Abstract: A method for evaluating a driving risk level in a tunnel based on vehicle bus data and a system therefor are provided. The method uses the Controller Area Network (CAN) bus data collected in a vehicle driving process, designs and extracts a driving risk characteristic feature index reflecting the driving behavior of a driver through a sliding time window method, writes a feature codebook to symbolize an extracted sequence feature, and then randomly samples all of the samples, and based on the sampled symbolic data, using a Latent Dirichlet Allocation (LDA) theme model to evaluate the driving risk level. The training method of the model is to acquire the optimal number of risk levels by evaluating the perplexity and the coherence scores, and to analyze the driving risk of the driving data in all of the samples.

Abstract: A method for evaluating a driving risk level in a tunnel based on vehicle bus data and a system therefor are provided. The method uses the Controller Area Network (CAN) bus data collected in a vehicle driving process, designs and extracts a driving risk characteristic feature index reflecting the driving behavior of a driver through a sliding time window method, writes a feature codebook to symbolize an extracted sequence feature, and then randomly samples all of the samples, and based on the sampled symbolic data, using a Latent Dirichlet Allocation (LDA) theme model to evaluate the driving risk level. The training method of the model is to acquire the optimal number of risk levels by evaluating the perplexity and the coherence scores, and to analyze the driving risk of the driving data in all of the samples.

Abstract: Provided are a road traffic incident detection method, system and device based on small sample learning. According to the invention, traffic flow data and incident data are utilized to construct non-incident samples by a case contrast study method, traffic flow feature indexes are extracted to construct incident and non-incident sample feature sets, then sample division and pairing are performed on the feature sets to obtain training and test sample pair sets, a traffic incident detection model is constructed by adopting a twin network architecture in small sample learning, and the model is trained and tested by a sample pair mode. The invention is conductive to accurately detecting accidental traffic incidents, and providing supports for avoiding secondary accidents and improving the traffic safety level.