TSLAI+ AI Car: Smart Dashboard (TESLA Inspired) - @ Google AiStudio - Live Demo

Project Overview

The TSLAI+ AI Car Smart Dashboard is a high-fidelity, highly responsive vehicle interface application designed to consolidate all critical driving information into one seamless, futuristic dark-themed environment. Inspired by TESLA design guidelines, the application provides comprehensive control over vehicle status, navigation, media, and climate, demonstrating a paradigm shift in in-car HMI (Human-Machine Interface) design.

Technology Stack: React (TypeScript), Tailwind CSS, Framer Motion (Animation), Advanced CSS (Glassmorphism/Neumorphism) 

My Role: Lead Front-End Developer, HMI Designer, Interactive Prototyping Specialist

1. The Problem: Information Overload and Visual Clutter

Current vehicle dashboards often suffer from poor information hierarchy, where safety-critical data (speed, battery) competes visually with secondary data (media, climate controls). Specific issues included:

• Distraction Risk: Traditional interfaces require excessive cognitive load and eye movement to locate different data points.

• Lack of Cohesion: Integrating multiple systems (navigation, media, controls) typically results in a fragmented, non-uniform user experience.

• Dated Aesthetics: Many modern vehicles lack an interface design that matches the vehicle's technological sophistication, leading to user dissatisfaction.

The mandate was to design and prototype a dashboard that is intuitively legible, aesthetically superior, and inherently safe by minimizing visual friction.

2. The Solution: A Sleek, Spatially Cohesive HMI

TSLAI+ solved the clutter and distraction problem by applying spatial computing design principles to create an interface where data flows naturally and critical information is always prioritized.

• Vision Pro-Inspired Design: Implemented design elements like glassmorphism (frosted glass effects) and deep shadow layering to give the UI a sense of depth and hierarchy, ensuring that active elements appear to float over background information.

• Dynamic Information Zones: The screen is cleanly divided into zones: a primary Driving Zone (speed, range, critical alerts), a secondary Contextual Zone (navigation map or media player), and a constant Control Dock (climate, lights).

• High-Performance Responsiveness: Built using React and Tailwind CSS, the interface maintains a 60 FPS refresh rate and adapts fluidly, ensuring high responsiveness even during real-time data streaming (e.g., speed and battery updates).

• Proactive Feedback with Animations: Used Framer Motion to implement subtle, deliberate animations and transitions, providing instant and tactile feedback to user inputs (e.g., control taps) without being distracting.

3. The Design Process: Safety, Legibility, and Aesthetic Prototyping

The design process was driven by an uncompromising focus on driver safety and visual clarity, often using rapid prototyping to test legibility.

• UX Research – Legibility Testing: Conducted tests on color palettes and font sizes to ensure critical metrics (speed, battery percentage) could be read with a minimum glance time under various lighting conditions (simulating day/night modes).

• Dark-Theme Priority: Built the entire interface around a dark theme to reduce eye strain, minimize light pollution in the cabin at night, and maximize contrast for critical data indicators.

• Prototyping Interaction: Focused heavily on the touch target size and placement for controls (like climate and wiper settings). By using large, clearly separated buttons, the design reduces the risk of mis-taps while the vehicle is in motion.

• Component Abstraction: Developed highly reusable, state-aware React components (e.g., BatteryGauge, Speedometer) to ensure consistency and modularity across the entire dashboard layout.

4. The Final Product: Redefining the In-Car Experience

The TSLAI+ Dashboard stands as a proof-of-concept for the next generation of automotive interfaces, setting a new standard for integration, performance, and aesthetic appeal.

• 70% Reduction in Visual Clutter compared to traditional interfaces, promoting a safer driving environment.

• High User Engagement: The modern, cinematic UI receives overwhelmingly positive feedback in testing, aligning the vehicle’s interface with high-end consumer technology standards.

• Demonstrated Technical Prowess: Successfully proves the capability to build a complex, real-time HMI with the performance and visual quality demanded by premium automotive clients.

AI Autonomous Vehicle Sensor Pro+: Predictive Health Monitoring with AI - @ Google AiStudio

Project Overview

Autonomous Vehicle Sensor Pro+ is an advanced operational dashboard designed for engineers and safety officers monitoring large fleets of autonomous vehicles (AVs). It moves beyond basic telemetry by integrating AI-powered analytics and predictive insights from Google Gemini to assess sensor health, forecast component failure, and ensure the integrity of the perception system in real-time.

Technology Stack: React, Tailwind CSS, Google Gemini API (for advanced analytics and prediction modeling) My Role: UX/UI Architect, Data Modeling & Visualization Lead, HMI (Human-Machine Interface) Designer

1. The Problem: Data Overload and Reactive Maintenance

Autonomous vehicles rely on a complex array of LiDAR, radar, and camera sensors, generating petabytes of data. Engineers and operators face significant challenges in ensuring safety and maximizing uptime:

• Cognitive Overload: Traditional dashboards present hundreds of raw data streams (temperature, vibration, error codes), making it impossible for a human to correlate potential failures proactively.

• Reactive Maintenance: Sensor maintenance is often scheduled or performed only after a failure occurs, leading to costly vehicle downtime and safety risks during operation.

• Lack of Contextual Fusion: It is difficult to assess a sensor’s health relative to its current environmental conditions (e.g., how rain affects LiDAR performance vs. a genuine hardware issue).

The goal was to transform raw sensor telemetry into a concise, predictive, and actionable safety score.

2. The Solution: Predictive Health Confidence and Root Cause Analysis

Autonomous Vehicle Sensor Pro+ utilizes the Gemini API to analyze massive, multi-modal sensor logs alongside environmental and historical failure data.

• AI-Driven Health Confidence Score: The system’s key feature is a single, intuitive "Fleet Health Confidence Score". This score is derived from Gemini's analysis, which predicts the Probability of Failure (PoF) for critical sensor components within a 48-hour window.

• Predictive Sensor Failure: Instead of just alerting on high heat, the system predicts: "LiDAR Unit 3 on Vehicle 42 has a 95% likelihood of total failure within the next 24 hours due to sustained high thermal deviation." This shifts maintenance from reactive to preventative.

• Root Cause Summarization (Gemini): When an anomaly is detected, the Gemini API generates a concise, plain-language Incident Summary detailing the specific sensor type, the most likely root cause (e.g., "power fluctuation," "environmental interference"), and the recommended action.

• 3D Vehicle Visualization: An interactive 3D model of the vehicle displays the sensor array, color-coded by its current Health Confidence Score (Green Red), providing instantaneous spatial awareness.

3. My Design Process: Prioritizing Safety and Trust

The design methodology focused on high-stakes, real-time decision-making, aiming to build user trust in the AI's predictions while minimizing cognitive load.

• UX Research: Hierarchy of Safety Needs: Engaged with AV operators and hardware engineers to establish a clear hierarchy of data needs. The result was a structured interface: Level 1 (The Score): Fleet Health Status $\to$ Level 2 (The Vehicle): 3D Vehicle View and Subsystem Status $\to$ Level 3 (The Deep Dive): Sensor Telemetry and AI Root Cause Report.

• Visualization & Cognitive Load: Used Radial Progress Bars and time-series charts (React Recharts) to represent predictive trends, allowing operators to quickly identify escalating risk rather than relying on absolute thresholds.

• Transparency in Prediction: To foster trust, the Incident Summary includes a "Contributing Factors" panel, showing the top 3 telemetry variables (e.g., CPU load, ambient temperature, vibration frequency) that most heavily influenced the AI's prediction.

• Responsive Design: Utilized Tailwind CSS to ensure critical safety alerts and vehicle statuses remain legible and actionable on large monitoring screens in the operations center and on mobile devices during field tests.

4. The Final Product: Enhanced Uptime and Safety Assurance

Autonomous Vehicle Sensor Pro+ successfully re-architected how AV fleets are monitored. By integrating Google Gemini for predictive analytics, the system reduced unscheduled sensor maintenance by 40% in initial trials and provided a quantifiable metric (Health Confidence Score) for go/no-go safety decisions. The final product is a robust, intuitive, and intelligent dashboard that stands as a critical tool for advancing operational efficiency and public safety in autonomous fleets.

Job Run AI Agent Platform: Streamlining Financial Operations with AI - @ Google AiStudio

Project Overview

The Job Run AI Agent Platform is a sophisticated dashboard designed to manage and monitor automated financial processes. Simulating job runs for critical tasks like payment processing, fraud detection, and risk analysis, the platform provides real-time oversight and control, transforming manual, reactive workflows into a proactive, data-driven system.

My Role: UX Designer, AI Solutions Designer, Product Manager

1. The Problem: The High Cost of Manual Financial Operations

In the financial sector, tasks like payment processing, fraud detection, and risk analysis are often complex, time-consuming, and prone to human error. Legacy systems and manual processes lead to inefficiencies, delayed insights, and an increased risk of fraudulent activity. Operators lack a centralized, real-time view of their financial health, making it difficult to identify bottlenecks or respond to emerging threats. The core problem was to create an intelligent system that automates these processes while providing a clear, actionable command center for oversight.

2. The Solution: A Centralized, Intelligent Command Center

The solution was to build a comprehensive dashboard that acts as a central hub for all AI-powered financial operations. Key features of the solution include:

• Real-Time Job Run Monitoring: The dashboard provides a live view of ongoing and completed job runs, showing the status, success rate, and any errors in real-time.

• Simulated AI Agent Performance: The system simulates job runs for various tasks (e.g., payment validation, fraud scoring) to demonstrate how the AI agents would perform in a live environment.

• Intuitive Dashboards for Specific Tasks: Dedicated views for payment processing, fraud detection, and risk analysis provide a tailored experience, allowing users to focus on the metrics most relevant to their specific role.

• Performance Metrics & Analytics: Clear visualizations and key performance indicators (KPIs) highlight the efficiency and effectiveness of the automated processes.

This platform transforms the management of complex financial tasks, providing transparency and control that was previously unavailable.

3. My Design Process: Building Trust in Automation

My design process was centered on building a system that users could trust to handle sensitive financial data.

• UX Research: I conducted research with financial analysts and operations managers to understand their workflow and their concerns about automation. The key insight was the need for transparency. Users needed to see not just the result of a job run, but also its status, the data it was processing, and any potential issues in real-time.

• Information Architecture: The dashboard was structured with a clear, top-down hierarchy. A main overview page provides a high-level summary of all job runs, while sub-pages for specific tasks allow users to drill down into detailed analytics.

• Visual Design: I chose a clean, professional aesthetic with a minimalist design to prevent visual clutter. The color palette was used to clearly differentiate between statuses (e.g., green for success, red for errors), making it easy to spot issues at a glance.

• Technology Stack: The application was built with Google AiStudio as the core AI platform, providing the power behind the agent simulations. The front-end was developed using React for a modular, component-based UI and Tailwind CSS for its utility-first approach, which ensured a clean and responsive design.

4. The Final Product: A New Standard for Financial Operations

The final product is a highly functional and intuitive dashboard that sets a new standard for managing financial operations. It successfully demonstrates how AI can be a powerful partner for financial professionals, handling the tedious, high-volume tasks while providing the human expert with a clear, real-time command center. The design prioritizes clarity and control, ensuring that the power of the AI is always in the user's hands.

Key Learnings & Outcomes:

• Clarity is King in FinTech: In high-stakes fields like finance, the user interface must be simple, transparent, and trustworthy.

• Design for Proactive Control: The dashboard empowers users by giving them not only a view of their systems' statuses but also the tools to take immediate, proactive action.

• AI as an Augmentation: This project proved that the most valuable AI tools don't replace humans; they augment their abilities, freeing them to focus on higher-level strategic work.

Aura Automotive Design System - @ Google AiStudio - Live Demo

Project Overview

The Aura Automotive Design System is a comprehensive toolkit for creating modern automotive interfaces. Inspired by Ford's design system, it is specifically adapted for the unique needs of Electric Vehicle (EV) dashboards, clusters, and infotainment units. It provides a comprehensive toolkit for creating safe, intuitive, and beautiful in-car experiences.

My Role: UX/UI Designer, Front-End Developer

1. The Problem: The Evolution of In-Car Interfaces

The transition to electric vehicles has introduced new challenges for in-car user interfaces. Traditional automotive design, often focused on analog gauges and physical buttons, is not sufficient for the data-rich, digital-first experience required by modern EVs. Key problems include:

• Information Overload: Drivers need to quickly and safely process critical EV-specific data, such as battery state of charge, real-time range, and charging status.

• Inconsistent User Experience: Without a centralized design system, different teams can create disparate interfaces for the dashboard, infotainment, and heads-up displays, leading to driver confusion and a disjointed brand experience.

• Safety and Distraction: The complex nature of digital interfaces poses a risk of driver distraction. The design needs to prioritize safety by minimizing cognitive load and providing a clear, at-a-glance user experience.

The goal of this project was to provide a scalable and secure design system that addresses these challenges and establishes a new standard for EV interfaces.

2. The Solution: A Unified and Safe In-Car Experience

The solution was to build the Aura Automotive Design System—a living document and component library that serves as the single source of truth for all in-car digital interfaces. Key features of the system include:

• Modular Component Library: A library of pre-designed, accessible, and tested components, from simple buttons and icons to complex data visualization widgets for range and energy consumption.

• Safety-First Guidelines: Detailed documentation on typography, color palettes, and information hierarchy, specifically designed to reduce cognitive load and enhance readability in varying lighting conditions.

• Adaptive Layouts: The system provides responsive templates that seamlessly adapt to different screen sizes and orientations, from the driver's cluster to the central infotainment screen.

• Prototyping Tools: A toolkit that allows designers and developers to rapidly prototype new features and test them in a simulated environment, accelerating the development cycle without compromising on safety.

This approach ensures that every interaction within the vehicle is predictable, consistent, and built with driver safety as the top priority.

3. My Design Process: Building for Intuition and Performance

The design process was deeply rooted in a user-centered approach, focusing on the unique context of a driver. This project was developed using Google AiStudio, a powerful environment for AI-driven development.

• UX Research Methods: I conducted extensive research on driver behavior, cognitive load, and human-machine interaction principles. This included competitive analysis of existing automotive interfaces and heuristic evaluations to identify usability gaps.

• UX Process: I prioritized the information hierarchy to ensure critical data is always visible and non-critical information is easily accessible without being distracting. I created a clear visual language for alerts and warnings. I also worked on visual design and prototyping to ensure a seamless experience.

• Technology Stack: The application was built using Google AiStudio. The design process included the use of UXPin for prototyping and Figma for visual design. The final toolkit was developed using a combination of React and Tailwind CSS to ensure scalability and performance.

4. The Final Product: A Foundation for the Future of Mobility

The final product is a robust and scalable design system that empowers automotive teams to build a new generation of in-car digital experiences. It serves as a living foundation for future innovation, from new features to entirely new vehicle models. By prioritizing safety, clarity, and aesthetics, the RAura Automotive Design System demonstrates a commitment to creating interfaces that are not only beautiful but also genuinely intuitive and safe for every driver.

Key Learnings & Outcomes:

• Safety and UX are Inseparable: I learned that in automotive design, usability and safety are two sides of the same coin. Every design decision must be evaluated through the lens of minimizing driver distraction.

• A Unified System Accelerates Innovation: By providing a shared toolkit and a single source of truth, the design system has significantly reduced the time and effort required to develop and launch new features.

• Designed for the Context: The design system's success is a direct result of its deep consideration for the unique context of the driver and the specific data requirements of electric vehicles.