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Towards Risk Minimizing Trajectory Planning in On-Road Scenarios
KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL. KTH.
KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL.
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Trajectory planning for autonomous vehicles should attempt to minimize expected risk given noisy sensor data and uncertain predictions of the near future. In this paper, we present a trajectory planning approach for on-road scenarios where we use a graph search approximation. Uncertain predictions of other vehicles are accounted for by anovel inference technique that allows efficient calculation of the probability of dangerous outcomes for set of modeled situation types.

Keywords [en]
Trajectory planning, autonomous vehicles, risk
National Category
Robotics
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-224800OAI: oai:DiVA.org:kth-224800DiVA, id: diva2:1193109
Note

QC 20180327

Available from: 2018-03-26 Created: 2018-03-26 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
Keywords
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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