Effects of assignments of dedicated automated vehicle lanes and inter-vehicle distances of automated vehicle platoons on car-following performance of nearby manual vehicle drivers.

Deploying dedicated lanes for automated vehicles (AVs) can effectively alleviate the coordination issues between AVs and manual vehicles (MVs). However, AV platoons running on dedicated AV lanes (DAVLs) have a prominent collective behavior characteristic of small inter-vehicle distance. The nearby MV drivers' imitation of this characteristic may reduce their car-following time headway (THW). The researchers conducted a simulation experiment to investigate the influence of DAVL assignments, inter-vehicle distances of AV platoons and AV platoon speed on the car-following performance of nearby MV drivers. The data of mean THW, standard deviation of THW, standard deviation of lateral position, standard deviation of velocity, standard deviation of horizontal gaze position and mean saccadic peak velocity were collected from 36 participants. Statistical analysis results show that the three factors considerably affected the MV drivers' car-following performance. In particular, the MV drivers showed a worse car-following safety but a better driving stability when the left lane was dedicated to AVs than when the right lane was dedicated to AVs (Note the experiments were done in a drive-on-the-left environment.). With respect to the inter-vehicle distances of AV platoons, the MV drivers' car-following safety was poorer under the 4 m condition than that under the 10 and 18 m conditions. In addition, the MV drivers showed a poorer car-following safety and bore a larger mental workload when driving next to the AV platoons running at 110 km/h. This study may provide some suggestions for DAVLs. Assigning the right lane of a three-lane motorway as the DAVL may have a slighter negative impact on the nearby MV drivers in China. In terms of traffic management in DAVLs, the inter-vehicle distance of AV platoons can be reduced to 10 m, and the speed of AVs should not be higher than the design speed of adjacent MV lanes.

Modeling takeover behavior in level 3 automated driving via a structural equation model: Considering the mediating role of trust.

The takeover process in level 3 automated driving determines the controllability of the functions of automated vehicles and thereby traffic safety. In this study, we attempted to explain drivers' takeover performance variation in a level 3 automated vehicle in consideration of the effects of trust, system characteristics, environmental characteristics, and driver characteristics with a structural equation model. The model was built by incorporating drivers' takeover time and quality as endogenous variables. A theoretical framework of the model was hypothesized on the basis of the ACT-R cognitive architecture and relevant research results. The validity of the model was confirmed using data collected from 136 driving simulator samples under the condition of voluntary non-driving-related tasks. Results revealed that takeover time budget was the most critical factor in promoting the safety and stability of takeover process, which, together with traffic density, drivers' age and manual driving experience, determined drivers' takeover quality directly. In addition, the pre-existing experience with an automated system or a similar technology and self-confidence of the driver, as well as takeover time budget, strongly influenced the takeover time directly. Apart from the direct effects mentioned above, trust, as an intermediary variable, explained a major portion of the variance in takeover time. Theoretically, these findings suggest that takeover behavior could be comprehensively evaluated from the two dimensions of takeover time and quality through the combination of trust, driver characteristics, environmental characteristics, and vehicle characteristics. The influence mechanism of the above factors is complex and multidimensional. In addition to the form of direct influence, trust, as an intermediary variable, could reflect the internal mechanism of the takeover behavior variation. Practically, the findings emphasize the crucial role of trust in the change in takeover behavior through the dimensions of subjective trust level and monitoring strategy, which may provide new insights into the function design of takeover process.

Research on the Relationship between Reaction Ability and Mental State for Online Assessment of Driving Fatigue.

Background: Driving fatigue affects the reaction ability of a driver. The aim of this research is to analyze the relationship between driving fatigue, physiological signals and driver's reaction time. Methods: Twenty subjects were tested during driving. Data pertaining to reaction time and physiological signals including electroencephalograph (EEG) were collected from twenty simulation experiments. Grey correlation analysis was used to select the input variable of the classification model. A support vector machine was used to divide the mental state into three levels. The penalty factor for the model was optimized using a genetic algorithm. Results: The results show that α/ß has the greatest correlation to reaction time. The classification results show an accuracy of 86%, a sensitivity of 87.5% and a specificity of 85.53%. The average increase of reaction time is 16.72% from alert state to fatigued state. Females have a faster decrease in reaction ability than males as driving fatigue accumulates. Elderly drivers have longer reaction times than the young. Conclusions: A grey correlation analysis can be used to improve the classification accuracy of the support vector machine (SVM) model. This paper provides basic research that online detection of fatigue can be performed using only a simple device, which is more comfortable for users.