Siemens PLM Software Generative Design For Autonomous .
Siemens PLM SoftwareGenerative design forautonomous vehicleelectrical systemsExecutive summaryThe complexity inherent in autonomous vehicle design will push the toolsand methodologies used by automotive engineers to their limits. This isespecially true in the electrical and electronic systems domains as theycome to dominate the operation of a vehicle’s safety-critical systems andconvenience features. To compete, autonomous car manufacturers willneed a new design methodology that enables young engineers to designaccurate and optimized systems, which can only be done by capturing theexperience and knowledge of veteran engineers. They will need generativedesign.Doug Burcicki,Director AutomotiveSiemens PLM Softwarewww.siemens.com/electrical-systems
White paper Generative design for autonomous vehicle electrical systemsMoving towards level 5 autonomyAutonomous vehicles will require an extensive systemof advanced sensors, on-board computers, high-speedand high-bandwidth data networks, and wiring to connect it all. This complex network of cameras, radar,LIDAR sensors and electronic control units (ECUs) will beresponsible for detecting and interpreting dynamicenvironmental conditions to inform real-time drivingdecisions. This means gathering, processing, and distributing gigabits of data every second to enable thealgorithms and ECUs to respond to a rapidly changingdriving environment.The complexity and criticality of the electrical and electronic systems required for autonomous driving willdramatically increase the challenge of vehicle designand engineering. This is due to the extensive testingand validation required to ensure the safety of thesesystems. Most estimates predict that autonomous vehicles will require billions of miles-worth of testing toensure their safety. Manufacturers will need to incorporate the lessons learned through simulated and realworld testing into their autonomous vehicle designs toremain competitive.The technological ramp to fully autonomous vehiclespresents significant challenges for the engineers taskedwith their design. Advanced sensor technology, highspeed and high-bandwidth data networks, and cuttingedge artificial intelligence are all crucial to thefunctional and commercial success of autonomousvehicles. The real challenge, however, begins whenthese advanced technologies are integrated into a single system that must perceive, communicate, anddecide on a course of action (figure 1).A car with level two autonomy, for example, may feature active cruise control, a lane departure warningsystem, lane keep assist, and parking assistance. Intotal, this car requires about seventeen sensors toenable its driver assistance systems. These sensorsconsist of ultrasonic, long-range radar, short-rangeradar, and surround cameras to monitor the vehiclesenvironment. Furthermore, the computations performed by this car’s automated systems are relativelyprimitive. The lane keep assist system, for instance, isonly tasked with monitoring the vehicle’s position relative to the lines of the road. Should the driver begin tostray, the system will notify the driver or take correctiveaction, but ultimate responsibility for control of thevehicle lies with the driver.Figure 1: Autonomous vehicle platforms must connect an array of advanced sensors and computers through high-speed datanetworks to perceive, assess, and act on environmental stimuli.Siemens PLM Software2
White paper Generative design for autonomous vehicle electrical systemsFigure 2: A fully autonomous vehicle will require many types of sensors to accurately perceive dynamic driving environments.During design, engineers will need to perform architecture and tradeoff analyses to investigate architecturalproposals, such as a centralized vs. domain vs. distributed architecture. For an autonomous vehicle platform,these analyses will need to account for hundreds ofcomponents and millions of signals while optimizingfunction locations, network latency, error rates andmore.Despite these challenges, autonomous drive is a burgeoning market. At least 144 companies haveannounced autonomous vehicle programs, and annualspending on semiconductors for ADAS applications isprojected to grow year over year (figure 3). Some ofthese are major automotive manufacturers seeking tostay ahead of the coming industry disruption, but mostare startups or companies from other industries seekingto enter a traditionally impenetrable market. Thesecompanies lack industry-specific experience and theSiemens PLM Softwareengineering resources to brute force their way throughthe complexities of autonomous vehicle design. Eventhe major automotive OEMs will face problems thattheir legacy design flows are ill-equipped to handle.This will be true especially as companies move theirautonomous vehicle projects from research, development, and one-off prototyping into full-scale production. Autonomous systems will need to be optimized forcost, weight, and power consumption while adhering tothe most stringent safety requirements the automotiveindustry has ever faced. To compete, these companieswill need a new design methodology that enablesyoung engineers to design accurate and optimizedsystems, which can only be done by capturing the experience and knowledge of veteran engineers. They willneed generative design.1412ADAS semiconductor by deviceCAAGR 2017-22 17.7%Image sensorsSmall-sig. disc.Other ICOptoASICLinearMemoryPowerMPU/DSP/SoCMCU10 billionsA level five autonomous vehicle will have completeresponsibility for control over the driving task, requiringno human input. As a result, a level five car is projectedto have more than thirty additional sensors of a muchwider variety to cover the immense number of tasks anautonomous vehicle will need to perform (figure 2). Ontop of the ultrasonic, surround camera, and long- andshort-range radar sensors of a level two car, level fivewill require long range and stereo cameras, LiDAR, anddead reckoning sensors. The increase in sensors willincrease the amount of wiring needed in the harnessand the necessary computational resources to handlethe gigabits of data being produced by the sensors.86422016 2017 2018 2019 2020 2021 2022 2023 2024 2025Sourcestrategy analyticsFigure 3: Annual spending on semiconductor devices for ADAS applicationsis expected to grow year over year.3
White paper Generative design for autonomous vehicle electrical systemsGenerative design and engineeringGenerative design takes system definitions and requirements as input and generates architectural proposalsfor the logic, software, hardware, and networks of theelectrical and electronic systems using rules-basedautomation (figure 4). These rules capture the knowledge and experience of the veteran engineers to guideyounger engineers throughout the design. Capturingthis IP helps companies to develop both vehicle architectures and new generations of engineers as they learnand implement existing company knowledge.A generative design flow begins with functional models.A functional model represents the functionality of theelectrical system to be implemented, without specifyinghow it should be implemented. It accounts for aspectssuch as communication networks, power sources, andcomponents. These models may be captured in a varietyof formats such as spreadsheets, SysML files, and MSVisio diagrams.Design teams then normalize these various functionalmodels into a unified format within their electricalsystems design environment, such as Capital. Oncenormalized, the engineers can generate potential architectures for the E/E system logic, networks, hardware,and software. Valuable company IP is integrated automatically into these proposals through the design rulesthat govern proposal generation. At this stage, theelectrical engineers can rapidly generate, assess, andcompare multiple architectural proposals, optimizingthe design from the initial solutions presented.From the selected architectural proposal, the engineerscan extract discreet logical systems to generate platform- level network designs and the electrical distribution system (EDS). With this in place, the team cansynthesize wire harness designs for each subsystem,generate manufacturing aids and bills-of-process costs,publish electrical service data, and generate VIN-specificservice documentation.Generative design – WorkflowEngineer at workAlternativesEngineering dataProduct in useCaptureExploreDiscoverFigure 4: Generative design uses rules-based automation to generate proposals for the logic, software, hardware, and networks of the E/E system.Siemens PLM Software4
White paper Generative design for autonomous vehicle electrical systemsWhy generative?The increasing electrical and electronic content of modern vehicles is already pushing current design methodsto their limits, yet the complexity of automotive systems will only continue to grow in the future.Autonomous cars will contain the most complicatedelectrical and electronic systems yet seen in the automotive industry. More than thirty sensors, miles ofwiring, and hundreds of ECUs will be required to gather,move, and process the data necessary for autonomousdriving. The data networks will need to be extremelyfast to support real-time perception, decision-making,and action to prevent collisions and harm to humanpassengers or pedestrians. Engineers developing thesevehicles will also need to balance performance requirements against power consumption, physical spaceconstraints, weight, and thermal considerations.Generative design empowers automotive engineers totackle the challenges of electrical and electronic systems design for autonomous vehicles. It employs rulesbased automation for rapid design synthesis, enablesengineers to design in the context of a full vehicle platform, and tightly integrates various design domains toensure data continuity.Firstly, employing automation throughout the processwill help design teams manage design complexitywithout increasing time-to-market. Automation helpsengineers focus on the most critical aspects of thedesign and verification of the functionality of the E/Esystem and reduces errors from manual data entry. Thisempowers engineers to focus more of their time onapplying their creativity and ingenuity to creating thenext generation of automotive technology breakthroughs. Automation also applies company IP to thegenerated proposals through design rules, increasingthe accuracy and quality of the designs.Next, designing in the full platform context helps engineers to understanding the way signals, wires, andother components are implemented across the entirevehicle platform, thereby reducing errors at interfacesor due to the intricacy of the harness. This design flowalso enables teams to re-use validated data across vehicle platforms to improve quality and reduce development costs.Finally, a tightly integrated environment enables theelectrical engineers to share data with engineers andtools in other domains, such as mechanical or PCBdesign. The interactions between the electrical,mechanical, and software components of a vehicle areincreasing. Seamless synchronization of data betweenthese domains improves the integration of them into asingle system.Traceability supporting compliance and certificationAll abstractions and domains natively connected and integrated into ALM and PLMBidirectional traceability System definitionSystem modelsSystem analysisAsset managementChange managementConfigurationWorkflowsEngineering viewpointsE/E architecture functions,electrical, electronics,software, hardware,networks Generative automationOutput synthesisSeparationRedundancyDesign rule decksMetricsRequirements traceabilitySoftware AUTOSAR Application design Behaviour modelingCommunications Firewalls/GatewayNetworks designProtocols and timingGuaranteed deliveryElectrical Generative automation Logical, wiring andharness systems Electrical analysis Manufacturing and serviceECU softwareimplementation Service architecture Cybersecurity libraries Secure bootloader, end-toend protection End-to-end encryption Watchdogs, diagnosticsComplianceRequirementsMulti-domain systemmodelingCERTIFICATION Fn safety Security EvidenceEnsuring digital continuity, multi-domain traceability, safety and security of autonomous systemsFigure 5: Generative design ensures data continuity from initial system definitions through production and after-sales for full traceability and compliancewith requirements.Siemens PLM Software5
White paper Generative design for autonomous vehicle electrical systemsData continuityGenerative design creates a continuous thread of datafrom the initial system definition and requirements tofull-scale production and service. The same data feedseach stage of the generative design flow so that nothing is lost between design stages or design domains.This continuous thread of data keeps all engineeringteam members up to date and working with the mostcurrent data while also ensuring that designs are meeting various requirements for functionality, safety,weight and so forth (figure 5).Built-in design rules enable engineers to check designsfor flaws automatically, flaws that can easily be lost inthe sheer complexity of an autonomous vehicle. Thesedesign rule checks can catch unterminated wire ends,inconsistencies in graphical and physical bundlelengths, and check for current loads on wires, generated heat, and other faults. Again, generative designemploys company IP through these design rule checksto catch design flaws that have caused trouble in thepast or that new engineers may not think to check.Additionally, data continuity enhances the engineer’sability to analyze the impact of design changes.Traditional design methodologies struggle to quantifythe knock-on effects of design changes. Each changeaffects the rest of the system, and the second- andthird-order effects can be very difficult to predict.Migrating an ECU to a new location or network in thearchitecture may affect performance elsewhere in thesystem. This change in behavior may cascade, invalidating any number of subsystems.Data continuity ensures that projects have a single datasource, providing a clear picture of the myriad interdomain and inter-system interactions. As changes aremade to the design, they can be examined with detailedimpact analysis that will inform the engineer of issuesthe change may cause in other domains. For instance,moving or removing an ECU could be assessed for itsimpact on network timing, signal integrity, or physicalclearance and collision issues. As a result, changes aremade knowing their full impact on the system.Enabling the autonomous drive winnersGenerative design will be a key enabler for new andestablished automotive companies in their pursuit ofdeveloping fully autonomous vehicles. The ability togenerate electrical system architectures automaticallyenables early exploration and optimization of designswhile embedding company IP into the design flow.Additionally, a singular source of data promotes consistency between domains, design reuse, and enhancesthe analysis of change impact. Finally, tight integrationsbetween the electrical domains and with mechanicaland PLM tools streamlines the entire design flow fromconception through production.The massive complexity inherent in autonomous vehicledesign will continue to push the tools and methodologies used by automotive engineers. This is especiallytrue in the electrical and electronic systems domains asthey come to dominate the operation of a vehicle’ssafety-critical systems and amenities. The winners inthis disruptive technology will be those companies thatcan most effectively integrate the advanced technologies required for autonomous drive into a package thatis reliable, safe, and attractive to consumers, and thenget those technologies to market quickly and with ahigh level of quality.References1.Strategy analytics (2018, August). ADAS semiconductor demandforecast 2016-2025. Retrieved from detail/ADAS-Semi-Forecast-AVS-Aug-2018.Siemens PLM Software6
Siemens PLM SoftwareHeadquartersGranite Park One5800 Granite ParkwaySuite 600Plano, TX 75024USA 1 972 987 3000AmericasGranite Park One5800 Granite ParkwaySuite 600Plano, TX 75024USA 1 314 264 8499About Siemens PLM SoftwareSiemens PLM Software, a business unit of the SiemensDigital Factory Division, is a leading global provider ofsoftware solutions to drive the digital transformation ofindustry, creating new opportunities for manufacturersto realize innovation. With headquarters in Plano, Texas,and over 140,000 customers worldwide, Siemens PLMSoftware works with companies of all sizes to transformthe way ideas come to life, the way products arerealized, and the way products and assets in operationare used and understood. For more information onSiemens PLM Software products and services, visitwww.siemens.com/plm.EuropeStephenson HouseSir William Siemens SquareFrimley, CamberleySurrey, GU16 8QD 44 (0) 1276 413200Asia-PacificUnit 901-902, 9/FTower B, Manulife Financial Centre223-231 Wai Yip Street, Kwun TongKowloon, Hong Kong 852 2230 3333www.siemens.com/plm 2019 Siemens Product Lifecycle Management Software Inc. Siemens, the Siemens logo andSIMATIC IT are registered trademarks of Siemens AG. Camstar, D-Cubed, Femap, Fibersim,Geolus, GO PLM, I-deas, JT, NX, Parasolid, Solid Edge, Syncrofit, Teamcenter and Tecnomatixare trademarks or registered trademarks of Siemens Product Lifecycle Management SoftwareInc. or its subsidiaries in the United States and in other countries. Simcenter is a trademark orregistered trademark of Siemens Industry Software NV or its affiliates. All other trademarks,registered trademarks or service marks belong to their respective holders.76145-A7 2/19 H7
Generative design empowers automotive engineers to tackle the challenges of electrical and electronic sys-tems design for autonomous vehicles. It employs rules-based automation for rapid design synthesis, enables engineers to design in the context of a full vehicle plat-form, and tightly integrates various design domains to ensure data continuity.
Siemens PLM Software, a business unit of the Siemens Digital Factory Division, is a world-leading provider of product lifecycle management (PLM) software, systems and services with nine million licensed seats and 77,000 customers worldwide. Headquartered in Plano, Texas, Siemens PLM
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About Siemens PLM Software Siemens PLM Software is a leading global provider of product lifecycle management (PLM) software and services with 7 million licensed seats and 71,000 customers worldwide. Headquartered in Plano, Texas, Siemens PLM Software works collaboratively with companies to deliver open solutions that help them turn more
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About Siemens PLM Software Siemens PLM Software, a business unit of the Siemens Digital Factory Division, is a leading global provider of software solutions to drive the digital transformation of industry, creating new opportunities for manufacturers to realize innovation. With headquarters in Plano, Texas,
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