A context aware and proactive in-vehicle information
system:
Matching infotainment with safety

Luca Contini Akhela
10/10/...
Agenda
•
•
•
•
•
•
•
•
•
•
•

SOTA and current limitations
The Solution
Car Sensors
Software Reference Architecture
Aggreg...
SOTA
Current IVI systems are difficult to navigate: user must dig into too
many levels
SOTA
Information is not filtered or recommended proactively
SOTA
The interface can be distractive and unsafe
The Solution
A minimal, context sensitive and proactive user interface
The Solution
Featuring a warning-based recommendation system
The Solution

To achieve context modelling and thus proactivity goals,
sensors must be used
Car Sensors
Location
sensor

Visual algorithm
dedicated HW

Local and remote
database

Visual
Odometry

External cameras f...
Car Sensors
Location
sensor
OBD Fuel
Level
Visual
Odometry
OBD RPM

Visual
Search

OBD Engine
Temperature

Weather

OBD Sp...
Car Sensors
Location
sensor
OBD Fuel
Level
Visual
Odometry

CONTEXT
DEFINITION

Visual
Search

OBD RPM

PROACTIVITY
Weathe...
Car Sensors
Visual
Odometry
Visual
Search
Weather

PROACTIVITY

Distance to
destination
Cruise Range

CONTEXT
DEFINITION

...
Software Reference Architecture
Visual
Odometry

ADAPTIVE HMI
ENGINE

Visual
Search
Weather

PROACTIVE
Distance to
destina...
Reference Architecture: Aggregator
CONTEXT
ENGINE

Cruise Range
The Aggregator component collects sensor data, aggregates ...
Reference Architecture: Context Engine
PROACTIVE
DECISION ENGINE

m_ContextAction0= "Show Default Panel"

The Contextm_Con...
Reference Architecture: Proactive Decision Engine
The Proactive Decision Engine is composed by separated subengines commun...
Reference Architecture: Proactive Decision Engine
Each sub-engine filters its specific category of facts and creates and
o...
Reference Architecture: Adaptive HMI Engine
Finally, the Adaptive HMi Engine selects the proper modality
Show fuel
warning...
Database
Remote database manages:
•
•
•

Normal Points of Interest
Visual Search
Multimedia information ofr Augmented Real...
Results
The result is an HMI proactively presenting the information to the driver
Results
When a route is not set, the system calculates the cruise range based
on current fuel level and shows it on the ma...
Results
When the fuel level is low, the system recommends the closest gas
stations
Results
3D Bubbles are used for Augmented Reality trip preview
Results
Visual Odometry algorithms are used when the GPS signal gets lost in
urban canyons, keeping the car position on th...
Results
Visual Search algorithms find a visual match on what the camera is
shooting, allowing specific POI information to ...
Results
Augmented reality is used only when the car is stopped
Results
Safety is achieved by reducing the amount of information when
dangerous or not needed
Future Work
•
•
•
•

Improve
Improve
Improve
Improve

Context Models and Rules/Engine
Proactive Decision Rules/Engines
men...
Thank You
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Astute symposium 2013-10-10_smart_automotiveinfotainmentsystem_lucacontini_mirkofalchetto

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Astute symposium 2013-10-10_smart_automotiveinfotainmentsystem_lucacontini_mirkofalchetto

  1. 1. A context aware and proactive in-vehicle information system: Matching infotainment with safety Luca Contini Akhela 10/10/2013
  2. 2. Agenda • • • • • • • • • • • SOTA and current limitations The Solution Car Sensors Software Reference Architecture Aggregator Context Engine Proactive Decision Engine Adaptive HMI Engine Database Results Future Work
  3. 3. SOTA Current IVI systems are difficult to navigate: user must dig into too many levels
  4. 4. SOTA Information is not filtered or recommended proactively
  5. 5. SOTA The interface can be distractive and unsafe
  6. 6. The Solution A minimal, context sensitive and proactive user interface
  7. 7. The Solution Featuring a warning-based recommendation system
  8. 8. The Solution To achieve context modelling and thus proactivity goals, sensors must be used
  9. 9. Car Sensors Location sensor Visual algorithm dedicated HW Local and remote database Visual Odometry External cameras for video processing Weather Visual Search OBD Sensors OBD Fuel Level Proactive HMI OBD RPM OBD Engine Temperature Virtual Sensors Weather Distance to destination Cruise Range OBD Speed
  10. 10. Car Sensors Location sensor OBD Fuel Level Visual Odometry OBD RPM Visual Search OBD Engine Temperature Weather OBD Speed Distance to destination Cruise Range
  11. 11. Car Sensors Location sensor OBD Fuel Level Visual Odometry CONTEXT DEFINITION Visual Search OBD RPM PROACTIVITY Weather OBD Engine Temperature OBD Speed Distance to destination Cruise Range
  12. 12. Car Sensors Visual Odometry Visual Search Weather PROACTIVITY Distance to destination Cruise Range CONTEXT DEFINITION Location sensor OBD Fuel Level OBD RPM OBD Engine Temperature OBD Speed
  13. 13. Software Reference Architecture Visual Odometry ADAPTIVE HMI ENGINE Visual Search Weather PROACTIVE Distance to destination DECISION ENGINE To achieve context definitition and proactivity we use a 4 layer Cruise Range software stack CONTEXT ENGINE Location sensor OBD Fuel Level OBD RPM AGGREGATOR Reference Architecture OBD Engine Temperature OBD Speed
  14. 14. Reference Architecture: Aggregator CONTEXT ENGINE Cruise Range The Aggregator component collects sensor data, aggregates them according to specific rules, and pushes them to the context engine AGGREGATOR Location sensor OBD Fuel Level OBD Speed
  15. 15. Reference Architecture: Context Engine PROACTIVE DECISION ENGINE m_ContextAction0= "Show Default Panel" The Contextm_ContextAction1= "Showthe ontology rules to the sensor Engine applies Parking Warning" m_ContextAction2= "Show Fuel Warning" values and generates lists of facts m_ContextAction3= "Show POI Available Warning" m_ContextAction4= "Show VS Match Warning" Context Facts Ontology Rules CONTEXT ENGINE
  16. 16. Reference Architecture: Proactive Decision Engine The Proactive Decision Engine is composed by separated subengines communicating via task-board Adaptive HMI Engine Proactive Decision Engine Taskboard Navigation Manager POI Manager Panel Manager Warning Manager
  17. 17. Reference Architecture: Proactive Decision Engine Each sub-engine filters its specific category of facts and creates and ordered (priority based) list of facts (actions) to be shown in the HMI Adaptive HMI Engine PDE takes “decisions” between possible solutions Show default panel Show fuel warning Proactive Decision Engine Navigation Manager POI Manager Panel Manager Warning Manager
  18. 18. Reference Architecture: Adaptive HMI Engine Finally, the Adaptive HMi Engine selects the proper modality Show fuel warning Show default panel AHE selects modality Adaptive HMI Engine Show default panel PDE takes “decisions” Show fuel warning Proactive Decision Engine Navigation Manager POI Manager Panel Manager Warning Manager
  19. 19. Database Remote database manages: • • • Normal Points of Interest Visual Search Multimedia information ofr Augmented Reality The database is locally buffered when a specific route is selected, to avoid connection issues during the trip
  20. 20. Results The result is an HMI proactively presenting the information to the driver
  21. 21. Results When a route is not set, the system calculates the cruise range based on current fuel level and shows it on the map
  22. 22. Results When the fuel level is low, the system recommends the closest gas stations
  23. 23. Results 3D Bubbles are used for Augmented Reality trip preview
  24. 24. Results Visual Odometry algorithms are used when the GPS signal gets lost in urban canyons, keeping the car position on the map up to date
  25. 25. Results Visual Search algorithms find a visual match on what the camera is shooting, allowing specific POI information to be delivered when actually facing a meaningful building
  26. 26. Results Augmented reality is used only when the car is stopped
  27. 27. Results Safety is achieved by reducing the amount of information when dangerous or not needed
  28. 28. Future Work • • • • Improve Improve Improve Improve Context Models and Rules/Engine Proactive Decision Rules/Engines mental workload control user state detection
  29. 29. Thank You
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