Learning Steering Bounds for Parallel Autonomous Systems

Deep learning has been successfully applied to “end-to-end” learning of the autonomous driving task, where a deep neural network learns to predict steering control commands from camera data input. However, the learned representations do not support higher-level decision making required for autonomous navigation, nor the uncertainty estimates required for parallel autonomy, where vehicle control is shared between human and robot. This paper tackles the problem of learning a representation to predict a continuous control probability distribution, and thus steering control options and bounds for those options, which can be used for autonomous navigation. Each mode of the distribution encodes a possible macro-action that the system could execute at that instant, and the covariances of the modes place bounds on safe steering control values. Our approach has the added advantage of being trained on unlabeled data collected from inexpensive cameras. The deep neural network based algorithm generates a probability distribution over the space of steering angles, from which we leverage Variational Bayesian methods to extract a mixture model and compute the different possible actions in the environment. A bound, which the autonomous vehicle must respect in our parallel autonomy setting, is then computed for each of these actions. We evaluate our approach on a challenging dataset containing a wide variety of driving conditions, and show that our algorithm is capable of parameterizing Gaussian Mixture Models for possible actions, and extract steering bounds with a mean error of only 2 degrees. Additionally, we demonstrate our system working on a full scale autonomous vehicle and evaluate its ability to successful handle various different parallel autonomy situations.

Authors

• Alexander Amini
• Liam Paull
• Thomas Balch
• Sertac Karaman
• Daniela Rus

Keywords

• Autonomous vehicles
• Navigation
• Neural networks
• Probability distribution
• Decision making
• Machine learning
• Bayes methods
• Autonomic System
• Neural Network
• Deep Learning
• Error Of The Mean
• Deep Neural Network
• Vehicle Control
• Mixture Model
• Actual Environment
• Continuous Distribution
• Gaussian Mixture Model
• Challenging Dataset
• Variational Inference
• Continuous Probability
• Steering Angle
• Steering Control
• High-level Decision
• Discretion
• Convolutional Neural Network
• Discrete Distribution
• Human Drivers
• Autonomic Control
• Human Input
• Multiple Activities
• Discrete Output
• Recurrent Neural Network
• Shared Control
• Types Of Drivers
• Single-frame Images