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FUTURE OF IN-VEHICLE RECOMMENDATION SYSTEMS @ BOSCH
13th ACM Conference on Recommender Systems (RecSys), 16th September 2019
Juergen Luettin, Susanne Rothermel, Mark AndrewRobert Bosch GmbH, Corporate Research, Germany
CR/AEX3 | 2019-09-02© Robert Bosch GmbH 2019. All rights reserved, also regarding any disposal, exploitation, reproduction, editing, distribution, as well as in the event of applications for industrial property rights.
Content In-Vehicle
Future of In-Vehicle Recommender Systems
Example Application: Convenience Charging
Conclusions
CR/AEX3 | 2019-09-02© Robert Bosch GmbH 2019. All rights reserved, also regarding any disposal, exploitation, reproduction, editing, distribution, as well as in the event of applications for industrial property rights.
Future of Mobility
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Personalized Automated
Connected Electrified
CR/AEX3 | 2019-09-02© Robert Bosch GmbH 2019. All rights reserved, also regarding any disposal, exploitation, reproduction, editing, distribution, as well as in the event of applications for industrial property rights.
Examples of In-Vehicle Recommendation Applications
Location based services POIs Fueling, Charging, Parking Social network services
Vehicle control Seat, mirror, HVAC,
windows, ambient light Driver assistance
Navigation Routing Shared mobility Tourism
Smart Home Heating Alarm Kitchen
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Infotainment Music Communication Information
Vehicle Maintenance Automatic emergency call Predictive Diagnostics Roadside Assistance
Multi-task recommendations need triggering, prioritization and orchestration
CR/AEX3 | 2019-09-02© Robert Bosch GmbH 2019. All rights reserved, also regarding any disposal, exploitation, reproduction, editing, distribution, as well as in the event of applications for industrial property rights.
Context Aware RecommendationDriver and Occupant Preferences, needs, calendar, to-do list Trip purpose, history, temporal information Physical, personality, emotion, mood
Vehicle Context Interior: internal sensors, devices, infotainment Driving: engine, battery, steering, braking External sensors: video, radar, lidar, ultrasonic
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Driver Monitoring
Occupant Monitoring
Passenger and vehicle context plays a crucial role for in-vehicle recommender systems
CR/AEX3 | 2019-09-02© Robert Bosch GmbH 2019. All rights reserved, also regarding any disposal, exploitation, reproduction, editing, distribution, as well as in the event of applications for industrial property rights.
Connected Recommendation
Connected Buildings Smart home Smart city
Location based POIs Services Weather
Connected vehicles
Location Based Social Networks
ActivityPOI
User
Time
Location
UserContent Tips Rating Photos Videos
Heterogeneous, time and location dependent information provides opportunities and challenges
CR/AEX3 | 2019-09-02© Robert Bosch GmbH 2019. All rights reserved, also regarding any disposal, exploitation, reproduction, editing, distribution, as well as in the event of applications for industrial property rights.
Human Machine InterfaceMulti-Modal input and output
Speech, gesture, touch, graphic, haptic
Non-distractive
Short interaction
Interplay with other devices
Explainable
Prioritization
From recommendations to decisions
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The cockpit of the near future thinks ahead and prioritizes information in real time.
Choosing the right interaction modality based on application and context
CR/AEX3 | 2019-09-02© Robert Bosch GmbH 2019. All rights reserved, also regarding any disposal, exploitation, reproduction, editing, distribution, as well as in the event of applications for industrial property rights.
Privacy & Security
User defined privacy settings
Access control to vehicle and data
Data encryption
Local vs cloud storage
Secure multi-party computation
Smart contracts between vehicle and service
e.g. using distributed ledger technologies
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CR/AEX3 | 2019-09-02© Robert Bosch GmbH 2019. All rights reserved, also regarding any disposal, exploitation, reproduction, editing, distribution, as well as in the event of applications for industrial property rights.
Recommendation in Automated Driving
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Bosch has developed all technologies required for fully automated driving.
Driver assistance
Fully Automated
HighlyAutomated
ConditionallyAutomated
PartiallyAutomated
Dependent on Automation level Driver’s involvement in driving Driver’s available time Driver’s alertness, distraction and take-over requirements
Who is driving?you must drive when requested
CR/AEX3 | 2019-09-02© Robert Bosch GmbH 2019. All rights reserved, also regarding any disposal, exploitation, reproduction, editing, distribution, as well as in the event of applications for industrial property rights.
Our Vision
Future of In-Vehicle Recommendation Systems @ Bosch
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The ultimate in-vehicle recommender systemunderstands me, my preferences and needs,
knows about my context and environmentoptimally assists me
to give me a personalized and unique experience that I can trust
before, during and after my trip.
EXAMPLE APPLICATION: CONVENIENCE CHARGING
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Convenience Charging: “Turns charging into a great experience”
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Goal: Routing with combined charging station and location based service recommendation
Features: Ahead trip planning on smartphone Synchronization between smartphone and
vehicle Accurate range prediction of electric vehicle Dynamic adaptation to changing situation Location based service recommendations Access management to charging stations
Video link: https://www.bosch-mobility-solutions.com/en/products-and-services/mobility-services/connected-charging-solutions-for-electromobility/convenience-charging/
CR/AEX3 | 2019-09-02© Robert Bosch GmbH 2019. All rights reserved, also regarding any disposal, exploitation, reproduction, editing, distribution, as well as in the event of applications for industrial property rights.
Convenience Charging
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Route planning Location-based servicesRange projectionAccess management
1 3 42
Customer tailored vehicle head unit
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Architecture
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Cognitive Services
Vehicle/PassengerContext
Observations
World context &knowledgeCognitive Architecture
…
Knowledge Graph
Serviceoffers
HMI Privacy Dashboard
Context Manager
Machine Learning
Question Answering
Reasoning Recommending
Convenience Charging
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Meta-Routing
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Multi-Criteria Preference
Ranking
Entered or predicted
destination
Range Prediction
do
S21
S11
S12
S22
S23
S24
Ao11
A1121
Sm1
Sm2
Sm3
Sm4
…
…
…
…
Am1d
Am2d
Am3d
Am4d
origin destination
Basic Routing
Location based Services
Vehicle & Passenger Context
Passenger Preferences
Directed graph with meta- information
CR/AEX3 | 2019-09-02© Robert Bosch GmbH 2019. All rights reserved, also regarding any disposal, exploitation, reproduction, editing, distribution, as well as in the event of applications for industrial property rights.
Conclusions
High complexity of In-Vehicle recommender systems due to rich context, challenging user
interaction, challenging driving situation and integration with other applications
High potential in combining of data-driven and knowledge driven methods
Privacy and Security becomes more important as vehicle will become the 3rd living space
Convenience Charging: first application with rich context dependent location based
recommendations
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