BAE Systems and GlobalFoundries are teaming up to strengthen the supply of chips for national security programs, aligning technology roadmaps and collaborating on innovation and manufacturing. Focus areas include advanced packaging, GaN-on-silicon chips, silicon photonics, and advanced technology process development.

Onsemi plans to build a $2 billion silicon carbide production plant in the Czech Republic. The site would produce smart power semiconductors for electric vehicles, renewable energy technology, and data centers.

The global chip manufacturing industry is projected to boost capacity by 6% in 2024 and 7% in 2025, reaching 33.7 million 8-inch (200mm) wafers per month, according to SEMI‘s latest World Fab Forecast report. Leading-edge capacity for 5nm nodes and below is expected to grow by 13% in 2024, driven by AI demand for data center applications. Additionally, Intel, Samsung, and TSMC will begin producing 2nm chips using gate-all-around (GAA) FETs next year, boosting leading-edge capacity by 17% in 2025.

At the IEEE Symposium on VLSI Technology & Circuits, imec introduced:

• Functional CMOS-based CFETs with stacked bottom and top source/drain contacts.
• CMOS-based 56Gb/s zero-IF D-band beamforming transmitters to support next-gen short-range, high-speed wireless services at frequencies above 100GHz.
• ADCs for base stations and handsets, a key step toward scalable, high-performance beyond-5G solutions, such as cloud-based AI and extended reality apps.

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Global

Wolfspeed postponed plans to construct a $3 billion chip plant in Germany, underscoring the EU‘s challenges in boosting semiconductor production, reports Reuters. The North Carolina-based company cited reduced capital spending due to a weakened EV market, saying it now aims to start construction in mid-2025, two years later than 0riginally planned.

Micron is building a pilot production line for high-bandwidth memory (HBM) in the U.S., and considering HBM production in Malaysia to meet growing AI demand, according to a Nikkei report. The company is expanding HBM R&D facilities in Boise, Idaho, and eyeing production capacity in Malaysia, while also enhancing its largest HBM facility in Taichung, Taiwan.

Kioxia restored its Yokkaichi and Kitakami plants in Japan to full capacity, ending production cuts as the memory market recovers, according to Nikkei. The company, which is focusing on NAND flash production, has secured new bank credit support, including refinancing a ¥540 billion loan and establishing a ¥210 billion credit line. Kioxia had reduced output by more than 30% in October 2022 due to weak smartphone demand.

Europe’s NATO Innovation Fund announced its first direct investments, which includes semiconductor materials. Twenty-three NATO allies co-invested in this over $1B fund devoted to address critical defense and security challenges.

The second meeting of the U.S.–India Initiative on Critical and Emerging Technology (iCET) was held in New Delhi, with various funding and initiatives announced to support semiconductor technology, next-gen telecommunications, connected and autonomous vehicles, ML, and more.

Amazon announced investments of €10 billion in Germany to drive innovation and support the expansion of its logistics network and cloud infrastructure.

Quantum Machines opened the Israeli Quantum Computing Center (IQCC) research facility, backed by the Israel Innovation Authority and located at Tel Aviv University. Also, Israel-based Classiq is collaborating with NVIDIA and BMW, using quantum computing to find the optimal automotive architecture of electrical and mechanical systems.

Global data center vacancy rates are at historic lows, and power availability is becoming less available, according to a Siemens report featured on Broadband Breakfast. The company called for an influx of financing to find new ways to optimize data center technology and sustainability.

In-Depth

Semiconductor Engineering published its Manufacturing, Packaging & Materials newsletter this week, featuring these top stories:

• Single Vs. Multi-Patterning Advancements For EUV
• Precise Control Of Copper Plating And CMP
• Ruthenium Interconnects On Tap
• Opportunities Grow For GPU Acceleration

More reporting this week:

• IC Industry’s Growing Role In Sustainability
• What’s Missing In Test

Market Reports

Renesas completed its acquisition of Transphorm and will immediately start offering GaN-based power products and reference designs to meet the demand for wide-bandgap (WBG) chips.

Revenues for the top five wafer fab equipment (WFE) companies fell 9% YoY in Q1 2024, according to Counterpoint. This was offset partially by increased demand for NAND and DRAM, which increased 33% YoY, and strong growth in sales to China, which were up 116% YoY.

The SiC power devices industry saw robust growth in 2023, primarily driven by the BEV market, according to TrendForce. The top five suppliers, led by ST with a 32.6% market share and onsemi in second place, accounted for 91.9% of total revenue. However, the anticipated slowdown in BEV sales and weakening industrial demand are expected to significantly decelerate revenue growth in 2024. 

About 30% of vehicles produced globally will have E/E architectures with zonal controllers by 2032, according to McKinsey & Co. The market for automotive micro-components and logic semiconductors is predicted to reach $60 billion in 2032, and the overall automotive semiconductor market is expected to grow from $60 billion to $140 billion in the same period, at a 10% CAGR.

The automotive processor market generated US$20 billion in revenue in 2023, according to Yole. US$7.8 billion was from APUs and FPGAs and $12.2 billion was from MCUs. The ADAS and infotainment processors market was worth US$7.8 billion in 2023 and is predicted to grow to $16.4 billion by 2029 at a 13% CAGR. The market for ADAS sensing is expected to grow at a 7% CAGR.

Security

The CHERI Alliance was established to drive adoption of memory safety and scalable software compartmentalization via the security technology CHERI, or Capability Hardware Enhanced RISC Instructions. Founding members include Capabilities Limited, Codasip, the FreeBSD Foundation, lowRISC, SCI Semiconductor, and the University of Cambridge.

In security research:

• Japan and China researchers explored a NAND-XOR ring oscillator structure to design an entropy source architecture for a true random number generator (TRNG).
• University of Toronto and Carleton University researchers presented a survey examining how hardware is applied to achieve security and how reported attacks have exploited certain defects in hardware.
• University of North Texas and Texas Woman’s University researchers explored the potential of hardware security primitive Physical Unclonable Functions (PUF) for mitigation of visual deepfakes.
• Villanova University researchers proposed the Boolean DERIVativE attack, which generalizes Boolean domain leakage.

Post-quantum cryptography firm PQShield raised $37 million in Series B funding.

Former OpenAI executive, Ilya Sutskever, who quit over safety concerns, launched Safe Superintelligence Inc. (SSI).

EU industry groups warned the European Commission that its proposed cybersecurity certification scheme (EUCS) for cloud services should not discriminate against Amazon, Google, and Microsoft, reported Reuters.

Cyber Europe tested EU cyber preparedness in the energy sector by simulating a series of large-scale cyber incidents in an exercise organized by the European Union Agency for Cybersecurity (ENISA).

The Cybersecurity and Infrastructure Security Agency (CISA) issued a number of alerts/advisories.

Education and Training

New York non-profit NY CREATES and South Korea’s National Nano Fab Center partnered to develop a hub for joint research, aligned technology services, testbed support, and an engineer exchange program to bolster chips-centered R&D, workforce development, and each nation’s high-tech ecosystem.

New York and the Netherlands agreed on a partnership to promote sustainability within the semiconductor industry, enhance workforce development, and boost semiconductor R&D.

Rapidus is set to send 200 engineers to AI chip developer Tenstorrent in the U.S. for training over the next five years, reports Nikkei. This initiative, led by Japan’s Leading-edge Semiconductor Technology Center (LSTC), aims to bolster Japan’s AI chip industry.

Product News

UMC announced its 22nm embedded high voltage (eHV) technology platform for premium smartphone and mobile device displays. The 22eHV platform reduces core device power consumption by up to 30% compared to previous 28nm processes. Die area is reduced by 10% with the industry’s smallest SRAM bit cells.​

Alphawave Semi announced a new 9.2 Gbps HBM3E sub-system silicon platform capable of 1.2 terabytes per second. Based on the HBM3E IP, the sub-system is aimed at addressing the demand for ultra-high-speed connectivity in high-performance compute applications.

Movellus introduced the Aeonic Power product family for on-die voltage regulation, targeting the challenging area of power delivery.

Cadence partnered with Semiwise and sureCore to develop new cryogenic CMOS circuits with possible quantum computing applications. The circuits are based on modified transistors found in the Cadence Spectre Simulation Platform and are capable of processing analog, mixed-signal, and digital circuit simulation and verification at cryogenic temperatures.

Renesas launched R-Car Open Access (RoX), an integrated development platform for software-defined vehicles (SDVs), designed for Renesas R-Car SoCs and MCUs with tools for deployment of AI applications, reducing complexity and saving time and money for car OEMs and Tier 1s.

Infineon released industry-first radiation-hardened 1 and 2 Mb parallel interface ferroelectric-RAM (F-RAM) nonvolatile memory devices, with up to 120 years of data retention at 85-degree Celsius, along with random access and full memory write at bus speeds. Plus, a CoolGaN Transistor 700 V G4 product family for efficient power conversion up to 700 V, ideal for consumer chargers and notebook adapters, data center power supplies, renewable energy inverters, and more.

Ansys adopted NVIDIA’s Omniverse application programming interfaces for its multi-die chip designers. Those APIs will be used for 5G/6G, IoT, AI/ML, cloud computing, and autonomous vehicle applications. The company also announced ConceptEV, an SaaS solution for automotive concept design for EVs.

Fig. 1: Field visualization of 3D-IC with Omniverse. Source: Ansys

QP Technologies announced a new dicing saw for its manufacturing line that can process a full cassette of 300mm wafers 7% faster than existing tools, improving throughput and productivity.

NXP introduced its SAF9xxx of audio DSPs to support the demand for AI-based audio in software-defined vehicles (SDVs) by using Cadence’s Tensilica HiFi 5 DSPs combined with dedicated neural-network engines and hardware-based accelerators.

Avionyx, a provider of software lifecycle engineering in the aerospace and safety-critical systems sector, partnered with Siemens and will leverage its Polarion application lifecycle management (ALM) tool. Also, Dovetail Electric Aviation adopted Siemens Xcelerator to support sustainable aviation.

Research

Researchers from imec and KU Leuven released a +70 page paper “Selecting Alternative Metals for Advanced Interconnects,” addressing interconnect resistance and reliability.

A comprehensive review article — “Future of plasma etching for microelectronics: Challenges and opportunities” — was created by a team of experts from the University of Maryland, Lam Research, IBM, Intel, and many others.

Researchers from the Institut Polytechnique de Paris’s Laboratory of Condensed Matter for Physics developed an approach to investigate defects in semiconductors. The team “determined the spin-dependent electronic structure linked to defects in the arrangement of semiconductor atoms,” the first time this structure has been measured, according to a release.

Lawrence Berkeley National Laboratory-led researchers developed a small enclosed chamber that can hold all the components of an electrochemical reaction, which can be paired with transmission electron microscopy (TEM) to generate precise views of a reaction at atomic scale, and can be frozen to stop the reaction at specific time points. They used the technique to study a copper catalyst.

The Federal Drug Administration (FDA) approved a clinical trial to test a device with 1,024 nanoscale sensors that records brain activity during surgery, developed by engineers at the University of California San Diego (UC San Diego).

Events and Further Reading

Find upcoming chip industry events here, including:

Upcoming webinars are here.

Semiconductor Engineering’s latest newsletters:

Automotive, Security and Pervasive Computing
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The post Chip Industry Week In Review appeared first on Semiconductor Engineering.

The amount of data being collected, processed, and stored in vehicles is exploding, and so is the value of that data. That raises questions that are still not fully answered about how that data will be used, by whom, and how it will be secured.

Automakers are competing based on the latest versions of advanced technologies such as ADAS, 5G, and V2X, but the ECUs, software-defined vehicles, and in-cabin monitoring also demand more and more data, and they are using that data for purposes that extend beyond just getting the vehicle from point A to point B safely. They now are vying to offer additional subscription-based services according to customers’ interests, as various entities, including insurance companies, indicate a willingness to pay for information on drivers’ habits.

Collecting this data can help OEMs gain insights and potentially generate additional revenue. However, gathering it raises privacy and security concerns about who will own this massive amount of data and how it should be managed and used. And as automotive data use increases, how will it impact future automotive design?

Fig. 1: Connected vehicles rely on software to communicate between vehicles and the cloud. Source: McKinsey & Co.

“Much of the data generated in the vehicle will have immense value to OEMs and their partners for analyzing driver behavior and vehicle performance and for developing new or enhanced features,” said Sven Kopacz, autonomous vehicle section manager at Keysight Technologies. “On the other hand, the privacy of data use can be viewed as a risk to some. But the real value – as already implemented and used by Tesla and others – is the constant feedback to improve those ADAS algorithms, enable a CI/CD DevOps software development model, and allow the rapid download of updates. Only time will tell if law enforcement and the courts will demand this data and how lawmakers will respond.”

Types of data generated
According to Precedence Research, the global automotive data market size will grow from $2.19 billion in 2022 to $14.29 billion by 2032, with many types of data collected, including:

• Autonomous driving: Data on all levels, from L1 to L5, including that collected from the multiple sensors installed on vehicles.
• Infrastructure: Remote monitoring, OTA updates, and data used for remote control by control centers, V2X, and traffic patterns.
• Infotainment: Information on how customers are using applications, such as voice control, gesture, maps, and parking.
• Connected information: Information on payment to third-party parking apps, accident information, data from dashboard cameras, handheld devices, mobile applications, and driver behavior monitoring.
• Vehicle health: Repair and maintenance records, insurance underwriting, fuel consumption, telematics.

This information may be useful for future automotive design, predictive maintenance, and safety improvements, and insurance companies are expected to be able to reduce underwriting costs with more comprehensive information on accidents. Based on the information collected, OEMs should be able to design more reliable and safer cars, and to stay in close touch with customer wants. For example, experiments can be conducted to gauge customer demand for subscription-based services such as automatic parking and more sophisticated voice input and commands.

“Diagnostic data for service and repair has been a core of automotive data analytics for decades,” noted Lorin Kennedy, senior staff product management manager for SLM in-field analytics at Synopsys. “With the advent of connected vehicles and advanced machine learning (ML) analytics, which enable a greater quantity of data to be routinely processed, this data has gained exponentially in value. As data drives feature enhancements such as mobile-like experiences and advanced driver assist capabilities, OEMs increasingly need to better understand the dependability and reliability of the semiconductor systems powering these new features. The collection of monitoring and sensor data from electronic components and the semiconductors themselves will be a growing diagnostic data requirement across all types of automotive technologies like ADAS, IVI, ECUs, etc. to ensure quality and reliability on these more advanced nodes.”

Anticipated updates to ISO 26262 regulations regarding the application of predictive maintenance to hardware, identifying degrading intermittent faults caused by silicon aging, and over-stress conditions in the field are areas to be addressed, as well. Those can include silicon lifecycle management (SLM) technologies, which can deliver more comprehensive knowledge about the health and remaining useful life of silicon as it ages.

“That knowledge, in turn, will enable service updates and future OTA releases that leverage additional semiconductor compute power,” Kennedy said. “Overall fleet performance will benefit, and the semiconductor and system design process will, too, as new insights help achieve greater efficiencies. OEM, Tier One, and semiconductor supplier collaboration on what the data brings to light – from silicon to software system performance – will enable vehicles to meet the functional safety design parameters that are becoming increasingly crucial in advanced electronics.”

Still, for data generated in vehicles, OEMs will need to prioritize which data can provide value for drivers immediately, and which data should be sent to the cloud via 5G connections.

“Tradeoffs between on-board processing to reduce data volume and data transmission network costs will likely dictate prioritization,” Keysight’s Kopacz said. “For example, camera, lidar, and radar sensor data for ADAS applications may have value for training ADAS algorithms, but the volume of raw data will be very costly to transmit and store. Likewise, driver attention data can have high value in UI design, and would be best gathered in a meta-data form. V2X data has a relatively lower data volume and should ultimately be a key data source for ADAS, providing in-car non-line-of-sight visibility of other vehicles, road infrastructure, and road conditions. Sharing this over V2N links can enable effective safety applications, but angle random walk (ARW) sensor data needs to be considered more carefully due to its complex nature. Infotainment streaming content into the vehicle also can be a valuable revenue stream for OEMs, and the content providers as well, as network operators working together.”

Impacts on automotive cybersecurity
As vehicles become more autonomous and connected, data use will increase, and so will the value of that data. This raises cybersecurity and data privacy concerns. Hackers want to steal personal data collected by the vehicles, and can use ransomware and other attacks to do so. The idea of taking control of vehicles — or worse, stealing them — also attracts hackers. Techniques used include hacking vehicle apps and wireless connections on the vehicles (diagnostics, key fob attacks and keyless jamming). Protecting data access, vehicles, and infrastructure from attacks is increasingly important and challenging.

Cybersecurity risks increase with software-defined vehicles. Memory especially will need to be safeguarded.

“The integration of advanced technology into EVs poses significant cybersecurity challenges that demand immediate attention and sophisticated solutions,” said Ilia Stolov, center head of secure memory solution at Winbond. “Central to the digital fortresses within modern electronic platforms are flash non-volatile memories, housing invaluable assets like code, private data, and company credentials. Unfortunately, their ubiquity has rendered them attractive targets for hackers seeking unauthorized access to sensitive information.”

Stolov noted that Winbond has been actively working to secure flash memory from hacks.

Additionally, there are important considerations in securing memory designs, such as:

• DICE root of trust: The Device Identifier Composition Engine (DICE) should be used to create the secure flash root of trust for hardware security. This secure identity forms the basis for building trust in the hardware. Other security measures can therefore rely on the authenticity and integrity of the boot code, protecting against firmware and software attacks. The initial boot process and subsequent software execution are based on trusted and verified measurements, helping prevent the injection of malicious code into the system.
• Code and data protection: Protecting code and data is crucial for maintaining system-wide integrity. Unauthorized modifications to code or data can lead to malfunctions, system instability, or the introduction of malicious code, compromising the hardware’s intended functionality or exploiting system vulnerabilities.
• Authentication protocols: Authentication is a fundamental and crucial component of cybersecurity, serving as the frontline defense against unauthorized access and potential security breaches. Employing authentication protocols to restrict access to authorized actors and approved software layers only using cryptography credentials is important.
• Secure software updates with rollback protection: Regular updates extend beyond bug fixes including remote firmware over-the-air (OTA) updates, guards against rollback attacks, and ensures the execution of only legitimate updates.
• Post-quantum cryptography: Anticipating the post-quantum computing era to include NIST 800-208 Leighton-Micali Signature (LMS) cryptography safeguards EVs against the potential threats posed by future quantum computers.
• Platform resiliency: Automatic detection of unauthorized code changes enables swift recovery to a secure state, effectively thwarting potential cyber threats. Adhering to NIST 800-193 recommendations for platform resiliency ensures a robust defense mechanism.
• Secure supply chain: Guaranteeing the origin and integrity of flash content throughout the supply chain, these secure flash devices prevent content tampering and misconfiguration during platform assembly, transportation, and configuration. This, in turn, safeguards against cyber adversaries.

Considering the transition to SDVs and connected cars, data vulnerability becomes even more significant.

“Depending on where data resides, different protection measures are in place,” said Keysight’s Kopacz. “Intrusion detection systems (IDS), crypto services, and key management are becoming standard solutions in vehicles. Especially sensitive data for safety features needs to be protected and verified. Thus, redundancy becomes more relevant. With SDVs, the vehicle software is constantly updated or changed throughout the entire vehicle life cycle. Ever-evolving cyber threats are particularly challenging. Accordingly, the entire vehicle software must be continuously checked for new security gaps. OEMs are going to need comprehensive testing solutions to minimize security threats. This will need to include the cybersecurity testing of the entire attack surface, covering all vehicle interfaces – wired vehicle communication networks such as CAN or automotive Ethernet or wireless connections via Wi-Fi, Bluetooth, or cellular communications. OEMs will also need to test the backend that provides over-the-air (OTA) software updates. Such solutions can reduce the risk of damage or data theft by cybercriminals.”

Data management and privacy concerns
Another issue to be resolved is how the massive amount of data collected will be managed and used. Ideally, data will be analyzed to yield commercial value without causing privacy concerns. For example, infotainment platform data might reveal what types of music are most popular, helping the music industry to improve marketing strategies. Who will monitor the transfer of such data, though? How will customers be made aware of the data collection? And will they have an opportunity to opt out of having their data sold?

As with airplanes, vehicle black boxes are installed to record information for analysis of the data after an accident occurs. The information recorded includes vehicle speed, the braking situation, and the activation of air bags, among other things. If an accident occurs resulting in a fatality, and the data from ADAS and ECU uncover vulnerability in the designs, could that data be used as evidence in court against manufacturers or their supply chains? Armed with this information, the insurance industry may decline claims. Would one or more manufacturers of the ADAS/ECU be required to hand over the data when ordered by the authorities?

“Quality requirements for sophisticated electronic parts will continue to become more rigid and strict, allowing only a few defective parts per billion (DPPB) due to the impact failed components can have on the safety and well-being of human life,” noted Guy Cortez, senior staff product management manager for SLM analytics at Synopsys. “SLM data analytics will continue to play a substantial role in the health, maintainability, and sustainability of these devices throughout their life within the vehicle. Through the power of analytics, you can do proper root cause analysis of any failed device (e.g., return merchandise authorization, or RMA). What’s more, you will also be able to find ‘like’ devices that ultimately may exhibit similar failed behavior over time. Thus empowered, you can proactively recall these like devices before they fail during operation in the field. Upon further analysis, the device(s) in question may require a design re-spin by the device developer in order to correct any identified issue. With a proper SLM solution deployed throughout the automotive ecosystem, you can achieve a higher level of predictability, and thus higher quality and safety for the automotive manufacturer and consumer.”

OEM impact
While modern cars have been described as computers on wheels, they are now more like mobile phones on wheels. OEMs are designing cars that do not skimp on features. Semi-autonomous driving, voice-controlled infotainment systems, and the monitoring of many functions—including driver behavior— are yielding a large amount of data. While that data can be used to improve future designs. OEMs’ approaches to security and privacy vary, with some offering stronger security and privacy protection than others.

Mercedes-Benz is paying attention to data security and privacy, and is compliant to UN ECE R155 / R156, a European norm for cybersecurity and software update management systems, according to the company. Which data is processed in connection with digital vehicle services depends on which services the customer selects. Only the data required for the respective service will be processed. Additionally, the “Mercedes me connect” app’s terms of use and privacy information make it transparent for customers to see what data is needed for and how it is processed. Customers can determine which services they want to use.

Hyundai indicated it would follow a user-centric focus, prioritizing safety, information security, and data privacy with fault-tolerant software architectures to enhance cybersecurity. Hyundai Motor Group’s global software center, 42dot, is currently developing integrated hardware/software security solutions that detect and block data tampering, hacking, and external cyber threats, as well as abnormal communication using big data and AI algorithms.

And according to the BMW Group, the company manages a connected fleet of more than 20 million vehicles globally. More than 6 million vehicles are updated over-the-air on a regular basis. Together with other services, more than 110 terabytes of data traffic per day are processed between the connected vehicles and cloud-backend. All BMW vehicle interfaces permit consumers to opt in or out of various types of data collection and processing that may happen on their vehicles. If preferred, BMW customers may opt out of all optional data collection relating to their vehicles at any time by visiting the BMW iDrive screen in their vehicle. Additionally, to completely stop the transfer of any data from BMW vehicles to BMW services, customers can contact the company to request that the embedded SIM on their vehicles be disabled.

Not all OEMs hold the same philosophy on privacy. According to a study on 25 brands conducted by the Mozilla Foundation, a nonprofit organization, 56% will share data with law enforcement in response to an informal request, 84% share or sell personal data, and 100% earned the foundation’s “privacy not included” warning label.

More importantly, are customers educated or informed on the privacy issue?

Fig. 2: Once data is collected from a vehicle, it can go to multiple destinations without the knowledge of customers. Source: Mozilla, *Privacy Not Included.

Applying data to automotive design in the future
OEMs collect many different types of automotive data in relation to autonomous driving, infrastructure, infotainment, connected vehicles, and vehicle health and maintenance. The ultimate goal, however, is not just to compile massive raw data; rather, it is to extract value from it. One of the questions OEMs need to ask is how to apply technology to extract information that is really useful in future automotive design.

“OEMs are trying to test and validate the various functions of their vehicles,” said David Fritz, vice president of virtual and hybrid systems at Siemens EDA. “This can involve millions of terabytes of data. Sometimes, a huge portion of the data is redundant and useless. The real value in the data is, once it gets distilled, that it’s in a form where humans can relate to the meaning of the data, and it also can be pushed into the systems while they’re being developed and tested and before the vehicles are even on the ground. We’ve known for quite some time that many countries and regulatory bodies around the world have been collecting what they call an accident database. When an accident occurs, the police show up on the scene collecting relevant data. ‘There was an intersection here, a stop sign there. And this car was traveling in this direction roughly this many miles an hour. The weather condition is this. The car entered the intersection in the yellow light and caused an accident, etc.’ This is an accident scenario. Technologies are available to take those scenarios and put them in a standard form called Open Scenario. Based on the information, a new set of data can be generated to determine what the sensors would be seeing in those accident situations, and then push it through both a virtual version of the vehicle and environment and in the future, and push those scenarios through the sensors in this physical vehicle itself. This is really the distillation of that data into a form that a human can wrap their mind around. Otherwise, you could collect billions of terabytes of raw data and try to push that into these systems, and it wouldn’t actually help you any more than if someone were sitting in a car and dragging those for billions of miles.”

But that data also can be very useful. “If an OEM wants to obtain safety certification, say in Germany, the OEM can provide a set of data of scenarios on how the vehicle will navigate,” Fritz said. “An OEM can provide a set of data to the German authority, with a set of scenarios to prove the vehicle will navigate in a safe manner under various conditions. By comparing that with the data in the accident database, the German government can say that as long as you avoid 95% of the accidents in that database, you’re certified. That’s actionable from the perspectives of human drivers, insurance, engineering, and visual simulation. The data prove the vehicle is going to behave as expected. The alternative is to drive around, as in the case of autonomous vehicles, and try to justify the accident was not caused by the vehicle, while facing the lawsuit. It does not seem to make sense, but that’s what’s happening today.”

Related Reading
Curbing Automotive Cybersecurity Attacks
A growing number of standards and regulations within the automotive ecosystem promises to save developments costs by fending off cyberattacks.
Software-Defined Vehicles Ready To Roll
New approach could have big effects on cost, safety, security, and time to market.

The post Increased Automotive Data Use Raises Privacy, Security Concerns appeared first on Semiconductor Engineering.