Almost every sector has been affected, and purposely integrated with AI for its efficient operations. The automotive sector is not an exception. Artificial intelligence in the automotive industry is giving chance to create and manage things that they could only have once dreamed of. Therefore, the applications of AI in the automotive industry are exciting and far reaching.

AI enables automakers and other industry participants to make significant technological advancements and increase efficiencies that were previously thought to be impossible. This includes the development of self-driving cars, the use of AI-driven robots in manufacturing settings, preventive maintenance techniques, and more. Drivers are given real-time information about their surroundings thanks to AI's facilitation of autonomous driving. Hence, benefits of AI integration in automotive industry are farfetched.

Adoption Of AI in Automotive Value Chain: 2023 Scenario

Processes including manufacturing, design, supply chain, production, post-production, driver risk assessment system, etc. are being benefitted by AI in the entire automotive value chain. Not only from the performance aspect, but this integration has advanced other operations of the same value chain. For instance:

• Logistics and distribution have purposely embraced AI towards optimizing supply chain operations. Predicting demand fluctuations through AI algorithms help streamline delivery routes and maintain real-time inventory levels. The synergy between AI and automobile manufacturing has and is on the path of consistent succession.
• From marketing and sales perspective, AI has enabled data-driven personalization, allowing automotive companies to tailor marketing campaigns and predict customer preferences. Virtual showrooms and AI-driven chatbots have elevated customer engagement by providing immersive experiences.

• After-Sales Service is also improved via this integration. With continuous monitoring of vehicle health via dedicated sensors, AI driven systems alert about potential issues, reducing downtime and enhancing overall satisfaction.

Moving through the supply chain, AI’s predictive capabilities have transformed demand forecasting, empowering manufacturers, and suppliers to adjust production with market alignment.

According to TechSci Research Report “Automotive Artificial Intelligence Market - Global Industry Size, Share, Trends, Competition, Opportunity and Forecast, 2017-2027F”, Segmented By Component (Hardware, Software, Service), By Technology (Deep Learning, Machine Learning, Context Awareness, Computer Vision, Natural Language Processing, Others), By Process (Signal Recognition, Image Recognition, Data Mining), By Application (Human–Machine Interface, Semi-autonomous Driving, Autonomous Driving), By Vehicle Type (Passenger Cars v/s Commercial Vehicles), By Demand Category (OEM v/s Aftermarket), By Company, By Region, ” the global automotive artificial intelligence market is expected to grow at a steady rate during the forecast period. The global automotive artificial intelligence market is driven by the growing adoption of advanced automotive solutions such as advanced driver assistance system (ADAS), adaptive cruise control (ACC), blind sport alert, among others by different OEMs. Additionally, government regulations to improve the safety in vehicles while assuring environmental sustainability is further expected to propel the market.

The most prominent example of AI integration in automobiles surrounds the realm of autonomous driving. Built upon the progress of the previous years, vehicles equipped with ADAS (Advanced Driver Assistance Systems) have become prevalent on roads, which also include closer to fully autonomous operations. This synergy between AI algorithms, sensor integration, and high-definition mapping empower these vehicles to navigate through complex real-life scenarios with high level of autonomy, enhancing road safety and minimizing human intervention.

AI driven safety features have undergone a remarkable transformation. Pedestrian detection, automatic emergency braking, and lane-keeping assistance have evolved to offer more sophisticated responses to potential hazards. The integration of AI enabled vehicles to analyze vast amounts of real-time data from sensors, cameras, and radars, making split-second decisions, mitigate collision risks. This not only safeguards lives but also propels the automotive industry toward a safer autonomous driving.

Company Examples of AI Integration With Vehicle Models

·         AUDI

With years of indulgence and commitment towards safe driving, its main emphasis was in conjunction with basic road maneuvers, sampled with a human-to-machine interface. Their luxury A8 model came out seamlessly integrated with AI solutions, which used facial recognition to monitor driver’s expression and touch sensitivity of the steering wheel. It provided other companies with a blueprint of how innovation and technology interface should coexist with real life scenarios.

·         BMW

The renowned German car manufacturer, BMW, partnered with Allianz insurance group to establish and further innovate AI powered products to help improve overall fleet management and driver safety. Via integration with their deep learning technology and leveraging Nauto’s (AI software for drivers and fleet safety) cloud-based AI platform, the company developed a solution to track driver alertness, unsafe driving habits, and near misses.

·         Mobileye

Mobileye, which is an Israeli company responsible for supplying manufacturers with computer vision technology, has developed its own solution that augments drivers’ capabilities by identifying speed signs, automatically triggering an automatic braking system. Today, the company covers a wide range of services and technology, including sensor fusion, mapping, front and rear camera tech, etc. The company was acquired by Intel in 2017 and went public recently in 2022.

·         Toyota

The company is among the biggest car and truck manufacturers on the planet. Their new institute, Toyota Research Institute (TRI) conducts and focus on developing technologies to advance human centered artificial intelligence, human interactive driving, machine learning, etc. Toyota is currently taking multiple R&D initiatives in the form of Human Support Robots that help people maintain mobility.

However, everything isn’t as rainbow as it seems. The adoption of AI in automotive industry does come with several challenges that should be addressed. The adoption of AI in the automotive industry is facing several challenges that need to be addressed. These challenges include the obscurity of machine learning algorithms, difficulty in pinpointing errors, inability to apply Agile methodologies, testing and validation of unproven technology, integration of AI methodologies, level of complexity, and localization and security. The complexity and opacity of machine learning algorithms used in autonomous vehicles pose challenges for engineers, including identifying and resolving errors or issues in AI. Traditional Agile methodologies may not be easily applicable to algorithm development in the automotive industry, and testing and validation of autonomous driving technologies on public roads can be time-consuming and costly.