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News

July 1, 2023June 21, 2025 byMWB Team

Toyota and NTT Invest $3.3 Billion in AI Platform Development

Toyota Motor and Nippon Telegraph and Telephone (NTT) plan to invest a total 500 billion yen ($3.27 billion) by 2030 into an infrastructure and software platform using artificial intelligence to reduce traffic accidents.

The automaker and telecommunications firm said in a joint statement on Thursday they want to develop a mobility AI platform that uses large amounts of data to support driver assist technology, aiming to have a system ready by 2028.

(more…)

Mobility AI

May 12, 2023June 21, 2025 byMWB Team

Edge AI with IoT in Smart Mobility Solutions

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

News

May 1, 2023June 21, 2025 byMWB Team

First Level 4 Autonomous Baggage Towing in Japan

TractEasy, leading distributor of autonomous material handling solutions driven by EasyMile’s technology, has announced its EZTow autonomous tow tractor in the first-ever Level 4 autonomous driving operation for Ground Support Equipment (GSE) vehicles at Kansai International Airport.

The trial is being conducted in collaboration with Peach Aviation Limited, Panasonic Holdings Corporation, NAGASE TECHNO SERVICE CO., LTD., and Kansai Airports.

It is taking place within the ramp area of Terminal 2 and marks the first time Level 4 autonomous GSE operations are being trialed at Kansai Airport. The objective is to assess the practical application of autonomous towing vehicles in a live airport environment.

System Features: Onboard LIDAR-based detection, autonomous navigation, and remote operation fallback
Use Area: Vehicular traffic zones and apron areas at Terminal 2

The demonstration is designed to evaluate the vehicle’s ability to detect and respond to pedestrians, stop and resume movement at crosswalks, and navigate with precision in constrained spaces such as baggage return areas. These functions are tailored to the operational demands of airport environments.

This initiative follows a remote-controlled vehicle test conducted at Kansai Airport in April 2023. The current trial advances this work by integrating higher levels of autonomy, with the aim of exploring how such technologies can support labor savings and improve operational flow in airport ground handling.

In the long term, combining autonomous and remote-controlled systems could offer flexible fleet management solutions across multiple airport sites.

“This initiative will reduce the time between aircraft arrival and the start of baggage return to contribute to major groundbreaking in labour and workforce efficiency in ground handling operations” Company Representative

Source: FutureTransport-News

Mobility AI

March 7, 2023January 13, 2025 byMWB Team

Predictive Maintenance with AI For Urban Mobility Systems

Urban mobility systems are the lifelines of modern cities, encompassing infrastructure such as roads, bridges, tunnels, and transit networks. Maintaining these systems efficiently is paramount to ensuring uninterrupted operations, safety, and cost-effectiveness. Predictive maintenance, powered by Artificial Intelligence (AI), has emerged as a game-changer, enabling infrastructure managers to anticipate failures and optimize maintenance schedules.

Predictive maintenance involves the use of advanced analytics, machine learning (ML), and IoT sensors to monitor the condition of assets in real-time. By analyzing historical and real-time data, predictive models identify patterns and anomalies that signal potential failures. This approach contrasts with traditional reactive maintenance, which addresses issues after they occur, and preventive maintenance, which relies on fixed schedules irrespective of actual asset conditions.

Core Components of AI-Driven Predictive Maintenance

IoT Sensors and Data Collection:
Sensors capture diverse data streams such as temperature, vibration, pressure, and structural stress in real-time. For example, accelerometers and strain gauges embedded in bridges detect vibrations that could indicate structural weaknesses.
Machine Learning Algorithms: ML models process and analyze data to identify degradation patterns and predict time-to-failure. For example, Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks are used for time-series analysis in dynamic systems like escalators or traffic signals.
Digital Twins: Digital twins—virtual replicas of physical assets—simulate operational conditions to forecast failures and test maintenance strategies. For example: A digital twin of an urban rail system can model wear and tear based on real usage patterns.
Centralized Monitoring Platforms: SCADA and cloud-based platforms aggregate and visualize data, allowing operators to monitor infrastructure health remotely. Integration with AI enhances decision-making by providing actionable insights.

Applications in Urban Mobility Systems

AI-powered sensors monitor structural integrity, detecting cracks or corrosion before they escalate in the wide spectrum of urban infrastructure, including tunnel and bridges, traffic signal systems, public transit and roadway infrastructure. Predictive models optimize inspection schedules, reducing costly manual checks, downtime. Anomaly detection algorithms predict hardware failures, enabling timely replacements. Pavement sensors and ML algorithms can also predict pothole formation based on traffic load and environmental conditions for proactive repair schedules.

A Technical Glance at AI in Predictive Maintenance

The anomaly detection is usually carried out by identifying the outliers in sensor data using techniques like k-means clustering or autoencoders. For predictive modelling, failure probabilities can be forecasted through regression models, decision trees, and neural networks. The maintenance schedules can be further optimized by reinforcement learning from simulated environments.

However, the high frequency sensor data requires scalable storage solutions, which are often cloud based. The data quality remains as one of the biggest challenges which needs to be addressed by preprocessing techniques such as imputation and smoothing. The learning from the predictive models must integrate seamlessly with legacy systems and SCADA platforms, requiring robust APIs and middleware, which must be planned in advance.

“AI in predictive maintenance isn’t just about anticipating failures – it’s about redefining the way we maintain, operation, and extend the life of critical infrastructure.”
Head of ITS & TECH, MWB

What we can expect in future

Based on the findings from the research and prototype of predictive maintenance project through a strategic collaboration of MWB, we can expect significant accuracy (>80%) in predicting motor failures to reduce downtime (<20%) in addition to a estimated lower operational costs by 15% within the first year itself.

With the future technologies around Edge Computing (real-time analytics at the edge itself to reduce latency and improve responsiveness), Hybrid AI Models (combined physics-based models with ML for greater predictive accuracy) and Sustainability practices (reduced resource waste and energy consumption), we can only expect that AI driven predictive maintenance will only get better, more accurate and the DNA of urban infrastructure technology.

News

February 15, 2023June 21, 2025 byMWB Team

Driverless bus is tested in Barcelona

Commuters in downtown Barcelona will be able to ride the bus for free soon. There’s just one catch: this mini-bus has no one at the wheel. The bus pulls away from the stop with its passengers on its own, brakes before changing lanes and eases down one of Barcelona’s most fashionable boulevards.

Renault is testing a new driverless mini-bus in Barcelona. The autonomous vehicle runs on a 2.2-km (1.3-mile) circular route with four stops in the center of the Spanish city. Adventurous commuters can jump on free of charge.

The French carmaker has teamed up with WeRide, a company specializing in autonomous vehicles, to make the prototype. It unveiled the driverless bus at the French Open venue last year, but now it is testing it on the open road in Barcelona. It also has testing projects going in Valence, France, and at the Zurich airport.

Pau Cugat was one of the curious to step aboard for a short ride along Passeig de Gracia boulevard.

“We just passed by a regular, combustion-engine city bus, and I thought, ‘Look, there is a bus of the past, and right behind it you have the bus of the future,’” the 18-year-old student said.

“The US is doing a lot of experimentation with autonomous vehicles, the same thing in China, Until now we don’t have a lot in fact in Europe. And this is why we want to show that this works and prepare Europe to this route in public transportation.”
Patrick Vergelas, Head of Renault’s AutoNOmous Mobility Projects

Driverless taxis and buses are being tried out by companies in other cities, from San Francisco to Tokyo. But Renault’s initiative comes as Europe generally lags behind the United States and China in driverless vehicle technology, where companies are fiercely competing to get ahead.

The electric bus can run for 120 kilometers without a recharge and reach 40 kilometers per hour (25 mph). It is equipped with 10 cameras and eight lidars (sensor arrays) to help it navigate the streets filled with cars, motorbikes and pedestrians. The company says the bus is able to drive safely on a given course through a busy downtown like that of bustling Barcelona.

Source: AP News

Insight

January 5, 2023August 3, 2025 byMWB Team

Engineering Excellence in Complex Urban Infrastructure Projects

Urban infrastructure projects represent some of the most ambitious engineering undertakings, requiring innovative solutions and multidisciplinary collaboration to address challenges like traffic congestion, environmental impact, and safety. At MWB, we have developed a robust approach to delivering excellence in complex projects, such as undersea tunnels, multi-level junctions, and innovative designs—leveraging Intelligent Transportation Systems (ITS), SCADA, and other advanced technologies.

The Challenges of Modern Urban Infrastructure

Modern cities face ever-growing demands on their infrastructure. With increasing urbanization, traffic congestion, and environmental sustainability concerns, infrastructure projects are no longer just about building roads or bridges; they are about creating intelligent systems that integrate seamlessly into urban ecosystems. Some of the key challenges include:

Space constraints: Projects like multi-level junctions must maximize utility within limited urban spaces.
Environmental sensitivity: Undersea tunnels require designs that minimize ecological disruption while maintaining structural integrity under extreme conditions.
Safety and efficiency: High-traffic areas demand systems that ensure smooth flow and rapid incident response.
Scalability: Infrastructure must accommodate future growth without requiring constant overhauls.

Leveraging ITS for Smarter Solutions

Intelligent Transportation Systems (ITS) integrate advanced communication, computation, and sensor technologies to optimize traffic flow, reduce emissions, and enhance safety for smarter infrastructure. It enables real-time monitoring of vehicle movement, air quality, and tunnel integrity along with Incident Detection Systems, which is integrated with adaptive lighting and ventilation to improve safety while reducing energy consumption within under-sea tunnels. It enables dynamic traffic signals and lane management systems to optimize traffic flow based on real-time data and predictive analytics to reduce bottlenecks for enhancing the commuter experience. ITS enabled innovative designs allow automated tolling and congestion pricing to encourage better traffic distribution.

In addition, Supervisory Control and Data Acquisition (SCADA) systems are vital for managing the complexity of large-scale infrastructure. By offering centralized monitoring and control, SCADA ensures seamless operations even under challenging conditions.

MWB’s Approach to Excellence

MWB brings deep expertise with all the underlying technologies, nuances and its integration with the current infrastructure to build a resilient technology integration solution. As an advocate to the cost-effective solutions, we introduce the right technology solutions and platforms for the customer need, whether GIS for spatial analysis or any proprietary programming based framework. What separates us apart from others is our comprehensive understanding of complexity in projects and the technology blueprint to provide the innovative layouts, best of the communication networks, a solution that reduces operational costs and enhances safety.

MWB’s expertise in multi-level junctions shines in projects where urban constraints demand innovative layouts. Our use of 3D modeling, simulation tools, and ITS-enabled traffic management systems resulted in junctions that improved throughput significantly compared to the traditional designs. MWB has embraced sustainability as a core design principle. These designs incorporate ITS for dynamic traffic and environmental monitoring, ensuring long-term viability and reduced ecological impact.

“At MWB, our mission is to transform challenges into opportunities. With innovation and collaboration at our core, we design infrastructure that is not only functional but also forward-thinking and sustainable.”
Founder & CEO, MWB

Looking Forward,

Engineering excellence in urban infrastructure projects requires a blend of innovation, adaptability, and technical mastery. MWB’s experience in deploying ITS, SCADA, and sustainable design principles positions us as leaders in addressing the complexities of modern urban development. By continuously pushing boundaries and embracing emerging technologies, we aim to shape resilient, intelligent cities that meet the demands of today and tomorrow.