In the landscape of AI development, computing capabilities are expanding from the cloud and data centres into devices, including vehicles. For smart devices to improve and learn, they require access to data, which must be stored and processed effectively. Embedded AI computing can facilitate this by integrating AI into an electronic device or system – such as mobile devices, autonomous vehicles, industrial automation systems and robotics.
However, for this to happen, the need for ample storage capacity within the device itself is increasingly important. This is especially so when it comes to smart vehicles and traffic management, as these technologies are also tapping into the benefits of embedded AI computing.
Smarter vehicles: Better experiences
By storing and processing data locally, smart vehicles can continuously refine their algorithms and functionality without relying solely on cloud-based services. This local approach not only enhances the vehicle’s autonomy but also ensures that crucial data is readily accessible for learning and improvement.
Moreover, as data is recorded, replicated and reworked to facilitate learning, the demand for storage capacity escalates. In this case, latency is key for smart vehicles as they need access to data fast – especially for security features on the road. This requires the integration of advanced CPUs, often referred to as the “brains” of the device, to enable efficient processing and analysis of data.
In addition, while local storage and processing enhance device intelligence, data retention is essential to sustain learning over time. Therefore, there must be a balance between local processing and cloud storage. This ensures that devices can leverage historical data effectively without compromising real-time performance.
In the context of vehicles, this approach translates into onboard systems that will be able to learn from past experiences, adapt to changing environments, and communicate with other vehicles and infrastructure elements – like traffic lights. Safety is, of course, of huge importance for smart vehicles. Automobiles equipped with sensors and embedded AI will be able to flag risks in real time, such as congestion or even obstacles in the road, improving the safety of the vehicle. In some vehicles, these systems will even be able to proactively steer the vehicle away from an obstacle or bring the vehicle to a safe stop.
Ultimately, this integration of AI-driven technology will allow vehicles to become smarter, safer, and more responsive, revolutionising the future of transportation. To facilitate these advanced capabilities, quick access to robust data storage is key.
Smart cities and traffic management
Smart cities run as an Internet of Things (IoT), allowing various elements to interact with one another. In these urban environments, connected infrastructure elements such as smart cars will form part of a wider system to allow the city to run more efficiently. This is underpinned by data and data storage.
The integration of AI-driven technology into vehicles has significant implications for smart traffic management. With onboard systems capable of learning from past experiences and adapting to dynamic environments, vehicles can contribute to more efficient and safer traffic flows.
Additionally, vehicles will be able to communicate with each other and with infrastructure elements, such as traffic lights, to enable coordinated decision-making. This communication network facilitated by AI-driven technology will allow for real-time adjustments to traffic patterns, optimising traffic flow, reducing congestion and minimising the likelihood of accidents.
For any central government department of transport and local government bodies, insights from connected vehicles can better prepare a built environment to handle peaks in traffic. When traffic levels are likely to be high, management teams can limit roadworks and other disruptions on roads. In the longer term, understanding the busiest roads can also inform the construction of bus lanes, cycle paths and infrastructure upgrades in the areas where these are most needed.
Storage plays a foundational role in enabling vehicles to leverage AI-driven technology for smart traffic management. It supports data retention, learning, communication, and system reliability, contributing to the efficient and safe operation of smart transportation networks.
Final thoughts
Ultimately, the integration of AI into vehicles lays the foundation for a comprehensive smart traffic management system. By leveraging data-driven insights and facilitating seamless communication between vehicles and infrastructure, this approach promises to revolutionise transportation, making it safer, more efficient, and ultimately more sustainable – all made possible with appropriate storage solutions and tools.
- Data & AI
- Infrastructure & Cloud
