Automotive AI Market - Size, Share, Industry Trends, and Forecasts (2025-2032)

By Component:

Based on component, the market is segmented into hardware and software.

Trends in the component:

• Increasing technological advancements associated with integration of AI in automotive market are driving the market demand.
• The rise in adoption of AI processors in modern vehicles are propelling the market adoption.

Hardware segment accounted for the largest revenue in the overall automotive AI market growth in 2024.

• In automobile AI, hardware includes specialised AI processors such as system-on-a-chip (SoCs), graphic processing units (GPUs), neural processing units (NPUs) and others.
• Moreover, AI chips and sensors are used in ADAS and autonomous driving (AD) to process data from the camera, lidar, radar and others.
• Additionally, GPUs are highly used in AI due to their ability to handle large amount of data and design for rendering graphics.
• For instance, Waymo utilizes a single integrated system of sensors such as lidar, which paints a 3D picture of the surroundings for crash avoidance. Moreover, Waymo performs comprehensive analysis on the hardware, behavioral and embedded software controls to avoid accidents.
• Therefore, hardware plays an important role in the automobile AI sector, further driving the market.

Software segment is anticipated to register a substantial CAGR development during the forecast period.

• In automobile AI, software facilitates advanced technologies such as ADAS and AD, which are collection of technologies that are used in integration with camera, sensors, and detection systems.
• Moreover, sensor data fusion enables various data to be embedded through inertial measurement unit (IMUs), radars and others to improve accuracy.
• For instance, Bosch offers sensor fusion software in its solution offering. Bosch SensorTec provides dynamic and accurate orientation of the target platform.
• Therefore, the rising advancements associated with AI software are expected to drive the market during the forecast period.

By Technology:

Based on technology, the market is segmented into machine learning and deep learning, computer vision, natural language processing and others.

Trends in the technology:

• Autonomous vehicles are one of the prime innovations of machine learning and deep learning, driving the adoption of automotive AI market.
• Rising trend in utilization of natural language processing in the automotive sector for facilitating autonomous features is driving the automotive AI market.

Machine learning and deep learning segment accounted for the largest revenue in the overall automotive AI market in 2024.

• By integrating machine learning (ML) algorithms and deep learning models, automotive has boosted its capacity to operate on voluminous data.
• However, self-driving cars and smart energy systems, plays a crucial role in applications such as ADAS, predictive maintenance and others.
• Various ML algorithms that are used in autonomous driving are perception algorithms, localization algorithms, mapping algorithms, particle filters and others.
• For instance, in July 2024, SoundHound AI, Inc. launched its voice assistant with integrated ChatGPT in automobile brands such as Peugeot, Opel, and Vauxhall around 11 European market.
• Therefore, the rise in advancements associated with machine learning and deep learning technologies are driving the automotive AI market demand.

Natural language processing segment is anticipated to register a substantial CAGR growth during the forecast period.

• Natural language processors (NLP) implements voice recognition feature to share real time alerts and assist driver accordingly.
• Moreover, the activation of NLP in ADAS warns drivers of the possible obstacles or hazards on the road.
• NLP can also share timely information with recommended solution if the driver is unaware about the vehicle issue including mechanical failure.
• By analysing different types of data input, NLP is able to recognise driver’s health condition for instance drowsiness.
• For instance, BMW launched its personal assistant where in it is able to understand user’s moods and commands giving appropriate solution to it. Personal assistant has the understanding of every detail of user’s car from servicing requirements to fuel levels.
• Therefore, the rising adoption of NLP in automotive sector is projected to drive the market during the forecast period.

By Vehicle Type:

Based on vehicle type, the market is segmented into passenger cars, light commercial vehicles, and heavy commercial vehicles.

Trends in the vehicle type:

• There is a rise in adoption of semi-autonomous and autonomous vehicles, which is further propelling the market demand.
• Increasing utilization of AI in heavy commercial vehicles to support automated features is driving the global automotive AI market.

Passenger cars accounted for the largest revenue share of 66.42% in the automotive AI market share in 2024.

• Artificial intelligence in passenger cars plays a vital role in enhancing safety and drivers experience from applications such as advanced driver assistance systems.
• Moreover, infotainment systems provides various recommendations for in-car features such as music, seat belt sensor, navigation and others.
• Moreover, AI helps to monitor and maintain speed while providing users with warnings accordingly. It also assists on lane departure warning when users unintentionally drift from the lane.
• For instance, according to the analysis by International Organization of Motor Vehicle Manufacturers (OICA), Asia-Pacific region witnesses an increase of 17% in total passenger car sales in 2023 as compared to 2019.
• Therefore, rising passenger cars sales are driving the automotive AI market trends.

Heavy commercial vehicles is anticipated to register a substantial CAGR growth during the forecast period.

• Heavy commercial vehicles utilizes AI to optimize operations such as improved safety, enhanced operations, navigation and others.
• Moreover, AI features that are used in heavy commercial vehicles include automated routing and dispatch, safety compliance integration, automated workflow, advanced driver assistance technology and protection of data and systems.
• Additionally, NLP is used in heavy commercial vehicles to assist navigation and provide warning for lane switch.
• For instance, according to the analysis OICA, the production of heavy trucks in Europe reached 423,359 units in 2023, representing the increase of 33% from 2022.
• Hence, the rising production of heavy commercial vehicles is anticipated to boost the automotive AI market trends during the forecast period.

By Application:

Based on application, the market is segmented into autonomous vehicles and semi-autonomous vehicles.

Trends in the application:

• The rapid adoption of AI in the semi-autonomous vehicles, driven by the technological advancements, is boosting the market growth.
• Rising trend in utilization of autonomous vehicle solutions for increasing operational safety and efficiency.

Semi-autonomous vehicles accounted for the largest revenue in the overall automotive AI market share in 2024.

• Semi-autonomous vehicles also known as partial automation, uses advanced technology to assist drive with certain tasks such as lane keeping but it still requires human to oversight operations.
• There are two levels of automation, one is partial automation where the vehicle can perform some tasks autonomously but compulsory requires a driver.
• Moreover, another level of automation is conditional automation, where the vehicle can handle specific conditions, allowing the driver to overtake when required.
• For instance, Bosch Mobility offers solutions for road sign information assistance by displaying road sign information of nearby signs in the driver’s view. This can be integrated in semi-autonomous vehicles to assist the driver while they drive.

• Therefore, the rising advancements associated with AI for integration in semi-autonomous vehicles are driving the market growth.

Autonomous vehicles is anticipated to register a substantial CAGR growth during the forecast period.

• Autonomous vehicles are vehicles that can drive with minimal human- intervention with the help of sensors and technology
• Moreover, autonomous vehicles uses sensors like radar, lidar, and cameras to understand their surroundings and take decisions accordingly.
• Additionally, autonomous vehicles use adaptive cruise control, anti-braking system and GPS navigation with the integration of artificial intelligence.
• For instance, Nvidia DriveOS is an automotive AI operating system designed to meet advanced AI inference, advanced graphics, high end audio and complex safety, and security for the users. DriveOS is also capable of supporting autonomous driving and AI-powered cockpit experiences.
• Thus, rising advancements in autonomous vehicles are anticipated to drive the market during the forecast period.