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

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Content In-Vehicle

Future of In-Vehicle Recommender Systems

Example Application: Convenience Charging

Conclusions

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Future of Mobility

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Personalized Automated

Connected Electrified

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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

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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

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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

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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

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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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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

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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/

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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

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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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THANK YOU

Juergen.Luettin@de.bosch.comwww.bosch.com/research