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Multi-classification of Driver Intentions in Yielding Scenarios
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. (CVAP)
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. (CAS/CVAP/CSC)ORCID iD: 0000-0002-7796-1438
2015 (English)In: Intelligent Transportation Systems (ITSC), 2015 IEEE 18th International Conference on, IEEE , 2015, p. 678-685Conference paper, Published paper (Refereed)
Abstract [en]

Predictions of the future motion of other vehicles in the vicinity of an autonomous vehicle is required for safe operation on trafficked roads. An important step in order to use proper behavioral models for trajectory prediction is correctly classifying the intentions of drivers. This paper focuses on recognizing the intention of drivers without priority in yielding scenarios at intersections - where the behavior of the driver depends on interaction with other drivers with priority. In these scenarios the behavior can be divided into multiple classes for which we have compared three common classification algorithms: k-nearest neighbors, random forests and support vector machines. Evaluation on a data set of tracked vehicles recorded at an unsignalized intersection show that multiple intentions can be learned and that the support vector machine algorithm exhibits superior classification performance.

Place, publisher, year, edition, pages
IEEE , 2015. p. 678-685
Keyword [en]
Autonomous Driving, Intention Estimation
National Category
Robotics
Identifiers
URN: urn:nbn:se:kth:diva-183496DOI: 10.1109/ITSC.2015.116ISI: 000376668800109Scopus ID: 2-s2.0-84950245947OAI: oai:DiVA.org:kth-183496DiVA, id: diva2:911785
Conference
Intelligent Transportation Systems (ITSC), 2015 IEEE 18th International Conference on
Funder
VINNOVA, 2012-04626
Note

QC 20160329

Available from: 2016-03-14 Created: 2016-03-14 Last updated: 2018-03-27Bibliographically approved
In thesis
1. Models Supporting Trajectory Planning in Autonomous Vehicles
Open this publication in new window or tab >>Models Supporting Trajectory Planning in Autonomous Vehicles
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Autonomous vehicles have the potential to drastically improve the safety, efficiency and cost of transportation. Instead of a driver, an autonomous vehicle is controlled by an algorithm, offering improved consistency and the potential to eliminate human error from driving: by far the most common cause of accidents.

Data collected from different types of sensors, along with prior information such as maps, are used to build models of the surrounding traffic scene, encoding relevant aspects of the driving problem.These models allow the autonomous vehicle to plan how it will drive, optimizing comfort, safety and progress towards its destination. To do so we must first encode the context of the current driving situation: the road geometry, where different traffic participants are, including the autonomous vehicle, and what routes are available to them. To plan the autonomous vehicle's trajectory, we also require models of how other traffic participants are likely to move in the near future, and what risks are incurred for different potential trajectories of the autonomous vehicle. In this thesis we present an overview of different trajectory planning approaches and the models enabling them along with our contributions towards localization, intention recognition, predictive behavior models and risk inference methods that support trajectory planning.

Our first contribution is a method that allows localization of anautonomous vehicle using automotive short range radars. Furthermore, we investigate behavior recognition and prediction using models at two different levels of abstraction. We have also explored the integration of two different trajectory planning algorithms and probabilistic environment models which allow us to optimize the expected cost of chosen trajectories.

Place, publisher, year, edition, pages
Kungliga Tekniska högskolan, 2018. p. 55
Keyword
Autonomous vehicles, planning, modeling
National Category
Robotics
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-224870 (URN)978-91-7729-724-6 (ISBN)
Public defence
2018-04-19, F3, Lindstedtsvägen 3, Stockholm, 14:00 (English)
Opponent
Supervisors
Note

QC 20180327

Available from: 2018-03-27 Created: 2018-03-27 Last updated: 2018-03-27Bibliographically approved

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