Series ISSN: 1947-945X
Deep Learning
for Autonomous
Vehicle Control
Algorithms, State-of-the-Art,
and Future Prospects
Sampo Kuutti
Saber Fallah
Richard Bowden
Phil Barber
Series Editor: Amir Khajepour, University of Waterloo
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
e next generation of autonomous vehicles will provide major improvements in trac ow, fuel
eciency, 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 signicant 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 eld, and provide insights into the future trends in this rapidly
evolving eld.
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About SYNTHESIS
This volume is a printed version of a work that appears in the Synthesis
Digital Library of Engineering and Computer Science. Synthesis
books provide concise, original presentations of important research and
development topics, published quickly, in digital and print formats.
KUUTTI • FALLAH • BOWDEN • BARBER DEEP LEARNING FOR AUTONOMOUS VEHICLE CONTROL MORGAN & CLAYPOOL
Synthesis Lectures on
Advances in Automotive Technology
Synthesis Lectures on
Advances in Automotive Technology
Series Editor: Amir Khajepour, University of Waterloo
Series ISSN: 2576-8107
Deep Learning for
Autonomous Vehicle Control
Algorithms, State-of-the-Art, and Future Prospects
Synthesis Lectures on
Advances in Automotive
Technology
Editor
Amir Khajepour, University of Waterloo
e automotive industry has entered a transformational period that will see an unprecedented
evolution in the technological capabilities of vehicles. Significant advances in new manufacturing
techniques, low-cost sensors, high processing power, and ubiquitous real-time access to information
mean that vehicles are rapidly changing and growing in complexity. ese new
technologies—including the inevitable evolution toward autonomous vehicles—will ultimately
deliver substantial benefits to drivers, passengers, and the environment. Synthesis Lectures on
Advances in Automotive Technology Series is intended to introduce such new transformational
technologies in the automotive industry to its readers.
Deep Learning for Autonomous Vehicle Control: Algorithms, State-of-the-Art, and
Future Prospects
Sampo Kuutti, Saber Fallah, Richard Bowden, and Phil Barber
2019
Narrow Tilting Vehicles: Mechanism, Dynamics, and Control
Chen Tang and Amir Khajepour
2019
Dynamic Stability and Control of Tripped and Untripped Vehicle Rollover
Zhilin Jin, Bin Li, and Jungxuan Li
2019
Real-Time Road Profile Identification and Monitoring: eory and Application
Yechen Qin, Hong Wang, Yanjun Huang, and Xiaolin Tang
2019
Noise and Torsional Vibration Analysis of Hybrid Vehicles
Xiaolin Tang, Yanjun Huang, Hong Wang, and Yechen Qin
2018
iii
Smart Charging and Anti-Idling Systems
Yanjun Huang, Soheil Mohagheghi Fard, Milad Khazraee, Hong Wang, and Amir Khajepour
2018
Design and Avanced Robust Chassis Dynamics Control for X-by-Wire Unmanned
Ground Vehicle
Jun Ni, Jibin Hu, and Changle Xiang
2018
Electrification of Heavy-Duty Construction Vehicles
Hong Wang, Yanjun Huang, Amir Khajepour, and Chuan Hu
2017
Vehicle Suspension System Technology and Design
Avesta Goodarzi and Amir Khajepour
2017
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