Aircraft assembly is the most important part of aircraft manufacturing. A large number of assembly fixtures must be used to ensure the assembly accuracy in the aircraft assembly process. Traditional fixed assembly fixture could not satisfy the change of the aircraft types, so the digital flexible assembly fixture was developed and was gradually applied in the aircraft assembly. Digital flexible assembly technology has also become one of the research directions in the field of aircraft manufacturing. The aircraft flexible assembly can be divided into three assembly stages that include component-level flexible assembly, large component-level flexible assembly, and large components alignment and joining. This article introduces the architecture of flexible assembly systems and the principles of three types of flexible assembly fixtures. The key technologies of the digital flexible assembly are also discussed. The digital metrology system provides the basis for the accurate digital flexible assembly. Aircraft flexible assembly systems mainly use laser tracking metrology systems and indoor Global Positioning System metrology systems. With the development of flexible assembly technology, the digital flexible assembly system will be widely used in current aircraft manufacturing.

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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