Integration and implementation of a lane keeping system with vehicle dynamics control

Hsu, Jin-Yan; Ku, Kun-Lung; Jhang, Tong-Kai; Lin, Hung-Pang; Yeh, Chih‐Jung

doi:10.1109/cacs.2017.8284234

Cited by 10 publications

(1 citation statement)

References 7 publications

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“…With the development of advanced sensor technology and artificial intelligence method, ADAS have been studied a lot in recent decades, which can effectively ensure driving safety, avoid traffic accidents, reduce energy consumption and improve ride comfort [1]- [4]. For example, collision avoidance system (CA) [5], [6], adaptive cruise control (ACC) [7]- [9], active front steering (AFS) [10], [11], autonomous emergency brake system (AEB) [12], [13] and lane keeping assistance (LKA) [14], [15]. Driving intention prediction is one of the core technologies of the next generation of ADAS products, which have the ability to infer the future intentions of drivers to predict the likelihood of potential collisions and take measures in advance to avoid accidents [16].…”

Section: Introductionmentioning

confidence: 99%

Driver Lane Change Intention Recognition of Intelligent Vehicle Based on Long Short-Term Memory Network

et al. 2020

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Driving intention prediction is one of the key technologies for the development of advanced assisted driving systems (ADAS), which could greatly reduce traffic accidents caused by lane change and ensure driving safety. In this paper, an advanced predictive method based on Multi-LSTM (Long Short-Term Memory) is proposed to predict lane change intention effectively. First, the training data set and test set based on real road information data set NGSIM (Next Generation SIMulation) are built considering ego vehicle driving state and the influence of surrounding vehicles. Second, the Multi-LSTM-based prediction controller is constructed to learn vehicle behavior characteristics and time series relation of various states in the process of lane change. Then, the influences of prediction model structure change and data structure change on test results are verified. Finally, the verification tests based on HIL (Hardware-in-the-Loop) simulation are constructed. The results show that the proposed prediction model can accurately predict the vehicle lane change intention in highway scenarios and the maximum prediction accuracy can reach 83.75%, which is higher than that of common method SVM (Support Vector Machine).INDEX TERMS Intelligent vehicle, lane change, driving intention prediction, advanced assisted driving systems, multi-LSTM.

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Section: Introductionmentioning

confidence: 99%

Driver Lane Change Intention Recognition of Intelligent Vehicle Based on Long Short-Term Memory Network

et al. 2020

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AR-NET: lane detection model with feature balance concerns for autonomous driving

Tong,

2023

Neural Comput & Applic

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Using strain gages to assess the bearing properties of underlying surface with a robot crawler

Mashkov

Pozdnyakov

2020

IOP Conf. Ser.: Mater. Sci. Eng.

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The advent of military robotic systems has raised requirements for control systems, especially independent motion control, in particular, lateral possibility and flotation when moving in partially defined or undefined environment and in the presence of external perturbations. This paper proposes approaches to determine bearing reactions under the dynamic interaction of RV running gear with the bearing surface. It demonstrates a set of gages built into the tracked running gear allowing to evaluate the underlying surface properties. It proposes a computer-aided engineering system based on the finite element method to identify the most efficient locations to install strain gages.

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Integration and implementation of a lane keeping system with vehicle dynamics control

Cited by 10 publications

References 7 publications

Driver Lane Change Intention Recognition of Intelligent Vehicle Based on Long Short-Term Memory Network

Driver Lane Change Intention Recognition of Intelligent Vehicle Based on Long Short-Term Memory Network

AR-NET: lane detection model with feature balance concerns for autonomous driving

Using strain gages to assess the bearing properties of underlying surface with a robot crawler

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