Sampling-based Bi-level Optimization aided by Behaviour Cloning for Autonomous Driving
| dc.contributor.author | Shrestha, Jatan | |
| dc.contributor.other | Tartu Ülikool. Loodus- ja täppisteaduste valdkond | et |
| dc.contributor.other | Tartu Ülikool. Tehnoloogiainstituut | et |
| dc.date.accessioned | 2023-10-09T14:16:06Z | |
| dc.date.available | 2023-10-09T14:16:06Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | Autonomous 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.uri | https://hdl.handle.net/10062/93448 | |
| dc.language.iso | eng | et |
| dc.publisher | Tartu Ülikool | et |
| dc.rights | openAccess | et |
| dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
| dc.subject | Autonomous Driving, Bi-level Optimization, Behavioural Cloning, Differentiable Optimization, Conditional Variational Autoencoder | et |
| dc.subject.other | magistritööd | et |
| dc.title | Sampling-based Bi-level Optimization aided by Behaviour Cloning for Autonomous Driving | et |
| dc.type | Thesis | et |