PS-1 Renault Nissan - Early Alert System for Dropout Risk in Girl Students using AI

Description (Background, Detailed Description and Expected Solution)

Background: In many regions of India, especially rural and underserved communities, girls face disproportionately high dropout rates from school due to a complex interplay of social norms, economic constraints, health issues, early marriage, poor infrastructure, and safety concerns. According to reports, over 1 in 5 girls drop out before completing secondary education. These dropouts not only limit personal growth but also have long-term implications on economic development, gender equality, and health outcomes in the community. Early intervention—through timely identification of at-risk students—can significantly reduce dropouts by enabling support from educators, NGOs, local authorities, or community volunteers.

Detailed Description: The challenge is to build a data-driven AI/ML-powered solution that can predict which girl students are at risk of dropping out by analysing various indicators such as:

• Attendance records (frequent absences, irregular patterns)
• Academic performance trends (sudden drops, consistent underperformance)
• Socio-economic background (family income, parents' education, access to digital devices)
• Health records (menstrual hygiene, malnutrition, absenteeism during specific times)
• School infrastructure (lack of toilets, safety, distance from home)
• Cultural or regional factors (child marriage prevalence, caste/community issues)

This model should be trained on anonymized, historical school data (either synthetic for hackathon or from public datasets) and be able to assign risk scores to students. It should also generate actionable alerts to stakeholders like:

• Teachers or principals for counselling or academic intervention
• NGOs or social workers to assess family support or financial aid
• Local authorities or community leaders for addressing systemic challenges

The system should be scalable, privacy-preserving, and easy to deploy in resource-constrained settings.

Expected Solution

• Web/mobile dashboard showing risk scores (low/medium/high).
• Correlation analysis between attendance, grades, and risk level.
• Automated alerts via SMS/WhatsApp to stakeholders.
• Action tracking for NGOs, teachers, and administrators.

Category: Software

PS-2 Renault Nissan - Smart EV Charging Scheduler for Community Living

Description (Background, Detailed Description and Expected Solution)

Background: India is witnessing a surge in Electric Vehicle (EV) adoption, driven by environmental concerns, government policies, and increasing fuel prices. However, this transition is creating new challenges, especially in residential and community settings like apartments, hostels, and shared societies. One critical issue is load balancing and grid stress, particularly during peak electricity demand hours. If multiple EVs are charged simultaneously without coordination, it can overload local transformers, increase electricity costs for the entire community, and even cause power outages. At the same time, many communities are investing in rooftop solar systems, creating an opportunity to align EV charging with clean energy generation. Unfortunately, there are few tools that intelligently manage when and how EVs are charged based on solar availability or grid efficiency.

Detailed Description: The objective is to design and develop a smart EV charging scheduling system tailored for community environments. This solution should intelligently determine the optimal time slots for charging, based on:

• Time-of-day electricity tariffs (to charge during low-cost, off-peak hours)
• Grid load availability (to avoid charging during peak usage and reduce stress on transformers)
• Solar generation patterns (to maximize the use of clean, renewable energy)
• User preferences and urgency (for example, if the user needs a full charge by morning)

The system can be deployed in environments such as gated apartment complexes, college hostels and faculty quarters, and public/shared EV charging points in residential areas. The solution should include a mobile app interface for EV owners to set preferred charging times and priorities, view cost estimates and clean energy usage stats, and receive alerts when charging starts or ends. The backend should ideally use machine learning to learn user patterns and predict grid load or solar generation using historical data and weather forecasts.

Expected Solution

Mobile App for EV users to:

• Register their EV and charging requirements
• Set charging preferences
• View real-time and scheduled charging status
• Receive cost-saving and carbon-saving tips

Smart Scheduler Backend:

• Consumes input from grid API (tariff/load)
• Integrates solar generation forecast (if rooftop solar exists)
• Uses an algorithm to suggest and trigger optimal charging slots
• Manages multiple users without overloading grid capacity

Category: Software

PS-3 Renault Nissan - AI-Powered Urban and Sub-Urban Flood Detection Using Existing CCTV Infrastructure

Description (Background, Detailed description and Expected Solution)

Background:
Urban flooding is a growing concern in many developing cities, especially during monsoon seasons. Cities like Chennai experience frequent waterlogging and flash floods, often with little to no early warning. While advanced flood monitoring systems are expensive and require robust infrastructure, many cities already have widespread CCTV and traffic camera networks that remain underutilized for disaster management.

Detailed Description:
This project aims to leverage existing CCTV and traffic camera infrastructure in urban areas to detect and monitor flooding in real time using AI. By applying computer vision techniques to live video feeds, the system can identify signs of flooding such as rising water levels, submerged roads, and stalled vehicles. The AI model should be capable of working with low-resolution footage, varying lighting conditions, and different camera angles. The system should also integrate with mobile-based alert platforms to notify authorities and the public in real time.
The solution should be scalable, cost-effective, and adaptable to other cities with similar infrastructure. It should also consider privacy, data security, and the limitations of internet connectivity in some areas.

Expected Solution:
A computer vision-based AI model that can detect flood conditions from CCTV footage.
A dashboard or alert system that visualizes flood-prone areas in real time.
Integration with SMS or mobile alert systems for public and emergency response notifications.
A pilot implementation plan for a city like Chennai, using government-shared camera feeds.
Partners can include but are not limited to Government Partners such as Greater Chennai Corporation (GCC), Tamil Nadu State Disaster Management Authority (TNSDMA), and Chennai Traffic Police; technical partners such as IIT Madras and local startups or tech companies; and NGOs working in disaster relief and Resident Welfare Associations (RWAs).
Documentation on model accuracy, limitations, and recommendations for scaling.

Category: Software

PS-4 Renault Nissan - AI-Driven Urban Freight Routing and Consolidation System for Sustainable City Logistics

Description (Background, Detailed description and Expected Solution)

Background:
Urban areas are experiencing increasing pressure on transportation infrastructure due to the surge in last-mile delivery, ride-sharing services, and growing e-commerce demands. A major challenge lies in managing urban freight traffic, which contributes to congestion, pollution, and inefficient logistics operations. There is a need for a smart logistics orchestration system that balances freight movements, reduces idle runs, and optimizes delivery operations in city zones.

Detailed Description:
Design a smart logistics platform powered by AI/ML and geospatial analytics that enables real-time, optimized routing and freight load consolidation for delivery partners and logistics providers operating in urban areas. The system should:

• Analyze city-wide traffic, environmental zones, and delivery density to suggest dynamic routes.
• Allow co-loading and hub-sharing by multiple vendors to reduce empty or underutilized runs.
• Offer predictive scheduling based on peak hours, zone restrictions, and vehicle types.
• Integrate with municipal APIs to account for construction zones, festivals, or temporary traffic regulations.
• Provide real-time dashboards for city authorities to monitor logistics load across zones.
• Support incentives for eco-friendly transport modes (electric vehicles, bicycles, etc.).

Expected Solution:
A software platform (mobile/web dashboard + backend system) that:

• Uses AI/ML for route optimization and load sharing.
• Supports API integrations with government and private logistics systems.
• Provides an intuitive UI for logistics operators, vehicle drivers, and city planners.
• Tracks KPIs like carbon footprint reduction, cost per delivery, and fleet utilization.
• Scalable to Tier 1 and Tier 2 cities with minimal customization.

Category: Software

PS-5 Renault Nissan - AI-Powered Crop Advisory and Marketplace Platform for Smallholder Farmers

Description (Background, Detailed description and Expected Solution)

Background:
A large percentage of India's farmers are smallholders who lack access to timely, localized, and reliable information on crop selection, pest management, weather patterns, and market prices. Due to fragmented supply chains and lack of direct market access, they are often forced to sell their produce to intermediaries at unfair prices. Bridging the information gap and enabling direct market linkage through technology can significantly enhance their income and resilience.

Detailed Description:
Design a comprehensive AI-powered digital platform tailored for small and marginal farmers that offers real-time, localized crop advisory and connects them directly to buyers, agro-input providers, and government schemes. The platform should include:

• AI/ML-based crop recommendation system based on soil type, weather forecast, and region-specific trends.
• Pest/disease prediction and treatment suggestions using computer vision (image upload of affected crops).
• Voice-based assistance in local languages for non-literate farmers.
• Integration with geospatial and satellite data for precision advisory.
• Digital mandi/marketplace feature to enable farmers to list and sell their produce directly to consumers, processors, or aggregators.
• Notifications about government subsidies, schemes, and insurance programs.

Expected Solution:
A software-based solution (mobile app and web portal) that:

• Uses AI, computer vision, and geospatial data for crop and pest advisory.
• Provides market price trends, nearest mandi rates, and connects farmers with buyers.
• Supports multi-language voice navigation and farmer-friendly UX.
• Ensures low bandwidth performance and offline mode for rural connectivity challenges.
• Includes dashboards for government and agri-extension officers to monitor usage, feedback, and impact.

Category: Software

PS-6 Renault Nissan - Smart Talent Identification and Progress Monitoring System for Grassroots Sports Development

Description (Background, Detailed description and Expected Solution)

Background:
India has a vast pool of young talent in sports, especially in rural and semi-urban areas. However, due to the lack of structured identification systems, real-time performance monitoring, and coaching support, many promising athletes are left unnoticed. Existing systems often rely on manual scouting and subjective evaluation, leading to talent gaps and missed opportunities.

Detailed Description:
Design a smart, digital platform that leverages AI/ML, computer vision, and data analytics to identify, track, and support emerging sports talent from schools, colleges, and local academies. The system should work as an end-to-end Talent Identification and Performance Management System (TIPMS) for young athletes. The platform should include:

• A mobile app or kiosk interface for schools/coaches to upload athlete performance data and videos.
• AI-based video analysis of athletic movements (e.g., sprint, jump, throw) for talent benchmarking.
• Dashboards to track athletes' progress across physical, technical, and psychological parameters.
• Skill and fitness benchmarking engine based on sport-specific parameters (e.g., endurance, agility, reflexes).
• Recommendation of training modules or coaching plans based on gaps and goals.
• Integration with federations/sports bodies to create a national athlete database.
• Option to host virtual selection trials with verified performance metrics.

Expected Solution:
A software-based platform (mobile/web) with:

• Role-based access for athletes, coaches, school admins, and government officials.
• AI/computer vision module to evaluate athletic performance via video input.
• Sport-specific data models for tracking and forecasting athlete growth.
• Secure, cloud-based athlete records for nationwide access and collaboration.
• Real-time analytics for district/state/national level sports authorities to plan interventions.

Category: Software

PS-1 Turbo Energy Private Limited - Beyond Words: Neural Touch Communication Between Humans and AI

(Integrating Emotional, Verbal, Written, and Voice-Based Interaction with Neural and Tactile Feedback)

Current AI communication systems—like chatbots, voice assistants, and conversational models—rely heavily on verbal and written language to understand user intent. While these systems can simulate empathy through text or tone, they lack the ability to perceive and respond to real human emotion, touch, and neural feedback. Humans communicate not only through words but also through touch, emotion, tone, and physiological responses. The absence of these sensory and emotional cues in AI communication creates a disconnect between human feeling and machine understanding. Hence, there is a need for a multi-sensory Human–AI interface that can:

• Interpret nerve and touch signals to understand emotional context
• Combine verbal, written, and vocal communication with emotional feedback
• Respond through tactile or haptic feedback, providing a more natural, human-like interaction

Project Objective

To develop a Neural Touch Communication Interface that bridges emotional and physical interaction between humans and AI, enabling communication beyond words through:

1. Sensing human emotional states via nerve and touch signals (EEG/EMG/GSR sensors)
2. Integrating multimodal communication (voice, text, and emotion)
3. Providing physical feedback (vibration, warmth, or pressure) as a form of AI response
4. Enhancing human trust, empathy, and immersion in AI interactions

Expected Outcome

• A working prototype that interprets human emotional or touch input using biosensors (like EMG, EEG, or GSR)
• An AI communication layer (using models like ChatGPT or Gemini) that adapts responses based on the detected emotional state
• A haptic feedback system (vibration motors or thermal actuators) that delivers physical feedback corresponding to AI responses
• A seamless emotional interaction loop — where the user’s touch, emotion, and speech influence the AI’s behavior, and the AI provides human-perceptible feedback in return

Benefits and Applications

Human–AI Understanding

• Creates emotionally aware AI systems capable of interpreting non-verbal human cues
• Enhances empathy and realism in virtual assistants, therapy bots, and robots

Improved Communication

• Reduces emotional gap between humans and AI by combining touch, tone, and emotion with text or speech
• Enables inclusive communication for individuals with speech or hearing impairments via touch feedback

Healthcare and Therapy

• Useful in mental health monitoring, stress management, and neurorehabilitation, where emotional awareness and touch feedback are critical

Human–Robot Collaboration

• Improves safety and coordination in robotic or industrial applications through nerve-signal-based intent detection and tactile confirmation

Future AR/VR and Metaverse Integration

• Enhances immersion through neuro-haptic feedback in gaming, education, and virtual interaction environments

Vision Statement

“To evolve AI communication from words and sounds into touch, emotion, and empathy — enabling machines to truly feel and respond like humans.”

PS-2 Turbo Energy Private Limited - AI-Driven Smart Safety and Predictive Monitoring System for Industrial Environments

Industrial environments such as manufacturing plants, medical facilities, data centers, and firecracker industries face significant safety challenges due to equipment failures, hazardous material leaks, temperature surges, and human errors. Traditional safety systems rely on reactive measures—detecting incidents only after they occur—leading to delays in response, equipment damage, loss of productivity, and potential harm to personnel. There is a growing need for an intelligent, real-time, and predictive system that can analyze environmental and operational data, anticipate risks, and trigger preventive actions automatically before a critical failure or accident happens.

Proposed Solution

The project proposes an AI-driven smart safety system that integrates IoT sensors, machine learning algorithms, and edge computing to continuously monitor environmental parameters (temperature, gas, vibration, noise, and electrical load). The system will:

• Predict hazards using AI-based anomaly detection
• Alert operators in real-time through visual and auditory warnings
• Automatically trigger safety protocols (e.g., equipment shutdown, ventilation activation, or fire suppression)
• Provide a cloud dashboard for centralized data visualization, risk analytics, and decision-making

Expected Outcomes

1. Predictive Hazard Detection: Early identification of unsafe conditions such as overheating, toxic gas buildup, or short circuits using AI models.
2. Real-time Monitoring Dashboard: Centralized platform displaying live safety metrics and alerts from multiple sites.
3. Automated Preventive Response: AI-triggered emergency actions (e.g., alarm activation, power isolation, or system shutdown).
4. Data-Driven Risk Analytics: Historical trend analysis to identify recurring risk zones and improve maintenance planning.
5. Scalable & Modular Design: Adaptable across industries (manufacturing, medical labs, data centers, and firecracker units).

Benefits

• Enhanced Worker Safety: Continuous real-time surveillance reduces the likelihood of human injury and fatalities.
• Predictive Maintenance: AI anticipates equipment failure, reducing downtime and maintenance costs.
• Rapid Emergency Response: Automated alerts and actions minimize damage during critical events.
• Energy and Cost Efficiency: Preventing system breakdowns improves energy utilization and lowers repair costs.
• Regulatory Compliance: Helps industries adhere to occupational health and safety (OHS) and environmental norms.
• Scalability & Flexibility: Easily deployable in diverse industrial sectors with minimal customization.

Vision Statement

“To create a safer, smarter, and self-aware industrial ecosystem where AI-driven intelligence and real-time data work together to prevent accidents before they happen, ensuring zero harm, maximum efficiency, and sustainable operations across all industrial sectors — from manufacturing and medical facilities to data centers and firecracker industries.”

PS-3 Turbo Energy Private Limited AI-Sustain: Smart Enforcement and Real-Time Compliance System for Zero-Waste Communities

(Leveraging AI, IoT, and Blockchain for Practical Sustainability Implementation)

Despite the existence of comprehensive sustainability and waste management policies, implementation at the ground level remains weak. Current systems rely on manual reporting, paper-based verification, and irregular inspections, leading to low compliance, poor accountability, and minimal citizen participation. Builders and households often complete sustainability formalities only on paper, as there is no continuous digital tracking or proof of compliance. Furthermore, public awareness and motivation are low, with no effective incentive system or feedback mechanism to reward sustainable actions. Hence, there is a strong need for a technologically enforced, transparent, and reward-based AI system that ensures every household and construction project genuinely adheres to sustainability laws through automation, monitoring, and engagement.

Project Objective

To design and implement an AI-driven digital ecosystem that continuously monitors, enforces, and rewards sustainability compliance at household and building levels — transforming existing paper-based policies into a real-time, transparent, and accountable system.

Proposed Solution – “AI-Sustain System”

A unified platform integrating AI, IoT sensors, and Blockchain to ensure real-time waste tracking, sustainability compliance, citizen participation, and automated reporting to government systems.

Core Module	Functionality
AI Monitoring Layer	Uses sensors, smart bins, and cameras to track waste segregation and recycling in real time.
IoT Data Network	Sends live compliance data to a central sustainability dashboard.
Blockchain Ledger	Stores verified waste transactions for transparent government auditing.
Gamified Citizen App	Encourages participation through green points and rewards.
AI Compliance Engine	Automatically detects violations, sends predictive alerts, and generates sustainability reports.

Expected Outcomes

Area	Current Status	After AI Integration
Waste Compliance Tracking	Manual & irregular	Real-time, automated with IoT + AI
Public Awareness	Low	Gamified, visible, and AI-assisted
Accountability	Weak	Continuous digital audit with predictive alerts
Incentives	Absent	Automated green credit system for citizens & builders
Policy Enforcement	Paper-based	Blockchain-backed, real-time transparent data

Expected Outcomes (Detailed)

1. 100% digital traceability of household and construction waste.
2. Real-time monitoring ensures violations are detected immediately.
3. AI-generated compliance reports automatically sent to authorities.
4. Enhanced citizen engagement through gamified eco-reward apps.
5. Drastic reduction in landfill contribution and unsorted waste.
6. Policy enforcement becomes data-driven, reducing manual inspection load.

Benefits

Category	Benefits
Environmental	Major reduction in landfill waste, CO₂ footprint, and pollution through continuous segregation and recycling.
Technological	Demonstrates AI, IoT, and Blockchain integration for real-world sustainability tracking.
Economic	Reduces waste collection costs and encourages recycling-based revenue systems.
Governance	Transparent, automated enforcement ensures real compliance and supports green certification programs.
Social	Creates awareness, motivates citizens, and builds a culture of sustainability through rewards and data visibility.
Urban Planning	Enables data-driven waste forecasting and better resource allocation for smart cities.

Vision Statement

“To bridge the gap between sustainability policies and real-world practice by creating an intelligent, transparent, and self-regulating ecosystem — where AI ensures that every home, builder, and community becomes a true partner in achieving zero-waste living.”