Autonomous Driving Chips Market

Autonomous Driving Chips Market Size

The global autonomous driving chips market size was estimated at USD 24.22 billion in 2024. The market is expected to grow from USD 29.73 billion in 2025 to USD 191.07 billion in 2034, at a CAGR of 23%, according to latest report published by Global Market Insights Inc.

Autonomous driving chips are dedicated processors that power self-driving vehicles. Which carry out important tasks for self-driving vehicles, including object detection, path planning, decision-making, and vehicle control. These chips must meet requirements for high reliability, ultra-low latency, and real-time processing capabilities. As vehicles adopt more advanced driver assistance systems (ADAS) and transition from Level 2 partial automation towards aspirational Level 4 and 5 full autonomy, these chips act as the computing engine for intelligent mobility.

The growth of the autonomous driving chips market is driven by multiple factors, such as, governments around the world are attempting to enforce safer roads through regulations and safety standards that will require ADAS technologies to be used. These regulations drive automotive companies to adopt high performance computing for their passenger vehicles. In addition, the transition to electric vehicles (EVs) has increased the demand to adopt advanced driver-assistance systems (ADAS), as manufacturers want to distinguish their models, resulting in an increase in the demand for more high-performance automotive chips.

The automotive industry is transitioning from conventional processor-based systems to using chips. This transition allows for improved scalability of technology and lower-cost options. Research shows that chips have improved data throughput and can use less energy compared to old systems with only one processor. At the same time, adding new AI models (e.g. transformers) and new sensor modalities (e.g. image, radar, and LiDAR) requires chip manufacturers to develop faster or more powerful chips or accelerators that process data quickly, and accurately, but with lower-precision requirements.

On the regulatory side, countries like the U.S. and China are creating regulations for autonomous driving. In the U.S., for example, the National Highway Traffic Safety Administration (NHTSA) is working on regulatory and certification processes to safely bring self-driving vehicles onto the roads.

China's evolving national guidelines for intelligent connected vehicles is comprehensive and advanced the process of innovations and implementation within the context of the domestic semiconductor sector. Systematic regulatory processes are helping improve standardization of safety protocol, enhancing the stability and authenticity of safety features, contributing towards more commercialized options for autonomous systems.

The prominent companies in this category are NVIDIA, Qualcomm, Intel (Mobileye), and some new companies like Horizon Robotics and Black Sesame Technologies located in China. There are many OEMs developing their own custom chips to reduce dependency on third-party chip suppliers and improve the integration and performance of the system.

For instance, XPeng introduced "Turing" chip, and claimed that it outperforms the competitors in performance and efficiency. Qualcomm and BMW launched an associated solution called Snapdragon Ride Pilot, which is distributed across international market. Similarly, Tenstorrent and BOS Semiconductor supported by Hyundai introduced a chip-based AI processor for car use.

Autonomous Driving Chips Market Trends

The market for chips used in autonomous driving is experiencing one of the most transformational shifts it has ever faced, because of regulatory requirements, geopolitical shifts, and real-world implementation milestones. The most important transformation occurring is an increased emphasis of regulators in influencing chip design and function. In the EU, updated regulatory requirements for safety now specify that autonomous driving systems, including the chips that power them, must be certified to safety standards.

These standards include a legal framework for reliability, software updates, and traceability. In China, new regulations require regulators to approve Over-the-Air upgrades for autonomous vehicles. These OTA upgrades represent a safety recall from the perspective of regulators. Another mega trend is a movement towards localizing more chip production and domination from major semiconductor manufacturing countries like China, South Korea, and Taiwan. In China, XPENG has developed and internally designed "Turing" chip that the company claims are superior yet less expensive than NVIDIA's Orin-X and will be used in future Volkswagen models in China.

The progression of technology in the field of chip architecture is also shifting the landscape of the marketplace. The conventional monolithic system-on-chip (SoC) model is being augmented, and even replaced, by modular, and chiplet-based architectures. These types of chiplets have advantages regarding scalability, manufacturing yields, and efficiency with customization. For instance, the new chip designs Eagle-N, a collaboration between Tenstorrent and BOS Semiconductors, is a chiplet architecture targeting specifically automotive AI workloads, and plans to go into production using a 5-nm process.

Chip developers are advancing beyond prototype phases and into more real-world deployments in mass-market vehicles. Major automakers (especially in China) are implementing high-performance chips such as NVIDIA’s Thor into their mass-market production vehicles. Automakers like Li Auto, Zeekr, and Xiaomi have even announced vehicles with these chips highlighting a clear trend towards mass-market of advanced autonomy technology.

As the industry progresses, there is increased focus on safety, auditability, and standardization. Technical and academic organizations are requesting changes to existing safety standards (i.e. ISO 26262) to better account for risks that are specific to AI (i.e. neural network transparency, adversarial robustness, and edge case failures).

Autonomous Driving Chips Market Analysis

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Based on chip, the autonomous driving chips market is divided among microcontrollers (MCUs), GPU, FPGA, ASIC, and others. The ASIC segment dominated the market, accounting for 36% in 2024 and is expected to grow at a CAGR of over 25% through 2025 to 2034.

• Application-specific integrated circuits (ASIC) are the leading chip because of their efficiency, performance, and flexibility to run in automotive environments. ASIC are not general-purpose processors which can be adapted to each role, instead they are custom chips to perform a particular workload (e.g., object detection, sensor fusion, neural network inference). These are all major functions for autonomous operation. This specialization enables ASICs to provide increased performance, lower latency, and greater thermal efficiency, which are all important attributes in an automotive environment where both power and size are limited, and safety is crucial.
  
• Intel (Mobileye) has some of the best-known ASIC with its EyeQ series of chips and has deployed over 200 million of these chips in vehicles. The fifth generation of the EyeQ series, the EyeQ6, is optimized for advanced driver assistance systems (ADAS) and autonomous operation, providing a high-compute capability under strict regulations. Tesla has similarly developed its own ASIC, called the Full Self-Driving (FSD) chip, which provides it with an opportunity to control the entire hardware-software stack, and optimize for purposes of their self-driving architecture.
  
• A new development in the industry is Alchip Technologies’ automotive ASIC design platform that was announced to seamlessly meet the increasing demand for custom chips designed for advanced driver assistance systems (ADAS) and autonomous driving functions. These types of platforms also have stringent automotive safety and dependability requirements for ASIC design, particularly for Level 3 and Level 4 autonomous driving systems. Additionally, these platforms are being developed with cutting-edge nodes like 5-nanometer, and as low as 3-nanometer designs, which offers additional power efficiencies and processing speeds.
  
• Graphics processing units (GPUs) are commonly used in development environments, especially during early-stage testing. They are well-suited for parallel image processing and are often used to train deep learning models. On the other hand, Field-programmable gate arrays (FPGAs) offer greater flexibility and are typically used in prototype development or in systems where algorithms need to be updated frequently, making them ideal for evolving in-vehicle applications.
  
• First-generation microcontrollers (MCUs), such as Infineon Technologies' AURIX family, are designed to handle essential vehicle functions. It manages tasks like controlling basic operations, interfacing sensors and safety systems, and ensuring system redundancy. These MCUs play a key role in maintaining functional safety, even in vehicles with lower levels of autonomy.
  

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Based on autonomy level, autonomous driving chips market is bifurcated among Level 1 (driver assistance), Level 2 (partial automation), Level 3 (conditional automation), Level 4 (high automation), Level 5 (full automation). Level 1 (driver assistance) segment dominates the market with 45% share in 2024, and the segment is expected to grow at a CAGR of over 18.8% from 2025 to 2034.

• Level 1 (driver assistance) dominates the market, but Level 2 (partial automation) is rapidly accelerating in popularity, with consumer interest shifting toward fully automated driving and the acceptance of key safety elements by regulators. Convenience features and additional Level 2 (partial automation) solutions are expected to be developed in a similar timeframe. Level 1 (driver assistance) remains widely recognized and favored, because it is simply more affordable and its construction depends on less complex and expensive technologies, providing a better opportunity to advance mass-market applications.
  
• These systems are generic and only support one function, such as adaptive cruise control, lane departure warning, or emergency braking. Also, these systems do not exhibit extensive sensor fusion and use lower-level computational resources which allows them to be implemented into a different variety of vehicles (increased use in more affordable vehicles at this level).
  
• Automakers have exponentially deployed Level 1 (driver assistance) systems in recent years. For example, the entire Toyota global safety package that encompasses Level 1 (driver assistance) technologies such as pre-collision systems and lane departure warnings are now on over 40 million vehicles. This represents a fundamental change in vehicle regulation and consumer expectation and applies even to entry level vehicles. Driver assistance is now expected in lower price point models.
  
• Level 2 (partial automation) systems that perform two or more functions concurrently, such as steering and acceleration, are witnessing significant growth, especially in the mid-to-high-end vehicle market. Level 2 (partial automation) systems require continuous monitoring by the driver but enable driving hands-free or semi-automated driving in specific environments.
  
• Level 2 (partial automation) systems like Tesla’s Autopilot and Ford’s BlueCruise are useful examples that demonstrate how quickly Level 2 (partial automation) systems are gaining market acceptance.  All Level 2 (partial automation) systems use more advanced chips with greater computing capacity and, with Level 2 (partial automation) systems, may use better sensor fusion and real-time monitoring of the driver, which adds further to greater chipset demand.
  
• Classification of Level 3 (conditional automation) comes with additional layers of complexity. Level 3 (conditional automation) signifies that the car can drive independently in some scenarios, and the driver doesn't have to be paying attention the entire time but must be able to take back control if necessary. Although regulatory and technical barriers have kept the technology from widespread deployment, some advances have been made toward Level 3 (conditional automation).
  
• Level 4 (high automation) and Level 5 (full automation), representing high and full automation respectively, are currently in the testing and pilot stage of development. As these levels necessitate the highest computing power, safety, and reliability based on safety-critical criteria, chipsets must carry out real-time perception, localization, planning, and control with possibly multiple redundant sensors such as LiDAR, radar, and cameras. While companies have tested services including robotaxi and autonomous shuttles in controlled environments, mass-market deployment will have to wait for the normal vehicle infrastructure, regulatory, and costs to meet current capabilities.
  

Based on function, the autonomous driving chips market is segmented among perception chips, decision-making chips, and control chips. The perception chips segment dominates the market with market share of 38% in 2024.

• The market is dominated by perception chips segment, owing to their critical functions including basic image processing, object detection, depth estimation, environmental modeling, and other forms of visual understanding. In turn, without precise and real-time perception capabilities, no higher-level decision-making or control functions can occur, thus, perception chips are strictly the foundation for autonomous driving at every level.
  
• This importance of perception chips at early levels of autonomy is reflected in the wide variation of vehicle models or grades of autonomy in which perception chips have been deployed. For instance, the EyeQ6 Lite chip from Mobileye is a dedicated perception chip made especially to assist driving and advanced driver assistance systems (ADAS) functions. Similarly, Xpeng's Turing chip has dedicated image signal processors (ISPs) and neural processing units (NPUs) to enable real-time visual perception, indicating this perception task is growing in complexity and importance in traditional vehicle architecture.
  
• Decision chips also play an important role in the system. They perform functions such as sensor merging, path planning and movement prediction functions that are essential in high autonomy levels, but not in all vehicles. For example, systems such as Qualcomm’s Snapdragon Ride or NVIDIA's drive platforms are integrated to do perception and decision making, but only on high or fully autonomous vehicles. These chips often need to handle operations such as real-time location detection, map interpretation and relevant decision-making, which increases their complexity, but limits the amount of deployment compared to the perception of chips.
  
• On the other hand, control chips, handle actuation, braking, steering, throttle control. They work under strict safety and time constraints and are often based on microcontrollers or automotive processors. Infineon’s AURIX family is a leading example, providing reliable performance and essential safety features for fail-safe operation. Although control chips are crucial, they have lower computational demands, and their core functions remain relatively consistent across different levels of autonomy. As a result, this segment experiences steady but slower market growth.
  

Based on vehicle, the autonomous driving chips market is segmented into passenger and commercial. Passenger segment dominates the market with market share of 79% in 2024.

• Passenger vehicles, especially in urbanizing countries, tend to be the consumer's choice of vehicle for several reasons such as increased levels of disposable income, established road systems, and lifestyles that promote personal vehicles. An illustrative example of passenger vehicle production levels, in India for example, in 2024, passenger vehicles were produced at levels of more than 4.9 million units. In fact, SUVs are encroaching into passenger vehicle space from the "passenger vehicles" sub-category based on safety and versatility.
  
• Electric vehicle (EV) adoption is also growing within the passenger vehicle category. In early 2025, electric passenger vehicle registrations in India showed strong month-on-month and year-on-year growth. This rapid increase reflects rising consumer awareness, government incentives, and active efforts by manufacturers. Companies like Tata Motors, Hyundai, and MG have started offering EV models targeting both middle-income and premium consumer segments.
  
• On the other hand, the International Council on Clean Transportation (ICCT) reports that the commercial vehicle segment, including trucks, buses, vans or any other transport vehicle, is developing at a slower pace. Cost sensitivity is a major barrier to adoption in the commercial vehicle sector, as fleet operators prioritize total cost of ownership. This means the higher upfront cost of many electric commercial vehicles (ECVs) often makes them less competitive compared to diesel vehicles. Although ECV sales have surged in some regions reaching growth rates of up to 124% year-on-year by mid-2024 they still represent less than 2% of the market share in most areas.
  

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North America dominated the autonomous driving chips market with around 35% market share and generated around USD 8.54 billion revenue in 2024.

• North America, particularly the U.S., is the global leader in autonomous vehicle chips due to a confluence of factors such as, favorable regulations, a strong industry ecosystem, real-world testing, and robust chip manufacturers that offer an optimistic ground for the innovation, manufacturing and real-world use of autonomous driving technologies.
  
• At the Federal Level, the U.S. government took steps to begin moving autonomous vehicles into acceptance and use from a regulatory point of view. In 2025, the National Highway Traffic Safety and Administration (NHTSA) expanded its Automated Vehicle Exemption Program to provide manufacturers with more flexibility to test Lexan manufactured AVs in the United States. The expansion of the NHTSA program was important not only from a regulatory standpoint for manufacturers that test real-world settings but also on demand for the specialized, tested, and certified autonomous driving chips.
  
• At the state level, California continues to support the R&D of autonomous vehicles, providing AV testing permits to tech-forward companies like NVIDIA, Tesla, Waymo, and Zoox, who build, manufacture, or incorporate autonomous driving chips into their product or service.
  
• In North America, leading companies in autonomous chip development include key players in the automated driving ecosystem like NVIDIA and Qualcomm, both creating advanced autonomous driving platforms. In early 2025, NVIDIA achieved high-level safety and cybersecurity certifications for its DRIVE Hyperion platform, setting a standard for AV chip makers in the coming year. Additionally, Ambarella, a U.S. consumer technology company, entered the market with low-power, high-efficiency image processing SoCs designed to support computer vision in autonomous vehicles.
  
• The wealth of road-testing data consolidates the position of North America, where in 2024, California alone had reported over 3.9 million autonomous vehicle miles and where companies like Waymo, and Zoox are responsible for much of that testing. These extensive trials produce invaluable performance data, which then can be used to improve chip design cycle, especially for data latency, sensor fusion, and decision-making algorithms.
  

U.S. dominated the North America autonomous driving chips market with around 89% market share and generated USD 7.57 billion revenue in 2024.

• The U.S. maintains a leadership position in the North American market for autonomous driving chips, due to the country's innovative ecosystem, leading semiconductor companies, supportive government actions, and the rate of autonomous vehicle (AV) technology uptake. U.S. leadership has been established not just because the best technology in each category comes from the U.S., but because of strategic initial investments, and sensibly regulated observation processes.
  
• With U.S. companies like NVIDIA, Intel (Mobileye), and Ambarella having built an impressive portfolio of high-performance system-on-chips (SoCs) and AI accelerators for autonomous driving and advanced driver-assistance systems (ADAS). NVIDIA's drive platform, for instance, is an integral component in GM's Super Cruise and Ultra Cruise systems, especially the Drive Orin and Drive Thor implementations. Mobileye's EyeQ chips are a common component of many vehicles in the U.S. fleet and have successfully met the challenging visual-based perception and decision-making tasks required for AV applications. Ambarella has produced quality camera processing and sensor fusion sensors that are also enabling AV features.
  
• Support from regulators has been imperative to driving chip adoption. In the latter half of 2024, for instance, the National Highway Traffic Safety Administration (NHTSA) announced that nearly all new passenger vehicles sold in the United States by model year 2029 will be required to have automatic emergency braking (AEB) systems. Enforceable action has led to vehicle manufacturers and tier-one vehicle suppliers installing sophisticated processing chips that can sense and make decisions in real-time.
  
• The United States also leads the national agenda on AV testing and deployment. Leading AV companies such as Cruise, Waymo, and Tesla are collecting a massive amount of data on real world driving across a variety of terrains and conditions which can help improve chip performance and the integration of software and hardware. Government platforms such as the Automated Vehicle Hub created by U.S. Department of Transportation have been useful in developing knowledge sharing and public-private partnerships to drive research and transparency in AV safety research and data processes.
  

Europe autonomous driving chips accounted for USD 6.74 billion in 2024 and is anticipated to show lucrative growth over the forecast period.

• Europe's autonomous driving chip market is expected to experience significant growth during the forecast period, driven by strict safety regulations, strategic industrial investments, and technological advancements from key regional players. Countries like Germany, France, and Italy are leading this progress by leveraging their large base of vehicles and rapidly adopting integrated semiconductor strategies.
  
• The EU has already implemented extensive vehicle safety rules that form the demand for chips. Effective from General Safety Regulation (EU 2019/2144), from July 2022, it suggests that all new vehicles should be equipped with many advanced driver assistance systems (ADAS), such as emergency braking, lane and intelligent speed aid.
  
• Germany has played a major role in implementing the autonomous driving law and the affiliated regulation, which allows driverless vehicles to work under specific conditions on public roads. This legal framework requires strong data processing platforms that are capable of real-time views, decision-making and compliance with security protocols.
  
• Industrial investment is another important growth-driver. European countries take active steps to make the chip production local and reduce their dependence on foreign semiconductor suppliers. For example, Infineon Technologies develops a large-scale semiconductor production facility in Dresden with the support of the German government. The Megafab project is expected to operate the automotive industry, including a time operation, including autonomous systems. Similarly, Continental has launched a new Advanced Electronics and Semiconductor Solutions Division with the aim of designing their own chips and increasing hardware software.
  
• France and Italy also take steps through STMicroelectronics, investing in silicon carbide (SiC) disc plants to support electric and autonomous vehicles. These materials are necessary for high-power applications, such as sensors and driving controls found on autonomous platforms. In addition, large chipmakers such as Infineon are expanding their car portfolio of the automotive industry.
  
• Recent development reflects this regional growth. At the beginning of 2025, Infineon Technologies secured more than one billion euros future contracts for silicon carbide chips. In addition, Continental's decision to integrate semiconductor design suggests that large European motor vehicles provide more control over important technologies. The European Union's cross-regulating adjustment also develops, with initiatives to cohesion of autonomous vehicle testing and approval procedures in the member states by 2026.
  

Asia Pacific autonomous driving chips accounted for USD 6.65 billion in 2024 and is anticipated to show lucrative growth over the forecast period.

• The Asia-Pacific region appears to be a very attractive market for autonomous driving chips, which is inspired by strong government support, leads technical abilities and grows in large countries such as China, Japan and South Korea. These factors are jointly determined for a significant increase in the forecast period.
  
• China has been particularly active in accelerating autonomous driving development through extensive political initiatives. Several government ministries have installed pilot zones in cities such as Beijing, Shanghai and Guangzhou to test intelligent network vehicles and connected-connected vehicle integration systems.
  
• These provide a regulator and infrastructure structure to support testing and final distribution of level 3 automation and high autonomous driving options. In addition, China has reduced domestic production of car chips to around 5% in recent years. Large Chinese companies such as XPeng develop their own AI-operated chips, improve the power of their vehicles and continue autonomous driving technologies.
  
• Japan is also continuously moving forward in this market. Through ministries such as the Japanese government, METI, domestic semiconductor subsidies to promote production. Large Japanese chip producers, including Renesas Electronics, Socionext, Mitsubishi Electric, Rohm and Toshiba, expand the capacity of large Japanese chip producers, electrical appliances, image sensors, logic chips and car micro controllers. Japan's "Advanced SoC Research for Automotive" (ASRA) and Consortium Renesas with semiconductor companies such as Toyota, Nissan, Honda, Denso and Panasonic bring out large vehicle manufacturers and component suppliers together. Their collaborative goals are to develop advanced automotive system-on chips with spontaneous technology, which is the target of mass production around 2030.
  
• In the South Korea, efforts to strengthen both design and production skills for autonomous driving chips are focused on. Hyundai has collaborated with Samsung Electronics to produce advanced domestic chips, using state process nodes such as 5nm. The purpose of this partnership is to reduce the dependence on external foundry, improve the flexibility of the supply chain and reduce production costs. Samsung delivers chips to many ADAS companies in motor vehicles, which improves South Korea's role in autonomous driving ecosystem.
  
• The collection of real-world data is crucial for developing autonomous driving technologies. In China, companies like Pony.ai have logged millions of autonomous driving kilometers through Robotaxi and ADAS testing. In Beijing, hundreds of autonomous vehicles have been deployed, driving advancements in mapping, sensing, regulations, and operational standards.
  

Latin America accounted for around USD 1 billion in 2024 and is anticipated to show lucrative growth over the forecast period.

• Latin America emerges as a promising market for autonomous driving, which is inspired to use strategic investments, government initiative and advanced driver assistance system (ADAS) in major countries such as Brazil and Mexico.
  
• In Brazil, the government has created important resources for technology and industrial digitalization. A major initiative includes an allocation of BRL 186.6 billion to pursue industrial digitalization, focusing on chip construction, robotics and cloud computing. As part of this test, the Brazil Semion-program provides 21 billion dollar with encouragement to promote domestic semiconductor production by 2026. The goal of these investments is to strengthen the country's capacity to produce the semiconductor required for the autonomous driving system.
  
• In addition, for 2024–2028, Brazil's "IA para o Bem de Todos" (AI for all plans) emphasized a comprehensive strategy for developing advanced AI technologies including autonomous driving applications. The scheme estimates a total investment of around USD 54.5 billion from several sources to support these goals and emphasizes Brazil's duty to continue AI and autonomous vehicle technologies.
  
• In Mexico, the motor vehicle industry plays an important role in using autonomous driving technologies. The country has experienced an increase of 25% in investments related to AI over the past year, with a sufficient proportion in the motor vehicles and the Internet of Things (IoT) sectors. Major technology leaders such as Intel and Qualcomm establish new research and development hubs in Mexico and use the country's strategic proximity and rapidly growing technical ecosystems for the United States.
  
• In addition, Mexico's electric vehicle market is expanded rapidly, it is expected to double by 2025 with production. This growth has increased the integration of AI and neuromorphic chips, as companies such as General Motors and Nissan include these techniques in their autonomous vehicle systems to increase sensor treatment and decision-making ability for real time.
  

Middle East and Africa autonomous driving chips accounted for USD 1.2 billion in 2024 and is anticipated to show lucrative growth over the forecast period.

• The region of the Middle East and Africa appear to be an important player in the autonomous driving chips market, run by the strategic government initiative, international participation and technological progress.
  
• In the Middle East, countries like the United Arab Emirates (UAE) and Saudi Arabia are actively promoting autonomous driving technologies. The UAE’s Roads and Transport Authority (RTA) is partnering with major companies selling autonomous vehicles, aiming for 25% of all city trips to be autonomous by 2030. Similarly, Saudi Arabia plans for 15% of vehicles to be sustainable as part of its Vision 2030. Innovative projects like Neom are also funding companies such as Pony.ai to accelerate autonomous vehicle development in the region.
  
• In Africa, Kenya Konja develops in autonomous vehicle development through the Technopolis initiative, also known as "Silicon Savannah". Located south of Nairobi, this large technology is designed to promote innovation and attract technology companies, possibly the basis for future progress in autonomous driving technologies.
  
• Despite this promising development, the MEA area faces challenges, including regulatory barriers, limited infrastructure and the need to develop an effective workforce. Nevertheless, ongoing investments and strategic initiatives show a strong obligation to continue autonomous driving technologies and keep the area in position as a cumbersome hub for innovation in the field.
  

Autonomous Driving Chips Market Share

• In 2024, top seven companies in the autonomous driving chips industry are NVIDIA, Intel (Mobileye), Renesas Electronics, STMicroelectronics, NXP Semiconductors, Infineon Technologies, Texas Instruments around 56% of revenue.
  
• NVIDIA is a world leader in AI data treatment and provides high performance for autonomous driving through its drive platform. NVIDIA is known for its altitude demonstration GPU and AI processing solutions that are advanced in terms of perception, sensor fusion and decision-making. The company's scalable data processing architecture supports all levels of autonomous driving and is widely adopted by car manufacturers and technology companies.
  
• Intel (Mobileye) is a pioneer in visual advanced driver assessment system (ADAS) and autonomous driving technology and Intel a subsidiary. Mobile's IQ chip series has been used extensively to avoid object detection, track stay and confrontation. With emphasis on the use of strong partnerships with vehicle manufacturers and camera-based perception, Mobileye is a dominant player in the market for autonomous driving chips and works against automated automatic driving solutions that include improved mapping and AI skills.
  
• Renesas is a manufacturer and prominent supplier of electronics microcontrollers and system-on-chips (SOCs), which is used for vehicle control and sensor treatment in autonomous driving applications. Renesas provides functional protection in its solutions to support reliability and real-time performance, as well as levels from level 1 to level 3. Renesas develops scalable platforms through partnerships with car manufacturers and Tier 1 suppliers, using sensor reindeer, AI processing and connection that allows vehicles to function safely.
  
• STMicroelectronics offers a wide range of semiconductor products, including sensors, microcontrollers and power management semiconductors, which are necessary for autonomous driving applications. Many vehicles such as chips LiDAR, Radar and cameras facilitate perception, data collection and processing. Their low power consumption and safety facilities make the company globally a reputable supplier for advanced driver assistance systems (ADAS), which are necessary to operate effectively for reliable autonomous driving systems.
  
• The NXP Semiconductors is a leader all over the world in the car's semiconductors, focusing on the connection, safety and processing of semiconductors for autonomous driving applications. Their solutions provide some (V2X) communication and secure data processing and sensor merging, which are essential for self-driving vehicles.
  
• Infineon Technologies plays a key role in autonomous driving by providing radar sensors, microcontrollers (like AURIX TC4x), and safety-certified chips for ADAS and autonomous systems. Its 77/79 GHz radar and ISO 26262-compliant components support Level 2 to Level 4 driving. Infineon focuses on scalability, safety, and system integration, partnering with OEMs like ZF. It enables sensor fusion, object detection, and real-time control in next-gen autonomous vehicles.
  
• Texas Instruments (TI) offer a wide selection of car processors, microcontrollers and analog chips, designed for autonomous driving applications, including the processing of sensor data and controlling vehicles. The focus for ten has been on integrated solutions with real-time performance and low power consumption and supports several levels of vehicle autonomy.
  

Autonomous Driving Chips Market Companies

      Major players operating in the autonomous driving chips industry are:

• Analog Devices
• Infineon Technologies
• Intel (Mobileye)
• NVIDIA
• NXP Semiconductors
• Qualcomm
• Renesas Electronics
• STMicroelectronics
• Texas Instruments

• The autonomous driving chips market is shaped by a combination of semiconductor giants and specialized innovators, creating a competitive and technologically intensive landscape. Leading players such as NVIDIA, Mobileye (Intel), Qualcomm, NXP Semiconductors, Infineon Technologies, Texas Instruments, Renesas Electronics, STMicroelectronics, and Analog Devices collectively account for a significant share of the international market, driven by their robust portfolios across AI computing, sensor integration, and automotive-grade chip design.
  
• These companies maintain their leadership by investing in AI-based SoCs, real-time edge processing units, and safety-critical microcontrollers, while integrating features such as sensor fusion, V2X communication, and functional safety compliance. Their offerings support varying levels of driving autonomy, from ADAS to full self-driving platforms, meeting the needs of OEMs, Tier 1 suppliers, and emerging EV startups.
  
• To consolidate their positions, these firms are deploying multi-layered strategies, including platform scalability, strategic alliances with automakers and tech firms, vertical integration, and specialization in key chip domains such as NVIDIA's AI dominance, Mobileye’s camera-based vision systems, and Infineon’s power-efficient safety solutions. Many are also expanding into automotive cloud ecosystems, enabling end-to-end autonomous solutions.
  
• Additionally, the rise of regional manufacturing hubs and government support for semiconductor self-sufficiency especially in Asia, Europe, and North America is prompting these players to localize R&D and production, ensuring supply chain resilience and reduced time-to-market for new technologies.
  
• Alongside these dominant players, emerging chipmakers and regional specialists are gaining traction by focusing on niche areas such as radar front ends, neuromorphic computing, and AI accelerators. These entrants, while smaller in scale, are contributing to innovation and diversification in the market, particularly in electric autonomous vehicles, where low-latency processing and energy efficiency are paramount.
  

Autonomous Driving Chips Industry News

• In December 2024, Infineon and Stellantis undertook contracts establishing both supply and capacity commitments for their PROFET power switches and SiC (silicon carbide) CoolSiC components. These arrangements support Stellantis's power architectures for next-generation vehicles, improving efficiency, overall range, and driver experience.
  
• In November 2024, Infineon and Siemens announced a collaboration to pair Siemens' embedded software based on AUTOSAR ("Capital Embedded AR Classic" platform) with Infineon's AURIX TC4x microcontrollers to create production-ready ECUs (electronic control units) for next generation SDVs, targeting OEMs like BMW.
  
• In April 2024, Mobileye announced an orders receivable of 46 million chips of its EyeQ6 Lite for the next few years. These chips would be based on TSMC's 7-nm process and deliver roughly 4.5× as much computing power as prior chips while also including Advanced Driver Assistance Systems (ADAS) features such as lane-changing and automatic cruise control.
  
• In March 2024, NVIDIA announced its DRIVE Thor computing platform (which follows the DRIVE Orin product) and that it was adopted by a number of companies (e.g. BYD, Waabi, XPeng, Fareshare, and WeRide) for future connected electric vehicles, robotaxis and autonomous delivery vehicles. DRIVE Thor also has generative AI features based on NVIDIA's new Blackwell architecture.
  

The autonomous driving chips market research report includes in-depth coverage of the industry with estimates & forecasts in terms of revenue ($ Bn) and volume (Million Units) from 2021 to 2034, for the following segments:

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Market, By Chip

• Microcontrollers (MCUs)
• GPU
• FPGA
• ASIC
• Others

Market, By Autonomy Level

• Level 1 (driver assistance)
• Level 2 (partial automation)
• Level 3 (conditional automation)
• Level 4 (high automation)
• Level 5 (full automation)

Market, By Function

• Perception chips
• Decision-making chips
• Control chips 

Market, By Vehicle

• Passenger
• Commercial               

The above information is provided for the following regions and countries:

• North America
  • US
  • Canada
• Europe
  • Germany
  • UK
  • France
  • Italy
  • Spain
  • Nordics
  • Russia
• Asia Pacific
  • China
  • India
  • Japan
  • Australia
  • Indonesia
  • Philippines
  • Thailand
  • South Korea
  • Singapore
• Latin America
  • Brazil
  • Mexico
  • Argentina
• Middle East and Africa
  • Saudi Arabia
  • South Africa
  • UAE