Tactical Decision-Making for Highway Driving

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dc.contributor.authorNordmark, Anders
dc.contributor.authorSundell, Oliver
dc.contributor.departmentChalmers tekniska högskola / Institutionen för matematiska vetenskapersv
dc.contributor.departmentChalmers University of Technology / Department of Mathematical Sciencesen
dc.date.accessioned2019-07-03T14:55:01Z
dc.date.available2019-07-03T14:55:01Z
dc.date.issued2018
dc.description.abstractThis thesis investigates three different Monte Carlo tree search (MCTS) algorithms for optimizing tactical decision-making during highway driving. The optimization problem was expressed in a partially observable Markov decision process (POMDP) framework, where the behaviors of the surrounding vehicles were modeled as nonobservable variables. The motion of the vehicles were governed by a generative model, which used two conventional driver models; the intelligent driver model (IDM) and minimizing overall braking induced by lane changes (MOBIL). These models together contain eight parameters for each vehicle which estimate a vehicle’s behaviour with respect to its longitudinal motion and lane changes. These eight non-observable parameters were inferred by a particle filter. The algorithms were tested in a simulated environment, where the objective was to change lanes to reach an exit ramp in dense highway traffic. The results show that the partially observable Monte Carlo planning (POMCP) based algorithms require more computational effort to reach the same performance as the MCTS based one, due to the inherent complexity of the history node trees. However, both methods are feasible to implement as an online tactical decision-making algorithm, where the less complex MCTS method performs best during simulations with limited resources.
dc.identifier.urihttps://hdl.handle.net/20.500.12380/256127
dc.language.isoeng
dc.setspec.uppsokPhysicsChemistryMaths
dc.subjectTransport
dc.subjectGrundläggande vetenskaper
dc.subjectNumerisk analys
dc.subjectOptimeringslära, systemteori
dc.subjectTransport
dc.subjectBasic Sciences
dc.subjectNumerical analysis
dc.subjectOptimization, systems theory
dc.titleTactical Decision-Making for Highway Driving
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster Thesisen
dc.type.uppsokH
local.programmeEngineering mathematics and computational science (MPENM), MSc
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