machine learning for autonomous driving - kth · • mariusz bojarski, et al. "end to end...
TRANSCRIPT
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Machine Learning for Autonomous Driving
Nasser Mohammadiha
Senior Analysis Engineer at Volvo Cars/Zenuity & Adjunct Docent at Chalmers
Stockholm (KTH), 2017-04-03
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Zenuity!
• Develop software for autonomous driving and driver assistance systems
• Autoliv and Volvo Cars will own Zenuity50/50
• Starting with 200 employees from Autoliv and Volvo Cars
• Volvo Cars and Autoliv will license and transfer the intellectual property for their ADAS systems to Zenuity
• Headquartered in Gothenburg with additional operations in Munich, Germany, and Detroit, USA
CEO: Dennis Nobelius
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AD and AI among hottest trends
Gartner's 2016 Hype Cycle for Emerging TechnologiesFörarlöst i
praktiken Ai i allt
Top 10 trends from Nyteknik
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Outline
Autonomous Driving (AD)
ML in the era of AD
• Interest in ML in the IV/ITS community
• Applications
• Some examples
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Drive Me: Self driving cars for sustainable mobility
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Global Trends
• Urbanisation
• Growing mega cities
• Air quality major health problem
• Traffic accident global health issue
• Time for commuting
• Desire for time efficiency
• Desire for constant connectivity
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Today’s reality
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SHAPING THE FUTURE MOBILITY
AD will be important for a sustainable mobility• Improved traffic safety• Improved environmental outcomes• Regain time
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Volvo vision 2020
No one should be killed or seriously injured
in a new Volvo car by 2020
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It was always about freedom
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And it’s still about freedom
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joint effort and proper reseach
Global challenges – Demand a joint effort
Drive Me – Nordic model of collaboration
Research platform – How autonomous cars can contribute to a sustainable
development
Pilots with real customers in real traffic
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The pilots – all about learning
• Traffic environments
• Customer preferences
• Exporting the Nordic model
of collaboration
Gothenburg – proof of concept
China and London – verify our technology
Gothenburg
2013London
2017China
2017
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well-defined commute highways
• Customer focus
• Simplification
• Risk Management
A B
F r u s t r a t i n g c o m m u t eN e i g h b o r h o o d C l o s e t o w o r k
Your neighborhood, children,
no lane markings, roundabouts, ...Traffic lights, pedestrians, bicyclists,
...
Well defined use case on city highways
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Pilot Assist versus Autopilot
• Driver is responsible, should monitor and supervise
• Driver responsible to intervene whenever needed
• Limitations: Lane markings, road design, oncoming objects,
pedestrians, animals, restrictions in steering/braking/acceleration
force that can be applied
Autopilot / UnSupervisedPilot assist / Supervised
• Manufacturer responsible
• Tested on and expects extreme situations
• Takes precautions, takes decisions
• Driver free to do something else
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Unsupervised
AD (Level 4)
Supervised
AD (Level 1&2)
Manual
(Level 0)
AD
Car
Safety Benefits
Injury ReductionCrash Avoidance
SAFETY Impact
Low-risk
driving
Risky driving
behaivours
& situations
Conflict/
Net-CrashCrash After crash
Precautionary Safety
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We assume liability
“Volvo will assume liability for its
autonomous technology, when used properly.”
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TECHNOLOGY
• Camera
• Radar
• Laser
• Ultrasonic
• Map data
• Cloud connection
• Traffic Control Centre
“Machine learning for sensor signal processing is in the core!”
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redundancy
ActionDecisionPerception
Sensor
Fusion 1
Sensor
Fusion 2
Decision &
Control 1
Decision &
Control 2
Vehicle
Dynamics
Management 1
Vehicle
Dynamics
Management 1
Brake
Control 2
Brake
Control 1
Steering
Control 1
Steering
Control 2
Vision
Radar
Lidar
Ultrasonic
Brake
Brake
Brake
Brake
Power steering
Power steeringCloud
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Interest in ML, related to AD and ITS
0
10
20
30
40
50
60
70
80
90
100
Records in Google Scholar ITSC 2016 (out of 430 papers)
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Improving the Results over THE Years
• Object detection rate in
KITTI
• Moderate: Min. bounding
box height: 25 Px, Max.
occlusion level: Partly
occluded, Max.
truncation: 30 %
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Grouping the ML Methods
Where is it trained?
• Locally
• In the cloud
When is it trained?
• Offline
• Online
How is it trained?
• Supervised
• Un-supervised (semi-supervised)
Where is it executed?
• On-board
• Off-board (e.g., in the cloud)
When is it executed?
• Real-time
• Offline (or batch processing)
Continuous learning?
• Yes
• No
Application
• System design
• Verification
Training Deployment
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Modular systems vs Holistic Design
- Modular systems and ML to design individual components
- Holistic and end to end learning for driving
Sensor data Vehicle control
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Modular systems
(1) Well-defined task-specific modules (2) Could have parallel modules
Examples:
• Semantic segmentation and free space and drivable area detection
• Object detection and tracking and information (speed, heading, type, ...)
• Road information and geometry of the routes
• Sensor fusion
• Scene Semantics such as traffic and signs, turn indicators, on-road markings etc
• Maps and updating them over time
• Positioning and localization
• Path planning
• Driving policy learning and decision making
• Other road user behavior analysis such as intention prediction
• Driver monitoring
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Holistic and end to end learning
• First implemented around 28 years ago in the
ALVINN system
• Number of parameters<50,000
• Dave-2
• 9 layers (5 convolutional and 3 fully
connected)
• 250 000 parameters
• Dean A. Pomerleau, ”ALVINN, an autonomous land vehicle in a neural network”., No. AIP-77, CMU, 1989.
• Mariusz Bojarski, et al. "End to end learning for self-driving cars“, arXiv, Apr. 2016.
• Christopher Innocenti, Henrik Lindén, “Deep Learning for Autonomous Driving: A Holistic Approach for Decision Making”, thesis.
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AD Verification
• Requirements: setting safety scope
for function and AD test scenarios
• Test methods• Test track
• Test in real traffic and expeditions
• Virtual testing and simulations
• Analyzing logged data• Need to have tools such as reference system
• Need to have advanced analysis methods
• Nidhi Kalra et al. "Driving to safety: How many miles of driving would it take to demonstrate autonomous vehicle reliability?." Transportation
Research Part A: Policy and Practice 94 (2016): 182-193.
[Kalra, 2016]
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Some Examples
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Object Detection
(1) Accuracy (2) Speed
• Ross Girshick et al., “Rich feature hierarchies for accurate object detection and semantic segmentation”, CVPR, 2014.
• Shaoqing Ren et al., “Faster R-CNN: Towards real-time object detection with region proposal networks”, NIPS, 2015.
• Jifeng Dai et al., “R-FCN: Object Detection via Region-based Fully Convolutional Networks”, arXiv, Jun., 2016.
• Donal Scanlan, Lucia Diego, “Robust vehicle detection using convolutional networks“, Master’s thesis, ongoing.
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Semantic Segmentation
• Jonathan Long et al., “Fully Convolutional Networks for Semantic Segmentation”, CVPR 2015.
• Jifeng Dai et al., “Instance-aware Semantic Segmentation via Multi-task Network Cascades”, arXiv, 2015
• Vijay Badrinarayanan et al., “SegNet: A Deep Convolutional Encoder-Decoder Architecture for Scene Segmentation”, PAMI 2017
FCN
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Road Information
• Christian Lipski et al. "A fast and robust approach to lane marking detection and lane tracking“, SSIAI, 2008.
• Florian Janda et al., "A road edge detection approach for marked and unmarked lanes based on video and radar“, FUSION, 2013.
• Jihun Kim et al., “Robust lane detection based on convolutional neural network and random sample consensus”, ICONIP, 2014.
• Bei He et al., “Accurate and Robust Lane Detection based on Dual-View Convolutional Neutral Network”, IV, 2016.
• Gabriel L. Oliveira et al., "Efficient deep models for monocular road segmentation“, IROS, 2016.
[He 2016]
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Traffic Sign Recognition
German Traffic Sign Recognition Benchmark (GTSRB) [Stallkamp 2012]
• Johannes Stallkamp et al. "Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition“, Neural networks, 2012.
• Pierre Sermanet et al., "Traffic sign recognition with multi-scale convolutional networks“, IJCNN, 2011.
• Konstantinos Mitritsakis, “Study Real World Traffic Environment Using Street View”, Master’s thesis, 2016.
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Interacting with Humans
• Eshed Ohn-Bar et al., “Looking at Humans in the Age of Self-Driving and Highly Automated Vehicles”, Trans. on IV, 2015
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Driver monitoring
• Kevan Yuen et al. ,”Looking at Faces in a Vehicle: A Deep CNN Based Approach and Evaluation”, ITSC, 2016.
• Akshay R. Siddharth et al., “Driver Hand Localization and Grasp Analysis: A Vision-based Real-time Approach”, ITSC, 2016.
• Tianchi Liu et al. "Driver distraction detection using semi-supervised machine learning." IEEE Transactions on ITS, 2016.
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Road Friction Estimation from Connected Vehicles Data
• Prediction using both
historical friction data from the
connected cars and data from
weather stations
• Busy roads
• Missing data
• Ghazaleh Panahandeh, Erik Ek, Nasser Mohammadiha, “Supervised Road Friction Prediction from Fleet of Car Data”, IV 2017.
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Driver Route and Destination Prediction
• History of driving for
individuals
• Use of metadata such as
driver id, the number of
passengers, time-of-day, day-
of-week
• Destination clustering
• Ghazaleh Panahandeh, “Driver Route and Destination Prediction”, IV 2017
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Compressed networks
• Pruning or quantizing weights
• Devising new and smaller
architectures
• Forrest N. Iandola et al. "SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size“, arXiv, Nov. 2016
• Michael Treml et al., “Speeding up Semantic Segmentation for Autonomous Driving”, NIPS Workshop, 2016
• Adam Paszke et al. "ENet: A deep neural network architecture for real-time semantic segmentation “, arXiv, Jun. 2016
• Song Han et al., “Deep compression: Compressing deep neural network with pruning, trained quantization and huffman coding” arXiv , 2015.
• Alireza Aghasi et al., “Net-Trim: A Layer-wise Convex Pruning of Deep Neural Networks”, arXiv, Nov. 2016.
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And many more ...
• Deep network understanding and interpretation
• Secure and privacy-preserving deep learning
• Transfer learning
• Pedestrian intention estimation
• Applications of generative adversarial networks
• Learning to attend
• Instance segmentation
• Object tracking
• Harsh weather conditions
• Traffic understanding such as brake light detection
• ...
• Benjamin Völz et al. “A data-driven approach for pedestrian intention estimation”, ITSC, 2016
• Arna Ghosh et al., “SAD-GAN: Synthetic Autonomous Driving using Generative Adversarial Networks”, arXiv, Nov. 2016
• Volodymyr Mnih et al. "Recurrent models of visual attention." NIPS, 2014.
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Classification of 3D Point Clouds
• Patrik Nygren, Michael Jasinski, “A Comparative Study of Segmentation and Classification Methods for 3D Point Clouds”, Master’s thesis, 2016.
• Axel Bender, Elías Marel Þorsteinsson, “Object Classification using 3D Convolutional Neural Networks“, Master’s thesis, 2016.
• N. Mohammadiha, P Nygren, M. Jasinski, “A Comparison of Classification Methods for 3D Point Clouds”, Fast-zero 2017.
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Ground truth for positioning
• High-accuracy
positioning in GPS-
denied environments
• Scalability to new
locations
• David Bennehag, Yanuar Nugraha, “Global Positioning inside Tunnels Using Camera Pose Estimation and Point Clouds”,
Master’s thesis, 2016.
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Sensor comparison framework
Data from
Sensor 2
Post-
processingMatching
Analysis
methods
Results
Data from
Sensor 1
• J. Florbäck, L. Tornberg, N. Mohammadiha, “Offline Object Matching and Evaluation Process for Verification of Autonomous
Driving”, ITSC, 2016.
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Wrap-up
• High expectations from ML to overcome some of the biggest
challenges in autonomous driving
• Successful applications of ML especially for perception already
being used in the industry
• New challenges arise in designing complete ML systems
(integration, updating, safety, interpretation...)
• Wide range of applications for ML from raw sensor data
processing to developing offline methods for verification purposes
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Opportunities
• Industrial PhD position on “Reinforcement Learning for Autonomous Driving” at Zenuity/Chalmers
• Postdoc position on “Big Sensor Data Analysis for Verification of Autonomous Driving” at Zenuity/Chalmers
• Research Engineer position on “Big Sensor Data Analysis for Verification of Autonomous Driving” at Zenuity/Chalmers
Contact me: [email protected]
More positions on:
• http://career.zenuity.com/
• http://www.volvocars.com/intl/about/our-company/careers