# Extracting Traffic Primitives Directly from Naturalistically Logged Data for Self-Driving Applications

arXiv: https://arxiv.org/abs/1709.03553 · HTML: https://mlsanity.com/paper/1709.03553/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2017.

## Built on by (most cited first, 6 of 6)

- [Enhancing Autonomous Vehicle Training with Language Model Integration and Critical Scenario Generation](https://mlsanity.com/paper/2404.08570/) (2024)
- [A Comprehensive Survey of Logging in Software: From Logging Statements Automation to Log Mining and Analysis](https://mlsanity.com/paper/2110.12489/) (2021)
- [(Re)2H2O: Autonomous Driving Scenario Generation via Reversely Regularized Hybrid Offline-and-Online Reinforcement Learning](https://mlsanity.com/paper/2302.13726/) (2023)
- [DIDER: Discovering Interpretable Dynamically Evolving Relations](https://mlsanity.com/paper/2208.10592/) (2022)
- [Planning Automated Driving with Accident Experience Referencing and Common-sense Inferencing](https://mlsanity.com/paper/2301.10892/) (2023)
- [REARL: A Closed-loop Autonomous Driving Simulation Enhancement Framework with Real Traffic Data and Large Language Models](https://mlsanity.com/paper/2609.19903/) (2026)

## Abstract (arXiv)

Developing an automated vehicle, that can handle complicated driving scenarios and appropriately interact with other road users, requires the ability to semantically learn and understand driving environment, oftentimes, based on analyzing massive amounts of naturalistic driving data. An important paradigm that allows automated vehicles to both learn from human drivers and gain insights is understanding the principal compositions of the entire traffic, termed as traffic primitives. However, the exploding data growth presents a great challenge in extracting primitives from high-dimensional time-series traffic data with various types of road users engaged. Therefore, automatically extracting primitives is becoming one of the cost-efficient ways to help autonomous vehicles understand and predict the complex traffic scenarios. In addition, the extracted primitives from raw data should 1) be appropriate for automated driving applications and also 2) be easily used to generate new traffic scenarios. However, existing literature does not provide a method to automatically learn these primitives from large-scale traffic data. The contribution of this paper has two manifolds. The first one is that we proposed a new framework to generate new traffic scenarios from a handful of limited traffic data. The second one is that we introduce a nonparametric Bayesian learning method -- a sticky hierarchical Dirichlet process hidden Markov model -- to automatically extract primitives from multidimensional traffic data without prior knowledge of the primitive settings. The developed method is then validated using one day of naturalistic driving data. Experiment results show that the nonparametric Bayesian learning method is able to extract primitives from traffic scenarios where both the binary and continuous events coexist.