Clustering Vehicle Maneuver Trajectories Using Mixtures of Hidden Markov Models
Paper i proceeding, 2018

The safety of autonomous vehicles needs to be verified and validated by rigorous testing. It is expensive to test autonomous vehicles in the field, and therefore virtual testing methods are needed. Generative models of maneuvers such as cut-ins, overtakes, and lane-keeping are needed to thoroughly test the autonomous vehicle in a virtual environment. To train such models we need ground truth maneuver labels and obtaining such labels can be time-consuming and costly. In this work, we use a mixture of hidden Markov models to find clusters in maneuver trajectories, which can be used to speed up the labeling process. The maneuver trajectories are noisy, asynchronous and of uneven length, which make hidden Markov models a good fit for the data. The method is evaluated on labeled data from a test track consisting of cut-ins and overtakes with favorable results. Further, it is applied to natural data where many of the clusters found can be interpreted as driver maneuvers under reasonable assumptions. We show that mixtures of hidden Markov models can be used to find motion patterns in driver maneuver data from highways and country roads.


John Martinsson

Chalmers, Data- och informationsteknik, Datavetenskap

Nasser Mohammadiha

Chalmers, Data- och informationsteknik, Datavetenskap

Zenuity AB

Alexander Schliep

Göteborgs universitet

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

2153-0017 (eISSN)

Vol. 2018-November 3698-3705 8569418
978-172810323-5 (ISBN)

21st IEEE International Conference on Intelligent Transportation Systems, ITSC 2018
Maui, USA,

Big Automotive Data Analytics - SEnsor Modeling and Performance Analysis (BADA-SEMPA)

VINNOVA (2015-04787), 2016-03-01 -- 2018-02-28.


Transportteknik och logistik





Mer information

Senast uppdaterat