI and learningalgorithms,and automating key

6、tasks at each level.Additionally,the document discusses the variation in software package sizesacross different autonomy levels.Index TermsArtificial Intelligence(AI),Machine Learning(ML),Deep Neural Networks(DNNs),Natural Language Process-ing(NLP),Autonomous Vehicles(AVs),Safety,Security,Ethics,Eme

7、rging Trends,Trucks vs.Cars,Autonomy Levels,OperationalDesign Domain(ODD),Software-Defined Vehicles(SDVs),Con-nected and Automated Vehicles(CAVs),In-Vehicle AI Assistant,Internet Of Things(IOT),Natural Language Processing(NLP),Generative AI(GenAI).I.INTRODUCTIONARTIFICIAL Intelligence(AI)and learnin

8、g algorithmssuch as Machine Learning(ML),Deep Learning usingDeep Neural Networks(DNNs)and Natural Language Pro-cessing(NLP)currently play a crucial role in the develop-ment and operation of autonomous vehicles.The integrationof AI and learning algorithms enable autonomous vehiclesto navigate,perceiv

9、e,and adapt to dynamic environments,making them safer and more effi cient.Continuous advance-ments in AI technologies are expected to further enhance thecapabilities and safety of autonomous vehicles in the future.Autonomous system development has been experiencing atransformational evolution throug

10、h the integration of Artifi-cial Intelligence(AI).This revolutionary combination holdsCorresponding Author:Divya Garikapati is a Senior IEEE Member.E-mail:(divygariumich.edu,divya.garikapatiieee.org),Sneha.S.Shetiya is a Senior IEEE Member.Email:(sneha.shetiyaieee.org)Manuscript created January 20,2

11、024.the promise of reshaping traditional development processes,enhancing effi ciency,and accelerating innovation.AI tech-nologies are becoming integral in numerous facets of softwaredevelopment within autonomous vehicles making a paradigmshift towards Software-Defi ned Vehicles(SDVs)12.The success o

12、f autonomous vehicles hinges on balancingtheir potential benefi ts with addressing the challenges throughcollaborative efforts in technology development,regulation,and public communication.Some of the challenges include:Safety and Reliability:Ensuring flawless AI performancein all scenarios is param

13、ount.Regulations and Law:Clear standards for safety,insur-ance,and liability are needed.Public Trust and Acceptance:Addressing concerns aboutsafety,data privacy,and ethical dilemmas is crucial.Cybersecurity:Protecting against hacking and unautho-rized access is essential.Ethical Dilemmas:Defi ning A

14、I decision-making in am-biguous situations raises moral questions.Addressing Edge cases:Being able to handle unforeseenscenarios is challenging as those scenarios are rare andcould be hard to imagine in some cases.A.Benefits of AI/Learning Algorithms for AutonomousVehiclesAI/Learning Algorithms are

15、currently influencing variousstages from initial coding to post-deployment maintenance inautonomous vehicles.Some of the benefi ts include:Safety:AI can signifi cantly reduce accidents by eliminat-ing human error,leading to safer roads.Traffic Flow:Platooning and effi cient routing can easecongestio

16、n and improve effi ciency.Accessibility:People with physical impairments or dif-ferent abilities,the elderly,and the young can gainindependent mobility.Energy Savings:Optimized driving reduces fuel con-sumption and emissions.Productivity and Convenience:Passengers use traveltime productively while d

17、elivery services become moreeffi cient.AI in autonomous vehicles is poised for a bright future,shaping everyday life and creating exciting opportunities.Heres a glimpse of the possibilities:1)Technological Advancements:Sharper perception and decision-making:AI algorithmsare more adept at understandi

18、ng environments with ad-vanced sensors and robust machine learning.arXiv:2402.17690v2 cs.LG 28 Feb 20242Faster,more autonomous operation:Edge computingenables on-board AI processing for quicker decisions andgreater independence.Enhanced safety and reliability:Redundant systems andrigorous fail-safe

19、mechanisms prioritizes safety above allelse.2)Education and Career Boom:Surging demand for AI expertise:Specialized courses anddegrees in autonomous vehicle technology will cater toa growing need for AI,robotics,and self-driving carprofessionals.Interdisciplinary skills will be key:Professionals wit

20、hcross-functional skills bridging AI,robotics,and trans-portation will be highly sought after.New career paths in safety and ethics:Expertise inethical considerations,safety audits,and regulatory 3compliance will be crucial as self-driving cars becomewidespread.3)Regulatory Landscape:Standardized sa

21、fety guidelines:Governments will estab-lish common frameworks for performance and safety,building public trust and ensuring industry coherence.Stringent testing and validation:Autonomous systemswill undergo rigorous testing before deployment,guar-anteeing reliability and safety standards.Data privac

22、y and security safeguards:Laws and regula-tions will address data privacy and cybersecurity con-cerns,protecting personal information and mitigatingcyberattacks.Ethical and liability frameworks:Clearly defi ned legalframeworks will address ethical decision-making and de-termine liability in situatio

23、ns involving self-driving cars.This future holds immense potential for revolutionizing trans-portation,creating new jobs,and improving safety.However,navigating ethical dilemmas,ensuring robust regulations,andbuilding public trust will be crucial for harnessing this tech-nology responsibly and susta

24、inably.B.Operational Design Domains(ODDs)Expansions intonew areas and Diversity-The Current Industry LandscapeThese examples illustrate the diverse evolution of Opera-tional Design Domains(ODDs)4 across various vehicletypes,including trucks and cars,and within different geograph-ical locations such

25、as the United States,China,and Europe.Waymo Driver:Can handle a wider range of weatherconditions,city streets,and highway driving,but speedlimitations and geo-fencing restrictions apply.Tesla Autopilot:Primarily for highway driving with lanemarkings,under driver supervision,and within specifi cspeed

26、 ranges.Mobileye Cruise AV:Operates in sunny and dry weather,on highways with clearly marked lanes,and at speedsbelow 45 mph.Aurora and Waymo via:Wider range of weather condi-tions,including light rain/snow.Variable lighting(sun-rise/sunset),Multi-lane highways and rural roads withgood pavement qual

27、ity,Daytime and nighttime operation,moderate traffi c density,dynamic route planning,Traffi clight/stop sign recognition,intersection navigation,ma-neuvering in yards/warehouses etc.,TuSimple and Embark Trucks:Sunny,dry weather,clearvisibility.Temperature range-10C to 40C,Limited-access highways wit

28、h clearly marked lanes,Daytimeoperation only,maximum speed 70 mph,limited traf-fi c density,pre-mapped routes,Lane changes,highwaymerging/exiting,platooning with other AV trucks etc.,Pony.ai and Einride:Diverse weather conditions,in-cluding heavy rain/snow.Variable lighting and complexurban environm

29、ents,Narrow city streets,residential areas,and parking lots,Low speeds(20-30 mph),high traffi cdensity,frequent stops and turns,geo-fenced deliveryzones,Pedestrian and cyclist detection/avoidance,obsta-cle avoidance in tight spaces,dynamic rerouting due tocongestion etc.,Komatsu Autonomous Haul Truc

30、ks,Caterpillar MineS-tar Command for Haul Trucks:Harsh weather con-ditions(dust,heat,extreme temperatures).Limited orno network connectivity,Unpaved roads,uneven terrain,steep inclines/declines,Autonomous operation with re-mote monitoring,pre-programmed routes,high groundclearance,Obstacle detection

31、 in unstructured environ-ments,path planning around natural hazards,dust/fogmitigation,etc.,Baidu Apollo:Highways and city streets in specifi c zoneslike Beijing and Shenzhen.Operates in daytime andnighttime,under clear weather conditions,and limitedtraffi c density.Designed for passenger transporta

32、tion androbotaxis.Specifi c scenarios include Lane changes,high-way merging/exiting,traffi c light/stop sign recognition,intersection navigation,low-speed maneuvering in urbanareas.WeRide:Limited-access highways and urban streets inGuangzhou and Nanjing.Operates in daytime and night-time,under clear

33、 weather conditions.Targeted for rob-otaxi services and last-mile delivery.Specifi c scenariosinclude Lane changes,highway merging/exiting,traffi clight/stop sign recognition,intersection navigation,auto-mated pick-up and drop-off for passengers/packages.Bosch&Daimler:Motorways and specifi c highway

34、sin Germany.Operates in daytime and nighttime,undergood weather conditions.Focused on highway truckingapplications.Specifi c scenarios include Platooning withother AV trucks,automated lane changes and overtak-ing,emergency stopping procedures,communication withtraffi c management systems.Volvo Truck

35、s:Defi ned sections of Swedish highways.Operates in daytime and nighttime,under varying weatherconditions.Tailored for autonomous mining and quarryoperations.Specifi c scenarios include Obstacle detectionand avoidance in unstructured environments,path plan-ning around natural hazards,pre-programmed

36、routes withhigh precision,remote monitoring and control.In this paper,we discuss the AI-powered software develop-3ment lifecycle for autonomous vehicles and discuss the detailson how to ensure software quality,security and resolve ethicaldilemmas by taking different biases into account during thedev

37、elopment of the AI algorithms.We explain how the AIalgorithms have been emerging and evolving over time to havemore and more decision making capabilities without humaninvolvement using IOT as a future direction of expansion forautonomous vehicles to being more connected to other actorsin the driving

38、 environment.In-cabin experience enhancementsand Driver Assistant Systems were also discussed as part of theemerging trends.A literature survey of how the AI algorithmsare being used within autonomous vehicles has been providedin Section V.In Section VI,we have also provided certainstatistics on how

39、 the use of AI and Learning algorithms havebeen evolving over time,how the research in these areas hasbeen trending over time,different AI model parameters beingconsidered for autonomous trucks vs.passenger cars etc.,.Another interesting statistic on how the use of AI and Learningalgorithms change b

40、ased on the levels of autonomy was alsoprovided.II.THEAI-POWEREDDEVELOPMENTLIFE-CYCLE INAUTONOMOUSVEHICLESThis section describes about the key aspects involved withthe AI-powered development life cycles within autonomousvehicles and these could be applicable to other fi elds as wellin general.A.Mode

41、l Training and DeploymentAI model training and deployment in autonomous vehiclesinvolves a systematic process and typically includes severalstages:Data Collection and Pre-processing:Gathering a vastamount of data from real-world sensors,pre-existing datasetsand other sources such as synthetic datase

42、ts.Cleaning and pre-processing the data to make it suitable for machine learningmodels.Model Training:Employ learning models such as neuralnetworks,deep learning 5,or natural language processing(NLP)to understand patterns and structures based on the data.Training the models to a desired level of acc

43、uracy based oneach scenario or in generic abstract cases like being able toextract the patterns during the live operation of the vehicles.Model Generation:Train models to perform a certain de-cision making task,functions,or modules based on learnedpatterns.These models can use various architectures,

44、such asdecision trees,random forests,regression trees,deep layers,ensemble learning etc.,Code Refinement and Optimization6:Refi ne the gener-ated code to improve its quality,readability,and functionality.Post-generation processing ensures the code adheres to codingstandards,conventions 7 and require

45、ments.Quality Assessment:Evaluate the generated code for cor-rectness,effi ciency,and adherence to the intended functional-ities.This involves testing,debugging,and validation proce-dures.Fig.1.AI-Powered Development Life-CycleIntegration and Deployment:Integrate the model into thebroader system und

46、er development for autonomy implemen-tation.Deploy and test the software application incorporatingthe new model using multiple methods like software-in-the-loop,hardware-in-the-loop,human-in-the-loop etc.,using sim-ulation,closed course and limited public road environments.Some models are trained to

47、 improve their learning even afterdeployment.These models need to be tested for future direc-tions of learning to ensure compliance to ethical considerationsas explained in Section III and other requirements.Using a systematic process like this would help buildthe confi dence levels on each model be

48、ing developed anddeployed in various subsystems of autonomous vehicles likeperception,planning,controls and Human-Machine Interface(HMI)applications.B.Ensuring Software Quality and SecurityIn autonomous vehicles,the integration of AI in variousaspects of software development and maintenance plays ac

49、rucial role in ensuring the robustness and security of theoverall system.Automated testing,powered by AI-based tools,emerges as a key component in the testing process.These toolseffi ciently identify bugs,vulnerabilities,and ensure that thesoftware functions as intended,contributing to the reliabili

50、tyof autonomous vehicle software.Additionally,AI extends itscapabilities to code analysis and review,providing a thoroughexamination of the codebase for quality and highlighting po-tential issues or vulnerabilities.Predictive maintenance,facili-tated by AI,becomes essential for anticipating and addr

51、essingpotential software failures,ultimately reducing downtime andenhancing the overall operational effi ciency of autonomousvehicles.Moreover,AI-driven anomaly detection and securitymonitoring contribute signifi cantly to the safety of autonomousvehicles.By continuously monitoring the software envi

52、ron-ment,AI systems can identify abnormal patterns or behaviors,promptly responding to potential security threats in real time.Vulnerability assessment,another application of AI tools,con-ducts in-depth evaluations to pinpoint weaknesses in softwaresystems,providing valuable insights to mitigate ris

53、ks effec-tively.Behavioral analysis powered by AI proves instrumentalin understanding user interactions within the software.Thiscapability aids in detecting and preventing suspicious or ma-licious activities,fostering a secure and reliable autonomousvehicle ecosystem.Finally,AIs role in fraud detect

54、ion withinsoftware applications adds an extra layer of security,ensuring4the integrity of autonomous vehicle systems and safeguardingagainst potential security breaches.In summary,the integrationof AI in these diverse areas signifi cantly enhances the overallsafety,security,and effi ciency of autono

55、mous vehicles.III.ETHICALCONSIDERATIONS ANDBIAS INAI-DRIVENSOFTWAREDEVELOPMENT FORAUTONOMOUS VEHICLESTo address the challenges related to bias,understanding andaddressing these concerns are crucial for building responsibleand fair AI-driven software for autonomous vehicles.Here arekey points highlig

56、hting ethical considerations and bias in AI-driven software development:1)Data Bias:Challenge:AI models learn from historical data,and if the training data is biased,the model canperpetuate and amplify existing biases.Mitigation:Rigorous data pre-processing,diversityin training data,and continuous m

57、onitoring for biasare essential.Ethical data collection practices mustbe upheld.2)Algorithmic Bias:Challenge:Algorithms may inadvertently encodebiases present in the training data,leading to dis-criminatory outcomes.Mitigation:Regular audits of algorithms for bias,transparency in algorithmic decisio

58、n-making,andthe incorporation of fairness metrics during modelevaluation.3)Fairness and Accountability:Challenge:Ensuring fair outcomes and establishingaccountability for AI decisions is complex,espe-cially when models are opaque.Mitigation:Implementing explainable AI(XAI)techniques,defi ning clear

59、decision boundaries,andestablishingaccountabilityframeworksforAI-generated decisions.4)Explainability and Transparency:Challenge:Many AI models operate as”blackboxes,”making it challenging to understand howdecisions are reached.AI safety is another challengethat needs to be made sure is safety-criti

60、cal appli-cations like autonomous vehicles9Mitigation:Prioritizing explainability 8 in AImodels,using interpretable algorithms,and provid-ing clear documentation on model behavior.5)User Privacy:Challenge:AI systems often process vast amountsof personal data,raising concerns about user pri-vacy.Miti

61、gation:Implementing privacy-preserving tech-niques,obtaining informed consent,and adheringto data protection regulations(e.g.,GDPR 9)tosafeguard user privacy.6)Security Concerns:Challenge:AI models can be vulnerable to adver-sarial attacks,posing security risks.Mitigation:Robust testing against adve

62、rsarial sce-narios,incorporating security measures,and regularupdates to address emerging threats.7)Inclusivity and Accessibility:Challenge:Biases in AI can result in excludingcertain demographics,reinforcing digital divides.Mitigation:Prioritizing diversity in developmentteams,actively seeking user

63、 feedback,and conduct-ing accessibility assessments to ensure inclusivity.8)Social Impact:Challenge:The deployment of biased AI systemscan have negative social implications,affectingmarginalized communities disproportionately.Mitigation:Conducting thorough impact assess-ments,involving diverse stake

64、holders in the de-velopment process,and considering societal conse-quences during AI development.9)Continuous Monitoring and Adaptation:Challenge:AI models may encounter new biasesor ethical challenges as they operate in dynamicenvironments.Mitigation:Establishing mechanisms for ongoingmonitoring,fe

65、edback loops,and model adaptationto address evolving ethical considerations.10)Ethical Frameworks and Guidelines:Challenge:The absence of standardized ethicalframeworks can lead to inconsistent practices in AIdevelopment.Mitigation:Adhering to established ethical guide-lines,such as those provided b

66、y organizations likethe ISO,IEEE,SAE,Government regulatory boardsetc.,and actively participating in the development ofindustry-wide standards.Addressing ethical considerations and bias in AI-driven soft-ware development in autonomous vehicles requires a holisticand proactive approach10.It involves a

67、 commitment tofairness,transparency,user privacy,and social responsibilitythroughout the AI development lifecycle.As the fi eld evolves,continuous efforts are needed to refi ne ethical practices andpromote responsible AI deployment.IV.AIS ROLE IN THEEMERGING TREND OFINTERNET OFTHINGS(IOT)ECOSYSTEM F

68、ORAUTONOMOUS VEHICLESArtifi cial Intelligence(AI)plays a crucial role in shapingand enhancing the capabilities of the Internet of Things(IoT).Heres an overview of how AI contributes to the IoTEcosystem for Autonomous vehiclesIn the realm of Connected and Autonomous Vehicles(CAVs),AI and IoT converge

69、 to create a seamless networkof intelligence and connectivity,transforming the drivingexperience.Vehicles become intelligent agents,processingsensor data in real-time to make informed decisions:predictingtraffi c patterns,optimizing routes,detecting anomalies,andeven adapting to changing road condit

70、ions with dynamicadjustments.This intelligent ecosystem extends beyond indi-vidual vehicles,interconnecting with infrastructure and other5Fig.2.AIs role in the Internet of Things(IOT)Ecosystem for Autonomousvehiclesvehicles to optimize traffi c flow,anticipate potential hazards,and personalize the d

71、riving experience.Key AI-powered IoT capabilities in CAVs include thefollowing:Real-time data processing and analysis for insights intotraffi c,road conditions,and vehicle health.Predictive analytics for proactive maintenance,effi cientresource allocation,and informed decision-making.Enhanced automa

72、tion for autonomous driving tasks,adaptive cruise control,and dynamic route optimization.Effi cient resource management for optimizing energyconsumption,bandwidth usage,and load balancing.Security and anomaly detection for identifying potentialthreats and preventing cyberattacks.Personalized user ex

73、perience through customized set-tings,preferences,and tailored insights.Edge computing for real-time decision-making,reducinglatency and improving responsiveness.Challenges to address include ensuring data privacy,security,interoperability,and overcoming resource constraints in con-nected vehicles.T

74、he seamless integration of AI and IoT holdsthe potential to revolutionize transportation,leading to safer,more effi cient,and sustainable 11 mobility solutions.A.Enhancing User ExperiencePersonalization and Recommendation Systems in-cabin:AI-driven personalization and recommendation systems inAutono

75、mous vehicles use machine learning models to analyzeuser behavior and preferences,creating personalized recom-mendations for tools,libraries,and vehicle maneuvers.Theycollect and pre-process user data,create individual profi les,generate tailored suggestions,and continuously adapt basedon real-time

76、interactions,aiming to enhance user experienceand developer productivity.Natural Language Processing(NLP)in-cabin:NLP en-ables software to comprehend and process human language.This includes chat bots 12,virtual assistants and voicerecognition systems that understand and respond to naturallanguage q

77、ueries in vehicle cabins.It allows the vehicle sub-systems to analyze and derive insights from user requirementsand structuring requirements effectively to create responsesand certain vehicle maneuvers.Generative Artificial Intelligence(Gen AI):This technol-ogy uses machine learning algorithms to pr

78、oduce new andoriginal outputs based on the patterns and information it haslearned from training data.In the context of vehicles,genera-tive AI can be applied to various aspects,including natural lan-guage processing for in-car voice assistants,content generationfor infotainment systems,and even simu

79、lation scenarios fortesting autonomous driving systems.Large Language Models(LLMs)are a specifi c class of generative AI models thatare trained on massive amounts of text data to understandand generate human-like language.In vehicles,LLMs can beemployed for natural language understanding and generat

80、ion,allowing for more intuitive and context-aware interactionsbetween the vehicle and its occupants.This can enhancefeatures like voice-activated controls,virtual assistants,andcommunication systems within the vehicle.V.REVIEW OFEXISTINGRESEARCH ANDUSECASESH.J.Vishnukumar et.al.12 introduced that tr

81、aditionaldevelopment methods like Waterfall and Agile,fall short whentesting intricate autonomous vehicles and proposes a novelAI-powered methodology for both lab and real-world testingand validation(T&V)of ADAS and autonomous systems.Leveraging machine learning and deep neural networks,theAI core l

82、earns from existing test scenarios,generates neweffi cient cases,and controls diverse simulated environmentsfor exhaustive testing.Critical tests then translate to real-world validation with automated vehicles in controlled set-tings.Constant learning from each test iteration refi nes fu-ture testin

83、g,ultimately saving precious development time andboosting the effi ciency and quality of autonomous systems.This methodology lays the groundwork for AI to eventuallyhandle most T&V tasks,paving the way for safer and morereliable autonomous vehicles.Bachute,Mrinal R et.al.13 described the algorithmsc

84、rucial for various tasks in Autonomous Driving,recognizingthe multifaceted nature of the system.It discerns specifi calgorithmic preferences for tasks,such as employing Rein-forcement Learning(RL)models for effective velocity controlin car-following scenarios and utilizing the”Locally Decor-related

85、Channel Features(LDCF)”algorithm for superiorpedestrian detection.The study emphasizes the signifi canceof algorithmic choices in motion planning,fault diagnosiswith data imbalance,vehicle platoon scenarios,and more.Notably,it advocates for the continuous optimization andexpansion of algorithms to a

86、ddress the evolving challengesin Autonomous Driving.The paper serves as an insightfulfoundation,prompting future research endeavors to broadenthe scope of tasks,explore a diverse array of algorithms,andfi ne-tune their application in specifi c areas of interest withinthe Autonomous Driving System.Y.

87、Maet.al.14 explained the pivotal role ofartifi cial intelligence(AI)in propelling the developmentanddeploymentofautonomousvehicles(AVs)withinthe transportation sector.Fueled by extensive data fromdiverse sensors and robust computing resources,AI hasbecome integral for AVs to perceive their environme

88、nt and6make informed decisions while in motion.While existingresearch has explored various facets of AI applicationin AV development,this paper addresses a gap in theliterature by presenting a comprehensive survey of keystudies in this domain.The primary focus is on analyzinghow AI is employed in su

89、pporting crucial applications inAVs:1)perception,2)localization and mapping,and 3)decision-making.The paper scrutinizes current practicesto elucidate the utilization of AI,delineating associatedchallenges and issues.Furthermore,it offers insights intopotential opportunities by examining the integrat

90、ion of AIwith emerging technologies such as high-defi nition maps,big data,high-performance computing,augmented reality(AR)and virtual reality(VR)enhanced simulation platforms,and 5G communication for connected AVs.In essence,this paper serves as a valuable reference for researchersseeking a deeper

91、understanding of AIs role in AV research,providing a comprehensive overview of current practicesand paving the way for future opportunities and advancements.VI.AIANDLEARNINGALGORITHMS STATISTICS FORAUTONOMOUS VEHICLESThis Section extends the analysis of Artifi cial Intelligence(AI)and Learning Algor

92、ithms in autonomous vehicles,build-ing upon previous work as described in Section V.The focus ison providing additional statistical insights into the following:-evolution of different types of AI and learning algorithms overthe years,-research trends in application of AI in all fi elds vs.autonomous

93、 vehicles,-creation of a parameter set crucial forautonomous trucks versus cars,-evolution of AI and learningalgorithms at different autonomy levels,and-changes in thetypes of algorithms,software package size etc.,over time.A.Stat1:Trends of usage of AI,ML and DNN Algorithmsover the yearsToday,a veh

94、icles main goal is not limited to transportation,but also includes comfort,safety,and convenience.This ledto extensive research on improving vehicles and incorporatingtechnological breakthroughs and advancements.As per prior work done for the development for architectureand ADAS technology it is evi

95、dent that the research till nowhas limitations.These limitations are pertaining either to theauthors elaboration of his/her knowledge or not having propersources.Thus its a good exercise to have a look at the trendsover the years as our capabilities to develop these ML modelshave gotten better and a

96、lso the access to better computingunits1516 has led to the evolution of the algorithms.In the Table I,we have summarized different modellingalgorithms for various standard components of the ADASalgorithm.The second column illustrates the technology thatexists in todays date and the third column pred

97、icts potentialfuture development which is effi cient than the current.Below we have derived series of plots pertaining to re-search publications in AI(Artifi cial Intelligence),ML(MachineLearning)and DNN(Deep Neural Network)domains.Briefexplanations have been provided before to understand whattopics

98、 come under these domains.TABLE IAUTONOMOUSDRIVING:KEY TECHNOLOGIES EVOLUTIONTechnologyDeveloped over yearsFutureEnvironmentalPerceptionDL for object detectionYOLOv3 K-means cluster-ingVery challenging.Needs moreresearch to better detect objectsin blurry,extreme and rare con-ditions in real time.Ped

99、estriandetectionPVANETandRCNNmodel for object detectionduring blurry weatherOrientNet,RPN,and Predictor-Net to solve occlusion problemPath PlanningDLalgorithmbasedonCNNmultisensor fusion system,alongwith an INS,a GNSS,and a Li-DAR system,would be used toimplement a 3D SLAM.VehicleCyber-securitySecur

100、ity testing and TARARemote control of AV deployingIoT sensorsMotionPlanningHidden Markov model Q-learning algorithmGrey prediction model utilisingand Advanced model predictivecontrol for effective lane changea)AI(Artificial Intelligence):Expert Systems:Rule-based systems that mimic humanexpertise fo

101、r decision-making 17.Decision Trees:Hierarchical structures for classifi cationand prediction.ex:prognostics areaSearch Algorithms:Methods for fi nding optimal pathsor solutions,such as A*search and path planning algo-rithms.Generative AI:To create scenarios for training the systemand for balancing

102、data on high severity accident/non-accident cases.(CRSS dataset).Create a non-existentscenario dataset.Supplement the real datasets.Simulationtesting.NLP:AI Assistant(Yui,Concierge,Hey Mercedes,etc.,)-LLMsb)ML(Machine Learning):Supervised Learning:Algorithms that learn from labeleddata to make predi

103、ctions,such as:Linear Regression:For predicting continuous values.Support Vector Machines(SVMs):For classifi cationand outlier detection.Decision Trees:For classifi cation and rule genera-tion.Random Forests:Ensembles of decision trees forimproved accuracy.Unsupervised Learning:Algorithms that fi nd

104、 patterns inunlabeled data,such as:Clustering Algorithms(K-means,Hierarchical):Forgrouping similar data points.Dimensionality Reduction(PCA,t-SNE):For reduc-ing data complexity.c)DNN(Deep Neural Networks):Convolutional Neural Networks(CNNs):For image andvideo processing,used for object detection,lan

105、e segmen-tation,and traffi c sign recognition.Recurrent Neural Networks(RNNs):For sequential dataprocessing,used for trajectory prediction and behaviormodeling.Deep Reinforcement Learning(DRL):For learningthrough trial and error,used for control optimization anddecision-making.7d)Specific Examples i

106、n Autonomous Vehicles:Object Detection(DNN):CNNs like YOLO 18,SSD19,and Faster R-CNN are used to detect objectsaround the vehicle.Lane Detection(DNN):CNNs are used to identify lanemarkings and road boundaries.Path Planning(AI):Search algorithms like A*and RRTare used to plan safe and effi cient rout

107、es.MotionControl(ML):Regressionmodels202122 are used to predict vehicle dynamicsand control steering,acceleration,and braking.Behavior Prediction(ML):SVMs or RNNs are used toanticipate the behavior of other vehicles and pedestrians.IntheFigure3,weevaluatedthepapersfrom2324and found the trends to be

108、as shown.One canobserve that year 2013 the number of algorithms in DNNsurpasses that in generic AI and ML.This shows more researchwith deep neural networks and the traction it received in theAI community.However the main takeaway from the graphis the exponential upward trend in the number of algorit

109、hmsover the years developed for AI applications.Fig.3.Trends of usage of AI,ML and DNN algorithms over the yearsSome research was done considering platforms of IEE-EXplore,SAE Mobilus,MDPI and Science Direct to fi ndout the published research in AI/ML and also particularly inAutonomous vehicles.When

110、 fi ltering the MDPI Journals and articles,one canobserve that there is an additional fi lter relating to Data thatpops up after 2021.This indicates that pre-2020 not muchpapers related to data handling and analysis were publishedas the collected data was not huge.One also observes thatthe year 2020

111、(year of the COVID pandemic)for MDPI;sawminimal papers for autonomous technology.While several fac-tors may contribute to the rise in model deployments observedin 2021,a possible explanation is the limited opportunityfor previous models to undergo real-world testing throughvehicle deployment.Notably

112、,the number of deployed modelssurged to 737 in 2021,representing nearly a twofold increasecompared to earlier years.From the IEEE publications,one can see that although effec-tive research in AI/ML increases over time not much researchhas been published towards autonomous vehicle technology.Shifting

113、 Trends in IEEE Publications:Interestingly,post-2021,the upward trend in LMM and DNN publications(identifi ed through fi lters aligned with our previous analysis)appears to plateau.This suggests a potential shift in researchfocus within computer vision(CV)following the emergenceof Generative AI(GenA

114、I)and other advanced technologies.While LMM and DNN remain foundational,their prominenceas primary research subjects within classic CV might bedeclining.Considering CVPR Publications:Initially,we consideredincluding CVPR publications in our analysis.However,weultimately excluded them due to signifi

115、cant overlap with theIEEE dataset.As a signifi cant portion of CVPR papers aresubsequently published in IEEE journals,including both setswould introduce redundancy and potentially skew the analysis.Figure 4 focuses on all of AI/ML publications relatedto IEEEXplore 25,MDPI(Multidisciplinary Digital P

116、ub-lishing Institute)26 and SAE(Society Of AutomotiveEngineers)27 Figure 5 focuses on the trend changes inpublications for autonomous vehicles.Figure6 focuses onScience Direct 28 where we see the publications are inthousands with very little presence for autonomous vehicles.This is an indication of

117、how AI/ML applications have sur-passed engineering and are used everywhere from medical todefence.From the graphs we see comparatively less publications instarting years 2014-2018.There is a huge surge in 2018 wherealso we see Autonomous vehicles with advanced self drivingfeatures gained traction.Fr

118、om the trend,we expect in futurea similar exponential rise.However we do expect additionalparameters(for ex:data got introduced)to be in the list.WithAI/ML applications coming up in every industry along withautomotive,the future for research in the area is promising.Fig.4.No.of Publications related

119、to AI/learning Algorithms in all FieldsB.Stat2:Parameters for AI model(Trucks vs.Cars)As per American Trucking Associations(ATA),there will bea shortage of over 100,000 truck drivers in the US by 2030,which could potentially double by 2050 if trends continue.Bureau of Labor Statistics(BLS stats),whi

120、le not explicitlypredicting a shortage,the BLS projects a slower-than-averagejob growth for truck drivers through 2030,indicating potential8Fig.5.No.of Publications related to AI/learning Algorithms for AutonomousVehiclesFig.6.No.of Publications related to AI/learning Algorithms for AutonomousVehicl

121、es vs.all Fields in Science Directchallenges in meeting future demand.Aging work force,demanding job conditions and regulatory hurdles are few ofthe reasons which contribute towards the same.The above two results give a good business case for driver-less trucks in comparison to driverless cars.This

122、is also contra-dictory to the belief that truck drivers may loose jobs over theself-driving technology.In fact as per 29,30,driverlesstrucks can drastically reduce the driver costs,increase truckutilization and improve truck safety.Inspite of this,one cansee not enough research has been done on the

123、impacts of self-driving trucks compared to passenger transport 31.Thereis a need to ensure road freight transport has alignment withits current operations retaining its value chain.One cannotthink of cost reductions by taking out the driver cabin asmost self-driving technology developing trucking co

124、mpaniesare focusing on hub to hub transport and unlike passengercars,not from source to destination.One would still need adriver in the start and end of the journey.This refocuses onthe statement above for the need of truck drivers in future buteliminating the other drawbacks of long haul freight tr

125、ansport.As mentioned in 30 above,according to Daimler Ex-CEO Zetsche,future vehicles need to have four characteristics;connected,autonomous,shared,and electric,a so-called CASEvehicle.Nevertheless,each of these points has the potentialTABLE IIPARAMETER SET FOR DIFFERENCES IN USINGAIIN AUTONOMOUSTRUC

126、KS AND CARSParameterSub-classTrucksCarsEnvironmentTraffi cdensityoperateonhighwayswithpredictabletraffi cpatterns,encounter diverse,oftencongested,urban environ-ments.Roadinfrastructurenavigateprimarilyonwell-maintainedhighwaysdeal with varied road con-ditions and potentially un-marked streets.Weath

127、er con-ditionsmayprioritizestabilityand visibility for cargosafetymay prioritize maneuver-ability for passenger com-fort.Vehiclecharacteris-ticsSizeandweightLarger size and weightpresentdifferentsensorranges and dynamic re-sponse complexitiesSmaller size and weight incomparison to Trucks.Cargohandli

128、ngand safetyrequireAItomanagecargo weight distributionandpotentialshiftingcargoThis is not a concern forcarsFueleffi ciencyand emissionsTruck AI prioritizes ef-fi cient fuel consumptiondue to long-distance travelCarAImayprioritizesmoother acceleration anddeceleration for passengercomfort.Operational

129、considera-tions:Routeplanning andoptimizationrequirelong-distancerouteplanningwithconsiderationsforinfrastructure limitations,reststops,andcargodelivery schedules.generally focus on shorter,dynamic routes with real-time traffi c updates.Communicationandconnectivitymay rely on dedicated in-frastructu

130、re for communi-cation(platooning,V2X)primarily use existing cel-lular networks.Legalandreg-ulatoryland-scapeRegulations regarding au-tomation and liability aretight.regulations impacting AIand deployment are differ-ent than trucks.AIalgorithmandhardwareneedsPerceptionandsensorfusionmay prioritize ra

131、dar andLiDAR for long-range de-tectionbenefi tfromhigh-resolutioncamerasfornear-fi eldobstacleavoidance.Decision-makingandplanningAI focuses on safe,fuel-effi cientnavigationandtraffi c flow optimizationAIprioritizesdynamicrouteadjustments,pedestrian/cyclistdetection,and passengercomfort.Redundancya

132、ndsafetyprotocolsmay have stricter fail-safemeasures due to cargorisks.have safety protocols withredundant systemsAdditionalfactorsPublicperceptionandacceptancePublic trust in truck au-tomation might be slowerto build due to size andpotential cargo risks.Public trust in car automa-tion is higher due

133、 to lesserrisksEconomicandbusiness mod-elsautomationmodelsmayinvolvefleetmanagement and logisticsoptimizationsautomation may focus onride-sharing and individ-ual ownershipto turn the industry upside down.The paper clearly statesbacked up by a study that level 4 automation will be reachedby 2030 foll

134、owed by level 5 in 2040.Based on the interviewresults conducted in 29 and the delphi-based scenario studywith projections for the next 10 years,it is evident one needsto seriously consider the impact of automation on trucks.Lots of research revolves around passenger cars with manycompetitors in the

135、market.We found that not much data existsfor self-driving for trucks.This parameter set as shown in Table II serves as a startingpoint for understanding the key differences in how AI isapplied to autonomous trucks and cars.Each parameter canbe further explored and nuanced based on specifi c scenario

136、sand applications.Currently,the autonomous trucking has beenexpanding in 4 major categories such as Highway TruckingODD,Regional Delivery ODD,Urban logistics ODD andMining and Off-Road ODD.There are 3 different categoriesas well based on the different stages of logistics to handle9the movement of go

137、ods for autonomous trucking like LongHaul,Middle Mile and Last Mile.Understanding these cate-gorization and how the trucking industry has been evolving todeliver more autonomous vehicles is really important for thefuture of logistics to help optimize and streamline the entiresupply chain,ensuring ef

138、fi cient and timely delivery of goodsto their fi nal destination.C.Stat3:Usage of AI and Learning Algorithms at variousLevels of AutonomyAutonomous vehicles operate at various levels of autonomy,from Level 0 to Level 5,each presenting unique challengesandopportunities.Thissectionexploresthediversity

139、and evolution of AI algorithms across different levels ofautonomous vehicle capabilities.Autonomous vehicles arecategorized into different levels based on their autonomy,with increasing complexity and diversity of AI algorithmsas autonomy levels progress.The six levels 34 of AVautonomy defi ne the d

140、egree of driver involvement and vehicleautomation.At lower levels(L0-L2),driver assistance systemsprimarily utilize rule-based and probabilistic methods forspecifi c tasks like adaptive cruise control or lane departurewarning.Higher levels(L3-L4)rely heavily on machinelearninganddeeplearningalgorith

141、ms,particularlyforperception tasks like object detection and classifi cation usingconvolutional neural networks(CNNs).Advanced sensorfusion techniques combine data from cameras,LiDAR,radar,and other sensors to create a comprehensive understandingof the environment.Furthermore,reinforcement learninga

142、nd probabilistic roadmap planning algorithms contributeto complex decision-making and route planning in L3-L4AVs.L5(full automation)requires robust sensor fusion,3D mapping capabilities,and deep reinforcement learningapproaches for adaptive behavior prediction and high-levelroute planning.Some indus

143、try relevant examples have been illustrated be-low:KodiakStatus:Kodiak currently operates a fleet of Level 4autonomous trucks for commercial freight hauling onbehalf of shippers.Recent Developments:Kodiak is focusing on scaling its autonomous truck-ing service as a model,providing the driving system

144、to existing carriers.The company recently secured additional funding toexpand its operations and partnerships.No immediate news about deployment of driverlesstrucks beyond current operations.WaymoStatus:Waymo remains focused on Level 4 autonomousvehicle technology,primarily targeting robotaxi servic

145、esin specifi c geographies.Recent Developments:Waymo is expanding its robotaxi service in Phoenix,Arizona,with plans to eventually launch fully driver-less operations.The companys Waymo Via trucking division con-tinues testing autonomous trucks in California andTexas.No publicly announced timeline f

146、or nationwide de-ployment of driverless trucks.Overall:Both Kodiak and Waymo are making progress towardscommercializing Level 4 autonomous vehicles,but pri-marily focused on different segments(trucks vs.passen-ger cars).Driverless truck deployment timelines remain flexible anddependent on regulatory

147、 approvals and further testing aswas discussed previously.a)Key AI/Learning Components across Levels:Perception:L0-L2:Basic object detection and lane segmentationusing CNNs.L3-L4:LiDAR-based object detection,advanced sen-sor fusion algorithms for robust object recognition.L5:3D object mapping,robust

148、 sensor fusion andinterpretation.Decision-Making:L0-L2:Rule-based algorithms for lane change assis-tance,adaptive cruise control.L3-L4:Probabilisticroadmapplanning(PRM),decision-making models for route selection.L5:Deep reinforcement learning for adaptive behav-ior prediction,high-level route planni

149、ng.Control:L0-L2:PID controllers 22 for basic accelerationand braking adjustments.L3-L4:Model Predictive Control(MPC)35 forcomplex maneuvers,trajectory tracking algorithms.L5:Multi-task DNNs for real-time coordination ofall driving actions.The following Table III provides examples of AI algorithmsus

150、ed at different autonomy levels,from L0 to L5,highlightingkey techniques and applications.We have considered thepercentage of systems using AI algorithms,algorithm types,examples of AI algorithms at each level and the key tasksbeing automated at each level of autonomy.Please note thatat L0,the exten

151、t to which AI or learning algorithms being usedis very minimal and not complete algorithms in themselves,although there could be some partial techniques being usedlike data processing or detecting an object on roadThe level of autonomy in an AV directly correlates with thesize of its software packag

152、e.Imagine a pyramid,with Level 0at the base(smallest size)and Level 5 at the peak(largest size).Each level adds functionalities and complexities,reflected inthe increasing size of the pyramid.Challenges and Implications:Limited Storage&Processing Power:Current onboardstorage and processing capabilit

153、ies might not yet besuffi cient for larger Level 4 and 5 software packages.10TABLE IIISTATISTICS ONAIANDLEARNINGALGORITHMS INAUTONOMOUSVEHICLES BASED ONLEVELS OFAUTOMATIONLevel ofAuton-omy%ofSystemsUsingAI/LearningAlgo-rithmsAlgorithmTypesKeyAI/LearningAlgorithmsKey TasksAuto-matedNumberofAI/Learnin

154、gAlgo-rithmsL0(NoAu-toma-tion)0%N/AN/AN/A0L1(DriverAssis-tance)50-70%Rule-basedsystems,Decisiontrees,NaiveBayesAdaptiveCruiseControl,LaneDepartureWarning(LDW),AutomaticEmergencyBraking(AEB)Sensing,basicalerts andinterven-tions3-5L2(PartialAu-toma-tion)80-90%Rule-basedsystems,Decisiontrees,Rein-force

155、mentlearning(RL),SupportVectorMachines(SVM)Traffi c SignRecognition,HighwayAutopilot(ACC+lanecentering),Traffi cJamAssistNavigation,lanecontrol,stop-and-go,limitedenviron-mentaladaptation5-10L3(Con-ditionalAu-toma-tion)90-95%DeepLearning(DL)(e.g.,ConvolutionalNeuralNetworks,RecurrentNeuralNetworks),

156、RL,ProbabilisticmodelsUrbanAutopilot,ValetParkingFullcontrolunderspecifi ccondi-tions,dynamicenvi-ronmentadap-tation,complexdecision-making10-15L4(HighAu-toma-tion)95-99%AdvancedDL(e.g.,GenerativeAdversarialNetworks,Transform-ers),Multi-agentRL,Sensor fusionalgorithmsCityNavigation,HighwayChauffeurF

157、ullcontrol inspecifi cenviron-ments,high-levelnavi-gation,complextraffi cscenarios15-20L5(FullAu-toma-tion)100%AdvancedDL,Multi-agentRL,Hybridalgorithms(combiningvarioustypes),ExplainableAI(XAI)UniversalAutonomyFullcontrol inallenvi-ronments,self-learningand adap-tation,human-likedecision-making20+D

158、ownload and Update Challenges:Updating these largerpackages may require longer download times and poten-tially disrupt vehicle operation.Security Concerns:The more complex the software,thehigher the potential vulnerability to cyberattacks,neces-sitating robust security measures.AV software package s

159、ize is a major challenge for develop-ers like Nvidia and Qualcomm,as larger packages require:Increased processing power and memory:This translatesto higher hardware costs and potentially bulkier systems.Slower download and installation times:This can befrustrating for users,especially in areas with

160、limitedinternet connectivity.Security concerns:Larger packages offer more attackvectors for potential hackers.Heres how Nvidia and Qualcomm are tackling this chal-lenge:Nvidia:Drive Orin platform:Designed for high-performance AVapplications,Orin features a scalable architecture that canhandle large

161、software packages.Software optimization techniques:Nvidia uses varioustechniques like code compression and hardware-specifi coptimizations to reduce software size without sacrifi cingperformance.Cloud-based solutions:Offloading some processing anddata storage to the cloud can reduce the size of theo

162、nboard software package.15Qualcomm:Snapdragon Ride platform:Similar to Orin,SnapdragonRide is a scalable platform built for effi cient processingof large AV software packages.Heterogeneous computing:Qualcomm utilizes differentprocessing units like CPUs,GPUs,and NPUs to optimizeperformance and reduce

163、 software size by distributingtasks effi ciently.Modular software architecture:Breaking down the soft-ware into smaller,modular components allows for easierupdates and reduces the overall package size.16Additional approaches:Standardization:Industry-wide standards for AV softwarecan help reduce dupl

164、ication and fragmentation,leading tosmaller package sizes.Compression algorithms:Advanced compression algo-rithms can signifi cantly reduce the size of data and codewithout compromising functionality.Machine learning:Using machine learning to optimizesoftware performance and resource utilization can

165、 helpreduce the overall software footprint.The battle against AV software package size is ongoing,andboth Nvidia and Qualcomm are at the forefront of developinginnovative solutions.As technology advances and these ap-proaches mature,we can expect to see smaller,more effi cientAV software packages th

166、at pave the way for wider adoptionof self-driving vehicles.Heres a deeper dive as shown in the Table IV into thisrelationship36.TABLE IVSOFTWAREPACKAGESIZE BASED ON THELEVELS OFAUTONOMYLevel of AutonomyPackage Size0Few MB1100s of MB2100s MB to Few GBs3Few GB to 10s of GB410s of GB to 100s of GB5100s

167、 of GB to TBs11The level of autonomy directly influences the size of anAVs software package.While higher levels offer greaterconvenience and potential safety benefi ts,they come with thechallenge of managing increasingly complex and computation-ally intensive software packages that would require lar

168、ge stor-age spaces that the current processors cannot accommodate.Hence,the transformation towards zonal-based architectures isdesirable with multiple but small number of processors that aretasked to accomplish a particular function or task providingthe ample of storage space needed for moving towar

169、ds Level5 along with supporting connected and automated vehicleconcepts.VII.CONCLUSIONThis paper presents a comprehensive analysis of the roleof AI and learning algorithms in autonomous vehicles,divinginto various aspects such as the shifts from rule-based systemsto deep neural networks due to impro

170、ved model capabilitiesand computing power.The specifi c needs for trucks vs.carshave been detailed like the trucks prioritizing hub-to-hubtransport and effi cient long-haul journeys,while passengercars aiming for source-to-destination autonomy.Increasingcomplexity from basic object detection(L0-L2)t

171、o 3D mappingand adaptive behavior prediction(L3-L5)has been explained.Challenges and implications such as limited storage andprocessing power,software update concerns,and increasedsecurity vulnerabilities at higher autonomy levels have beendiscussed.Some of the key conclusions include that AI iscruc

172、ial for achieving different levels of autonomous vehiclefunctionality.Advanced techniques like deep learning and rein-forcement learning are essential for higher levels with complexdecision-making and adaptable behavior.Truck and car AIapplications have distinct requirements.Trucks focus on routeopt

173、imization and fuel effi ciency,while passenger cars prioritizepassenger comfort and dynamic adaptation to urban environ-ments.Software package size grows with autonomy level.Thisposes challenges for storage,processing,and software updates,highlighting the need for effi cient architectures and robust

174、security measures.Further research is needed on self-drivingtrucks despite their promising business potential.This arealags behind passenger car research,but its development couldsignifi cantly optimize logistics and address driver shortages.Certain aspects fall outside the scope of this paper,inclu

175、dingemerging technologies and AI algorithms like quantum AI,transfer learning,and Meta-Learning.Currently,these appli-cations in robotics and physical systems,such as vehicles,arelimited.However,the paper does not rule out their potentialuse within vehicles for tasks like edge computing or learningh

176、uman behaviors through transfer learning.In addition,thestudy on trends in published research in artifi cial intelligence(Stat 1)only considers the four most popular platforms withina broader automotive industry.The paper acknowledges theexistence of other journals and conferences,such as NeurIPS37,

177、which may have more publications.A quick study onthese new platforms revealed similar trends to those presentedin the paper.The exclusion of discussions on emerging trendsis intentional to narrow the scope and present relevant studiesfor drawing meaningful conclusions.The paper concludes bypresentin

178、g a clear image of the evolving AI landscape inautonomous vehicles,stressing its critical role in effi cient andsafe transportation solutions.It identifi es key challenges andsuggests areas for future research,contributing to a road mapfor researchers,practitioners,and enthusiasts interested in thed

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203、 serving as the standards committeemember within the IEEE Intelligent TransportationSystems Society(ITSS)and a peer reviewer forIEEE Intelligent Transportation Systems Confer-ences(ITSC).She actively participates in severalindustry level standards discussions within IEEE andSAE organizations.She is

204、also the working groupchair for the IEEE Vehicular Technology Societystandards discussions.She received her Masters inElectrical Engineering Systems from the Universityof Michigan,Ann Arbor in 2014.Prior to that,she received her Bachelors inElectronics and Communications Engineering from Andhra Univ

205、ersity Collegeof Engineering,Andhra Pradesh,India.Her current work focuses on Systemsand Safety research and development for Level 2,3 and 4 Autonomousvehicles.She has over 10 years of experience in the automotive industry.Sneha Sudhir Shetiya is a Senior IEEE Memberand received her Maters degree in

206、 Electrical Engi-neering with a major in computer vision and SignalProcessing from North Carolina State University,in2021.She received her Bachelors degree in Elec-tronics and Communication Engineering from theVisvesvaraya Technological University,Karnataka,India,in 2014.Her work involves middleware

207、 topicsfor embedded development of autonomous drivingstack,automotive diagnostics,systems engineeringand functional safety.She is an active volunteerwith IEEE region 4 and takes part in activities of Women In Engineering(WIE)groups in the region.She is part of the committee for senior memberevaluation at IEEE for 2024 and has been a proctor for IEEExtreme 24 hourcoding competition.