Motivated by the successful demonstrations of learning of Atari games and Go by Google DeepMind, it is possible to propose a framework for autonomous driving using deep reinforcement learning. It incorporates Recurrent Neural Networks for information integration, enabling the car to handle partially observable scenarios.

1. DEEP REINFORCEMENT LEARNING FRAMEWORK
FOR AUTONOMOUS DRIVING
PRESENTED BY,
GOPIKA GOPINATH TG

2. CONTENTS
• Introduction
• Existing System
• Issues in Existing System
• Objective
• Proposed System
• Block Diagram
• Conclusion
• References
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3. INTRODUCTION
• Reinforcement learning is considered to be a strong AI paradigm which can be used to teach machines through
interaction with the environment and learning from their mistakes.
• Motivated by the successful demonstrations of learning of Atari games and Go by Google DeepMind, it is
possible to propose a framework for autonomous driving using deep reinforcement learning.
• It incorporates Recurrent Neural Networks for information integration, enabling the car to handle partially
observable scenarios.
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4. EXISTING SYSTEM
• Current technology for highly automated driving in controlled environments is quite mature.
• These vehicles uses state-of-the-art sensors (radar, lidar, GPS and camera vision systems)
• These prototypes operate with a driver that must stand ready to take control of the vehicle
• There are also risks linked to the safety performance of these vehicles
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5. ISSUES IN EXISTING SYSTEM
• Vehicle-to-X connectivity (V2X)
• Decision and control algorithms
• Digital infrastructure
• Human factors
• Evaluating road automation
• Roadworthiness testing
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6. OBJECTIVE
• The purpose of this work is to develop a deep learning frame work for autonomous driving
• Autonomous driving promises many benefits: improved safety, reduced congestion.
• Evolved from the study of pattern recognition and computational learning theory in artificial
intelligence, machine learning explores the study and construction of algorithms that can learn from
and make predictions on data.
• To understand the limitations and merits of an algorithm and to develop efficient learning algorithms
is the goal in reinforcement learning.
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7. PROPOSED SYSTEM
• The machine learning algorithms are extensively used to find the solutions to various challenges
arising in manufacturing self-driving cars.
• Reinforcement learning is learning what to do--how to map situations to actions--so as to maximize
a numerical reward signal.
• These two characteristics--trial-and-error search and delayed reward--are the two most important
distinguishing features of reinforcement learning.
• A comparative study can be made from RNN, LSTM, GRNN
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8. Contd..
• The agent learns to behave in environment depending on these rewards
• Major task of a machine learning algorithm is continuous rendering of surrounding environment and
forecasting the changes that are possible to these surroundings.
These tasks are classified into 4 sub-tasks:
• The detection of an Object
• The Identification of an Object or recognition object classification
• The Object Localization and Prediction of Movement
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9. BLOCK DIAGRAM
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10. CONCLUSION
• In this work ,a survey of the recent advances in the field of Deep Reinforcement Learning, followed
by a proposal of a framework for an end-end Deep Reinforcement learning pipeline for Autonomous
driving will be done.
• The proposed framework integrates the RNN
• The future work includes to deploy the proposed framework first on a simulated environment, where
the sensors and actuators are artificially controlled, with ground truth available. Later on, the same
framework could be extended to real vehicle scenarios.
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11. REFERENCES
[1] Abbeel, P., & Ng, A. Y. (2004). Apprenticeship learning via inverse reinforcement learning. Proceedings of the
twenty-first international conference on Machine learning, (p. 1) 2014.
[2] Ba, J., Mnih, V., & Kavukcuoglu, K. (2014). Multiple object recognition with visual attention. arXiv preprint
arXiv:1412.7755 .
[3] Badrinarayanan, V., Kendall, A., & Cipolla, R. (2015). SegNet: A Deep Convolutional Encoder-Decoder
Architecture for Image Segmentation. arXiv preprint arXiv:1511.00561 .
[4] Balduzzi, D., & Ghifary, M. (2015). Compatible Value Gradients for Reinforcement Learning of Continuous Deep
Policies. arXiv preprint arXiv:1509.03005 .
[5] Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., et al. (2016). End to End Learning
for Self-Driving Cars. arXiv preprint arXiv:1604.07316 .
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