The automotive industry is quickly embracing an electric future fuelled by AI and automation. The global automotive AI market share is touted to increase by USD 10.73 billion by 2024. Additionally, a compound annual growth rate of 37% indicates the industry’s dynamism to innovate and ride the first wave of this new trend. Here is an insight into the evolving innovations of AI in the automotive industry.
Computer vision involves the analysis of image data or visual sequences to classify objects within a field of vision of a vehicle. The analysis includes the application of algorithms for image segmentation, scene parsing, 3D modeling, pattern recognition, 3D reconstructions, motion analysis, and object recognition. AI is being implemented in these areas and machine learning models with neural networks are being used for enabling computer vision solutions.
To truly understand the significance of computer vision in the global automotive AI market, one needs to look at its acquisition trends over recent years. From Apple acquiring Drive.ai, Baidu buying xPerception, Ford Motor Company acquiring SAIPS, and even the Indian cab aggregator – Ola – purchasing Pikup, it is evident that computer vision will be a primary component of AI integration in the automotives of the future.
AI-based data analytics
Data has become ubiquitous in the automotive ecosystem ranging from in-vehicle systems, sensors mounted on the cars, to location intelligence and other surrounding factors. New data collection mechanisms and sensor innovations integrated with AI-based analysis are potentially unlocking more opportunities across various automotive use cases including connected vehicles, insurance, telematics, mobility, and infotainment. Acquired tech companies are leveraging AI technologies that can learn patterns from the data collected from multiple sources and provide key insights for triggering necessary actions.
With every individual exhibiting a unique driving behavior, using traditional techniques to accurately gauge their patterns is difficult. The future will see AI-based data analytics globally deployed by manufacturers to improve driving experiences and provide predictive maintenance, safety tech to offer safety mapping, sellers to create dynamic vehicle pricing strategies for customers, insurance services to offer better solutions for car crash scenarios, among various other usages.
AI hardware and software
While the AI software industry is advancing rapidly, the hardware innovation to complement the processing is evolving with equal vigor. With more significance being weighed on in-vehicle computing to reduce latency and data processing during crucial times, system on chip (SoC) platforms are fulfilling the on-device computing requirements during mission-critical scenarios. Acquirers are spending huge sums for buying out SoC platform providers that are focused on developing on-device computing and computer vision solutions. While semiconductor establishments such as Intel and Molex are leading the acquisition race, car manufacturers like General Motors are competing to do the same.
Autonomous vehicles will be driven for thousands of miles for testing before hitting the consumer market. Predictably, this exercise is expensive, time-consuming, and limiting in terms of offering a dynamic environment and weather amongst various other elements. This is where simulation techniques come into the picture by offering simulation technologies to virtually recreate the car, its sensors, and driving characteristics in an automotive ecosystem.
Automakers are training AI systems for their self-driving vehicles using simulation techniques and virtual reality environments. In pursuit of achieving the next level of autonomy, industry participants are also exploring new ways to run neural networks with virtual data obtained from modeled cars and raw data from simulated sensors.
Navigation and mapping technology is considered to be the most vital aspect of today’s autonomous vehicle industry. With its evolution from human-readable maps to complex and intelligent machine-readable ones, companies are more than keen on redefining synergies between sensors and mapping technologies in machine-readable HD maps. LiDAR specialists are acquiring AI-based localization and mapping software providers for building a strategy of mapping technology that is inclusive of LiDAR-based 3D point clouds and semantic maps.
The automotive industry is currently undergoing dramatic changes with the rise of AI-driven intelligent systems. Such efforts by automotive companies are going to translate into AI products and services that will not only accelerate AI adoption in the sector but also reduce the time lag for implementing autonomous vehicles and other mobility applications. For in-depth research and analysis of the state of AI in the global automotive industry, connect with us at email@example.com