AI & Mobility Challenges & Opportunities for the Automotive Industry Jean-Marc David Expert Leader Artificial Intelligence Groupe Renault
SophIA Conference - 8th November 2018
SOPHIA CONFERENCE
8TH NOVEMBER 2018
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AGENDA
01 02 03 04 SOPHIA CONFERENCE
AI at Renault: a global picture
Delivering robust & safe autonomous vehicles
Mobility services & robo-taxis
Conclusion; Q/A
8TH NOVEMBER 2018
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01 AI AT RENAULT: A GLOBAL PICTURE
The AI lanscape today (simplified)
Chatbots for customer relationship
Chatbots for user support
Optimized design
Interaction with other road users
Virtual Personal Assistant
HMI for occupants
Driver & occupant behavior Monitoring
Planning and sheduling
Mobility service operations Usage-based design
Decision making & navigation
Quality data analysis
AI is everywhere !
Predictive models
Perception
Personalized services (e.g. predictive maintenance)
Computer vision for intelligent manufacturing
Social media analysis
Off-board analysis of recorded data
Mobility needs analysis & demand prediction
Advanced Knowldedge & Content Management
R&D SOPHIA CONFERENCE
Manufacturing & Supply Chain
Sales & Marketing 8TH NOVEMBER 2018
Virtual simulation & (re)validation
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Connected Services & Mobility Services
Autonomous Vehicles 3
01 AI AT RENAULT: A GLOBAL PICTURE
With 2 main drivers: AV & big data
Chatbots for customer relationship
Chatbots for user support
Optimized design
Interaction with other road users
Virtual Personal Assistant
HMI for occupants
Driver & occupant behavior Monitoring
Planning and sheduling
Mobility service operations Usage-based design
Decision making & navigation
Quality data analysis Perception Predictive models
Personalized services (e.g. predictive maintenance)
Big Data Computer vision for intelligent manufacturing
Social media analysis
Off-board analysis of recorded data
Mobility needs analysis & demand prediction
Advanced Knowldedge & Content Management
R&D SOPHIA CONFERENCE
Manufacturing & Supply Chain
Sales & Marketing 8TH NOVEMBER 2018
Virtual simulation & (re)validation
‌
Autonomous Vehicles
Connected Services & Mobility Services 4
02 AUTONOMOUS VEHICLES
Why is it very difficult Non-critical applications
Critical applications
Autonomous systems
e.g. Go game
Autonomous Vehicles
Decision-support systems
e.g. customer analysis
e.g. medical diagnosis
• Technology challenge: robustness is more important than performance - and has to be assessed (resilient to condition disturbances, that cannot be fooled…)
• Business challenge: value / cost
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02 AUTONOMOUS VEHICLES
Why and where is AI required for Autonomous Vehicles ? Interaction with other road users Interaction with occupants
Interaction
Driver & occupant monitoring
Decision making & navigation
Embedded intelligence Perception
Off-board analysis of recorded data
Off-board analysis and simulation
Virtual simulation & (re)validation SOPHIA CONFERENCE
8TH NOVEMBER 2018
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02 AUTONOMOUS VEHICLES
Perception
Interaction with other road users
• Object recognition (massive use of deep learning)
Interaction with occupants Driver & occupant monitoring
• Fusion between different types of sensors (e.g. fusion of a camera, in red, with a LIDAR, in blue)
Decision making & navigation
• Situation understanding
Perception
Off-board analysis of recorded data Virtual simulation & (re)validation
SOPHIA CONFERENCE
can we ‘understand’ all situations with deep learning only ? 8TH NOVEMBER 2018
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02 AUTONOMOUS VEHICLES
Decision Making
Interaction with other road users Interaction with occupants Driver & occupant monitoring
Decision making & navigation Perception
Off-board analysis of recorded data
• The most critical part to ensure robustness and safety of AD • Various AI techniques - in combination with other methods • Bayesian methods / POMDP • Rule-based systems • Constraint satisfaction techniques • End-to-end learning (?) • …
• Solutions must be ethical, trustable and explainable
Virtual simulation & (re)validation
SOPHIA CONFERENCE
8TH NOVEMBER 2018
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02 AUTONOMOUS VEHICLES
Interaction with occupants; driver & occupant monitoring
Interaction with other road users Interaction with occupants Driver & occupant monitoring
Decision making & navigation Perception
• Cognitive attention assessment before take over • Behavior monitoring to ensure safety of people & goods in driverless-vehicles
Off-board analysis of recorded data Virtual simulation & (re)validation
SOPHIA CONFERENCE
• Explanation of vehicle perception & decision to ensure trust 8TH NOVEMBER 2018
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02 AUTONOMOUS VEHICLES
Interaction with other road users
Interaction with other road users Interaction with occupants Driver & occupant monitoring
Decision making & navigation Perception
Off-board analysis of recorded data
• Road users (vehicles, pedestrian…) are constantly interacting and negociating at a very local level • E.g. lane insertion / changing, intersection crossing… • Interaction and negociation require modelling the alter perception of the situation, predicting intents and behaviors…
Virtual simulation & (re)validation
SOPHIA CONFERENCE
8TH NOVEMBER 2018
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02 AUTONOMOUS VEHICLES
Virtual simulation & (re)validation
Interaction with other road users Interaction with occupants Driver & occupant monitoring
Decision making & navigation Perception
Off-board analysis of recorded data
• Physical validation (& revalidation) with millions of km is not an option • Virtual simulation requires realistic models of sensors, driving & traffic simulation
Virtual simulation & (re)validation
SOPHIA CONFERENCE
8TH NOVEMBER 2018
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02 AUTONOMOUS VEHICLES
Off-board analysis of recorded data
Interaction with other road users Interaction with occupants Driver & occupant monitoring
Decision making & navigation Perception
Off-board analysis of recorded data
• Automatic analysis & labelling of data (cost & quality of labelling) • Situation clustering & scenario building from real data
Virtual simulation & (re)validation
SOPHIA CONFERENCE
8TH NOVEMBER 2018
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03 MOBILITY SERVICES & ROBO-TAXIS
Robo-taxis: why is it different from AD
• A shift from car ownership to MAAS (Mobility as-a-service)
• A new business for OEM: operating mobility services • Driverless cars • A different business model enabling more embedded technology • Localized services: cooperation with territories, public transport operators & the infrastructure
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03 MOBILITY SERVICES & ROBO-TAXIS
FOT
Several on-going Field Operation Tests to experiment Ride Hailing with AD level 4
SOPHIA CONFERENCE
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03 MOBILITY SERVICES & ROBO-TAXIS
Collaboration with the infrastructure
Intelligent infrastructure for robo-taxi FOT (Rouen & Saclay)
Roundabout in Rambouillet equipped with cameras to ease AV decision making (TORNADO FUI project)
SOPHIA CONFERENCE
8TH NOVEMBER 2018
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03 MOBILITY SERVICES & ROBO-TAXIS
And new products
EZ-ULTIMO EZ-PRO
EZ-GO
2018 Paris Motor Show the trilogy of shared mobility according to Groupe Renault
SOPHIA CONFERENCE
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03 MOBILITY SERVICES & ROBO-TAXIS
AI & New Mobility platforms – the next step
• Providing a global multimodal mobility service • Will become the unique contact point with mobility customers • A system of systems, integrating various services & data sources • With ‘AI inside’: • mobility need forecasts • planning & dynamic replanning • understanding user preferences • ...
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04 CONCLUSION
Final remarks The Automotive industry will be more disrupted in the next 5-10 years than it has been in the last 100 years…
And AI is raising hype (again)…
Some conditions for a successful deploiement of AI ▪ We need to explain opportunities and limitations – and build competencies ▪ We need to develop a ‘trustable’ AI (explainable, transparent, robust…) that probably requires to combine deep learning with other kinds of reasonning
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Editor-in-Chief: Prof. Frank Kirchner, DFKI / University of Bremen Associate Editors: Prof. Andreas Butz, LMU Dr. Jean-Marc David, Renault Group Prof. Malik Ghallab, LAAS-CNRS
Prof. Randy G. Goebel, University of Alberta Prof. Donia Scott, University of Sussex Prof. Oliviero Stock, FBK/ICT IRST Prof. Paolo Traverso, FBK/ICT IRST Aims and Scope
A new fully open access journal from Springer Nature aiperspectives.springeropen.com
• AI perspectives will cover the application of AI in industry, healthcare, transport, education, social sciences and humanities and business and economics. • Strict high-level selection process to ensure an excellent publication quality. • Publication of innovative applications of artificial intelligence with focus on: o An in-depth description how basic research enabled the application o How applied research triggers new questions for basic research o How integration of various AI Methods in application can be achieved • Interaction of data driven vs. model driven AI • System oriented and integrated research • Focus on responsibility/ethics of AI, explainability and transparency