Deep Reinforcement Learning framework for Autonomous Driving (1704.02532v1)

Abstract: 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. Despite its perceived utility, it has not yet been successfully applied in automotive applications. Motivated by the successful demonstrations of learning of Atari games and Go by Google DeepMind, we propose a framework for autonomous driving using deep reinforcement learning. This is of particular relevance as it is difficult to pose autonomous driving as a supervised learning problem due to strong interactions with the environment including other vehicles, pedestrians and roadworks. As it is a relatively new area of research for autonomous driving, we provide a short overview of deep reinforcement learning and then describe our proposed framework. It incorporates Recurrent Neural Networks for information integration, enabling the car to handle partially observable scenarios. It also integrates the recent work on attention models to focus on relevant information, thereby reducing the computational complexity for deployment on embedded hardware. The framework was tested in an open source 3D car racing simulator called TORCS. Our simulation results demonstrate learning of autonomous maneuvering in a scenario of complex road curvatures and simple interaction of other vehicles.

• The paper presents an end-to-end framework that combines CNN-based recognition, RNN-based prediction, and RL-based planning for autonomous driving.
• The authors compare DQN and DDAC models, demonstrating that continuous action approaches yield smoother and more efficient lane-keeping.
• The integration of attention mechanisms and sensor fusion reduces computational overhead, enabling effective real-time decision making.

Deep Reinforcement Learning Framework for Autonomous Driving: A Summary

The paper "Deep Reinforcement Learning Framework for Autonomous Driving," authored by Ahmad El Sallab, Mohammed Abdou, Etienne Perot, and Senthil Yogamani, provides a detailed exploration and proposal of an end-to-end framework for autonomous driving using deep reinforcement learning (DRL). The central thesis revolves around leveraging reinforcement learning (RL) to overcome the challenges inherent in autonomous driving, particularly those related to interaction with a dynamic environment. The significance of this work stems from the necessity to address autonomous driving as a problem that cannot be adequately handled through supervised learning due to the unpredictability of real-world interactions.

Key Components and Contributions

The authors start by dividing the autonomous driving task into three primary components:

1. Recognition: Leveraging deep learning, specifically Convolutional Neural Networks (CNNs), to identify and classify elements in the environment.
2. Prediction: Using Recurrent Neural Networks (RNNs) to predict future states based on past information, which is essential for tasks such as object tracking and environmental mapping.
3. Planning: Integrating recognition and prediction to generate efficient driving action sequences aimed at avoiding penalties and reaching destinations safely.

The innovative aspect of their proposed framework is the incorporation of RL with deep learning (DL), inspired by the success of Google's DeepMind in playing Atari games and the game of Go using Deep Q Networks (DQN). They extend this by integrating Recurrent Neural Networks (RNNs) to handle partially observable scenarios, which are prevalent in real-world driving conditions, and attention models to focus on relevant information, thereby enhancing computational efficiency and suitability for deployment on embedded hardware.

Methodological Approach

The paper provides a comprehensive survey of DRL algorithms, detailing their progression from traditional Markov Decision Processes (MDPs) and Q-learning to advanced models such as DQN, Deep Recurrent Q Networks (DRQN), and Deep Attention Recurrent Q Networks (DARQN). These models are foundational to the proposed framework.

Framework Components:

• Spatial Aggregation: Utilizes sensor fusion and CNNs to process raw sensor inputs (like LIDAR and camera data) into meaningful spatial features.
• Recurrent Temporal Aggregation: Employs RNNs and LSTMs to integrate temporal data, crucial for handling Partial Observability Markov Decision Problems (POMDPs), typical in dynamic driving environments.
• Planning: Deploys reinforcement learning techniques, primarily DQN for discrete actions and Deep Deterministic Actor-Critic (DDAC) for continuous actions, to derive driving actions from the processed sensor data.

Experimental Results

The framework was empirically tested using the open-source simulator TORCS, configured to simulate lane-keeping scenarios. Notably, the authors compared the efficacy of both DQN and DDAC models for action selection. They found that while DQN could achieve successful lane-keeping, the continuous action space model of DDAC provided smoother and more efficient steering actions.

Implications and Future Directions

The research delineates both theoretical and practical implications:

• Theoretical: The integration of DRL with attention models offers a robust method for information filtering, which is critical for reducing computational overhead and enhancing real-time application viability.
• Practical: The framework's success in simulation environments opens avenues for further refinement and eventual deployment in real-world scenarios. This includes extensions for more complex driving tasks beyond lane-keeping, such as navigation through intersections and dynamic obstacle avoidance.

The authors suggest future work to deploy this framework in more controlled simulation environments with labeled ground truth, paving the way toward practical applications in real driving conditions. This highlights the importance of further research in DRL for autonomous driving, with a focus on tackling the intricacies of real-world unpredictability and sensor noise.

In summary, this paper presents a comprehensive and nuanced approach to addressing autonomous driving challenges through deep reinforcement learning, proposing a framework that effectively integrates state-of-the-art DL and RL techniques.