Strategic Partnership to Advance Sustainable Infrastructure in India through Advanced Sensing Technology

Executive Summary

A strategic partnership has been formed between Sparsh, Cron AI, and Innoviz Technologies to deploy a unified, edge-native sensing platform across India. This initiative aims to integrate advanced LiDAR, AI-driven perception, and vision analytics to enhance security and transportation systems. The project is strategically aligned with several United Nations Sustainable Development Goals (SDGs), particularly SDG 9 (Industry, Innovation, and Infrastructure), SDG 11 (Sustainable Cities and Communities), and SDG 7 (Affordable and Clean Energy), by focusing on resilient infrastructure, urban safety, and energy-efficient technology.

Contribution to SDG 9: Industry, Innovation, and Infrastructure

The collaboration directly supports the modernization of India’s infrastructure, a core component of SDG 9. By introducing high-performance sensing technology, the partnership aims to build resilient and reliable systems for critical sectors.

1. Infrastructure Modernization: The technology is targeted at national programs like DFCCIL and Kavach, with over USD 1.3 billion invested in railway modernization. It will also serve the intelligent transport and perimeter security sectors, providing real-time insights to improve operational integrity and safety.
2. Fostering Innovation: The project combines best-in-class global technologies, including automotive-grade LiDAR and deep-learning perception, tailored for complex Indian environmental conditions. This fosters an ecosystem of advanced technological application.
3. Promoting Sustainable Industrialization: Sparsh, with an annual production capacity of over 500,000 devices, will lead go-to-market efforts and explore local manufacturing for the platform, contributing to domestic industrial capacity and supply chains.

Enhancing Urban Sustainability and Safety (SDG 11)

The deployment of this intelligent sensing platform is set to make significant contributions to creating safer, more resilient, and sustainable urban environments, directly addressing the targets of SDG 11.

• Safe and Sustainable Transport Systems: The platform will deliver real-time data on vehicle movement and pedestrian activity, forming the basis for intelligent transport systems that can reduce accidents and improve traffic efficiency in cities.
• Public Safety: By upgrading security at critical infrastructure and perimeters, the system enhances the safety and security of public spaces.
• Resilience to Environmental Conditions: The integrated platform is engineered to operate reliably in challenging Indian conditions such as heat, dust, fog, and electrical instability, ensuring continuous performance for critical city services.

Technological Innovation and Energy Efficiency (SDG 7)

A key feature of the platform is its edge-native processing, which aligns with SDG 7 by promoting energy efficiency in technological systems. Data is processed locally, reducing the need for energy-intensive cloud computing and extensive network infrastructure.

• InnovizSMART LiDAR: Provides long-range, high-resolution 3D environmental data, performing reliably in harsh weather and lighting.
• Cron AI’s senseEDGE: This deep-learning platform processes LiDAR data with high accuracy and context. Critically, it operates on just 8 watts of power without requiring a GPU, minimizing the system’s energy footprint.
• Sparsh’s Vision Systems: Add a layer of redundancy and intelligent classification through real-time visual analytics.

Market Opportunity and Economic Impact (SDG 8)

The initiative operates within rapidly growing markets, indicating a strong potential for economic growth and the creation of skilled employment, in line with SDG 8 (Decent Work and Economic Growth).

1. Perimeter Security: This sector in India was valued at USD 3.23 billion in 2022 and is projected to grow at a CAGR of 14% through 2030.
2. Intelligent Transport: The market is currently valued at USD 1.44 billion and is expected to more than double by 2033.
3. Addressable Opportunity: The combined sectors represent an estimated addressable opportunity of over USD 1 billion for edge-native LiDAR-camera systems, with significant scaling potential.

1. Which SDGs are addressed or connected to the issues highlighted in the article?

SDG 9: Industry, Innovation and Infrastructure

• The article focuses on technological innovation (LiDAR, AI, edge computing) and its application to upgrade national infrastructure. It explicitly mentions “railway modernization and infrastructure investments,” the “intelligent transport market,” and a “security infrastructure upgrade project,” all of which are central to building resilient infrastructure and fostering innovation.

SDG 11: Sustainable Cities and Communities

• The partnership aims to provide technology for “smart city initiatives.” The stated goals of delivering “real-time insights into vehicle movement, pedestrian safety, and perimeter activity” directly contribute to making cities and human settlements safer and more sustainable.

SDG 17: Partnerships for the Goals

• The entire article announces a multi-stakeholder partnership between three distinct entities: Sparsh (an Indian manufacturing and deployment leader), Cron AI (a deep-tech AI company), and Innoviz Technologies (a global LiDAR technology leader). This collaboration to share technology, expertise, and market access for sustainable development goals is a clear example of SDG 17 in action.

2. What specific targets under those SDGs can be identified based on the article’s content?

Targets for SDG 9

1. Target 9.1: Develop quality, reliable, sustainable and resilient infrastructure, including regional and transborder infrastructure, to support economic development and human well-being.
   • The article supports this target by describing a project to upgrade “critical infrastructure across India,” including transport and railway systems, with high-performance, reliable sensing technology designed to function in harsh Indian conditions.
2. Target 9.4: By 2030, upgrade infrastructure and retrofit industries to make them sustainable, with increased resource-use efficiency and greater adoption of clean and environmentally sound technologies.
   • The technology platform, specifically Cron AI’s senseEDGE, is highlighted for its energy efficiency, as it “runs at just 8 watts, with no GPU or external infrastructure.” This demonstrates a move towards more sustainable and resource-efficient technology in infrastructure projects.

Targets for SDG 11

1. Target 11.2: By 2030, provide access to safe, affordable, accessible and sustainable transport systems for all, improving road safety.
   • The integrated platform is designed to enhance “pedestrian safety” and provide insights into “vehicle movement.” This directly addresses the goal of improving the safety of transport systems within cities and on transport networks like railways.
2. Target 11.a: Support positive economic, social and environmental links between urban, peri-urban and rural areas by strengthening national and regional development planning.
   • The initiative is described as a national-level deployment, driven by “national programs like DFCCIL and Kavach” and covering “120+ cities.” This points to a strengthened national development plan for infrastructure that connects various regions.

Targets for SDG 17

1. Target 17.16: Enhance the global partnership for sustainable development, complemented by multi-stakeholder partnerships that mobilize and share knowledge, expertise, technology and financial resources.
   • The partnership combines “global best-in-class LiDAR” from Innoviz (Israel), advanced AI perception from Cron AI, and the national reach and manufacturing capabilities of Sparsh (India). This is a textbook case of sharing technology and expertise across borders.
2. Target 17.17: Encourage and promote effective public, public-private and civil society partnerships.
   • The article states that the partnership is “equipping our customers — across cities, industries, and government,” indicating that the technology will be deployed through collaborations between these private companies and public sector entities responsible for infrastructure and security.

3. Are there any indicators mentioned or implied in the article that can be used to measure progress towards the identified targets?

Indicators for SDG 9

1. Infrastructure Investment: The article mentions that “railway modernization and infrastructure investments exceed USD 1.3 billion,” providing a financial metric for investment in sustainable infrastructure (related to Target 9.1).
2. Manufacturing Capacity: Sparsh’s “annual production capacity of 500,000+ security devices” serves as an indicator of the industrial and manufacturing capability being leveraged for the project (related to Target 9.1).
3. Energy Efficiency of Technology: The specification that the AI platform “runs at just 8 watts” is a direct, quantifiable indicator of resource-use efficiency in new technology adoption (related to Target 9.4).

Indicators for SDG 11

1. Scale of Urban Deployment: The plan to deploy the system in “120+ cities” and at critical infrastructure points like “intersections” and “railways” serves as an indicator of the geographic scope and progress of implementing smart city solutions (related to Target 11.a).
2. Improved Safety Systems: The deployment of systems specifically designed to enhance “pedestrian safety” and monitor “vehicle movement” implies that progress could be measured by the number of intersections or kilometers of railway covered by this advanced safety technology (related to Target 11.2).

Indicators for SDG 17

1. Financial Scope of Partnership: The article identifies “over USD 1 billion in addressable opportunity” for these systems, which can be used as a proxy indicator for the financial resources being mobilized and targeted by this public-private partnership (related to Target 17.17).
2. Establishment of Multi-Stakeholder Partnership: The formal announcement of the collaboration between Sparsh, Cron AI, and Innoviz is itself an indicator that a partnership to share technology and knowledge has been successfully formed (related to Target 17.16).

4. Create a table with three columns titled ‘SDGs, Targets and Indicators” to present the findings from analyzing the article.

Source: prnewswire.com