Machine Learning for Autonomous Driving: Recent Advances and Research Opportunities (MLAuto)

Although dramatic progress has been made in the field of autonomous driving, there are many major challenges in achieving full-autonomy. For example, how to make perception accurate and robust to accomplish safe autonomous driving? How to reliably track cars, pedestrians, and cyclists? How to learn long term driving strategies (known as driving policies) so that autonomous vehicles can be equipped with adaptive human negotiation skills when merging, overtaking and giving way, etc? How to achieve near-zero fatality?

These complex challenges associated with autonomy in physical world naturally suggest that we take a machine learning approach. Deep learning and computer vision have found many real-world applications such as face tagging. However, perception for autonomous driving has a unique set of requirements such as safety and explainability. Autonomous vehicles need to choose actions, e.g. steering commands which will affect the subsequent inputs (driving scenes) encountered. This setting is well-suited to apply reinforcement learning to determine the best actions to take. Many autonomous driving tasks such as perception and tracking requires large data sets of labeled examples to learn rich and high-performance visual representation. However, the progress is hampered by the sheer expenses of human labelling needed. Naturally we would like to employ unsupervised learning, few-shot learning transfer learning leveraging simulators, and techniques can learn efficiently.

Autonomous driving has become one of the leading applications to drive the progress of AI. The goal of this workshop is to bring together researchers and practitioners in the field of autonomous driving to share the recent progresses, and discuss core challenges and research opportunities in machine learning. These challenges and research opportunities include, but are not limited to

    • accurate and efficient pedestrian detection, pedestrian intent detection,
    • machine learning for object tracking,
    • unsupervised representation learning for autonomous driving,
    • deep reinforcement learning for learning driving policies,
    • cross-modal and simulator to real-world transfer learning,
    • scene classification, real-time perception and prediction of traffic scenes,
    • uncertainty propagation in deep neural networks,
    • efficient inference with deep neural networks

The workshop will include invited speaker presentations and panels. We encourage researchers and practitioners interested in research opportunities in autonomous driving to participate.

Differences with 2018 NIPS Workshop on Machine Learning for Intelligent Transportation Systems:

Li Erran Li is also co-organizing the 2018 NIPS Workshop on Machine Learning for Intelligent Transportation Systems (MLITS). The differences with this Expo workshop, MLAuto are as follows. MLITS has a much broader scope than MLAuto. MLAuto will focus on research opportunities in machine learning for autonomous driving. MLAuto will cover key machine learning areas, e.g. research challenges in making representation learning robust and easy to generalize to variations of real-world traffic scenes, research opportunities in apply imitation learning and model-based reinforcement learning for planning and control in autonomous driving.