@inproceedings{acerbo24drividoc,

title = {Driving from Vision through Differentiable Optimal Control},

author = {Flavia Sofia Acerbo and Jan Swevers and Tinne Tuytelaars and Tong Duy Son},

url = {https://doi.org/10.48550/arXiv.2403.15102

},

doi = {10.1109/IROS58592.2024.10802306},

isbn = {979-8-3503-7770-5},

year  = {2024},

date = {2024-10-01},

booktitle = {2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},

pages = {2153-0866},

publisher = {IEEE},

address = {Abu Dhabi, United Arab Emirates},

abstract = {This paper proposes DriViDOC: a framework for Driving from Vision through Differentiable Optimal Control, and its application to learn autonomous driving controllers from human demonstrations. DriViDOC combines the automatic inference of relevant features from camera frames with the properties of nonlinear model predictive control (NMPC), such as constraint satisfaction. Our approach leverages the differentiability of parametric NMPC, allowing for end-to-end learning of the driving model from images to control. The model is trained on an offline dataset comprising various human demonstrations collected on a motion-base driving simulator. During online testing, the model demonstrates successful imitation of different driving styles, and the interpreted NMPC parameters provide insights into the achievement of specific driving behaviors. Our experimental results show that DriViDOC outperforms other methods involving NMPC and neural networks, exhibiting an average improvement of 20% in imitation scores.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{acerboEvaluationMPCbasedImitation2023,

title = {Evaluation of MPC-based Imitation Learning for Human-like Autonomous Driving},

author = {Flavia Sofia Acerbo and Jan Swevers and Tinne Tuytelaars and Tong Duy Son},

url = {https://www.sciencedirect.com/science/article/pii/S2405896323016610},

doi = {https://doi.org/10.1016/j.ifacol.2023.10.1257},

issn = {2405-8963},

year  = {2023},

date = {2023-11-30},

urldate = {2023-11-30},

booktitle = {22nd IFAC World Congress},

journal = { IFAC-PapersOnLine},

volume = {56},

number = {2},

pages = {4871-4876},

publisher = {Elsevier Ltd},

address = {Yokohama, Japan},

abstract = {This work evaluates and analyzes the combination of imitation learning (IL) and differentiable model predictive control (MPC) for the application of human-like autonomous driving. We combine MPC with a hierarchical learning-based policy, and measure its performance in open-loop and closed-loop with metrics related to safety, comfort and similarity to human driving characteristics. We also demonstrate the value of augmenting open-loop behavioral cloning with closed-loop training for a more robust learning, approximating the policy gradient through time with the state space model used by the MPC. We perform experimental evaluations on a lane keeping control system, learned from demonstrations collected on a fixed-base driving simulator, and show that our imitative policies approach the human driving style preferences.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}