The convergence of Edge AI and the Internet of Things (IoT) is revolutionizing smart mobility solutions by enabling real-time, low-latency decision-making critical for Intelligent Transportation Systems (ITS). This synergy is essential for applications like vehicle-to-everything (V2X) communication, autonomous vehicles, and smart parking systems, where responsiveness and scalability are paramount.
Before we dive deep:
Edge AI: Edge AI refers to deploying artificial intelligence (AI) algorithms directly on edge devices, such as sensors, cameras, and embedded systems, rather than relying on cloud computing. Key benefits include reduced latency, enhanced privacy, and real-time processing.
Internet of Things (IoT): IoT connects physical devices, enabling them to collect and exchange data. When integrated with Edge AI, IoT systems can process data locally, making them more efficient and responsive.
Why Edge AI for Smart Mobility?
Smart mobility solutions demand immediate responses to dynamic conditions, such as traffic congestion, pedestrian crossings, and emergency vehicle prioritization. Edge AI enables low latency in processing data at the source minimizing delays, high reliability wherein the systems can operate independently of network connectivity, and scalability as the local processing reduces the load on centralized cloud resources.
Applications of Edge AI and IoT in Smart Mobility
- Vehicle-to-Everything (V2X) Communication: V2X encompasses communication between vehicles, infrastructure, pedestrians, and the network. Edge AI enhances V2X through Collision Avoidance by processing the sensor data in edge devices installed in the vehicles, Traffic Signal Optimization with AI adjusted timings dynamically based on the real-time traffic conditions and Hardware Platforms e.g. NVIDIA Jetson AGX Xavier is commonly used in V2X systems for real-time perception and decision-making.
- Autonomous Vehicles: Autonomous vehicles rely heavily on Edge AI to process vast amounts of data from LiDAR, cameras, and other sensors. The outcome is used for Object Detection and Tracking wherein Edge AI processes camera feeds to identify pedestrians, vehicles, and obstacles, Path Planning to find optimal routes based on the road conditions as per the real-time algorithms calculations and Hardware Platforms which includes devices like NVIDIA Jetson Orin and Intel Movidius to facilitate high-performance AI computation at the edge.
- Smart Parking Systems: Edge AI transforms parking infrastructure by enabling Occupancy Detection wherein IoT sensors embedded in parking spots detect availability in real-time, Dynamic Pricing to optimize pricing based on demand and occupancy rates and Hardware Platforms like Raspberry Pi 4 with AI accelerators like Coral TPU provides cost-effective solutions for parking management.
Technical Architecture
A typical Edge AI-IoT architecture for smart mobility includes:
- Sensors: LiDAR, cameras, and ultrasonic sensors collect raw data
- Edge Devices: Devices like NVIDIA Jetson process data locally, running AI inference models.
- Connectivity Modules: 5G, DSRC, and LoRa facilitate communication between devices and infrastructure.
- Cloud Integration: While primary processing occurs at the edge, the cloud is used for long-term storage and analytics.
However the real challenge lies in Hardware Limitations because of the limited computational power in the Edge devices compared to the cloud systems, which can be addressed by specialized hardware accelators like GPUs and TPUs designed for AI workloads.
Another challenge lies in the data privacy and security because the Edge AI systems must secure the sensitive data locally. In addition, integrating thousands of devices in urban environments can strain resources. However, with the advent of newer encryption protocols the boot processes can be secured on edge devices and lightweight AI models can be leveraged for scalability.
“At MWB, we see the convergence of Edge AI and IoT as a pivotal force in shaping smart urban mobility—creating systems that not only adapt in real-time but also set new benchmarks for efficiency, safety, and sustainability.”
Director, ITS & TECH – MWB
Future Trends in Edge AI and IoT for Smart Mobility
We, at MWB, believe that the learning in this space is going to evolve tremendously in the coming years. We see already “Federated Learning”, which enables collaborative training of AI models across edge devices without sharing raw data. There is enormous amount of research and development in the area of Energy-Efficient AI for the development of low-power AI models to extend the battery life of edge devices. The “Integrated V2X Ecosystems” allow Seamless integration of edge AI and IoT with urban digital twins for predictive traffic and infrastructure management.
For further discussions around this and to know more about it, reach out to us at info@mwb-me.com