Driving risk assessment under the connected vehicle environment:a CNN-LSTM modeling approach

Connected vehicle(CV)is regarded as a typical feature of the future road transportation system.One core benefit of promoting CV is to improve traffic safety,and to achieve that,accurate driving risk assessment under Vehicle-to-Vehicle(V2V)communications is critical.There are two main differences concluded by comparing driving risk assessment under the CV environment with traditional ones:(1)the CV environment provides high-resolution and multi-dimensional data,e.g.,vehicle trajectory data,(2)Rare existing studies can comprehensively address the heterogeneity of the vehicle operating environment,e.g.,the multiple interacting objects and the time-series variability.Hence,this study proposes a driving risk assessment framework under the CV environment.Specifically,first,a set of time-series top views was proposed to describe the CV environment data,expressing the detailed information on the vehicles surrounding the subject vehicle.Then,a hybrid CNN-LSTM model was established with the CNN component extracting the spatial interaction with multiple interacting vehicles and the LSTM component solving the time-series variability of the driving environment.It is proved that this model can reach an AUC of 0.997,outperforming the existing machine learning algorithms.This study contributes to the improvement of driving risk assessment under the CV environment.

Preparation and Activity Identification of Recombinant Protein PACAP-PTD

[Objective] This study aimed to prepare recombinant protein PACAP-PTD and measure its activity. [Method] The gene that encodes fusion protein PACAP-PTD was cloned into the expression vector pKYB to construct recombinant expression vector pKYB-PACAP-PTD, which was then transformed into E. coli ER2566. The fusion protein consisting of PACAP-PTD, intein and chitin was expressed under the induction of IPTG. Finally, the target fusion protein PACAP-PTD was purified with IMPACT system （ Intein Mediated Purification with an Affinity of chitin-binding Tag）, and its activities to cross blood-brain barrier and to promote cell proliferation were measured. [ Result~ The molecular weight of the fusion protein PACAP-PTD determined with laser time-of-flight mass spectrometry was con- sistent with the theoretical value. In addition, the protein could effectively cross the blood-brain barrier and promote cell proliferation as well. [ Conclusion] The construction and preparation of the fusion protein PACAP-PTD not only lays foundation for further study on its biological function, but also improves the route of PACAP administration, and thus expands its scope of application.