According to 360 Quadrant analysis, the following have been identified as the top 10 vendors in the Best Automotive Artificial Intelligence:

Top 10 Automotive Artificial Intelligence vendors

  1. Nvidia Corporation
  2. Intel Corporation
  3. Alphabet Inc
  4. Baidu Inc
  5. IBM Corporation
  6. Microsoft Corporation
  7. Nuance Communications Inc
  8. BrainChip
  9. Qualcomm Inc
  10. Argo AI

Top Features of Automotive Artificial Intelligence

Artificial Intelligence used in the automotive industry enables users to find solutions to problems independently. AI mimics and supports human actions and provides reactions to machine-based systems.

  1. Traffic Agents
  2. Virtual Endurance
  3. Integration
  4. Map Processing
  5. Procedural Object Placement
  6. Export of 3D Scenes
  7. Virtual Sensor 
  8. Real-World Measurement Data
  9. Secure, Flexible and Modular

Impact of AI on Automotive Industry

Artificial Intelligence is highly impacting the automotive industry with its very useful applications. Below is the list of few applications of AI in the automotive industry:

  1. Cloud Services
  2. Personalized Marketing
  3. Predictive Maintenance
  4. AI Driving
  5. Self-Driving Cars
  6. Driver Assistance
  7. Smart Manufacturing
  8. Insurance
  9. Vehicles as Assistants
  10. HMI based Vehicles

What is Automotive Artificial Intelligence?

The concept of self-driving vehicles has been around for a long period of time. Artificial intelligence is now being used to realize self-driving cars as data generated from sensors such as includes LIDAR, video camera, position estimators, and distance sensors help the automobile identify the surrounding environment and evaluate the contextual implications. Vehicles are equipped with AI systems in order to provide improved safety and convenience features, which are designed to work in conjunction with the automated driving capabilities of the existing cars.


The competitive leadership mapping section provides information regarding key vendors offering automotive artificial intelligence and outlines the findings and analysis as well as rates them accordingly based on vendor performance within each evaluation criterion. The evaluation criteria are based on 2 broad categories, namely, Product Maturity and Company Maturity. Each category encompasses various parameters on the basis of which vendors are evaluated.

Parameters considered under  Product Maturity include breadth and depth of product offering, product features & functionality, focus on product innovation, product differentiation and impact on customer value, and product quality & reliability.

Parameters considered under Company Maturity include geographic footprint/presence in emerging markets, breadth of applications served, channel strategy and fit, the effectiveness of organic growth strategy, and mergers & acquisitions strategy.

The best Automotive Artificial Intelligence vendors are placed into 4 categories based on their performance and reviews in each criterion: “Visionary leaders,” “Innovators,” “Dynamic differentiators,” and “Emerging companies".
Among all the Automotive Artificial Intelligence vendors, the top 25 have been evaluated, including NVIDIA Inc. (US), Intel Corporation (US), Alphabet Inc. (US), IBM Corporation (US), and Baidu Inc. (China) and other players.


Visionary leaders are the leading market players in terms of new developments such as product launches, innovative technologies, and adoption of growth strategies. This quadrant receives high scores for the most evaluation criteria. They have a strong service portfolio, a robust market presence, and effective business strategies across the world. Visionary leaders primarily focus on acquiring the leading market position through their strong financial capabilities and well-established brand equity. NVIDIA Corporation (US), IBM Corporation (US), Intel Corporation (US), Microsoft Corporation (US), Alphabet Inc. (US), Nuance Communications, Inc. (US), and Baidu Inc. (China) are the major visionary leaders for the Automotive Artificial Intelligence market.


Dynamic differentiators have established vendors with effective business strategies and market presence. However, they have a weak service portfolio. They focus on a specific type of technology related to the product. Many companies in the Automotive Artificial Intelligence market are largely dependent on their competitive R&D activities. Most of the dynamic players are classified under tier 1 and 2 companies that have a presence in various regions worldwide. Xilinx, Inc. (the US), Harman International Industries, Inc. (the US), Qualcomm Inc. (US), ARM Holdings PLC (UK), General Vision, Inc. (the US), and Advanced Micro Devices Inc. (US) are a few key dynamic differentiators in the Automotive Artificial Intelligence.


Innovators demonstrate substantial product innovations compared with its competitors. They have a focused service portfolio. However, they do not have effective growth strategies for their overall business, and their geographical presence is low. Major innovators in the Automotive Artificial Intelligence market include Argo AI, LLC (US), AImotive (Hungary), Nauto Inc. (US), nuTonomy (US), BrainChip Holdings Ltd. (US), and NeuroControls Co. Ltd. (South Korea).


Emerging companies are the new players that provide value to diverse groups globally with a large group of suppliers, system integrators, end-users, and manufacturers that lead to the evolution of a new player. Some emerging companies in the Automotive Artificial Intelligence market are Zoox Inc. (US), (US), Nuro, Inc. (US), BRAIQ (US), Graphcore (UK), and FiveAI (UK).

What’s driving the Automotive Artificial Intelligence market?

  • Increasing government regulations for vehicle safety
  • Growing adoption of ADAS technology by OEMs
  • Rising demand for enhanced user experience and convenience features
  • Rising trend of autonomous vehicles

Different type of technology used in Automotive Artificial Intelligence market

  • Deep Learning- Deep learning is a class of machine learning based on multiple algorithms for creating relationships among data. Deep learning typically uses artificial neural networks to learn multiple levels of data such as text, images, and sound. Its algorithms help in identifying patterns from the set of unstructured data. The growing application of deep learning algorithms is the major driving force of the automotive AI market. The deep learning technology is widely being used in the developments of autonomous cars. Many companies are investing in the development of self-driving cars in which the deep learning technology is used for image processing, speech recognition, and data analysis. For instance, Google is heavily investing in autonomous vehicles through its spin-off Waymo and has an active system integrated into its self-driving vehicle with the deep learning technology to detect pedestrians in different situations.
  • Machine Learning- Machine learning gives cars the ability to analyse and learn from different driving situations, to learn better than any human being can, thus helping in reducing accidents and making the car safer and more efficient. Machine learning can create accurate models that can guide future actions and rapidly identify patterns at a scale that was not achievable before. Machine learning has various technologies such as supervised learning, unsupervised learning, deep learning, and reinforcement learning.
  • Computer Vision- The computer vision technology is concerned about the physical structure of three-dimensional (3D) objects attached with an intelligent-based computing system. Computer vision analyzes the information of different geometric shapes, volume, pattern, and provides visual feedback to the user, which is further used to draw the inference. The fundamental objective of the computer vision technology is to interpret the picture obtained through a high-resolution camera. The computer vision technology is very important in semi-autonomous and autonomous cars, as these cars cannot understand human hand signal or any other gestures without computer vision technology. Due to this, computer vision has a high market share for autonomous and semi-autonomous applications.
  • Context Awareness- Context awareness is an integral part of computer systems. The development of sophisticated hard and soft sensors has accelerated the growth of context aware processing. LIDAR sensors are used in autonomous and semi-autonomous vehicles for pedestrian detection applications to avoid possible collisions. The complete driving behavior is a combination of the intricate and complex interactions between the driver, vehicle, and the environment, and this behavior cannot be linearly predicted. However, a context-aware system could assist the driver in augmenting the probability of undertaking safe behavior. In-vehicle context aware systems aim at considering more contextual information related to the driving task to produce adapted or customized actions.
  • Natural Language Processing (NLP)- Natural language processing (NLP) is a form of automatic speech recognition and is among the fastest-growing AI technologies in recent years. Natural language processing (NLP) is the ability of a computer program to understand human speech. Natural language processing is developed for making real-time translation and development of systems that can interact through dialogue. The use of natural language processing technology for understanding human speech applications in autonomous driving projects by the companies such as Alphabet Inc. (US) and Uber Technologies, Inc. (US) is the key factor leading to its domination. Virtual assistants in vehicles are also on a rise.

What are the major applications of Automotive Artificial Intelligence?

  • Human-Machine Interface (HMI)- Human–machine interface (HMI) is a combination of devices with software applications that informs an operator or a user about the state of process and implement the operator's control instructions. In a vehicle, HMI allows the driver and the passenger to interact with the vehicle by seamlessly delivering convenience, information, and entertainment. The function also provides an opportunity for unique branding and differentiations such as including traditional radio tuners for low-end models and highly complex infotainments through touch panels for high-end models. Major components of HMI include electromechanical devices such as keypads, pointing device, indicators, and alarms. The infotainment category comprises of features such as speech recognition, eye tracking, monitoring driving, gesture recognition, and database of natural languages.
  • Semi-Autonomous driving- Semi-autonomous systems include features such as driver assist system involving steering, acceleration/deceleration using sensor gathered information, adaptive cruise control, lane-keeping assist, locating available parking spaces, stopping and starting with traffic, and self-parking. Most of the functions in a semi-autonomous system are controlled by drivers, but some functions such as steering and accelerating are automated. Cars can control specific environmental conditions and traffic, but a human intervention is still required. This technological advancement has been implemented with the deployment of AI in vehicles. AI addresses the issue of safety through an advanced driver assistance system (ADAS), which is considered under the semi-autonomous driving segment. AI tools help detect drivers’ drowsiness and will alert the driver to take a stop to rest. This technology is making the car semi-autonomous by equipping the vehicles with visual sensors, haptic feedback, for instance, steering wheel shakes or automatic steering control when the driver is about to collide, or facial tracking system. The aim of this technology is to translate the biometric data into car behavior.
  • Autonomous Vehicle- Autonomous vehicle is the main target of the AI technology in the automobile industry. There are various levels of autonomous vehicles. Automation of cruise control, lane change control, and automated parallel parking are the first level of automation. Combined function automation includes automation of multiple and integrated control systems such as adaptive cruise control with lane-centering where drivers are responsible for monitoring the roadway. Limited self-driving automation is where drivers can give up all safety-critical functions under certain conditions and rely on the vehicle to monitor when conditions require transition back to driver control. In full self-driving vehicles, the system performs all driving functions on all road types, at all speed ranges and environmental situations. For autonomous vehicle, various service and equipment requirements include automatic transmissions, diverse sensors (optical, infrared, radar, etc.), wireless networks, short-range and vehicle-to-vehicle communications, access to maps, and navigation, which includes GPS systems, automated controls (steering, braking, signals, etc.), and connected servers.

Best Automotive Artificial Intelligence

Comparing 35 vendors in Automotive Artificial Intelligence across 76 criteria.
All vendors(30)
NVIDIA DriveWorks software allows cars to receive over-the-air updates to add new features and capabilities throughout the life of a vehicle. The company invests mainly in R&D every year, with more than 20% of the overall revenue. NVIDIA, being ann important name in the AI market, has partnerships with various automotive companies such as Audi, Toyota, Tesla, Mercedes-Benz, Volvo, Honda, and BMW
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Intel has designed Deep Learning Inference Accelerator (DLIA), an integrated hardware, software frameworks and libraries such as Intel Math Kernel Library for Deep Neural Networks (MKL-DNN) and Caffe, which simplifies the neural-network acceleration for image processing applications. The company has a strong and successful R&D model. They invest approximately 20% of its revenue in R&D every year. Strong and successful R&D model has allowed the company to maintain technology leadership. In addition, the company's strong focus on R&D and product innovation enabled it to enhance the appeal of its portfolio
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Alphabet provides Tensor Processing Unit (TPU), a custom ASIC built specifically for machine learning and customised for TensorFlow. The company invests mainly in R&D, which is almost 15% of the total revenue, to provide enhanced product features. In addition to this, the company is expanding its product line in the AI segment. The company stands very high rated for its product strategies
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Accurate Perception - Various sensors, such as LiDAR, cameras and radar collect environmental data surrounding the vehicle. This autonomous perception system is backed by both Baidu’s big data and deep learning technologies, as well as a vast collection of real world labeled driving data. Simulation - Baidu's Automatic Grading System tests via ten metrics: Collision detection, Traffic light recognition and logic, Speed limit, Detection of objects out of lane, End-of-route logic etc HD Map and Localization - Their localization system is a comprehensive positioning solution with centimeter level accuracy based on GPS, IMU, HD map, and a variety of sensor inputs Intelligent Control - The Apollo intelligent vehicle control and canbus-proxy modules are precise, broadly applicable and adaptive to different environments. The modules handle different road conditions, speeds, vehicle types and canbus protocols. Apollo provides waypoint following capability with a control accuracy of ~10 cm
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IBM has developed an AI platform called IBM Watson. Watson learns from more than 30 sensors embedded throughout the vehicle to enhance its understanding of passengers’ needs and preferences IBM offers hardware and AI platform, which are used in predictive maintenance, performance analysis, and prescriptive maintenance. Such a vast breadth of offering with continuous technological innovations and expanding the features makes the rating of IBM products strategy very high. The chip provides advanced technology in image recognition and can accurately classify image data efficiently. The hardware can be deployed in mobile computing, IoT, robotics, autonomous cars, and high-performance computing (HPC)
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Microsoft offers various AI-based solutions in manufacturing such as Cortana Intelligence Suite and Azure. Cortana Intelligence Suite has been built with digital personal assistant and NLP technology. The company has a moderate number of AI-related offerings in the automotive segment. It invests around 12% of its revenue on R&D activities every year. Its strong portfolio, along with continuous technological advancements, is one of the reasons why Microsoft has a high rating for its product offerings
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Nuance’s Dragon Drive platform enables the user experience of tomorrow in the cars of today, understanding individual preferences, personalizing entertainment and executing complex, contextual commands. It can find preferred parking, play users' favorite songs, direct them to the best-priced gas stations and turn up the voices they need to hear while muting the ones they don’t. Nuance supports automakers with capabilities that maximize the safety, productivity and enjoyment of customer journeys while elevating the identity of each unique brand
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BrainChip Ships First BrainChip Accelerator To a Major European Car Maker for Evaluation in ADAS and AV Systems. BrainChip Accelerator will be evaluated for use in Advanced Driver Assisted (ADAS) and Autonomous Vehicle (AV) applications. BrainChip Accelerator increases the performance of object recognition provided by BrainChip Studio software and algorithms.
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Qualcomm develops, designs, manufactures, and markets digital telecommunication products and services. The company's strong R&D has enabled it to win a number of patents. According to the company, it has the most widely and extensively licensed portfolio in the industry with over 285 licensees of all commercially deployed forms of CDMA and their derivatives require the use of Qualcomm patents. The company's robust R&D capabilities that enabled it to develop a strong patents portfolio offer technological leadership to Qualcomm over its peers. It will enable it to introduce new products in a timely manner and enhance its existing products. This would enhance the appeal of its products thus enabling it to effectively retain and gain new customers.
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Argo AI develops robotics and artificial intelligence solutions for self-driving vehicles. Argo has acquired Princeton Lightwave, a company with extensive experience in the development and commercialization of LiDAR sensors. The technology that underpins their lineup of LiDAR sensors which already serves the commercial mapping and defense industries will help them extend the range and resolution needed to achieve self-driving capability in challenging urban environments
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Arm’s newest chip design called the Cortex-A65AE is designed around the idea of being able to handle the stream of data from a self-driving car’s sensors in near real-time, but with new safety features intended to make the chips better suited to cars, where glitches that are minor annoyances in consumer electronics could lead to crashes
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AImotive provides OEMs the necessary global scalability, accessibility and safety to rapidly meet the needs of billions of people all over the world. Their unique toolset is engineered to cater to all the challenges of autonomous mobility, powered by advanced artificial intelligence, simulation technology, and supporting hardware architectures
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The Radeon Instinct™ MI60 compute card is designed to deliver high levels of performance for deep learning, high performance computing (HPC), cloud computing, and rendering systems. This new accelerator is designed with optimized deep learning operations, class-leading double precision performance1, and hyper-fast HBM2 memory delivering 1 TB/s memory bandwidth speeds
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Xilinx was the first company in the programmable logic device (PLD) industry to ship 45-nm high-volume, 28-nm, 20-nm and 16nm FPGA devices. The company provides FPGAs for applications in various industries. For the automotive industry, the FPGAs are optimized for cost and power with small form-factor packaging for high-volume automotive applications. At the end of 2016, the company held over 3,500 issued US patents and over 300 pending US patent applications. The company's continuous focus on R&D enables it to address the diverse and evolving needs of the PLD market.
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Harman International is engaged in designing, manufacturing, and marketing audio and infotainment solutions for the automotive, consumer and professional markets. offers solutions in the connected car segment for applications such as embedded infotainment, telematics, connected safety, and security. The embedded infotainment application provides complete information, entertainment and communications capabilities with 3D and augmented navigation, multimedia support and smart apps for device integration, high speed connectivity, intuitive and multimodal user interfaces, and automotive cloud services
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nuTonomy is developing a complete solution for providing point-to-point mobility via large fleets of autonomous vehicles; this includes software for autonomous vehicle navigation in urban environments, smartphone-based ride hailing, fleet routing and management, and controlling a vehicle remotely through teleoperation
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Nauto is an autonomous vehicle technology system that offers an AI-powered connected camera network and smart cloud system for its clients. It identifies dangers and alerts the drivers and provides coaching and feedback at the end of trips. Nauto’s real-time sensors and visual data help fleet managers detect and understand the cause of accidents and reduce false liability claims. Nauto aims to gain understanding of what causes near-misses, scrapes, and accidents at every level and to deliver insights back to cities that can help them control traffic and even street design to eliminate fatal accidents
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Nuro's vehicle is a fully autonomous, on-road vehicle designed to transport goods — quickly, safely, and affordably. Nuro’s vehicle is designed to be fully self-driving, so it does not have space for a driver or passengers Nuro is focused on deliveries, specifically the kind that are low-speed, local, and last-mile: groceries, laundry, or your take-out order from Seamless
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BRAIQ personalizes the ride experience in autonomous vehicles by teaching artificial intelligence how to understand human emotions. BRAIQ uses various sensors to gather biometric information to recognize the driver’s emotional and stress states, and personalizes its driving style for automated driving using AI deep learning, to increase passenger comfort and enhance the automated driving experience.
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1.5 uses artificial intelligence to create self-driving transportation solutions that improve the state of mobility offers turnkey mobility solutions with self-driving cars for partners and communities, solving for their unique and everyday transportation needs What sets’s approach apart is its use of screens on the van’s exterior. These four panels—each 22.5 by 7.5 inches, on the hood, on the rear, and just above each of the front wheels—are the vehicle’s voice
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FiveAI is using the power of autonomy to transform Europe's autonomous driving scenario. The concept behind the project is to have a vehicle fitted out with sensors all being processed by a central computer capable of ‘machine learning’. In essence – capable of picking up information from encounters and applying it to future situations. “It uses sensors to perceive what’s out there – identifying things like road, pavement, street signs, pedestrians and other cars
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Zoox develops fully autonomous vehicles and the supporting ecosystem required to bring this technology to market. Using a combination of technologies such as robotics, machine learning, and design. Zoox aims to provide the next generation of mobility-as-a-service in urban environments
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Aeye's solution, iDAR (Intelligent Detection and Ranging), aims to solve the processing time and computing power issues in the current LiDAR sensor implementations. iDAR combines a solid-state agile LiDAR, with a low-light HD camera and computer vision artificial intelligence to speed up a vehicle's perception system. According to the company, iDAR can increase perception speeds by up to 10 times and reduce power consumption by five to 10 times. In a recent performance tests monitored and validated by VSI Labs, one of the nation’s leading automated vehicle technology advisors, AEye’s iDAR system detected and tracked a truck from one kilometer away, a distance four to five times beyond what current LiDAR systems are able to detect. Taking a completely fresh approach to artificial perception, the AEye team determined that a vehicle’s ability to scan the surroundings and quickly identify critical objects demanded that it perceive like a person, but think like a robot.
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Aurora develops self‑driving software and hardware products for electric vehicle fleets and mobility-on-demand systems. The company works at the intersection of rigorous engineering and applied machine learning to address one of the most challenging, important and interesting opportunities of this generation: transforming the way people and goods move
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Based on machine learning algorithms, advanced signal processing techniques, and NVH technologies, Carfit monitors and predicts maintenance needs. The company has created a self-diagnostic and predictive maintenance platform in the connected car space, providing dealers and service providers with customized lead generation. CARFIT is a self-diagnostic & predictive maintenance platform in the connected car space that provides the dealers/service companies with customized lead generation and car owners with individualized predictive maintenance info on their car.
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DarwinAI is a cutting-edge Artificial Intelligence startup which exists with a purpose to: 1.) Significantly reduce the size of deep neural networks while maintaining functional accuracy and reducing inference time; 2.) facilitate ‘explainable’ deep learning: the ability to understand why a network makes the decisions it does. Through their patented Generative Synthesis™ A.I.-building A.I., DarwinAI enables A.I. to work in the real world by making deep learning faster, portable, scalable, and understandable.
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DeepMap's objective is to accelerate safe autonomy by providing the world's best HD mapping and localization services. The company is involved in developing HD mapping and localization and big data management solutions for L4/5 autonomous vehicles.
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EasyMile is one of the leading AI companies that specializes in autonomous vehicle technology. The core interest of the company’s autonomous technology is inclined towards minibuses. For the past few years, the company has developed and deployed autonomous mobility solutions worldwide based on vehicles manufactured by recognised industrial partners.
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German Autolabs provides the Automotive Voice Assistance, AVA - a voice AI platform for the Automotive vertical. The company produces a signature voice assistant hardware, Chris. Commercially available in Europe, the retrofit assistant bypasses traditional automotive development cycles to rapidly iterate key AI / NLP models. Key assets of this underlying platform include the multimodal hybrid (offline/online) Dialogue Management System, deep domain knowledge of driver behavior, and OS-agnostic access to APIs and other assistant services. Founders Holger G. Weiss and Patrick Weissert strive to deliver an independent assistant solution to the automotive industry.
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Momenta develops autonomous driving technology for self-driving cars. Momenta's deep-learning-derived software in perception, semantic HD mapping, and data-driven path planning enables the realization of full autonomy. The company's solutions include AutoRing, Mpilot, Momenta Valet Parking (MVP), and Momenta L4 Urban Autonomous Driving (M4U).
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