The rise of artificial intelligence (AI) is transforming fleet management, with vehicle gateway equipment playing a pivotal role in enabling smarter, safer and more efficient transportation networks, but making robust edge storage ever-more crucial.
The rise of artificial intelligence (AI) is transforming fleet management, with vehicle gateway equipment playing a pivotal role in enabling smarter, safer and more efficient transportation networks. These gateways, embedded systems that connect onboard sensors, cameras, telematics units and cloud platforms, are evolving from simple data routers into intelligent edge computing hubs capable of real-time decision-making and analytics.
Revolutionizing fleet operations through AI integration
AI integration in vehicle gateways empowers fleets with enhanced operational intelligence. These systems can now process data from multiple sources, including GPS, cameras, engine diagnostics, and environmental sensors, to deliver insights that improve routing, safety, fuel efficiency, and predictive maintenance.
For example, AI-powered gateways can detect driver fatigue or distraction using in-cabin cameras and alert systems, helping prevent accidents. They can also analyze traffic patterns and road conditions to dynamically reroute vehicles, reducing delays and fuel consumption.
In logistics, AI-enabled gateways optimize delivery schedules by analyzing historical data, weather forecasts and traffic congestion. This leads to better asset utilization and lower operational costs. Additionally, real-time monitoring of cargo conditions (e.g., temperature, humidity, vibration) ensures compliance and quality assurance in sensitive shipments like pharmaceuticals or perishables.
Joey Lin,
Principal Segment Marketing Manager
AEBU IMM at Micron
Fleet operators also benefit from AI-driven predictive maintenance. By continuously analyzing engine performance and wear indicators, gateways can forecast component failures before they occur, minimizing downtime and repair costs.
As AI continues to advance, vehicle gateways will become even more integral to fleet operations, enabling advanced driver assistance systems (ADAS), real-time safety analytics and seamless vehicle-to-everything (V2X) communication.
Memory and storage demands of AI-enabled vehicle gateways
1. Memory requirements for edge AI inference
A gateway handling multiple camera feeds for ADAS or driver monitoring may require over 4GB of DRAM, depending on factors such as video resolution and frame rate. In more advanced configurations, particularly those involving fusion of video, telematics, and environmental sensor data, the memory requirements can increase significantly.
High-bandwidth DRAM, such as LPDDR5, is essential for handling real-time AI inference workloads, especially when processing multiple camera feeds and sensor streams simultaneously. Bandwidth directly impacts inference latency and responsiveness in safety-critical applications.
2. Storage demands for data logging and model hosting
Storage in vehicle gateways serves two key roles: hosting AI models and logging operational data. NAND flash-based storage (eMMC, UFS, NVMeTM SSDs, memory cards) is preferred for its reliability and compact form factor.
Key considerations include:
- Model storage: AI models vary in size from lightweight (e.g., MobileNet at ~50MB) to complex (e.g., YOLOv5 (you only look once, version 5) at several hundred MBs). Multiple models may be stored for different driving scenarios.
- Data logging: High-resolution video, telematics, and sensor data are logged for compliance, diagnostics and training. A single HD camera stream can consume several gigabytes per hour. When storing multiple video streams along with telematics and sensor data, total storage requirements can exceed 128GB, and in some cases, reach terabytes depending on retention policies and data resolution.
AI workloads in fleet gateways demand storage solutions with high endurance and reliability. Continuous video recording, frequent model updates, and harsh operating environments require NAND flash that can withstand sustained write cycles and temperature extremes.
3. Edge AI versus cloud offloading
While cloud-based analytics can reduce on-device compute and storage needs, vehicle gateways often operate in areas with limited connectivity. This necessitates robust edge AI capabilities. Distributed AI, where lightweight inference is done locally and deeper analytics are offloaded when connectivity allows, are increasingly common.
4. Thermal and power constraints
Memory and storage components must meet strict thermal and power budgets, especially in transportation environments such as commercial fleets, logistics vehicles and public transit systems. LPDDR5 offers high bandwidth with low power consumption, while rugged NAND solutions with thermal protection ensure consistent performance under extreme conditions.
Enabling the future of fleet intelligence with industrial-grade solutions
As AI-powered vehicle gateways become central to fleet intelligence, the need for high-performance, durable and efficient memory and storage solutions grows. Micron’s industrial-grade portfolio is engineered to meet these demands, enabling reliable edge computing in harsh environments.
Micron memory and storage solutions for intelligent transportation systems
Micron offers a comprehensive lineup of memory and storage products tailored for intelligent transportation systems:
- High-speed DRAM (LPDDR5, DDR5) for real-time AI inference
- Durable NAND flash (eMMC, UFS, NVMe SSDs, microSD cards) for data logging and model storage
- Compact multichip packages (MCPs) that integrate memory and storage in a single footprint
These components are optimized for wide temperature ranges, shock and vibration resistance, and low power consumption — making them excellent for vehicle gateways operating in demanding fleet environments.
Micron’s innovations, such as the i400 industrial microSD card with up to 1.5TB capacity, enable continuous high-resolution recording and analytics in space-constrained systems. With extended product lifecycles and a commitment to quality, Micron empowers fleet operators to deploy intelligent, AI-driven systems with confidence.
Micron’s industrial portfolio not only supports intelligent transportation systems but also extends to other edge applications such as video security and industrial automation, demonstrating the versatility and reliability of its memory and storage solutions in demanding environments.
In summary
AI integration in vehicle gateways is reshaping fleet management, driving efficiency, safety and sustainability. From reducing accidents through driver monitoring to optimizing routes and delivery schedules, the real-world impact is clear, as smarter fleets mean safer roads and lower costs. Selecting the right memory and storage architecture is critical to unlocking the full potential of these intelligent edge systems. Micron stands ready to support this transformation with proven, industrial-grade solutions built for the road ahead.
Joey Lin is Principal Segment Marketing Manager AEBU IMM at Micron
Learn more about Micron’s memory and storage solutions for Industrial IoT applications.