A High-accuracy Framework for Vehicle Dynamic Modeling in Autonomous Driving

Jiang, Shengyuan; Wang, Yu; Lin, Weiman; Cao, Yu; Lin, Le; Miao, Jinghao; Luo, Qi

doi:10.1109/iros51168.2021.9636861

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…Research in autonomous driving aims at improving our safety and driving experience. In order to derive and execute trustworthy actions autonomously, vehicles must be able to perceive and understand their surroundings [1], [2], [3], [4], [5]. To ensure the safety of the passengers, an autonomous vehicle needs to determine precisely whether there is a danger such as an obstruction or a pedestrian, and consequently decide whether to slow down or accelerate.…”

Section: Introductionmentioning

confidence: 99%

“…While collecting the images from vehicles is an effortless process, sending them to a central server to train a model could potentially violate the users privacy. Indeed, other nodes of the communication infrastructure may access 1 All the authors are supported by Politecnico di Torino, Turin, Italy. Corresponding author: lidia.fantauzzo@studenti.polito.it Other authors: name.surname@polito.it 2 Fabio Cermelli is with Italian Institute of Technology, Genoa, Italy.…”

Section: Introductionmentioning

confidence: 99%

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FedDrive: Generalizing Federated Learning to Semantic Segmentation in Autonomous Driving

Fantauzzo¹,

Fani'²,

Caldarola³

et al. 2022

Preprint

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Semantic Segmentation is essential to make selfdriving vehicles autonomous, enabling them to understand their surroundings by assigning individual pixels to known categories. However, it operates on sensible data collected from the users' cars; thus, protecting the clients' privacy becomes a primary concern. For similar reasons, Federated Learning has been recently introduced as a new machine learning paradigm aiming to learn a global model while preserving privacy and leveraging data on millions of remote devices. Despite several efforts on this topic, no work has explicitly addressed the challenges of federated learning in semantic segmentation for driving so far. To fill this gap, we propose FedDrive, a new benchmark consisting of three settings and two datasets, incorporating the real-world challenges of statistical heterogeneity and domain generalization. We benchmark state-of-the-art algorithms from the federated learning literature through an in-depth analysis, combining them with style transfer methods to improve their generalization ability. We demonstrate that correctly handling normalization statistics is crucial to deal with the aforementioned challenges. Furthermore, style transfer improves performance when dealing with significant appearance shifts. We plan to make both the code and the benchmark publicly available to the research community.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

FedDrive: Generalizing Federated Learning to Semantic Segmentation in Autonomous Driving

Fantauzzo¹,

Fani'²,

Caldarola³

et al. 2022

Preprint

View full text Add to dashboard Cite

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FedDrive: Generalizing Federated Learning to Semantic Segmentation in Autonomous Driving

Fantauzzo

Fani

Caldarola

et al. 2022

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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A Survey of Vehicle Dynamics Models for Autonomous Driving

Jin,

Zhao,

2024

SAE Technical Paper Series

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<div class="section abstract"><div class="htmlview paragraph">Autonomous driving technology is more and more important nowadays, it has been changing the living style of our society. As for autonomous driving planning and control, vehicle dynamics has strong nonlinearity and uncertainty, so vehicle dynamics and control is one of the most challenging parts. At present, many kinds of specific vehicle dynamics models have been proposed, this review attempts to give an overview of the state of the art of vehicle dynamics models for autonomous driving. Firstly, this review starts from the simple geometric model, vehicle kinematics model, dynamic bicycle model, double-track vehicle model and multi degree of freedom (DOF) dynamics model, and discusses the specific use of these classical models for autonomous driving state estimation, trajectory prediction, motion planning, motion control and so on. Secondly, data driven or AI based vehicle models have been reviewed, and their specific applications in automatic driving and their modeling and training processes are introduced. At the end of this review, the advantages and disadvantages of these vehicle models are summarized, and the future research directions and possible explorations are discussed to guide readers.</div></div>

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A High-accuracy Framework for Vehicle Dynamic Modeling in Autonomous Driving

Cited by 3 publications

References 23 publications

FedDrive: Generalizing Federated Learning to Semantic Segmentation in Autonomous Driving

FedDrive: Generalizing Federated Learning to Semantic Segmentation in Autonomous Driving

FedDrive: Generalizing Federated Learning to Semantic Segmentation in Autonomous Driving

A Survey of Vehicle Dynamics Models for Autonomous Driving

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