Extracting Traffic Primitives from Millions of Naturalistic Driving Encounters – A Synthesized Method based on Nonparametric Bayesian and Deep Unsupervised Learning
Project Abstract/Statement of Work:
Encounters where multiple road users meet and coordinate with each other are the key challenges for self-driving and driving-assistance systems. Methods that can automatically process, cluster, and analyze driving encounters from a massive database becomes imperative to reduce development cost and duration. The noisy, incomplete, and unbalanced nature of existing databases gives a great challenge to existing auto-encoding methods.
It is estimated that one hour of data requires 800 human hours to label them manually. In recent years, the idea of segmenting a long-term time-series data into primitives has been applied to other research fields, such as such as human motion trajectory learning. Similarly, we believe that it is worthy to develop tools that can automatically extract primitives from millions of naturalistic driving encounters, thus being applicable to automated driving both for Guardian and Chauffeur.
In our previous research, we developed preliminary techniques to automatically extract traffic primitives and driving behavior primitives from large and multi-scale traffic data from a vision-based sensor (Mobileye) using nonparametric Bayesian learning.
In this project, we aim to synthesize the advantages of these two approaches in dealing with complex driving encounters and then develop a new learning-based approach that is versatile to use but also mathematically trackable.
A new method to automatically study the driving encountering that inherits the modularity, uncertainty quantifiability, robustness and interpretability of the Bayesian approach, while retaining the deep unsupervised learning’s strength.
An automatically labeled database extracted from the UM database with estimated 10 million of driving encounters.
A pool of encountering primitives representing basic driver interaction patterns for design/test Guardian and Chauffeur systems