Modeling and Prediction of Driving Behaviors Using a Nonparametric Bayesian Method With AR Models

IEEE Robot. Autom. Lett.

Zhao

2018

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 highdimensional 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.

Section: Discussionmentioning

confidence: 99%

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

IEEE Robot. Autom. Lett.

Zhao

2018

“…3) Learning Procedure: We adopt a weak-limit Gibbs sampling algorithm for HDP-HSMM [16]; the duration variables are drawn from Poisson distribution. The observations are generated from a Gaussian model θ i = [µ i , Σ i ], we take µ i = 0 according to [18]. The hyperparameters α and γ are drawn from a gamma prior, and the hyperparameters for θ are determined by an Inverse-Wishart (IW) prior [19].…”

Section: B Autoencodersmentioning

confidence: 99%

A General Framework of Learning Multi-Vehicle Interaction Patterns from Video

Zhang

Zhu

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

et al. 2019

Semantic learning and understanding of multivehicle interaction patterns in a cluttered driving environment are essential but challenging for autonomous vehicles to make proper decisions. This paper presents a general framework to gain insights into intricate multi-vehicle interaction patterns from bird's-eye view traffic videos. We adopt a Gaussian velocity field to describe the time-varying multi-vehicle interaction behaviors and then use deep autoencoders to learn associated latent representations for each temporal frame. Then, we utilize a hidden semi-Markov model with a hierarchical Dirichlet process as a prior to segment these sequential representations into granular components, also called traffic primitives, corresponding to interaction patterns. Experimental results demonstrate that our proposed framework can extract traffic primitives from videos, thus providing a semantic way to analyze multi-vehicle interaction patterns, even for cluttered driving scenarios that are far messier than human beings can cope with.

“…Taniguchi, et al [18], [19] proposed a double articulation analyzer based on nonparametric Bayesian theory for predicting sixdimensional data. Hamada, et al [20] raised a nonparametric Bayesian approach with linear systems to learn and predict driver behaviors. Wang, et al [21] investigated three different nonparametric Bayesian approaches to analyze drivers' carfollowing styles.…”

Section: B Traffic Primitive Extractionmentioning

confidence: 99%

A Tempt to Unify Heterogeneous Driving Databases using Traffic Primitives

Zhu

2018 21st International Conference on Intelligent Transportation Systems (ITSC)

Zhao

2018

A multitude of publicly-available driving datasets and data platforms have been raised for autonomous vehicles (AV). However, the heterogeneities of databases in size, structure and driving context make existing datasets practically ineffective due to a lack of uniform frameworks and searchable indexes. In order to overcome these limitations on existing public datasets, this paper proposes a data unification framework based on traffic primitives with ability to automatically unify and label heterogeneous traffic data. This is achieved by two steps: 1) Carefully arrange raw multidimensional time series driving data into a relational database and then 2) automatically extract labeled and indexed traffic primitives from traffic data through a Bayesian nonparametric learning method. Finally, we evaluate the effectiveness of our developed framework using the collected real vehicle data.