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:
Operators lacked an intuitive way to track active and idle vehicles across different zones.
Ride dispatching was inefficient, as it did not account for real-time traffic conditions.
System monitoring required multiple disconnected tools, making vehicle maintenance and diagnostics harder to manage.
Optimizing vehicle routes dynamically based on demand, road congestion, and fleet distribution was a key challenge.
Design Challenges
Creating a scalable, real-time UI that simulates fleet monitoring without overwhelming users.
Designing for a system that doesn’t exist yet, requiring speculative research and user flow modeling.
Incorporating Google’s Maps API to accurately reflect traffic conditions and ride assignments.
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:
Running usability tests with fleet management professionals, gathering feedback on information clarity and usability.
Tracking system performance based on simulated ride data and efficiency metrics.
Iterating based on real-world fleet management challenges identified in research.
Takeaways & Learnings
Balancing Complexity & Simplicity: Managing an autonomous fleet involves massive data streams, requiring a highly intuitive UI to prevent cognitive overload.
Real-Time Traffic & Routing Are Key: Google Maps API and live traffic data integration significantly improved ride assignment efficiency in the simulation.
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.
The Power of Simulation Testing: Even without a real-world deployment, we were able to validate UX decisions through AI-driven vehicle simulations.