When it comes to AI, the power of computing is extending beyond traditional cloud and data centres, branching out to various end devices.
For these smart devices to evolve and learn, they require not only access to data but also the capacity to store, process and analyse it efficiently.
This is where embedded AI computing can help integrate AI capabilities directly into electronic devices – from PCs and mobile gadgets to self-driving cars and industrial automation setups and robotics.
However, to be successful, there’s a growing demand for substantial storage capacity and performance within the devices themselves.
This need is particularly precedent in smart vehicles and traffic management systems, which are increasingly leveraging the advantages of embedded AI computing.
Smarter Vehicles: Better Experiences
With local data storage and processing, smart vehicles are enhancing their algorithms and functions, reducing reliance on external cloud services.
This localised strategy boosts the vehicle’s autonomy and helps ensure fast availability of critical data for ongoing learning and improvements.
With the increasing need to store, duplicate, and process data to support such learning, there are surging storage capacity requirements. Smart vehicles need to swiftly retrieve data, particularly for road safety features, therefore low latency is key.
To meet this need, smart vehicles are equipped with sophisticated central processing units (CPUs) and electronic control units (ECUs), the ‘brains’ and sensory systems behind the rapid data processing and analysis.
Moreover, while local storage and processing increase intelligence, preserving data over time is crucial for continuous learning.
Therefore, a strategic balance needs to be struck between real-time local processing and long-term cloud storage, enabling devices to draw insights on historical data while still delivering instant operational output.
To ensure scalability, it’s imperative to have prompt and reliable access to substantial data storage.
Car manufacturers should also remember that the design must still appeal to the modern driver.
Therefore, storage and computing hardware within the cars must be powerful but compact to fit nicely with the interior.
This fusion of technology turns vehicles into increasingly intelligent, secure, and adaptive devices, potentially transforming transport dynamics.
Smart Cities and Traffic Management
Smart cities operate much like a vast Internet of Things (IoT) ecosystem, where smart vehicles interact with other cars and urban infrastructure, like traffic signals, seamlessly.
Within these digitally connected urban areas, smart vehicles are integral to the broader network, enhancing the city’s operational efficiency through data and advanced data storage capabilities.
This advanced vehicle interconnectivity offers invaluable insights for both central and local transportation authorities.
The capability of vehicles to communicate among themselves and with urban infrastructure, like traffic signals, paves the way for synchronised decision-making.
It allows for the anticipation of traffic peaks, facilitating strategic decisions such as temporarily suspending roadworks to alleviate congestion.
Over time, traffic pattern analyses can guide the development of dedicated bus lanes, bicycle paths, and infrastructure improvements where they are most needed.
Bringing AI technologies into vehicles creates transformative benefits for smart traffic management systems.
With the ability to learn from historical data and adjust to changing conditions, these intelligent vehicles play a key role in creating more streamlined and safer traffic networks.
Data storage is at the core of enabling smart vehicles to thoroughly harness AI for traffic and smart city management. It underlies the processes of data maintenance, learning, communication, and overall system dependability – all crucial for the effective and secure functioning of smart transport networks.
Infusing AI into vehicle technology is the cornerstone of a smart traffic management framework. It leverages data-centric insights and ensures streamlined and efficient communication between vehicles and city infrastructure.
However, this shift towards safer, more efficient, and sustainable urban mobility should be anchored on sophisticated storage solutions and technologies.
