Use case 2: Fuel management for fleet efficiency

Optimize fuel consumption by analyzing idle times and driving behaviour in order to lower operating costs and reduce the CO2 emissions of the fleet.

Simulation flow

Simulated vehicles report idle times, speeds, fuel consumption
Cloud aggregates and identifies unnecessary idling based on:
- Context (e.g., traffic vs. parked)
- Environment (cold vs. warm start)
Driver receives notification:
“Your idle time is 23% above average, costing you ~€15/week in fuel and 11kg CO₂. Would you like to enable EcoStart mode?”
Fleet manager sees heatmaps of idling across cities, identifies hotspot areas for rerouting or coaching.

Component	Description
In-Vehicle ECUs	Last state telemetry, idling duration, GPS location, speed, gear status. Basic reasoning
Customer Devices (Mobile App)	Visualizes personal fuel efficiency and receives feedback/coaching
Cloud/Backend Infrastructure	Data persistence (time series, driver profile, vehicle data,..), advanced reasoning
Cross-Domain Connections	V2C (Vehicle to Cloud), Device2V (Driver App gets live trip feedback), Device2C (Cloud alerts on trend detection)

Source	Data
Vehicle Sensors	Engine status, RPM, GPS, idle time, speed, fuel flow
Driver Data	Unique driver ID, preferences (e.g., eco-mode), driving style
External Sources	Traffic congestion zones (e.g., idling at red lights), weather (cold starts)

Component	Role
VSS (Vehicle Signal Specification)	Standardizes all signals: `Vehicle.Powertrain.CombustionEngine.IdleDuration`, `Vehicle.CurrentLocation`, etc.
User Profile Abstraction	Abstracts driver IDs with linked behavior history
Bidirectional Data Sync
Unified Access API (VISS/Info API)	VISS on-board vehicles, cloud middleware can be VSS compliant.
Time-Series Storage	Fuel and idling logs stored in time-series DB
Schema Generation	VSS-based schema used to define cloud DB schema

Component	Function
Semantic Rules
ML Models
Symbolic AI
Real-time Knowledge Conversion
AI Agents

Component

Role

Driver App

Shows fuel-efficiency score, idling history, behavior improvement suggestions

Fleet Dashboard

Aggregates vehicle-specific and driver-specific fuel reports

Decision Support

System recommends:

Area	Application
Vendors	Combines hardware (OEM ECUs), mobile apps, cloud DB, AI toolkits
Security/Privacy	Role-based data access, driver-anonymous behavioral tracking
Scalability
Diagnostics
Extensibility	Modular: can integrate new sensors or driving behavior types
Interoperability	Unified VSS-based APIs
Multi-Cloud / Edge Support	Pre-processing at edge for live feedback; cloud for batch learning
Efficient Pipelines
Industry Alignment