Learn about strategic moves from Porsche, KIA, Microsoft, PlaxidityX (ex-Argus Cyber Security), Panasonic, LG, Valeo, and Green Hills Software.

Automotive Cyber Security Regulatory Compliance Is a Top Priority

UNECE regulations R155 & R156 require OEMs to implement cybersecurity management systems and secure vehicle software updates. The broad impact of these regulations across 64 countries necessitates a rapid and widespread integration of cybersecurity management systems.

The growing attack vector is increasing potential vulnerabilities, with KIA in the spotlight for hacks.

However, automakers are not the only ones subject to cyber threats.

Dealerships lost $1.02 Billion in business after CDK cyberattack, says AEG study

The Anderson Economic Group estimated dealerships lost 56,200 new-vehicle sales and $1.02 billion between June 19 and July 15 because of the CDK Global cyberattacks and subsequent software outages.

The Rising Importance of CyberSecurity Certifications

Certifications emphasize the growing importance of demonstrating compliance through certifications to gain trust and market access in regulated markets.

LG, Valeo, and Green Hills Software are pursuing ISO/SAE 21434 and UNECE R155 certifications to align with global standards.

LG secured the highest level of automotive cyber security certification “Level 3 CSMS” (Cybersecurity Management Systems) Certification for Vehicle Cybersecurity from TÜV Rheinland, a global testing and certification agency.

Certifications like Level 3 CSMS reflect a company’s ability to manage cybersecurity risks throughout product lifecycles, enhancing their market position as reliable suppliers.

Penetration Testing As Part of Product Validation

OEMs such as Ford Trucks are conducting vehicle-level penetration testing to identify and address potential threats as part of regulatory compliance and product validation.

Focus on Regular Bug Bounty Programs to Secure Digital Services

Porsche launched a four-week bug bounty initiative to identify vulnerabilities in digital services and vehicle systems through external cybersecurity researchers. The company says it will regularly launch Bug Bounty programmes to secure its digital services.

Integration of Cybersecurity into Development Lifecycles

New Product Launches to Integrate Security to Automotive Software Development

dSPACE and Argus Cyber Security (now PlaxidityX) joined forces in December 2023 to introduce automated cybersecurity testing capabilities, supporting CI/CD pipelines in automotive software development.

Moreover, platforms like Argus DevSecOps (launched in May 2024) integrate cybersecurity testing early in the software development process (“shift-left” approach).

Technologies such as Argus Fuzzing, integrated with dSPACE’s HIL systems, enable automated security testing early in the development cycle, supporting the “shift-left” paradigm in software engineering.

Finally, Panasonic’s VERZEUSE™ for TARA (Threat Analysis and Risk Assessment) can streamline compliance and threat analysis during vehicle development.

Collaborations To Enhance Product Scalability

The collaboration between Argus and Microsoft in June 2024 for end-to-end security platforms underlines the shift towards comprehensive and scalable automotive cyber security solutions integrated into development lifecycles.

Aftermarket Anti-Theft Vehicle Solutions

Argus Cyber Security launched vDome, an aftermarket anti-theft solution that leverages AI to identify and neutralize unauthorized device connections in real time.

Rising Demand for Real-Time, Turnkey Solutions

Demand for Turnkey Solutions is Rising

Mid-sized OEMs are increasingly outsourcing cybersecurity needs to specialists. AVL’s Ajunic® ECU solutions with embedded IDPS cater to these needs.

This trend supports the broader adoption of ready-to-deploy solutions to meet regulatory needs without extensive in-house expertise.

Real-Time Threat Detection and Prevention Is the Need of the Hour

The use of AI for threat detection, predictive analytics, and real-time threat neutralization is increasing.

Solutions like Argus vDome detect and neutralize malicious devices in microseconds, adding layers of real-time protection against cyberattacks.

The Outlook of Automotive Cyber Security

Rise of Cybersecurity-as-a-Service

Silicom is offering standardized edge devices for security-as-a-service, enabling scalable and subscription-based security solutions.

Expanded Use of Threat Intelligence To Cover Supply Chain

Bitdefender’s partnership with Ferrari indicates how automakers are tapping into advanced threat analytics to enhance automotive cyber security beyond vehicles, covering supply chains and communications.

Cybersecurity in Data Centers

The expansion of cyber risk management to data centre physical security (e.g., DeNexus) underscores the convergence of cyber and physical security in the automotive ecosystem.

AI-Driven Cybersecurity for Autonomous Vehicles

Research into vulnerabilities of AI in autonomous systems highlights the need for resilient and adaptive security measures.

University at Buffalo’s research into AI vulnerabilities in Autonomous Vehicles highlights the potential risks of AI-powered autonomous systems and stresses the need for resilient detection systems against malicious tampering.

Key Takeaways

Strategic focus: Companies align with global regulations and emphasise partnerships to tackle automotive cyber security at scale, across the whole product lifecycle.

Technological evolution: The Automotive Cyber Security industry is advancing towards automated, AI-driven, and proactive security measures.

Market adoption: Regulatory mandates and the increasing sophistication of cyber threats are accelerating market penetration of cybersecurity solutions

For a deep dive into product offerings, competition, and market outlook check our report.

Players are concentrating efforts on AI integration across the vehicle lifecycle—design, production, operation, and customer support.

Auto2x analysed more than 100 applications of AI in cars to unveil strategic opportunities, and advise stakeholders on the technological readiness and value extraction of different use cases.

The insights come from our latest report: AI in Automotive: Opportunities, Applications and Competitor Analysis1

Real-World Integration of AI in Cars

We analysed more than 100 use cases of Artificial Intelligence in Automotive to understand:

• Which automotive applications of AI are gaining traction in 2024-25 and why?
• Which tech enablers do players use, e.g. GenAI, NLP, ML or acoustic AI?
• Which domain is it for, e.g. HMI in-car assistants, Level 4-Autonomous driving, vehicle design or manufacturing?
• Who are the partners enabling the tech integration, e.g. Amazon, Baidu. For example, NVIDIA is a key supplier of Generative AI for AI brain for autonomous driving

Navigate our database of AI use cases and enabling technologies in the automotive industry to learn about strategies and emerging trends.

To access the full database contact us here.

Exploring Generative AI Applications Beyond Voice Assistants

The VW Group, Stellantis, and Great Wall Motors are among the carmakers integrating Generative AI to in-car voice assistants to make communication between the driver and the system more accurate.

However, an interesting angle is to look at who supplies the technology at a regional level. In China, Baidu has a strong position compared to Cerence or OpenAI’s ChatGPT.

3 Chinese Players to Watch in LLMs in Autonomous Driving

Three Chinese players show how they have developed solutions for Autonomous Driving using large-scale models and often a vision-only approach.

• Haomo.ai with DriveGPT an autonomous driving generative large-scale model aiming at end-to-end self-driving cognition.
• Baidu, Inc. with their Level 4 Apollo Self Drive (ASD) and an end-to-end large-scale model
• Waabi in Autonomous Trucks. In March 2024, Waabi introduced their Copilot4D: A Foundation Model for Self-Driving. The integration of generative AI into Copilot4D enables it to learn from vast amounts of sensor data without requiring extensive human supervision. This is pivotal for scaling autonomous driving technologies effectively and safely.

Design and manufacturing

In terms of design, the benefit of using generative AI is leveraging the different types of iterations in a simulated environment to optimize resources and allow for better efficiency when you design a component or even a whole vehicle.

With regards to manufacturing, there are a lot of benefits to bringing elements of sustainability, such as the collaboration of Aston Martin with Provider Altair to create efficiency and sustainability.

The Key Pillars of the AI defined Vehicle

Unlike traditional vehicles defined by mechanical components and hardware, AI-defined vehicles are dynamic, software-centric platforms.

The AI defined vehicle represents the culmination of decades of innovation in AI, generative design, machine learning, and robotics. It continuously evolves delivering unparalleled safety, efficiency, and user experiences.

XPeng claims their P7+ is the world’s first “Ai defined vehicle²” featuring built-in AI-powered advanced ADAS, XPENG’s in-house XOS 5.4 and the AI Hawkeye Visual Solution.

The road ahead for AI in cars requires a unified effort across industries to overcome challenges and drive adoption at scale.

Generative AI Design

The adoption of Generative AI Design in the Automotive industry is growing fast to leverage its benefits for iterative efficiency and optimize solutions based on aesthetics, lightweight, aerodynamic and energy-efficient components.

Volkswagen harnesses generative AI to optimize the design of its Golf hatchback, generating numerous design options for the chassis, leading to a more lightweight and fuel-efficient vehicle.

Sustainable Production

AI plays a pivotal role in making the automotive industry more sustainable across the vehicle lifecycle.

1. Machine Learning (ML) and Predictive Analytics are popular for predictive maintenance, error identification, and infrastructure monitoring.AI enhances robot manufacturing efficiency and reduces production downtime
2. Battery Optimization: Machine learning can help optimize battery cell production
3. AI in Supply Chain Monitoring ensures transparent, sustainable procurement of materials, reducing carbon footprints.

Online AI Car Configurators and Metaverse

Carmakers like Rimac are already working on future car configurators with interactive personalization using Generative AI and real-time graphics.

Fiat has launched their Metaverse Store, a virtual dealership that covers every stage of the buying journey, from discovery to configuration and purchase.

AI Brain Vehicle Computer: The Nerve Center of Driverless Vehicles

The AI brain vehicle computer is the central processing unit, enabling vehicles to think, learn, and adapt over time. For EVs and autonomous vehicles, it provides the intelligence needed to meet the growing demand for smarter, greener, and more adaptable vehicles.

It combines inputs from multiple vehicle systems (e.g., navigation, sensors, user preferences) for more informed decision-making. In ADAS, AI brain computers process data from cameras, sensors, LIDAR, and RADAR in real-time, enabling autonomous driving functionalities (e.g., lane keeping, obstacle avoidance).

Continuous Learning with SDV-SOTA

Unlike traditional vehicles that degrade over time, Software-defined vehicles improve with each software update, offering new features and optimized performance.

The heart of AI defined Vehicle: Safety & Autonomous Driving

Autonomous driving technology is at the heart of the AI-defined vehicle. AI-driven systems reduce human error and enhance situational awareness.

• Computer Vision helps interpret road conditions, obstacles, and signals with precision.
• Edge AI allows real-time decision-making by processing data locally, reducing latency. Companies like Aptiv and Bosch are adopting edge computing to process data locally, reducing latency and dependence on cloud systems.
• Acoustic AI detects emergency vehicles and ambient noises, adding a layer of situational awareness. Cerence is supplying BMW’s L3 with emergency vehicle detection

Collaborative AI and V2X For Perception Beyond The Range of Sensors

Integrating Vehicle-to-Everything (V2X) communication enables vehicles to communicate with traffic infrastructure and other cars, paving the way for smart cities.

ZF is exploring collaborative AI models to enhance vehicle-to-vehicle interaction, particularly in dense traffic scenarios.

Ai defined vehicle for Advanced Electrification

AI in Electric vehicles is following a strong growth trajectory with innovations focused on energy management, personalisation and sustainability.

• Route Optimization: AI optimizes route planning for EVs, considering traffic, terrain, and weather to reduce energy consumption.
• Battery Performance: AI is used to optimize battery cell production and predict battery health for efficient energy usage. Predictive analytics helps with proactive maintenance and extending battery life.
• AI in-car voice assistants to help EV drivers drive more efficiently.

UX: Predictive Personalization and Vehicle Companions

Natural language processing (NLP) makes the car a true digital companion.

1. In-car Assistants: Voice-enabled AI assistants help drivers navigate, optimize routes, and even manage energy consumption in electric vehicles (EVs).
2. Digital Cockpits: Generative AI enhances in-car interfaces by providing predictive suggestions and personalized content.

Emotion AI to Prevent Driver Distraction & Health Emergencies

Safety is enhanced through Emotion AI, which monitors driver behaviour and state of mind:

1. Real-time emotion recognition detects fatigue or distraction, triggering alerts or interventions to ensure safety.
2. AI-based monitoring of surrounding vehicles and infrastructure enables predictive analytics to prevent accidents.

Efficiency Optimization During the Whole Vehicle LifeCycle

Analyzing vehicle health data systems can minimize downtime and ensure efficient energy use, particularly critical for electric vehicles (EVs). This real-time optimization helps companies manage costs and reduce carbon footprints while improving operational efficiency.

AI-Driven Maintenance and Repairs

1. Robots and AI systems monitor production lines to identify trends in errors.
2. Gen AI Mechanics-Acoustic AI for Quality Inspection in Production
3. Natural Language Processing in Technical Customer Service
4. In-field diagnostics analyze vehicle conditions, enabling proactive repairs before critical failures occur.

End of Life: Automated Dismantling, Recycling, Repurposing

By integrating AI into dismantling, recycling, and repurposing processes, the automotive industry can reduce waste, recover valuable materials, and contribute to a circular economy while minimizing environmental impact.

• AI-powered computer vision systems can identify and sort vehicle components for dismantling, and separating materials like metals, plastics, and electronic parts.
• AI evaluates the condition of components such as EV batteries to determine if they can be repurposed for energy storage in homes, businesses, or grid applications.

1. AI in Automotive: Opportunities, Applications and Competitor Analysis ↩︎
2. XPeng claims their P7+ is the world’s first “Ai defined vehicle ↩︎

New technology in automotive industry shaping the future of EVs, ADAS and Connected Cars

1. AI-Defined Vehicles to empower human-centric services and personalisation
2. Innovations in AI hardware for automotive to support suppliers building AI products
3. Unmanned factories and manufacturing digitalisation to boost vertical integration
4. The new battlefront in Autonomous Driving Software Stack: in-house vs. outsourced
5. AI and Generative AI to bridge the gap to Vision-based autonomous vehicles (Level 4)
6. The rise of Level 4 Autonomous Driver-as-a-service in robo-taxis and robo-trucks
7. Advancements in sensors, software and computing fuel the expansion of L2/2+ & L3
8. Software-defined vehicles will become the key differentiator in automotive
9. The race to create immersive in-vehicle experiences comes at a Premium
10. EV localisation strategies and ecosystem growth to succeed as competition intensifies
11. Closing the gap in EV charging infrastructure towards unified EV charging standards
12. Innovations in alternative powertrain technology are crucial to sustainable propulsion
13. Competition in energy storage and innovations could help make EVs more affordable

To learn more about opportunities in automotive, get access to our Opportunity Radar¹.

The most important new product launches and services coming in 2025

To derive opportunities, we analysed the top technologies in artificial intelligence, autonomous vehicles, electric cars, and connectivity in 2025.

In 2025, new technology in the automotive industry will feature AI hardware and software for autonomous driving and intelligent vehicles, solid-state batteries, flex-fuel hybrids, and modular EV platforms tailored for global markets.

Artificial Intelligence

Integration of AI in vehicles to empower human-centric services and personalisation

AI-Defined Vehicles In November 2024, XPeng showcased their latest P7+ sedan marking it as “the world’s first AI-Defined Vehicle”². What makes this vehicle different is the integration of AI to develop intelligent technologies across vehicle domains for premium passenger experiences.

XPeng’s P7+ integrates AI across the chassis, powertrain, driving system, smart cockpit, and XPENG’s AI Hawkeye Visual Solution, making intelligent driving an essential feature of the future mobility experience.

XPeng joins several automobile manufacturers, automotive suppliers and mobility players integrating artificial intelligence new technology in Automotive industry, such as Generative AI, NLP and Machine Learning.

Uber will launch an AI assistant for drivers in the U.S. powered by OpenAI’s GPT-4o in 2025.

Advancements in AI hardware for automotive to support suppliers building AI-enabled products

Arm and Intel’s new hardware solutions empower Tier-1 suppliers and OEMs to build AI-enabled products, promoting a decentralized ecosystem. New technology in automotive industry includes AI-automotive GPUs and chiplets.

³GPU Advancements: Intel’s Arc Graphics for Automotive marks a leap in automotive AI performance, enabling real-time processing for intelligent cockpits and immersive user experiences.

Automotive Chiplets: Arm’s Compute Subsystems (CSS) for chiplets accelerate the development of AI-driven vehicles, emphasizing modularity and faster iteration cycles for automotive electronics.

Digitalization in Manufacturing to boost Vertical Integration

BMW’s iFACTORY employs NVIDIA’s Omniverse platform to create a digital-first manufacturing process.

Denso’s commitment to a 24-hour unmanned operation facility highlights the use of robotics and digital twins to optimize efficiency, ensuring minimal human intervention while meeting future production demands.

Autonomous Driving New Technology in Automotive Industry in 2025

The new battlefront in Autonomous Driving Software Stack: in-house vs. outsourced

Unique software capabilities become key differentiators in a crowded market. Competition in AD-SW Stack is getting stronger because it’s a key enabler to achieving and monetizing autonomy.

Automakers follow three strategies in Autonomous Driving Software:

• developing software in-house to retain control and differentiation
• or outsourcing to tech partners to accelerate development and manage complexity.
• or a hybrid approach mixing the two approaches

Automakers are focusing on in-house and partner-led software development to customize and optimize autonomous driving.

Tesla, Mercedes-Benz, BMW, XPeng, Toyota are developing in-house expertise to leverage the benefits of full customisation with specific vehicle performance and brand identity, reduced dependency on external licensing fees and partnerships and seamless updates to improving the user experience over time. However, the face high upfront R&D costs, talent shortages and complexities in scaling.

General Motors, Volkswagen, Volvo (ecarx), Hyundai, Jaguar Land Rover (NVIDIA) are among the automakers following an outsourced development strategy. This strategy could enable faster time-to-market, cost-savings and flexibility in selecting state-of-the-art components.

The rise of software-defined platforms like Ecarx’s Cloudpeak and SiEngine’s AD1000 chip is driving innovation in autonomous driving stacks.

AI and Generative AI to bridge the gap to Vision-based autonomy

The integration of AI is bringing innovations in Autonomous driving, from Level 2-3 ADAS (XPENG vision-based ADAS without Lidar), to take-over control (Waymo) and traffic monitoring (SoftBank Group Corp.).

XPeng is among other carmakers (Baidu, Inc.–JIDU Auto, and Tesla) exploring Lidar-less, vision-based Autonomous Driving with their “AI-Defined Vehicle” P7+.

Plus SuperDrive Level 4 self-driving software built on Generative AI and open foundation models will be ready for driverless operations in 2025.

Waabi to Bring the First Generative AI-Powered Trucking Solution, Built on NVIDIA DRIVE Thor, to Market.

Expansion of Level 3-4 autonomous driving in cars and trucks

By 2025, Level 4-Autonomous driving will transition from pilot projects to scalable deployments, shaping the future of mobility and transportation ecosystems globally.

Automakers like Honda, Mercedes-Benz, and XPENG are pushing boundaries with advanced Level 3 (cruising autonomy).

New technology in automotive industry from the likes of NVIDIA (DRIVE Thor) and partnerships with firms like Momenta and Helm.ai enable significant leaps in capability.

Aurora and Waabi are pioneering autonomous truck solutions, targeting commercial deployments by 2025. Scania’s mining trucks and Continental’s autonomous agricultural robots showcase non-passenger use cases for autonomous technologies.

Next-generation Sensors for better performance and efficiency

Next-gen sensors like 4D imaging radar (Arbe chipset) and high-performance lidar (Hesai for Leapmotor) improve detection accuracy and range. This new technology in automotive industry is critical for enabling safer and more reliable autonomous functions.

Read the 10 biggest opportunities in Autonomous Driving in 2025.

Connected Cars

Software will become the key differentiator among automotive players

Partnerships to make all vehicles “Software-Defined”, i.e. always up-to-date and manage complexity are on the rise. New Technology in Automotive Industry in 2025 includes new automotive processors to support immersive experiences, automotive digital twins and more.

Hyundai’s SDV Transformation: Hyundai’s ambitious roadmap aims to make all its vehicles SDVs by 2025, enabling over-the-air (OTA) updates for functionality upgrades. This strategy emphasizes Hyundai’s commitment to creating vehicles that can evolve with user needs, making hardware a foundation for long-term software services.

JLR’s collaboration with NVIDIA DRIVE for SDVs focuses on AI-powered safety and autonomous features. This partnership highlights the growing trend of automakers teaming up with tech companies to develop robust SDV platforms.

Immersive Passenger Experiences

Create immersive In-vehicle experiences: Connected services, such as advanced IVI systems, create personalized and immersive experiences for drivers and passengers.

Hyundai’s adoption of Samsung’s Exynos Auto V920 processor for its next-generation in-vehicle infotainment (IVI) systems is a step forward in delivering high-performance, immersive driving environments. With this new technology in automotive industry, Hyundai’s vehicles will remain competitive in connected car experiences.

Electrification trends in 2025

Electrification technology in automotive industry to power efficiency and affordability includes Solid State Batteries, Alternative Powertrains and ultra-fast EV Charging.

Affordable Electric Vehicles on the Horizon

Suzuki-Toyota Collaboration: Suzuki will manufacture a nimble BEV SUV for Toyota at its Gujarat plant in India starting in 2025. Featuring a 4WD system, this model underscores Toyota’s focus on leveraging Suzuki’s cost-effective production capabilities to expand in emerging markets.

BMW Vision Neue Klasse: BMW’s next-generation electric platform emphasizes computing power and efficiency, with its iX3 crossover leading a series of innovative BEV models.

Affordable BEVs on the Horizon: According to Goldman Sachs, battery prices are projected to fall nearly 50% by 2026, potentially bringing EV ownership costs in line with gasoline cars. Advances in battery chemistry, such as cell-to-pack technology and higher energy density, are accelerating this shift.

Localization strategies in China and Europe

Brands are tailoring products for regional markets, such as Toyota’s flex-fuel hybrids for Brazil and BYD’s dealership expansion in Japan. These strategies ensure relevance in diverse regulatory and consumer landscapes.

Audi’s focus on the Q6L e-tron for the Chinese market underscores the importance of localized strategies in EV adoption.

Solid State Batteries

The falling cost of lithium-ion batteries and breakthroughs in solid-state and nuclear battery technologies drive competition and innovation in energy storage. The advancements of SSB technology in automotive industry is critical for improving EV performance and reducing costs.

Nissan believes all-solid-state batteries can be reduced to $75 per kWh in fiscal 2028 and to $65 per kWh thereafter, placing EVs at the same cost level as gasoline-powered vehicles.

Ilika’s shipment of prototype Goliath solid-state batteries to an automotive customer marks progress in battery energy density and safety. Expected to enter the market by 2025, these batteries promise significant efficiency gains.

Alternative Powertrain New Technology in Automotive Industry

Solutions like Toyota’s flex-fuel hybrids and MAN’s methanol retrofits offer transitional pathways to sustainability by diversifying powertrain options, catering to markets where full electrification faces infrastructure or cost barriers.

Toyota’s Hybrid-Flex Strategy in Brazil: Toyota’s $2.2 billion investment in flex-fuel hybrids underscores its commitment to region-specific solutions. Hybrid-flex vehicles that run on ethanol or gasoline are tailored for Brazil’s energy landscape, balancing electrification with fuel flexibility.

Methanol Retrofits for Commercial Vehicles: MAN PrimeServ’s methanol retrofit packages for diesel engines represent a step toward alternative fuels. By enabling dual-fuel operations, this innovation caters to commercial fleets looking for sustainable solutions.

Hydrogen Mobility

Toyota: The Mirai, already in its second generation, will receive updates in 2025 to improve range and performance.

Hyundai: The NEXO SUV continues to lead the hydrogen passenger car segment, with the next generation expected in 2025.

BMW: The company’s iX5 Hydrogen SUV will move beyond pilot testing to limited production.

Chinese Automakers: Companies like Great Wall Motors and Geely are expected to enter the hydrogen market with affordable FCEVs.

Nikola Motors plans to launch its Tre FCEV in the U.S. and Europe.

Hydrogen-powered buses from Toyota, Ballard, and Van Hool will be deployed across urban transit systems in Europe and Asia.

Close EV charging infrastructure gaps towards unified EV charging standards

Volvo and Tesla Superchargers: Volvo’s integration with Tesla’s North American Charging Standard (NACS) provides access to over 17,800 Tesla Supercharger stations. Starting with the 2025 model year, Volvo’s EX90 flagship will ship with NACS adapters.

Volkswagen and Electrify America: Volkswagen’s partnership ensures three years of discounted charging for ID. Buzz customers.

Cross-Adoption of NACS: Automakers like Toyota, BMW, and Mazda are aligning with Tesla’s NACS, creating a unified standard in North America to simplify charging for consumers.

The EU plans to add 1,000 hydrogen refueling stations as part of its hydrogen roadmap, with Germany and France leading deployment.

United States: California is targeting 200 hydrogen refueling stations by 2025, with expansion plans in other states like Texas and New York.

The expansion of green hydrogen production and refueling infrastructure could accelerate the adoption of new technology in the automotive industry, particularly in regions focused on sustainability.

By 2025, new technology in the automotive industry will include AI hardware and software for Level 4-Autonomous driving, intelligent cockpits, electrification.

🚀 Stay Ahead of the Curve!
Our reports are your ultimate guide to navigating innovation and automation in the automotive industry. Whether it’s electrification, AI, or market trends, we’ve got the insights you need to:

• Make data-driven decisions
• Spot emerging opportunities
• Outpace your competitors

I want to unlock exclusive industry intelligence and power my strategy

1. Current, Automated Intelligence Opportunity Radar ↩︎
2. XPEng AI-Defined Vehicle ↩︎
3. Intel brings GPU to the automotive industry to elevate in-vehicle AI capabilities. ↩︎

Auto2x brings real-time automated intelligence to its Live Scoring of Opportunities in Autonomous Driving to help players respond faster to market changes and empower their strategies with quantitative methods

Auto2x brings real-time data and a synthesis of business, innovation metrics and expert opinion to develop a Live Scoring of Opportunities in Autonomous Driving to help players develop effective strategies and business models.

• Real-time data helps players respond faster to changing market needs with real-time information about changes in scoring;
• Automated trend identification saves time from manual analysis with dynamic taxonomies across technologies, markets and players;
• Automated technology and innovation monitoring leverage expert methodologies to pinpoint breakthroughs and trigger points impacting the opportunity landscape.

To access the Live ranking of Opportunities in Autonomous Driving contact us here.

Autonomous Vehicles present huge opportunities but come with significant risks due to techno-commercial challenges

There are big opportunities to generate new revenue pools, expand to new promising markets, and embrace innovative business models to monetise and advance products to solidify their differentiation.

However, incumbents and new entrants must navigate the opportunity landscape carefully due to financial constraints and techno-commercial challenges.

• There is increasing pressure for better financial performance (ROI, EBIT, CAPEX), leading to organisational restructuring

Continental¹ evaluates making Automotive an Independent Company and to Prepare Spin-off in Q4 2024. Automotive is their biggest division, with 20.3 Billion Euros in revenue and covers ADAS, among other products.

Competition in ADAS, Autonomous Driving and Autonomous Vehicles is fierce² as vehicle manufacturers, automotive component suppliers, and new entrants strive to monetise the huge opportunities for new revenues from sensors, over-the-air (OTA) features and novel services.

• Despite significant investments, technological challenges in autonomous driving software persist leading to delays in deployment roadmaps and tech pivots.

Mobileye ³is ending its development of FMCW Lidar due to positive progress in EyeQ6 and imaging radar.

• Regulatory roadblocks and political uncertainty over tariffs (e.g. semicondictor components) affect the ADAS supply chain.

The Methodology for Scoring Opportunities in Autonomous Driving

Auto2x launches new Live Scoring of the Biggest Opportunities in Autonomous Driving segmented by Market Potential and Technological Readiness

Auto2x analysed business data, innovation metrics, investments and interviews with experts to rank Opportunities in Autonomous Driving across 3 parameters.

• Market Potential of opportunities across ADAS features, sensors, AI, computing, robotics, etc.
• Technological Readiness Level (TRL from 1 to 9-Commercialised)
• Opportunity Score: a quantitative metric of all the sub-parameters from Market Potential and TRL.

In total, we have identified more than 100 Opportunities in Autonomous Driving segmented into 10 categories.

1. Generate new Revenue Pools
2. Expand to new Markets
3. Expand to new Sectors
4. Improve Operational Efficiency
5. Develop your Ecosystem
6. Improve Customer Experience
7. Embrace innovative Business Models
8. Product Advancements & Innovation
9. Regulatory mandates and innovation for Compliance
10. Emerging Signals

The 10 Biggest Opportunities in Autonomous Driving in 2025

Learn which opportunities in Autonomous Driving across the 10 categories should be at the top of your agenda. This ranking considers the Market Potential and TRL in 2025.

This means that promising opportunities that are still not deployed to realise high revenues have a lower score.

The Live Scoring system for opportunities in autonomous driving identifies several promising opportunities that stakeholders can leverage as the technology matures and market dynamics evolve. Key opportunities include:

• Advanced Driver Assistance Systems (ADAS): The transition from basic ADAS to more sophisticated systems, such as Level 3 and Level 4 autonomy, is expected to create significant demand. Vehicles equipped with enhanced ADAS features can improve safety and user experience, leading to higher adoption rates.
• Robo-Shuttle Services: The pilot programs for autonomous shuttles in urban areas are gaining traction, particularly in regions like the U.S., China, and Europe. These services can optimize public transport and reduce congestion, presenting a lucrative market for operators.
• Sensor Technology Development: As vehicles become more autonomous, there is a growing need for advanced sensors (e.g., lidar, cameras) that can accurately perceive the environment. Companies specializing in sensor technology stand to benefit significantly from this demand.
• Data Monetization: Autonomous vehicles generate vast amounts of data that can be analyzed for insights into traffic patterns, driver behavior, and vehicle performance. Companies can monetize this data through partnerships with urban planners or transportation agencies.
• Smart City Integration: The integration of autonomous vehicles with smart city infrastructure (like traffic lights and road sensors) presents opportunities for enhancing urban mobility solutions. This synergy can lead to reduced traffic congestion and improved road safety.
• Insurance Innovations: As the liability landscape changes with the introduction of autonomous vehicles, there is potential for new insurance models tailored specifically for self-driving technology. This could include usage-based insurance or policies that account for reduced risk.
• Innovation for Compliance: With evolving regulations around autonomous driving, there is a growing need for expertise in compliance and safety standards. Consulting firms that specialize in navigating these regulatory frameworks can help find a niche market.

Which new Revenue Pools Players should Focus on

Sales of ADAS sensors and next-generation components is one of the biggest opportunities in Autonomous Driving: Redundant perception is crucial for safe path planning, leading to the rise of sensor set. Price competition, supply chain constraints, and evolving standards for interoperability across OEMs are the main challenges.

ADAS Average Content per Vehicle range from €489 for Lv.2, with 17 sensors per car, to €960-2,100 for Level 3 depending on the usage of lidar (L3+ HWP) or not for redundancy

Automotive Computing, Domain Controllers and Supercomputing: The computing power required for a Lv.2 vehicle is 10 TOPS (tera operations per second). Lv.4 exceeds 100 TOPS, Lv.5 vehicle will require a 1,000 TOPS computing processing power.

The potential of computing is high, drawing the attention of new entrants. Lenovo⁴ entered the Autonomous Driving market with 2,100 TOPS Domain Controller for Level 2-4 Autonomy starting production in 2025. However, there is risk because of the complexity and costs in developing scalable solutions; reliance on chip supply stability.

Memory: The massive data processing needs in autonomous systems fuels growth in production and specialization of memory systems for AI-driven tasks and high data processing in autonomous vehicles. Players need to consider the costs of production and maintaining performance standards in a rapidly changing tech landscape.

Features of demand: Capitalize on demand for Level 2-3 ADAS features through mass deployment and safety-focused offerings. However, there are market saturation risks and regulatory variability across regions.

Licence ADAS and Autonomous Driving Software to carmakers and suppliers is one of the most promising opportunities in Autonomous Driving. Players need to consider Intellectual property competition, cybersecurity risks, and dependency on integration partnerships.

Autonomous Truck Driver-as-a-Service: Deploy autonomous truck services tailored to on-demand logistics and long-haul freight solutions. The challenges are regulatory hurdles, operational safety challenges, and slow adoption from conservative logistics operators.

Flexible, dynamic pricing for ADAS, e.g. hourly pricing or based on usage patterns.

There is a big opportunity to bundle autonomous driving services as part of subscription-based or pay-per-use models, addressing customer demand for flexibility and convenience.

Expand to new Markets

China, Europe, the U.S and Japan are the Top Autonomous Driving Hubs based on deployment of autonomous driving and innovation intensity.

Honda and Nissan launched Level 3-Conditional Automation in Japan in 2021 leveraging the update of the UNECE regulation. Level 3 is coming to Germany and the US in 2024 from BMW and Mercedes-Benz, among others.

The potential of commercialising Level 2-ADAS in India is high. However, there are still techno-commercial challenges in systems and infrastructure.

Expand to new Sectors

Autonomous trucks are already expanding in the mining sector. Expand deployment of autonomous trucks in the mining sector for improved efficiency, safety, and cost reduction.

Other promising sectors include Autonomous deliveries, Autonomous logistics and Autonomous vehicles in AgiTech and Industrial robotics.

How to Improve Operational Efficiency

Some of the opportunities in autonomous driving to improve operational efficiency are: Dynamic Fleet Optimization, Flexible, Dynamic Pricing Models

Tips to Develop your Ecosystem

Connected Road Infrastructure: Implement connected infrastructure (e.g., V2X communication) to support safe, efficient autonomous vehicle navigation and traffic management. The risks are high costs, long deployment timelines, and the need for cohesive public-private collaboration.

Enhance integration of robotaxis and robo-shuttles into urban mobility networks and public transportation. Urban public transportation with autonomous vehicles presents a scalable and high-impact solution, driven by increasing urbanization and congestion relief needs. However, regulatory complexities, consumer acceptance, and infrastructure integration hurdles.

Create robotaxi platform open to different companies for Level 4 autonomous vehicles. However, there are technical complexities in creating scalable, universally compatible platforms.

Improving Customer Experience

There is potential to mitigate driver distraction and drowsiness with health monitoring and emotion recognition systems integrated in vehicles. Despite the high potential, there are limited commercial deployments in premium vehicles due to privacy concerns, security and integration challenges.

Passenger Wellbeing will become the key USP in future cabins, incorporating predictive personalisation for safety, incl. emotional recognition & health monitoring, and voice / gesture-operated functions for convenience

Embrace innovative Business Models

Component as a Service: Offer hardware components (sensors, processors) on a subscription or pay-per-use model to OEMs and tier suppliers.

Tier-0.5 Supplier: Integrate between traditional Tier-1 and OEMs to provide full-stack solutions, combining hardware, software, and system integration.

Tele-operation for robotaxis to handle emergency safety maneuvers. Early-stage commercial applications exist. Challenges include network latency, cybersecurity risks, and limited scalability for teleoperation centers.

Product Advancements & Innovation

Develop steer-by-wire systems to enhance vehicle agility, reduce weight, and enable customizable driving experiences. Safety certification, user acceptance, and regulatory approval challenges still remain.

Sensor integration: Level 3-4 require a robust sensor set for redundancy and sensor placement for performance and aesthetics. The downside is that players face high costs, complexity in system integration, and aesthetics vs. functionality trade-offs.

Precise positioning solutions are experiencing high demand, however, the future of Mapless Autonomy is still a debate. There is potential to develop systems that can operate with limited or no reliance on pre-built maps, enhancing scalability and flexibility in varying conditions.

Lidar-less autonomy: High interest from cost-conscious automakers, but there are safety and reliability concerns in complex, unstructured environments.

Autonomous Truck Convoying (Platooning): Significant fuel and operational savings in the logistics sector, along with increased safety and reduced labor costs.

Generative AI and Large Multimodal AI Models in Perception Software and Autonomous Simulations: Baidu, Haomo.ai and helm.ai are three innovative suppliers of Generative-AI and end-to-end models for Autonomous Driving, supplying JIDU Auto, WEY and Honda respectively. To leverage these opportunities in autonomous driving, players need to overcome the high computational demands, IP conflicts, and regulatory considerations for AI use.

Develop Level 4-Autonomous Driving-ready platforms. Some of the challenges are high R&D costs, regulatory hurdles, and public perception.

Regulatory mandates and innovation for Compliance

Collaborate with certification organisations for continuous validation and certification of new SOTA updates.

Emerging Signals

Integrate neuromorphic computing for more efficient, brain-like processing capabilities in autonomous systems. The tech is still in early research stage, with long development timelines, high complexity, and commercial uncertainties.

Quantum computing is a revolutionary technology with promising applications in: cryptography for unbreakable encryption to enhance vehicle cyber-security, supply chain optimization: inventory management to demand forecasting; traffic management; Environmental perception for autonomous driving.

Develop Brain-Machine Communication systems that facilitate direct communication between the human brain and the vehicle for enhanced control and safety.

The Live Scoring of Opportunities in Autonomous Driving is complemented by an in-depth report of strategies and technologies of major carmakers, suppliers and emerging start-ups.

Autonomous Vehicle Competitor Analysis: Carmakers, Suppliers and Start-ups

Auto2x examined the strategies, technologies & market position of carmakers, suppliers & start-ups in Autonomous Vehicles to find leaders, gaps & opportunities.

Learn about the strategies, technology roadmaps, strategic partners and marketing positioning of automakers, suppliers and start-ups in Autonomous Vehicles

References

1. Continental to Evaluate Making Automotive an Independent Company and to Prepare Spin-off (August 2024) ↩︎
2. Competition in Level 4 Autonomous Vehicles is intensifying powered by Generative AI, computing and collaborations (Auto2x) ↩︎
3. Mobileye is ending its development of FMCW Lidar (September 2024) ↩︎
4. Lenovo Unveils Vehicle Computing Roadmap Through Collaboration with NVIDIA (November 2023) ↩︎

In Level 3-Automated Mode, the Autonomous Driving System (ADS) monitors the road and drives the vehicle within its Operating Design Domain (ODD, e.g. highway driving or traffic jam). However, the driver is still the “fall-back” and must be “available” to take over vehicle control.

Level 3 autonomy is poised to transform how we drive and interact with vehicles allowing drivers to legally perform side tasks like answering emails or browsing to increase productivity and convenience

However, the transition of vehicle control poses safety challenges. Moreover, automotive players strive to find the perfect balance between system and component cost and robust performance. Finally, they must navigate the challenging regulatory framework requiring certifications and approvals which vary across major markets.

This article explores what Level 3 autonomous driving means, how it differs from other levels of autonomy, and its implications for automakers, suppliers, and consumers.

What is Level 3 Autonomous Driving?

Level 3 autonomous driving, as defined by the Society of Automotive Engineers (SAE), refers to a system where the vehicle can manage all aspects of driving in specific conditions without driver intervention. However, unlike Level 4 or Level 5 systems, the driver must be ready to take over control when requested by the system.

For instance, a Level 3-enabled vehicle can handle tasks like steering, acceleration, braking, and navigation in controlled environments, such as highways, but the driver must remain alert to intervene during unexpected situations.

Key Technological Building Blocks of Level 3 Autonomy

Operating Design Domain (ODD) for Conditional Automation: Unlike Level 2 systems that require constant driver supervision, Level 3 can operate independently in predefined conditions.

Carmakers are working on Level 3 features for:

• Cruising, such as Traffic Jam Pilot or Highway Pilot.
• Parking: summon, learnt-parking manoeuvres
• Understanding what these systems do and their limitations is crucial to avoid misuse and abuse.

Advanced Sensor Suite for robust performance: Level 3 vehicles rely on a combination of cameras, radar, lidar, high-definition mapping, AD-Domain Controllers, advanced AD-Software Stack and centralised E/E architecture.

There are different approaches to software and hardware stack, with some players working on vision-based autonomy, lidar-less and map-less Level 3.

Human-Machine Interface: The system notifies drivers when human intervention is necessary through visual, audio, or tactile alerts. Intuitive interaction with HMI and safe transition of control between the driver and the system are critical for higher levels of automated driving. The mandatory installation of driver-facing cameras and data storage aims at mitigating safety risks of ‘’transition demand’’

Over-the-air updates (OTA): system updates to enhance performance and fix issues are now delivered with SOTA-FOTA updates. Tesla gives the option to their customers to purchase ADAS after they purchase their vehicle (Feature on Demand) with OTA of software since the hardware is already installed.

Data Storage: The regulations also require vehicles to be equipped with a Data Storage System for Automated Driving — the so-called “black box” — which will record when ALKS is activated. This will help towards the determination of correct system operation as well as liability determination in case of an accident in “automated mode”.

Cyber Security and Functional Safety Requirements: ALKS will also need to comply with cyber-security and software update requirements set out in two new U.N. regulations (2021). Cyber Security becomes a regulatory requirement to tackle cyber threats and vulnerabilities. OTA updates will also play a crucial role to mitigate and patch threats.

How Level 3 Autonomous Driving Differs from Other Levels

Level 2 vs. Level 3

Level 2 systems provide partial automation, assisting with tasks like lane keeping or adaptive cruise control. However, the driver must actively supervise and remain in control. Level 3, by contrast, takes full responsibility under specific conditions, making it a significant leap forward.

Level 3 vs. Level 4

While Level 3 requires driver intervention in certain scenarios, Level 4 autonomy is designed to operate without human input in geofenced areas. Level 4 systems have higher reliability and can handle more complex scenarios autonomously.

Regulation for Level 3

The update of the UNECE R.79-Automated Steering to Regulation No.157- Automated Lane Keeping Systems (ALKS) unlocked Level 3-Driving features with speeds up to 60 km/h on motorways. The amendment was announced in mid-2020 and came into force in Jan’21 in Europe, Japan and China, among other signatories. In Jun’22, speed limits rose to 130 km/h.

Carmaker Roadmaps to Level 3 Autonomous Driving

Most carmakers follow an Incremental approach of adding longitudinal and lateral support to automate functions. For example, in Driving features, from L1-Cruise Control to Level 2-ACC to Cruise Assist, to Level 3-Highway Pilot.

A limited number of players aims at skipping L3. Collaborative business models to build L4 platforms and share costs and expertise are some of the key strategies for higher autonomy.

Carmakers are racing to commercialize Level 3 systems. For example:

• Audi: Audi showcased Level 3 capabilities in its A8 sedan but faced regulatory and infrastructure challenges that delayed wider deployment¹.
• In April 2022, BMW announced that its 7-Series to be equipped with a Level 3 autonomous system in limited scenarios on highways and moderate traffic at speeds up to 60 km/h. The BMW-7 Series got certification for Level 2+L3 in Germany
• Honda: In 2021, Honda introduced the Legend, equipped with the “Traffic Jam Pilot,” the world’s first certified Level 3 system, available in Japan². Honda’s Legend with L3 system called “Sensing Elite” went for sale in Mar’21.
• Mercedes-Benz: The 2023 S-Class and EQS models have received regulatory approval for Level 3 driving in Germany. This “Drive Pilot” system enables hands-free driving at speeds up to 60 km/h on designated highways³. The speed limit of Drive PILOT will increase to 95 km/h by 2025 (Sep’24).
• Nissan Skyline featured the ProPILOT 2.0 and the NissanConnect system introduced in 2020.
• Tesla has technical capability of L3+ in the US with the FSD, where regulations are different compared to Europe. L3 FSD is expected in 2025 in Europe and China pending approval (Sep’24)

Fragmented deployment of Level 3

Honda and Nissan launched Lv.3 in Japan only in 2021 leveraging the update of the UNECE regulation. BMW and Mercedes-Benz are launching Level 3 in 2024 in Germany & US. Changan claims they have Level 3 in China.

Lidar fitment strategies vary

In contrast to Tesla who does not feature a Lidar, BMW (iX & 7-Series) and Mercedes-Benz (L3-Drive Pilot) are among the carmakers equipping their new cars with Lidar for Lv.3+/4. They will offer L2+ until data validation and regulation allow Lv.3 via OTA.

Benefits of Level 3 Autonomous Driving

Enhanced Safety

Level 3 systems are designed to reduce human error, which is a leading cause of traffic accidents. By automating critical driving functions, these systems can improve reaction times and situational awareness.

Improved Driver Comfort

Drivers can delegate monotonous tasks like navigating traffic jams, allowing them to focus on other activities while the system takes control.

Industry Growth

The adoption of Level 3 technology is creating new opportunities in the automotive sector. According to a report by Auto2x, the global autonomous vehicle market is expected to reach $35 billion in 2025⁴.

Challenges and Considerations

Despite its promise, Level 3 autonomous driving faces several hurdles:

Regulatory Barriers

The rollout of Level 3 systems depends heavily on regional regulations. Some countries have yet to establish clear guidelines for conditional automation.

Infrastructure Limitations

Level 3 vehicles require high-quality road infrastructure, such as well-maintained lane markings and reliable GPS signals.

Safe Transition of Vehicle Control Between the System and the Driver

A significant concern is the “handoff” period—ensuring drivers can safely regain control when the system requests intervention.

The Future of Level 3 Autonomous Driving

The path to widespread adoption of Level 3 autonomy will be shaped by technological advancements, regulatory alignment, and consumer acceptance. With major automakers investing heavily in this space, the industry is moving closer to a future where vehicles can handle complex driving scenarios with minimal human input.

Artificial Intelligence to unlock Level 3 Autonomous Driving

AI plays a pivotal role in enabling Level 3 autonomous driving by enhancing a vehicle’s ability to make real-time decisions, interpret complex environments, and navigate with minimal human intervention.

In 2025, Honda will launch the Honda Series 0 with L3 Autonomy and Unsupervised Machine Learning from Helm.ai.

Honda’s Series 0 coming in 2025, equipped with Level 3 autonomy, showcases how advanced AI technologies, particularly unsupervised machine learning (UML), are transforming autonomous driving capabilities.

Unlike traditional supervised machine learning, which requires labeled datasets for training, Helm.ai’s unsupervised approach enables the AI to learn from unstructured and unlabeled data. This dramatically reduces the time and cost of training.

Demand for Sensors and Sofware to boost Suppliers’ Revenues

For Level 3 autonomy, a combination of high-performance sensors will be in high demand due to their critical roles in object detection, environment mapping, and decision-making.

The sensors that will dominate Level 3 autonomy are those that offer high precision, redundancy, and robustness across varying driving scenarios. LiDAR, radar, cameras, and driver monitoring systems will be especially in demand, supported by V2X communication and GNSS systems. With advancements in AI and sensor technology, the industry is moving toward more sophisticated, cost-effective, and scalable solutions for autonomous driving

Computing: Demand for computing will increase to process the data and enable motion planning.

Software: The autonomous driving software stack is a layered architecture designed to enable self-driving vehicles to perceive their environment, make decisions, and execute actions. Each of these components is powered by advanced algorithms, machine learning, and real-time computation to achieve reliable and safe autonomous driving. We expect more partnerships and market consolidation as players are building strong position in AD Software Stack.

ODD and HMI are Critical for Safe Adoption and Customer Acceptance

In addition to regulatory requirements, engineering requirements for functional safety will affect the design and operation of modern HMI for automated driving to deliver enhanced safety and take advantage of the side tasks unlocked by higher autonomy.

Consumer education

Understanding what these systems do and their limitations is crucial to avoid misuse and abuse.

Level 3 autonomous driving is not just a technological milestone—it’s a paradigm shift that redefines our relationship with mobility.

Innovation Hubs

Amendment of international regulations as well as national traffic laws will soon give the green light for deployment but the regulatory and legal framework differs across leading car markets. Adoption in emerging markets like China and India is crucial for democratization of Level 3.

References

1. Audi A8 Level 3 System, Audi Media Center ↩︎
2. Honda Traffic Jam Pilot Certification, Honda Global Newsroom ↩︎
3. Mercedes-Benz Drive Pilot System, Mercedes-Benz Official Website ↩︎
4. Auto2x, Advanced Driver Assistance System: Market Growth and Opportunities ↩︎

The advanced driver assistance system (ADAS) market is on a trajectory of unprecedented growth, with revenues expected to surpass €35 billion by 2025¹. This surge is driven by increasing demand for sensor content (e.g. radars, cameras, Lidar) required for Level 2-3 features and the development of Level 4 autonomous driving capabilities.

As the automotive industry pivots toward higher levels of automation, advanced driver assistance system suppliers stand poised to capitalise on emerging opportunities in sensors, AI, software stack and computing technologies.

ADAS revenues have more than doubled since 2021, showcasing the market’s dynamic evolution. This remarkable growth is largely attributed to:

• Sensor Innovations: Modern vehicles require advanced sensors such as forward-looking and side radars (e.g. 77GHz, mmwave), cameras (mono, stereo), lidar (solid state), and ultrasonics to enable features like Adaptive cruise control (ACC), parking assistance, and highway autonomy.
• Level 2-4 Automation: The shift from basic driving aids to advanced automation systems is fueling demand for high-performance hardware and software.
• Collaborations and Partnerships: OEMs and tier-1 suppliers are forming strategic alliances to accelerate the development of scalable AD platforms, AD Software Stacks and AI-driven capabilities.

Advanced Driver Assistance System Suppliers

The ADAS market is highly competitive, with established giants and newcomers vying for dominance. 

Bosch, Continental, ZF, and Valeo, the top European ADAS suppliers, have consistently driven innovation and captured significant market share.

Top Performers and Revenue Growth

• Bosch, the world’s biggest automotive supplier with €91.6 Billion in 2023, develops ADAS in the Mobility Solutions business segment. Even though the company does not report sales of ADAS, their revenues from radars and camera systems exceed 2 billion euros.
• ZF achieved 11% growth in ADAS revenues in 2023, with sales reaching €2.7 Billion, up from €2.4 billion in 2022.²
• Continental saw a 23% boost in 2022 ADAS revenues, reaching €2.1 billion and accounting for 11% of its automotive revenue³.

Among other non-European suppliers, Mobileye, a leading suppliers in the ADAS camera market with their EyeQ SoC, achieved sales of $2.1 Billion in 2023.

Emerging Players

Global suppliers such as Bosch, Continental, APTIV, Denso, Valeo, ZF, Mobileye, Magna, Hyundai Mobis dominate the market for advanced driver assistance systems.

However, there are several opportunities for new entrants to disrupt the existing supply chain in

• Next-gen Perception Hardware for Autonomous Driving;
• Autonomous driving software, e.g. AD SW Stack for L2-4;
• Computing, e.g. for Peta-ops chips for Autonomous Driving;
• AI for Autonomy, such as Generative AI and LLMs or HMI such as in-car AI assistants;
• Data-based Business models such as in-car e-commerce;
• 5G-6G, Connected Infrastructure and Smart Cities;
• Autonomous Shared Mobility and autonomous deliveries.

Chinese companies like Baidu and Huawei are gaining momentum by integrating advanced technologies such as lidar and AI solutions into their solutions across domestic and international markets.

Helm.ai, Haomo, Zenseact, Hitachi, Qualcomm emerge as serious contenders.

Lenovo entered the ADAS-DC (Domain Controller) market as Tier-1 supplier partnerting with NVIDIA.

Opportunities in Advanced Driver Assistance Systems

Sensor Market Boom

ADAS sensors represent the lion’s share of revenue, with innovations in radar, lidar, and cameras driving significant growth. For example:

• Bosch’s Supercomputers: Bosch secured €2.5 billion in vehicle supercomputer orders, highlighting the importance of processing power in autonomous systems.
• Continental’s Innovations: The company introduced AI-based object detection, enhancing its market position.
• Mobileye is launching Imaging radar in 2025, to expand its sensor portfolio beyond camera-based ADAS (EyeQ) to attract revenues from existing and new clients, while also getting closer to redundancy for L3-4 Autonomy.

Growth in Autonomous Driving Features

By 2025, the ADAS market will be defined by increased adoption of Level 2/2+ features, expansion of Level 3 and the introduction of Level 4 systems in autonomous trucks. Key features include:

• Enhanced redundancy systems, such as redundant computing and data storage
• Driver-facing cameras.
• Centralized electronic architectures.
• Autonomous driving software stacks

Regional and Global Market Expansion

In 2022, 42 Level 3-capable models were launched in Europe, a number expected to rise as regulatory hurdles are addressed.

The US and China are also key markets, with rapid adoption of automation technologies.

India is a promising market for Level 2 ADAS.

Challenges for Advanced Driver Assistance System Suppliers

Despite opportunities, ADAS suppliers face challenges such as:

• Intense competition from new entrants like Qualcomm and Microsoft.
• The transition from hardware-centric to software-driven business models.
• Meeting regulatory requirements across diverse markets.

Why Choose Auto2x’s ADAS Report?

Our report on Advanced Driver Assistance System provides:

• Access to a live dashboard to monitor players and technologies in real-time;
• Comprehensive rankings of major suppliers by revenue, sensor types, and geographies;
• Competitive benchmarking and strategy assessments for key players like Bosch, Continental, and Mobileye;
• Detailed forecasts for the 2025-2030 period;
• Insights into suppliers’ capabilities in radar, lidar, and AI integration.

Unlock the Future of ADAS

Don’t miss out on understanding the trends and technologies shaping the future of driving. Purchase our report to gain access to critical insights and stay ahead in the competitive ADAS market.

References

1. Auto2x ADAS Market Analysis 2023 ↩︎
2. ZF, Annual Report 2023 ↩︎
3. Continental, FY 2022 Results ↩︎

The Autonomous Vehicle Market Report offers an in-depth look at the competition among carmakers, suppliers, and start-ups in the ADAS and autonomous driving ecosystem.

The report highlights strategies, market leaders, and emerging trends that define the autonomous vehicle landscape, from new revenue opportunities from sensors, software, and computing to advanced technology adoption across markets.

The race to dominate the autonomous vehicle market is driven by:

• Opportunities to monetize ADAS sensor technology: Increasing integration of radars, cameras, and LiDAR, to support ADAS and expand to Level 3-4 automation.
• Intensifying competition in Autonomous Driving Software: building in-house capabilities in AD-SW Stack is a key differentiator for companies like Tesla
• Rising demand for data processing and computing: Introducing new cruising and parking features demands higher computational power and lower power consumption.
• Unlocking Driver-as-a-Service: Truck manufacturers and Commercial Vehicle operators are launching autonomous driver subscriptions to close the gap in truck driver shortage and meet demand for e-commerce and deliveries
• The democratisation of Software-Defined Vehicles: Ensuring vehicles remain updated and secure through Software Over-The-Ai (SOTA) updates for Features-on-Demand (e.g. Tesla FSD)
• Regulatory mandates fueling innovation for compliance: Leveraging evolving regulations to access new markets and customer segments.

The autonomous vehicle market report helps you understand the current status of ADAS-AD and the future outlook by

• assessing the market potential of key technologies to find whitespace
• the technology readiness of major players to benchmark competition
• and the strategies leading to competitive advantage and higher market share.

Read the full report description here including TOC and Methodology: Autonomous Vehicle Competitor Analysis.

Key Insights from Autonomous Vehicle Market Report

Leveling-up Autonomous Driving Features Level 3 in Private Cars

While automakers are unveiling their visions for full autonomy, the technology remains in the “highly assistive” stage (Level 2/2+) due to:

• Engineering constraints: Developing robust, compact, cost-efficient hardware and software for mass production is challenging
• Regulatory hurdles: Restrictions on Level 4 automation in private vehicles.
• Commercial challenges: Consumer adoption, pricing models, and subscription strategies.

Deployment strategies for Level 3 systems have gained traction in Japan, with systems coming to Germany, China and Germany. For example:

• Honda launched the Level 3 “Sensing Elite” system in Japan in 2021.
• Mercedes-Benz and BMW have approvals for Level 3 systems in Germany and the U.S.
• Tesla plans to roll out Level 3+ features by 2025.

Chinese carmakers are working on Level 4 such as Baidu’s Apollo Self Driving debuting in JIDU Auto’s Robocar 07, to realise the vision-based Level 4 system tailored for Chinese roads.

Level 4 Robo-Trucks: The Next Frontier

The shortage of truck drivers and enhanced safety requirements are critical for the future of commercial trucks pushing for autonomous driving systems.

Autonomous vehicle applications for commercial transportation, such as robo-trucks / autonomous trucks for mining and logistics, are progressing faster than private cars:

• Aurora, Continental, and Daimler Trucks are among the companies targeting full deployment of autonomous trucks by 2027.
• Plus together with TRATON GROUP Brands Scania, MAN, and International launch of Level 4 Autonomous Trucking Software in Europe and the U.S.
• Mars Auto is automating long-haul Class 8 trucks with a system priced at $7,000 per truck.

Robotaxis are Expanding from Pilots to Public Deployment

Waymo’s AMOD launched Level 4 geo-fenced robotaxis in 2019.

TIER IV launched Japan’s First Level 4 Autonomous Bus in Shiojiri, approved to operate up to 35 km/h on public roads alongside pedestrians and regular traffic.

Tesla and Verne are among other companies launching robotaxis to cater to the autonomous shared mobility on demand (AMOD) market.

Rising Demand High-Performance Computing to Power Automation

As vehicles advance from Level 2 to Level 5 automation, computing power requirements increase:

• Level 2: 10 trillion operations per second (TOPS).
• Level 4: Over 100 TOPS.
• Level 5: Estimated at 1,000 TOPS.

Leading suppliers like NVIDIA are at the forefront, offering AI-driven solutions for carmakers and trucking companies.

However,  APTIV, Continental, and Valeo are also expanding their market presence with advanced computing systems.

ADAS Revenue Growth

According to Auto2x, the global ADAS market is projected to grow from €19.3 billion in 2020 to €35 billion by 2025.

By 2030, 67% of vehicles sold globally are expected to have Level 2 or 3 automation capabilities.

Notably:

• Level 2: Average content per vehicle valued at €489 with 17 sensors.
• Level 3: Ranges from €960 to €2,100 depending on LiDAR usage.

Leading suppliers like Bosch, ZF, Valeo, and Continental dominate the market, but competition from tech giants such as Amazon and Baidu poses significant disruption risks.

ADAS Supplier Readiness

Competition in Autonomous Driving is getting stronger.

Auto2x examined the strategies, technologies and market leadership of carmakers, suppliers and start-ups in Autonomous Driving to find leaders, gaps and opportunities.

• Learn about the capabilities of ADAS suppliers in sensors, features, computing and software.
• Understand the strategies and technologies to realise Level 3-4
• Get insights on who is better positioned in the marketplace.

Ranking the Biggest Opportunities in Autonomous Mobility

The Autonomous Vehicle Market Report includes a Live scoring of opportunities that gets updates instantly to help you respond to market changes faster and not miss out on big opportunities.

The ranking unveils untapped sectors, such as mining, industrial logistics, new markets, and innovation opportunities to enhance high-performance ADAS systems.