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Merging behavior requires multiple tasks such as cognition, decision-making, and driving operation. Previously, driving assistance systems, which instruct drivers on making accelerations, have been studied to support the decision-making task. The importance of improving driver comfort with adjusting system variables has been revealed through these studies. The present study aims to propose assistance methods for merging, which decreases driver’s workload and difficulty in decision-making. The proposed methods recognize drivers’ decision ambiguity using a decision-making model for respective drivers and instruct them on acceleration to decrease the ambiguity. First, we develop a decision-making model to predict where drivers merge based on a logistic function. Furthermore, we propose acoustic assistance methods, which instruct the acceleration and deceleration. The systems continuously calculate the optimal instruction based on driving history from the beginning of the assistance. Driving simulator experiments demonstrated that drivers’ workload and decision ambiguity decreased with our proposed methods.

There has been an increase in the number of on-road vehicles of all types, especially in some developing countries. The rise of traffic heterogeneity causes larger mixed traffic congestion. This study examines the impact of next-nearest leading vehicles on the driving of following drivers in mixed traffic. Although previous studies reported that traffic stability can be improved with the introduction of followers' anticipatory driving that refers to multiple leaders, the effect of anticipatory driving on mixed traffic has not yet been examined. Using data collected from experiments conducted with groups of two and three vehicles, we found that operational delay, maximum acceleration and deceleration of the followers were affected by the presence of next-nearest leaders. In addition, we developed regression models of the affected followers' behaviours with respect to the next-nearest leaders and identified the factors affecting these behaviours. For example, the followers’ deceleration is directly affected by the height of the next-nearest leading vehicles. Hence, the model parameters for determining the deceleration of following vehicles should take the height of the next-nearest leading vehicle into consideration. Finally, based on the regression models, we estimated values of parameters in the intelligent driver model when the type of the next-nearest leader was changed. Stability analysis based on these estimated parameters implied that a tall or short next-nearest leader with a large engine power would stabilise traffic when anticipatory driving of followers is possible. Keywords: Transportation engineering, Mixed traffic, Car-following behaviour, Next-nearest leader, Traffic stability, Intelligent driver model

The dynamics of mixed traffic were analysed by examining the driving features of various vehicles based on their car-following behaviours in a test-driving circuit. By applying decision tree analys...

The analysis of the microscopic view of mixed traffic offers a basis for resolving traffic jams which are inhomogeneous due to several types of vehicles. In this study, we research the dependence of driving characteristics upon vehicle order in a platoon. By focusing particularly upon the manner in which the driving characteristics of a follower are affected by both their own vehicle type and that of their leader, we measured the trajectories of platoons comprising two vehicles selected from motorcycles, passenger cars, and trucks on a test course. Analysis based on vehicle order showed that the vehicle types of both the leader and the follower as well as the leader’s driving characteristics affected the velocity, acceleration, deceleration, operational delay of followers, and the distance gap between leaders and followers in different ways. In addition, we investigated the factors affecting driving characteristics by multiple regression analysis. We revealed that the operational delay and the maximum distance gap tend to be large when the length of leaders is large. Furthermore, as long as a follower can follow, we need not consider vehicle types among the parameters determining maximum velocity in car-following models. The vehicle types of the leader and the follower should be considered to determine maximum acceleration. When determining maximum deceleration, the vehicle types of the follower should be considered.

While automated driving systems (ADSs), which correspond to level-two automated driving of Society of Automotive Engineers (SAE), are being developed actively, the ADS that learns the preference of drivers at an opportune time is required. In addition, there is a lack of assessments on ADS comfort. In this study, we focused on collision avoidance and developed an ADS that learns driver preference by driver intervention. By applying the gain-tuning and learning methods for gradual learning, the ADS can stably modify its behavior at an opportune time. The results of a driving simulator experiment indicated that driver comfort improved by using the proposed ADS. In addition, we confirmed that the ADS can guarantee usability during authority transfer in the learning process.