Sampling-based Bi-level Optimization aided by Behaviour Cloning for Autonomous Driving

dc.contributor.authorShrestha, Jatan
dc.contributor.otherTartu Ülikool. Loodus- ja täppisteaduste valdkondet
dc.contributor.otherTartu Ülikool. Tehnoloogiainstituutet
dc.date.accessioned2023-10-09T14:16:06Z
dc.date.available2023-10-09T14:16:06Z
dc.date.issued2023
dc.description.abstractAutonomous driving has a natural bi-level structure. The upper behavioural layer aims to provide appropriate lane change, speeding up, and braking decisions to optimize a given driving task. The upper layer can only indirectly influence the driving efficiency through the lower-level trajectory planner, which takes in the behavioural inputs to produce motion commands for the controller. Existing sampling-based approaches do not fully exploit the strong coupling between the behavioural and planning layer. On the other hand, Reinforcement Learning (RL) can learn a behavioural layer while incorporating feedback from the lower-level planner. However, purely data-driven approaches often fail regarding safety metrics in dense and rash traffic environments. This thesis presents a novel alternative; a parameterized bi-level optimization that jointly computes the optimal behavioural decisions and the resulting downstream trajectory. The proposed approach runs in real-time using a custom Graphics Processing Unit (GPU)-accelerated batch optimizer and a Conditional Variational Autoencoder (CVAE) learnt warm-start strategy and extensive experiments on challenging traffic scenarios show that it outperforms state-of-the-art Model Predictive Control (MPC) and RL approaches regarding collision rate while being competitive in driving efficiency.et
dc.identifier.urihttps://hdl.handle.net/10062/93448
dc.language.isoenget
dc.publisherTartu Ülikoolet
dc.rightsopenAccesset
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectAutonomous Driving, Bi-level Optimization, Behavioural Cloning, Differentiable Optimization, Conditional Variational Autoencoderet
dc.subject.othermagistritöödet
dc.titleSampling-based Bi-level Optimization aided by Behaviour Cloning for Autonomous Drivinget
dc.typeThesiset

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