Fleet Management Dashboard for Autonomous Cabs
Designing a simulated fleet management system for autonomous taxis as part of a Google apprenticeship project.
Overview
🗓 Date: Fall 2024
💼 Client: Google (Apprenticeship Project)
👩💻 Role: UX Designer & Frontend Developer
🔒 Due to NDA, I’m not allowed to reveal complete design processes and deliverables. All data is redacted and used for portfolio-building purposes only.
Background
As part of an apprenticeship project assigned by Google, my team and I designed a simulated fleet management dashboard for autonomous taxis. This was not a real-world application but rather a conceptual exploration to evaluate how fleet operators might manage autonomous vehicles at scale. The dashboard provides an interactive interface where operators can monitor vehicle statuses, assign rides dynamically, track real-time traffic conditions, and manage system diagnostics.
The project also included simulating vehicle routes and real-life traffic conditions using Google’s Maps API and predictive modeling, allowing us to explore how ride assignments and routing could be optimized for efficiency in a future autonomous taxi service.
User Problems
Managing a fleet of autonomous taxis requires real-time visibility into vehicle status, ride requests, traffic conditions, and system diagnostics. However:
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Operators lacked an intuitive way to track active and idle vehicles across different zones.
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Ride dispatching was inefficient, as it did not account for real-time traffic conditions.
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System monitoring required multiple disconnected tools, making vehicle maintenance and diagnostics harder to manage.
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Optimizing vehicle routes dynamically based on demand, road congestion, and fleet distribution was a key challenge.
Design Challenges
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Creating a scalable, real-time UI that simulates fleet monitoring without overwhelming users.
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Designing for a system that doesn’t exist yet, requiring speculative research and user flow modeling.
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Incorporating Google’s Maps API to accurately reflect traffic conditions and ride assignments.
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Simulating vehicle movement and operator interactions to validate dashboard usability.
Mapping out Critical User Journey
To uncover pain points, I conducted interviews with fleet operators and ride-sharing system analysts to understand their daily workflows. The key user journeys identified were:
Building the Minimum Viable Product (MVP)
Since this was a proof-of-concept project, we focused on delivering a simplified, high-impact version of the dashboard:
Dashboard Design & Features
Fleet Overview Panel
📍 Displays real-time vehicle status (Active, Idle, Needs Service) using color-coded indicators.
📊 Provides key performance metrics like total completed trips, average wait times, and fleet efficiency.
Live GPS Tracking & Ride Assignment
🗺 Google Maps API integration for real-time vehicle tracking and route optimization.
⚡ Auto-dispatch system assigns vehicles to passengers based on proximity and estimated arrival time.
Vehicle Health & Diagnostics
🔋 Battery levels, sensor health, and maintenance logs available at a glance.
🚨 Instant alerts for urgent maintenance issues or vehicle malfunctions.
Traffic Simulation & Route Optimization
🚦 Uses historical and real-time traffic data to optimize routes.
🛣 Dynamic rerouting capabilities adjust based on congestion and estimated trip durations.
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Validating the Design in a Simulated Environment
Since this project was fully simulated, we tested the dashboard by:
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Running usability tests with fleet management professionals, gathering feedback on information clarity and usability.
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Tracking system performance based on simulated ride data and efficiency metrics.
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Iterating based on real-world fleet management challenges identified in research.
Takeaways & Learnings
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Balancing Complexity & Simplicity: Managing an autonomous fleet involves massive data streams, requiring a highly intuitive UI to prevent cognitive overload.
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Real-Time Traffic & Routing Are Key: Google Maps API and live traffic data integration significantly improved ride assignment efficiency in the simulation.
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Speculative Design for Future Systems: Since fully autonomous ride-sharing doesn’t yet exist at scale, designing for an emerging industry required creative problem-solving.
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The Power of Simulation Testing: Even without a real-world deployment, we were able to validate UX decisions through AI-driven vehicle simulations.