Automotive Cybersecurity

NOVI, Mich. — As automakers transition to building more connected and software-defined vehicles that can regularly receive over-the-air updates, OEMs must now contend with integrating vehicle hardware from different suppliers to ensure seamless connectivity.

A software-defined vehicle using a top-layer operating system still relies heavily on “middleware,” which acts as the “glue” that connects a vehicle’s various hardware components, experts said during a panel discussion moderated by Maite Bezerra, principal analyst for software-defined vehicles at Wards Intelligence at the AutoTech 2025 conference in Michigan.

According to a December 2024 report from Wards Intelligence, the term “software-defined vehicle” refers to a broader digital transformation that’s occurring in the global auto industry. It includes advancements in vehicle platforms, such as such consolidated electrical/electronic (E/E) architectures, and the decoupling of vehicle hardware and software. But this transformation also extends to an automakers’ organizational processes, structures and Tier 1 supplier relationships, all of which play key roles in the vehicle development process, according to the Wards Intelligence report.

But with a large network of global suppliers providing hardware components to dozens of OEMs, the shift to building SDVs will also require a higher level of standardization, according to the panel participants, as well as a deeper collaboration with software developers that have traditionally served clients outside of the auto industry. 

“As more OEMs adopt the software defined vehicle approach, the middleware shifts, as you said, from the background in terms of being kind of this glue that would connect software components and enable communication for different systems within a vehicle to now it shifts into this more critical role,” said panelist Brandy Goolsby, director business development and sales, embedded software at Vector North America.

Middleware can help automakers to manage increasing complexity

Middleware serves as an intermediary software layer that connects a vehicle’s different applications, systems and hardware components, allowing each to communicate and exchange data. It can help automakers better manage the growing complexity of SDVs, according to the panel participants. 

Automakers have previously sourced hardware using embedded software developed by suppliers. But in order to create better digital experiences for customers, building SDVs requires an entirely different approach, and automakers must now consider the role of middleware in a modern vehicle. Customers today expect that their vehicles will always stay updated and evolve at the pace of personal consumer electronics, such as smartphones. 

“This is a layer that will integrate and facilitate the interoperability of the different services and systems that will need to interact within this new modern vehicle architecture,” said Goolsby.

Vector is a developer of embedded electronics and software for networking microcontrollers and high-performance computers. The company also offers a vehicle OS and an open and modular software ecosystem for OEMs.

Additional challenges of SDV development

SDVs also will require a high level of interoperability between systems, which is a new challenge for legacy automakers that for decades adopted a hardware-centric approach to vehicle development. This interoperability ensures that automakers are able to deliver connected services that more customers expect. 

But the development of SDVs also requires that automakers implement organizational changes within their corporate operations to support new product development, according to experts.

Another challenge for OEMs is ensuring that vehicle software meets a high level of cybersecurity, and automakers typically do not specialize in this critical area.

“You have to break the silos inside of the house, you have to break the silos outside of the house because we have to come together as an industry in order to really understand what can create together,” said Goolsby.

NOVI, MICHIGAN — With the auto industry’s shift toward building more connected vehicles powered by software continuously updated over-the-air, OEMs are rapidly moving from hardware-centric vehicle development processes to a software-first approach. This pivot also includes the integration of AI and adoption of a cloud-based development environment for software-based vehicles.

However, to support this transition, legacy automakers still face challenges in data management and technology integration, according to a recent panel discussion on the topic at the AutoTech 2025 conference in Michigan. 

The panel, which was moderated by Maite Bezerra, principal analyst for software-defined vehicles at Wards Intelligence, included industry experts from Bosch, Stellantis, Toptal, and the Scalable Open Architecture for Embedded Edge (SOAFEE) industry group, which is working with automakers to expedite development of software-defined vehicles. 

SOAFEE aims to create an open source vehicle platform using cloud-native architecture that supports multiple hardware configurations. 

A shift toward a cloud development model

“SOAFEE is really kind of more about bringing some of the modern software techniques to automotive software development,” said panelist Robert Day, the group's governing body representative. “Over the last couple or three years, people are actually starting to do their development in the cloud using the tools, technologies and methodologies that are well developed and well used in cloud development.”

Although adopting a cloud-based software development approach is a common practice for developers working in the tech space, it's an entirely new field for some legacy automakers. 

"The problem is the car is not the cloud,” said Day. “It has things like safety and things like mutual physicality, heterogeneous computing.” 

The software development challenges for automakers also create the need for OEMs to recruit top talent to integrate the technology into next-generation vehicles, often from outside of the industry. Some companies are providing services to expedite such recruitment. For example, Toptal operates a freelancing platform that connects companies with in-demand software engineers and other technology specialists. 

“We have a lot of partners in the automotive space,” said panelist Paul Timmermann, VP of product at Toptal. 

Stellantis is one of the automakers encountering the challenges of shifting towards SDVs for its future vehicles. 

"We [automakers] are always hardware first, and now the switch is happening to, you know, software, and then comes the hardware," said panelist Sangeeta Theru, director of virtual validation platforms at Stellantis. “Tools, processes…everything is changing,” she said. 

Theru also highlighted the importance of training internal teams at Stellantis, adding that the automaker recently launched "big training on AWS cloud and architecture” for employees. “There was a lot of effort in upskilling and training internal people,” she said.

Automakers building SDVs must be adept at managing large data sets

A major driver of increasing vehicle complexity is automakers launching more advanced driver assist systems and autonomous driving functionality using AI-powered software, according to the panelists. 

Vehicles with automated driving capabilities, for example, are equipped with dozens of cameras and sensors, generating "many, many terabytes of data" for a single car, scaling to "well beyond petabytes" across large fleets, explained panelist Steven Miller, product management of ADAS and technical expert at Bosch. 

“Clearly you're not going to upload all of that data,” he said. “The other even harder data problem is okay, what's the right data to upload to the cloud?”

With rollout of more advanced autonomous driving features, automakers need to be adept at processing and merging extremely large data sets. One of these challenges is processing high volumes of vehicle data in real-time, as well as making it more manageable to transfer to and from the cloud. 

Automakers must also decide which vehicle data to upload to the cloud to train AI models. Therefore, the panelists emphasized the need for OEMs to create efficient data pipelines to manage this complexity. The panelists also foresee AI being integrated into other vehicle systems, such as remote diagnostics and infotainment. The use of AI will also likely extend to corporate organizational processes.

"This is one of the most transformational shifts that we are seeing in the automotive industry," said Bezerra.

Using software with a higher degree of standardization

The panel discussion also delved into automakers’ adopting open source software with a higher level of standardization to reduce development times and costs.

In November 2024, Panasonic Automotive Systems and Arm announced a collaboration to standardize automotive architecture. The two companies said they recognized the need for the industry to shift from a hardware-centric to a software-first development model to address challenges created by high-cost, vendor-specific proprietary interfaces for vehicles. 

While the use of open source automotive software has traditionally been met with caution due to safety and liability concerns, an April 2025 report from the Eclipse Foundation found a significant jump in industry appetite to use it for safety-critical vehicle systems. According to the report, 79% of automotive software professionals currently use open source tools and/or in-vehicle software for development, and the number of users actively contributing to open source projects increased by 4% from last year.

The big advantage of open source is it provides a standard between companies, explained Day. “If you're starting to use open standard or open source, it makes that collaboration easier,” he said. 

Day also highlighted another long-term strategy decision facing OEMs. "What would you choose to open source first? What would you actually keep in-house?" he said.

Despite the prospects of adopting open source software for vehicles, the panelists acknowledged that some key areas needed more attention, including cybersecurity. This area is even more critical for automated driving and connected infotainment systems that can be used to pay for goods and services, such as EV charging sessions. 

Day raised a critical point about security. “I don't think it's placed enough attention to, and certainly don't think [automakers] spend enough money on it,” he said.

According to chipmaker Arm, a modern vehicle can have up to 650 million lines of code, and this number will only increase in the future. But software will revolutionize how drivers interact with their vehicles and redefine the relationship between OEMs and vehicle owners, according to the company. 

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By Splunk

Modern vehicles are no longer just mechanical machines; they are data platforms on wheels tightly integrated into cloud ecosystems, manufacturing supply chains, and critical infrastructure like charging networks. With over 100 million lines of code in many models (KPMG), each new feature, sensor, and communication channel expands the attack surface.

For senior leaders across the automotive industry, this raises a fundamental question: how do you build digital resilience, the ability to anticipate, withstand, and recover from disruption, in a world where software and cyber threats define safety, trust, and competitive edge?

This article explores how agentic AI and evolving Security Operations Centers (SOCs) can help decision-makers in the automotive ecosystem from OEMs to suppliers and fleet operators, design resilient strategies for an increasingly complex environment.

The Expanding Cyber Landscape in Automotive

The automotive ecosystem faces growing cyber pressure:

• Cyber incidents targeting EV charging infrastructure grew by 50% in 2024, with nearly three-quarters resulting in service disruptions (Cisco). For operators, that translates directly into downtime and lost revenue.

• Reports show that vehicles themselves were compromised in over 35% of industry cyberattack cases (WardsAuto) ranging from remote telemetry exploits to OTA hijacks.
• In supply chains, a single compromised supplier can introduce vulnerabilities across multiple OEMs. With the majority of vulnerabilities found in in-vehicle systems, upstream weaknesses cascade downstream fast.

Attacks are real. Past vulnerabilities have enabled unauthorized access to telemetry data and even remote vehicle functions (Wikipedia). With vehicles integrating 100+ ECUs and multiple connectivity channels including cellular, Wi-Fi, Bluetooth, and V2X, the challenge of defending them is formidable.

Key implication: Digital resilience in automotive requires more than perimeter defenses. Digital resilience requires end-to-end observability across vehicles, infrastructure, and the supply chain.

Agentic AI: What It Means for Automotive Security

Agentic AI refers to AI systems capable of acting autonomously, making context-aware decisions, and executing tasks without constant human direction. In practice, this means:

• AI agents that monitor ECU health, identify anomalies in drive-by-wire commands, and initiate self-isolation of compromised subsystems.
• Automated risk detection in firmware updates or component telemetry before distribution.
• AI agents that detect unusual power draw patterns in charging networks indicative of cyber tampering and reroute sessions.
• Predictive AI that spots malicious attempts at GPS spoofing or unauthorized remote access across thousands of vehicles simultaneously for fleet managers.

The promise of agentic AI is scale: human analysts cannot inspect millions of data points and logs per vehicle, per fleet, per supply chain. AI-driven autonomy offers a way to scale monitoring and response enabling response at the speed and breadth required.

The SOC of the Future: From Reactive to Proactive

Traditional SOCs focus on detection and response, often after incidents are already underway. They struggle to keep up with the distributed nature of the automotive ecosystem. The AI powered SOC of the future will look very different:

• Virtual SOCs (vSOCs): Cloud-driven operations that ingest telemetry from vehicles, factories, chargers, and cloud systems into a unified view.
• Adaptive detection engines: Continuously learning models that tune themselves based on emerging attack patterns.
• Automated response playbooks: Machine-speed containment of threats, from halting suspicious OTA updates to quarantining a compromised charger.
• Lifecycle coverage: Observability not just during operation but across manufacturing, distribution, and decommissioning, and aligned with standards like ISO/SAE 21434 and UNECE WP.29.

This approach moves from reactive “find and fix” toward proactive, adaptive resilience.

Why Digital Resilience Matters Beyond Compliance

While compliance provides a baseline, resilience is ultimately about business continuity and customer trust. Consider these dimensions:

• Protecting vehicle software integrity is not just a compliance requirement, it is central to safety and brand reputation. A hacked steering control undermines consumer trust faster than any recall.
• With just-in-time manufacturing, a single compromised ECU batch can trigger recalls across thousands of vehicles with implications that reach beyond cost alone. Visibility into supplier security posture is essential.
• For EV charging infrastructure operators, downtime doesn’t only affect customers; it can destabilize regional energy grids if attacks scale. Resilience here is both a cybersecurity and sustainability issue.
• With logistics and mobility-as-a-service fleets, even short outages from cyber incidents mean lost revenue and reputational damage. Real-time monitoring of telematics is business-critical.
• Consumers are highly sensitive to reports of “hacked cars.” Hard-won brand reputation is easily damaged and can take years to repair. Sustained trust in connected technology depends on visible resilience.

A Practical Roadmap Toward Resilience

Each stakeholder in the automotive ecosystem has unique challenges, but a shared roadmap is emerging:

1. Comprehensive Visibility
   • OEMs: integrate ECU and OTA telemetry.
   • Suppliers: map firmware provenance and integrity checks.
   • Charging networks: monitor charger-to-cloud traffic.
   • Fleets: centralize telematics data from diverse vehicle types.
2. Pilot Agentic AI
   • Start with non-safety-critical domains (infotainment, energy management) to validate autonomous detection and response workflows.
3. Develop vSOC Capabilities
   • Build data pipelines across IT, OT, and IoT.
   • Automate triage to reduce analyst workload and dwell time.
4. Align With Standards
   • ISO/SAE 21434 and WP.29 should guide lifecycle monitoring, not just annual audits.
5. Scale & Share Intelligence
   • Encourage industry collaboration to share threat intelligence across OEMs, suppliers, and operators because attackers already collaborate.

Looking Ahead

The shift toward software-defined, connected, and autonomous vehicles makes resilience a first-order concern for every stakeholder in the automotive value chain. Agentic AI and the evolution of the SOC provide the tools to meet this challenge: continuous visibility, adaptive learning, and automated response.

For leaders, the path forward is clear. Resilience is no longer about simply defending the perimeter; it is about ensuring that vehicles, infrastructure, and supply chains can operate safely and recover quickly, no matter the disruption.

In summary, digital resilience is the new roadworthiness

Ford Motor Co. Chief EV, Digital and Design Officer Doug Field shared an update of the company’s future strategy as the automaker navigates the auto industry’s transition to software-defined vehicles, in a blog post. 

Ford’s future-state software project, code-named “FNV4,” will combine with its existing vehicle architecture dubbed “FNV3” into a new version referred to as “FNV3.X.” Field said Ford plans to build on its FNV3.X architecture and offer it across a full portfolio of vehicles.

“In today’s automobiles, software is the single biggest lever in advancing a customer’s ownership experience,” Field said in the blog post. 

Field said Ford is already far ahead of its competitors in deploying vehicle software updates. The company delivered 9.5 million updates to its fleet in the first quarter of 2025, according to its blog post, and the shift to its FNV3.X vehicle architecture will deliver software innovations even more quickly to customers.

The new architecture will facilitate the rollout of new digital services to customers and improve vehicle quality. It will also support advanced vehicle security and deliver driving and infotainment enhancements to more Ford customers, according to Field. 

In addition to improving the customer experience, the FNV3.X architecture will allow the automaker to extend the current digital experience — currently offered in the Explorer, Lincoln Nautilus and Navigator — to other vehicles, including the Bronco, F-150, Mustang and Ranger. This approach will also enable Ford to more easily add its BlueCruise hands-free driving technology to additional models that otherwise wouldn’t support it, as the required architecture will be preinstalled.

“Whether it’s hands-free highway driving with BlueCruise, intelligent navigation, using your phone as a key, or our Ford Security Package to give you peace of mind, we’re integrating technology that actually makes life better, and doing it across as many of our vehicles as we can,” Field said in the blog post.

Unified software across the portfolio

Like other automakers, Ford is migrating from traditional vehicle architectures to electrical-electronic systems powered by software that can be regularly updated over-the-air. 

Field said software should be available across Ford’s lineup to reach as many customers as possible. Having a dedicated electrical architecture reserved for some vehicles and a legacy architecture for other models — while simultaneously trying to keep them all updated — is not a good strategy for Ford, according to the blog. 

Without a shared and scalable vehicle software platform, the automaker’s software engineers would have to re-develop the same features over and over and adapt them for different models, according to Field.

In the auto industry, more than 90% of new architectures being introduced by automakers are exclusive to their electric vehicles, according to Field. But if these architectures are not made available in other models, it can exclude a sizable portion of an automaker’s customer base driving ICE and hybrid models. As a result, OEMs will face challenges in delivering the best customer experiences without a more unified vehicle platform that can be shared among models, Field said. 

Since coming back to Ford in 2021, Field and his team have been working to develop a new software-based vehicle architecture for the automaker.

Field joined Ford from Apple in September 2021. Prior to that served as SVP of Engineering at Tesla, where he led the development of the Model 3 sedan. Now, Field is working to deliver to Ford customers the same type of software innovations developed by Apple and Tesla.

Ford created a “skunkworks” team to develop a low-cost electric vehicle platform to make the company more competitive against rivals like Tesla, CEO Jim Farley revealed during the company’s earnings call in early 2024. Farley said at the time that Ford’s new EVs should be profitable within 12 months of launching, and its low-cost EV platform powered by software will be key to achieving that goal.

“We will not be on the sidelines of what can be done on a next-generation, ground-up electric vehicle,” Field said in the blog post. “Our electric vehicle ‘skunkworks’ project is moving full speed ahead to show what we can do when we start with a clean sheet software and electrical architecture.”

American Honda laid out a generative AI strategy in 2023, focused on immediate risks, bringing capabilities in-house, training and long-term planning. As the company advanced on its broader goals, executives increased the focus on responsible AI and the data foundation that powers the technology.

“We’re trying to improve our decision-making speed and our decision-making quality at every level of the organization,” said Bob Brizendine, SVP of IT at American Honda and deputy head of the digital unit in the corporate administration operations at Honda Motor. “The reason we put brakes on a car is so we can go safer, faster, and data governance and AI governance are really in that same domain.”

As enterprises invested in generative AI, adequate governance became critical to reach AI ambitions. Poor frameworks can lead to a legion of roadblocks, from increased costs and subpar performance to project delays and biased results.

To up the ante, strengthening governance isn’t a one-time affair. Plus, aiming for what the industry has labeled as AI-ready data is a “mythical” endeavor, Brizendine told CIO Dive. 

“I really do use the word mythical because I don’t know how to measure exactly when data is ready for AI, or for what decision,” Brizendine said. “It’s a tall order. It comes down to the individual use case, the risk of the decision being made and the importance of that decision.” 

American Honda has worked to get its arms around data and AI governance by establishing a responsible AI council, creating a unified data platform and fostering digital literacy.

The council, composed of representatives from various business units, keeps an eye on model drift, privacy-related compliance obligations and how AI-generated outputs are used. The group advises teams on expectations around AI use and when to use what model. Around mid-year, Brizendine said the group will evaluate how it’s executed and adjust if necessary.

To support the council’s efforts and the overall strategy, American Honda employees are participating in digital literacy training. The automotive manufacturer created a program with research and consulting firm Gallup to establish a digital literacy metric to see where teams stand. 

“If we know the digital literacy of a team, we can target the education programs for their level, whether it’s the executives who need to learn how to drive innovation using these capabilities or the staff,” Brizendine said. 

Strengthening the data engine

In addition to its governance efforts, American Honda has elevated data ownership and worked to democratize AI and data tools across the organization in recent years.

Enterprise AI efforts have amplified data’s importance within most organizations. More than 4 in 5 technology decision-makers said data ownership has shifted over the last year as adoption initiatives expand, according to a Collibra and Harris Poll survey published Wednesday.

American Honda was an early adopter of Microsoft’s Copilot and rolled it out broadly in 2023. Since then, the company has explored AI tools tailored to several business units and tasks. The automotive company has also worked to build out a unified data platform and accelerate its data maturity, fueling its generative AI endeavors. 

“This is where all of these things tie together,” Brizendine said. “We’re making a pretty large play for building a unified platform for some of our strategic elements of our data: integration of various data, ensuring we have seamless data flow. We’re working on that in a priority order by data domain.”

As the organization continues to incorporate unstructured data — such as owners' manuals — and increases the quality of structured data, it’s identifying room for improvement. 

“When we talk about quality or defect information in manufacturing, if you don’t have a common language, it becomes difficult to socialize and look beyond the boundaries of one particular plant,” Brizendine said, giving an example of a use case that’s not quite ready yet. 

American Honda’s Arun Yarlagadda, deputy general manager of global services and data platform and North American chief data officer, told CIO Dive that scrapping a potentially valuable use case is not as common as putting it in the not-for-now pile. 

“If we are using photos of a factory to chart some next-best action and the quality is not good, we would work on improving that source,” Yarlagadda said. “ We didn’t just turn away, but we have to postpone the activity.”

Long term, Yarlagadda said having the right talent, observability and monitoring capabilities will continue to be vital for its success. 

For organizations that get data and AI governance on the right track, technology leaders will need to maintain their diligence — and keep an eye on the horizon. 

“Data and AI are twin engines driving our digital transformation,” Brizendine said. “Keeping the finger on the pulse of this changing world is very difficult, but we’re investing more in advanced technology planning and proofs of concept to try to be ready.”

Hyundai Motor Group propelled its software-defined vehicle strategy forward when it announced a new mobility software brand and technology platform.

The automaker launched “Pleos” at its recent Pleos 25 developer conference in Seoul, according to a press release. The end-to-end platform will integrate high-performance vehicle chips and controllers, a connected vehicle operating system, cloud infrastructure, and mobility and logistics optimization for connected fleets.

The Pleos ecosystem's proprietary vehicle operating system is based on a scalable, zonal-based electrical/electronic architecture designed to boost the performance of its software-defined vehicles. Hyundai will also launch a “Pleos Connect” next-generation infotainment system in Q2 2026, which it plans to install in 20 million vehicles by 2030.

The news comes as Hyundai and other major automakers work to launch connected and software-based vehicles that can continuously be updated to deliver new features, and generate new revenue streams. These plans include new collaborations with technology partners that have traditionally operated outside of the auto industry.

Hyundai says the launch of Pleos is a significant step towards its evolution into a mobility tech company to achieve “Cloud Mobility,” a comprehensive ecosystem of connected mobility software.

“In the rapidly changing automotive industry, Hyundai Motor Group has been dedicated to SDV development since 2023,” the automaker said in the release. “Today, starting with Pleos 25, we are transforming into a company providing software-centered mobility experiences.”

Here's a look at two key aspects Hyundai shared at Pleos 25.

1. A new, smartphone-like interface

Hyundai's new infotainment system — called Pleos Connect — will feature a smartphone-like interface.

The infotainment screen, which is based on Android Automotive OS, will include split-view and multi-window functions, and offer customizable, user-friendly in-vehicle features, according to the press release.

Pleos Connect will also feature in-vehicle AI-powered voice technology called “Gleo AI” for intelligent control of vehicle functions using voice commands. Hyundai customers will be able to create a unique user profile and access their personalized settings from any Pleos Connect-based vehicle.

2. A new platform for developers to create in-vehicle apps

Hyundai is also launching a “Pleos Playground” for software developers to create in-vehicle apps for the automaker’s future software-based vehicles running Pleos Connect.

Pleos Playground will offer feature-rich software development kits that support plug & play functionality to test software in a real-world vehicle environment during the development process, according to the press release. Additional tools include design guides, application programming interfaces and sample code.

Developers will be able to distribute their apps through an “app market” where Hyundai customers can download them to their vehicles. The automaker says it will continually expand its developer support programs and partnerships based on its open ecosystem.

“Pleos serves to create a connected mobility experience expanding from a vehicle to fleets, hardware to software, and ultimately to the entire mobility infrastructure and cities,” the automaker said in the release.

Hyundai’s future vision for the Pleos ecosystem also includes connecting its software-based vehicles to smart city technology.

To prepare for the era of SDVs, Hyundai and its sister company Kia Corp. reorganized their research and development divisions in January of last year. The reorganization included establishing a new advanced vehicle platform division.

When a Nissan minivan equipped with autonomous driving technology successfully navigated the streets of Yokohama, Japan, during a recent test, it was a celebratory milestone for Liam Pedersen, chief scientist at Nissan’s Advanced Technology Center in Silicon Valley.

Pedersen, along with a team of 15 U.S.-based researchers, have worked alongside colleagues in Japan since 2013, developing the technology behind the automaker’s autonomous driving system. The test vehicle equipped with the latest generation of Nissan’s AV hardware and software traversed “a public road in a complex urban environment” without a safety driver onboard, according to a press release.

“This project is the culmination of a dozen years of collaboration across the Pacific,” Pedersen wrote in an email to Automotive Dive.

Pederson and his Silicon Valley team have focused on software technology, including creating AI algorithms, for Nissan. 

“It is immensely satisfying to see the real-world results of everyone’s work,” Pedersen said. “Our technology is advancing at a rapid pace, and we’re excited to be a pioneer in the autonomous driving space.” 

Nissan said the reveal of its latest generation autonomous technology advances its effort to address transportation challenges in communities dealing with driver shortages and an aging population. 

The automaker began demonstrating and testing its autonomous technology in fiscal year 2017, per the release. Nissan’s next test phase will involve 20 autonomous vehicles in Yokohama during its 2025 and 2026 fiscal years, with a goal of providing autonomous mobility services by 2027 using remote human supervision. 

The latest test vehicles are based on the Nissan Serena, which is one of Japan’s best selling minivans. The vehicles are equipped with 14 cameras, nine radars and six lidar sensors, which safely traveled the streets of Yokohama’s busy Minato Mirai central business district.

But reaching the latest system’s generation took years of research, Pedersen said. 

Early versions of the lidar sensors were tested in San Francisco on a 2019 Nissan Leaf EV. An updated AV system using cameras and radars was installed on an Ariya SUV and began testing in 2023.

Pedersen said that both test vehicles can still be regularly seen on roads near his office.

“Both projects have allowed us to make huge advancements in our autonomous drive technology’s ability to perceive, predict, make decisions and plan,” he said.   

Nissan’s Silicon Valley team’s research wasn’t limited to spending time in front of computers in the U.S. Pedersen said researchers were hands on, taking test rides and making multiple trips to Japan, delivering code and the test vehicles to their overseas colleagues. 

While Pedersen and his team members have seen how years of research and testing resulted in successful deployment of autonomous technology on public roads, they recognize their work has other applications.

“Autonomous drive tech is demanded by more car shoppers than ever, and Nissan is committed to meeting that need,” he said. “Nissan will continue to develop ADAS systems that help enhance safety, ease driver stress and relieve traffic congestion.”