With the continuous development of intelligent networks and autonomous driving technologies, heterogeneous traffic flow represented by conventional vehicles (CV), autonomous vehicles (AV), and connected and autonomous vehicles (CAV) have emerged, and consequently, the driving safety risk issues in this mixed driving environment have become increasingly complex. In a mixed and connected environment, different traffic streams in complex driving scenarios are intertwined with each other, and driving behaviours such as steering and lane-changing between various traffic streams are frequent, thus increasing the crash risk of vehicles. To understand the research methods, theoretical models, and system architectures in the field of driving safety analysis in this mixed-connected environment, this article reviews the driving safety risk progress from four major aspects: driving safety risk perception and identification, driving safety risk prediction, driving safety risk quantification, and driving safety risk early warning. By summarizing the existing research, it can be found that academics have achieved many achievements in urban driving safety risk evaluation in mixed-connected environments. Still, there are several problems and challenges that need to be solved, such as the stability and reliability of collaborative sensing systems of AV and CAV in complex traffic environments, the accuracy of object detection, the vehicle information security, the limitations of a single factor analysis used in driving safety risk assessment, and the accuracy of trajectory prediction for both the CV, AV, and CAV. By analyzing the limitations of the existing research, this article proposes a future research direction, which provides a reference for the development of driving safety risk research.

Human-like visual perception systems are indispensable and vital components of human-like autonomous vehicles. In the real driving environment, there is much unlabeled information and the total number of categories of information is uncertain. While human brains are adept at processing such information, current methods are not. Thus, this study presented a novel hybrid unsupervised deep learning method to model the information processing mechanism of the driver’s visual perception. The proposed approach (CAE-SOM) was a neuron-triggered method, which leveraged the virtues of a convolutional autoencoder (CAE) and a self-organizing map (SOM) neural network. The CAE mimicked the hierarchical structures of the driver’s visual system to extract the high-level features, whilst the SOM neural network simulated the working principle of human brain neurons during the information judgment process to perform unsupervised clustering. The CAE-SOM method was built by using a dataset with eight common types of objects in road environments, and then it was tested on a public dataset LabelMe. The results showed that the CAE-SOM method performed well with an average accuracy of 90%. Compared with current unsupervised methods, the CAE-SOM model could improve the accuracy by nearly 10%. Compared with current supervised methods, this new model was still competitive, and its accuracy was close to the highest one. More importantly, the CAE-SOM model could reduce the cost of human labeling work in an unsupervised way and handle data from new categories that had never appeared. The outcomes could contribute to the visual algorithm optimization and safety improvement for autonomous vehicles.

In this study, a novel traffic risk assessment framework of mega-events that integrate risk field and deep learning is proposed. Considering the inherent difference of different traffic risks, the risk quantification and standardization is conducted first. Then several risk field models are constructed to quantify the impacts of multi-source traffic risk superposition on mega-events. Then a time-series generative adversarial networks (TimeGAN) is used to predict the evolution of superposition risk. We select 2022 Beijing Winter Olympics as a case to explore the superposition effects of different traffic risks on the convoy entrance of this mega-event. The results illustrate the superposition risks are significantly associated with the strength of each traffic risk, and the distance from the traffic risk location to the convoy entrance. Furthermore, the temporal evolutions for different traffic risks and their superposition are forecasted using TimeGAN. The results show the unexpected traffic congestion risk presents the highest predictive performance (i.e., the average error for RMSE, MAE, and MSE is 0.135%) and the superposition traffic risks present the lowest predictive performance (the average error is 0.536%). Comparison between different methods demonstrates TimeGAN outperforms other methods in predicting both single traffic risks and superposition risks. The research findings could be potentially referenced in multi-source traffic risk management for mega-events.

In road transport systems, various traffic risks in certain condition could produce joint actions, which increases the complexity of traffic risk assessment. Previous single risk assessment fails to reflect the superposition effect of multi-type traffic risks, so the result may underestimate or overrate the total risk strength of transport system. To address this problem, a novel risk assessment perspective that aims to evaluate the superposition effect of several traffic risks is studied. In this study, the risk quantification and standardization for single traffic risks is conducted first. Then a GARCH-VaR model is developed to explore the superposition impact of these single traffic risks. The GARCH-VaR model integrates the VaR theory and GARCH model, from which the superposition traffic risk is obtained by assigning every single traffic risk a reasonable weight. Finally, an improved k-means clustering algorithm is proposed to classify the superposition risk level. Empirical results demonstrate that the superposition risk of crash risk and congestion risk is lower than a single traffic risk in certain condition, which attributes to the weak interactions between various traffic risks. This finding illustrates the superposition risk does not necessarily go up with the increase of the risk category. Then the superposition risks are classified into high-risk level, moderate-risk level, and low-risk level, among which the classification accuracy of high-risk level is 92.85%-95.23%. The proposed method provides a theoretical reference for collaborative assessment of multi-type traffic risks, and the results could be potentially used in the comprehensive management of traffic risks.

This study proposes a short-term traffic flow prediction framework. The vector autoregression (VAR) model based on econometric theory and the CNN-LSTM hybrid neural network model based on deep learning are employed in the analysis. An intrinsic association among traffic variables is first evaluated using the VAR model, and the predictable relationship of these variables is determined. Then the multi-features speed prediction for one spatial location using the CNN-LSTM hybrid neural network model is conducted, the prediction results prove that prediction with multi-feature is better than that with a single feature. Subsequently, several popular deep learning models and other shallow predicted models are proposed to be compared with the constructed CNN-LSTM network model, and the comparison illustrates that the model performance of the developed CNN-LSTM network model is superior to other models in forecasting the short-term traffic flow. Then the multi-feature speed predictions for a group of spatial locations are further conducted using the CNN-LSTM model. The result demonstrates the predictive accuracies are associated with the spatial correlation of traffic flow. Finally, a heatmap is produced to visualize the predicted speed, from which the spatial-temporal traffic condition can be presented clearly. The research results have the potential to be applied to the travel information releasing and traffic congestion management.

Proactive traffic safety management systems can reduce crashes by identifying crash precursors, evaluating real-time crash risks, and implementing suitable interventions. The basic prerequisite for developing such a system is to propose a reliable crash risk evaluation model that takes real-time traffic flow data as input. Previous studies have primarily focused on real-time crash prediction using some statistical or machine-learning methods. However, further quantitative evaluation and classification of crash risks have been ignored. In this study, we conduct a systematic crash risk evaluation workflow, including crash risk prediction, crash risk quantification, and crash risk classification. Specifically, the crash risk prediction using an extended logit model is proposed, from which CAS, CSD, UAS, DAS, DTV are identified to be contributing factors of crash risks. Then a crash risk quantification model based on the parameter evaluation of the extended logit model is developed. The crash risks of urban expressways and their spatial-temporal evolution trends are quantified. Finally, the crash risks are classified into high crash risk level, moderate crash risk level, and low crash risk level by the k-means cluster algorithm. Then the threshold boundaries of different crash risk levels are determined. The research results provide a proactive guidance for traffic safety management of urban expressways.