Driving behaviour is a complex and multidisciplinary research domain, and bad driving behaviours that threaten the safety of road users should be refrained. In order to better educate, manage, and restrain driver’s behaviours, from the perspective of human factors and psychology, the present study deconstructed driving behaviours based on theory of planned behaviour (TPB) into five categories: mistakes (Mis), lapses and slips (LaS), violations (Vio), driving experience (Exp), and safety attitude and awareness (SAA). According to today’s practical traffic rules and conditions in China, a driving behaviour questionnaire was built as an analysing tool and the survey data were collected in accordance with the demographic of Chinese drivers. Furthermore, a driving behaviour analysis model contains the aforementioned categories was established by using the structural equation model (SEM). Through the path analysis results among latent variables and manifested variables, it was found that Exp has an impact on Vio and LaS, and better SAA can inhibit Vio and Mis. In conclusion, the prime aim of improving road traffic safety is to reduce Vio by means of educating and improving the drivers’ Exp and SAA. Moreover, drivers’ LaS and Mis are transition processes which should be corrected timely and prevented from continuing to evolve into Vio.

Compared with the traditional environment, a connected environment in which multiple types of intelligent vehicles share roads will be more complicated. However, do changes in the traffic environment affect the driver's distraction level and distraction recognition? Relatively few studies have addressed these questions. This study constructs traditional and connected environments and conducts driving simulation experiments. Distracted driving data for 60 drivers were collected in each environment, and the one‐way repeated analysis of variance (ANOVA) method was used to select the distraction indices with significant differences as the input indices. An independent t ‐test was applied to analyse changes in the distraction indices in the two environments. The results show that the distraction indices, including the standard deviation (SD) of the horizontal fixation angles, blink frequency, mean pupil diameter, mean saccade speed, SD of the steering wheel angle, mean speed and SD of the lane acceleration, differ in the two environments; therefore, the distraction recognition model in the traditional environment is no longer suitable for connected environments. A distraction state recognition model developed with a stacking‐based ensemble learning algorithm composed of a support vector machine (SVM), extreme gradient boosting (XGBoost), long short‐term memory (LSTM), and bi‐directional LSTM with an attention mechanism (AT‐Bi‐LSTM) was proposed for each environment. The recognition accuracy reaches 95.71% in the traditional environment, and the accuracy rate reaches 97.92% in the connected environment. Compared with traditional algorithms, the proposed approach yields greater recognition accuracy and can be applied in intelligent vehicle distraction early warning assistance systems to improve road safety.

Distracted driving is a dominant cause of traffic accidents. In addition, with the rapid development of intelligent vehicles, mixed traffic environments are expected to become more complicated with multiple types of intelligent vehicles sharing the road, thereby increasing the opportunities for distracted driving. However, the existing research on detecting driver distraction in mixed traffic environments is limited. Therefore, in this study, we analysed the effect of cognitive distraction on the driver physiological measures and driving performance in traditional and mixed traffic environments and compared the parameters extracted in the two environments. Sixty drivers were involved in the data collection, which included normal driving and two distracting tasks while driving in a simulator. Repeated-measures analysis of variance (ANOVA) was performed to examine the effect of cognitive distraction and traffic environments on all parameters. The results indicate that the effects of the pupil diameter, standard deviations (SDs) of the horizontal and vertical fixation angles, blink frequency, speed, SD of the lane positioning (SDLP), SD of the steering wheel angle (SDSWA), and steering entropy (SE) were significant. These findings provide a theoretical foundation for identifying the most appropriate parameters to detect cognitive distraction in traditional and mixed traffic environments to help reduce traffic accidents.

Distracted driving has become a growing traffic safety concern. With advances in autonomous driving and connected vehicle technology, a mixture of various types of intelligent vehicles will become normal in the near future, while more factors that may cause driver cognitive distraction are emerging. However, there are rarely studies on distracted driving in mixed traffic environments. To fill this gap, we conducted a natural driving experiment with three representative events at a nonsignalized intersection in a mixed traffic environment and proposed a novel method of identifying cognitive distraction based on bidirectional long short‐term memory (Bi‐LSTM) with attention mechanism. Forty participants were recruited for each event, who completed three different cognitive distraction experiments induced by three different secondary tasks in contrast with a normal driving process when passing a nonsignalized intersection. Related driving performance and eye movement data were collected to train and test the Bi‐LSTM with attention mechanism model. Compared with the support vector machine (SVM) model, its recognition accuracy rate is 94.33%, which is 3.83% higher than that of the SVM in the total event, which has reasonable applicability for distraction recognition in a mixed traffic environment. Potential applications of this model include distraction alarm and autonomous driving assistance systems, which could avoid road traffic accidents.

Vehicle control requirements for longitudinal and lateral driver control are varied in different road geometries; this makes it irrational and superfluous to represent driving control characteristics with repetitive indices. To address this problem, the present study used multiple cross-analysis methods of vehicle running state parameters from experienced drivers in order to deeply study driving control characteristics in different road geometries. Six common road geometries with different driving control emphases were selected as typical road types and twenty-five experienced drivers were asked to perform an actual driving test. Taking the indices in the long straight road as the control variable, the indices in other roads were compared with it and judged according to the three methods: the overall distribution by box plots, significant difference test by analysis of variance (ANOVA) and relative distance calculation by technique for order preference by similarity to an ideal solution (TOPSIS). Moreover, the weight of the driving control characteristic index was calculated through the entropy weight method to reflect its importance. In this paper, the relationships between road geometry and driving control characteristics explicate the influence mechanism and interaction of road geometry on driving behavior, and the indicators that can reflect the control characteristics in different road types are obtained.