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Deep Learning for Autonomous Vehicle Control: Algorithms, State-of-the-Art, and Future Prospects
Sampo Kuutti, Saber Fallah, Richard Bowden, and Phil Barber
www.morganclaypool.com
ISBN: 9781681736075 paperback
ISBN: 9781681736082 ebook
ISBN: 9781681736167 hardcover
DOI 10.2200/S00932ED1V01Y201906AAT008
A Publication in the Morgan & Claypool Publishers series
SYNTHESIS LECTURES ON ADVANCES IN AUTOMOTIVE TECHNOLOGY
Lecture #8
Series Editor: Amir Khajepour, University of Waterloo
Series ISSN
Print 2576-8107 Electronic 2576-8131
Deep Learning for
Autonomous Vehicle Control
Algorithms, State-of-the-Art, and Future Prospects
Sampo Kuutti
University of Surrey, UK
Saber Fallah
University of Surrey, UK
Richard Bowden
University of Surrey, UK
Phil Barber
Jaguar Land Rover
SYNTHESIS LECTURES ON ADVANCES IN AUTOMOTIVE TECHNOLOGY
#8
C
M
&
cLaypoolMorgan publishers
&
ABSTRACT
e next generation of autonomous vehicles will provide major improvements in traffic flow, fuel
efficiency, and vehicle safety. Several challenges currently prevent the deployment of autonomous
vehicles, one aspect of which is robust and adaptable vehicle control. Designing a controller
for autonomous vehicles capable of providing adequate performance in all driving scenarios is
challenging due to the highly complex environment and inability to test the system in the wide
variety of scenarios which it may encounter after deployment. However, deep learning methods
have shown great promise in not only providing excellent performance for complex and non-
linear control problems, but also in generalizing previously learned rules to new scenarios. For
these reasons, the use of deep neural networks for vehicle control has gained significant interest.
In this book, we introduce relevant deep learning techniques, discuss recent algorithms
applied to autonomous vehicle control, identify strengths and limitations of available methods,
discuss research challenges in the field, and provide insights into the future trends in this rapidly
evolving field.
KEYWORDS
artificial intelligence, machine learning, deep learning, neural networks, computer
vision, autonomous vehicles, intelligent transportation systems, advanced driver as-
sistance systems, vehicle control, interpretability, safety validation
vii
Contents
List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix
List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii
1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
2
Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.1 Neural Network Architectures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2 Supervised Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.3 Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.4 Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3
Deep Learning for Vehicle Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
3.1 Autonomous Vehicle Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
3.1.1 Lateral Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
3.1.2 Longitudinal Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.1.3 Full Vehicle Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.2 Research Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.2.1 Computation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.2.2 Network Architectures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.2.3 Goal Specification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.2.4 Generalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.2.5 Verification and Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.2.6 Safety and Interpretability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
4
Safety Validation of Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
4.1 Validation Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
4.1.1 Formal Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
4.1.2 Run-Time Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
viii
4.1.3 Software Safety Cages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
4.1.4 Cross Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
4.1.5 Visualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
4.1.6 Black Box Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
4.2 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
4.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
5
Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
Authors’ Biographies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
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