Extracting Traffic Primitives Directly From Naturalistically Logged Data for Self-Driving Applications
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 pa...
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| Vydáno v: | IEEE robotics and automation letters Ročník 3; číslo 2; s. 1223 - 1229 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Piscataway
IEEE
01.04.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 2377-3766, 2377-3766 |
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| Abstract | 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 letter 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. |
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| AbstractList | 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 letter 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. |
| Author | Ding Zhao Wenshuo Wang |
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| References | ref24 ref12 ref23 ref15 ref14 wang (ref13) 0 ref11 ref22 ref10 ref2 ref17 ref16 zhao (ref21) 0 ref19 ref18 ref7 ref9 ref4 ref3 ref6 ref5 johnson (ref20) 2013; 14 (ref8) 2017 wang (ref1) 2017; 18 |
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| SubjectTerms | Automation Bayes methods Bayesian analysis big data in robotics and automation Data mining Dirichlet problem Hidden Markov models Intelligent transportation systems Learning systems Machine learning Markov chains Mechanical engineering Roads Teaching methods Traffic flow Traffic information Vehicles |
| Title | Extracting Traffic Primitives Directly From Naturalistically Logged Data for Self-Driving Applications |
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