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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...

In this study, we focus on the effects of the lateral shift of leading vehicles (leaders) and the steering operation of following vehicles (followers) on the response time and deceleration of the followers. A physical investigation on heterogeneous traffic with weak lane discipline is required because of heavy congestion in certain developing countries. Two-dimensional microscopic traffic models enabled researchers to simulate vehicles’ acceleration in traffic with weak lane discipline. However, to the best of our knowledge, there is a lack of research that discusses acceleration considering the variation of response time caused by lateral shifting. We conducted experiments with a driving simulator and observed the followers’ behaviors when the leaders suddenly commenced deceleration. The distributions of the deceleration and response time depending on the leaders’ position were different under respective conditions of steering operation and relative positions.

Road accidents are among the leading cause of death worldwide, with developing countries paying the highest toll. Among the different road users, pedestrians are the most vulnerable. A better understanding of the relation occurring between car drivers and pedestrians when they interact is necessary for the development of new research approaches and their fall in education/prevention. Given the risks involved in the study of car-pedestrian interactions, experimental studies are difficult and only simulators represent a feasible alternative. However, the high cost of driving simulators prevent developing countries from investing on these tools. In this research, we considered the possibility to use a simple low-cost (below 500 $) virtual reality (VR) based driving simulator to study the interaction between cars and pedestrians at unsignalized crosswalks. Young adults and elderlies were asked to drive in a virtual environment where simulated pedestrians would pass randomly on crosswalks. Driving behavior was numerically determined for both age groups using speed, time gap and driving accuracy. The obtained results fit with the behavior observed in real conditions, suggesting the empowerment of the application of low-cost VR-based systems for traffic safety research. Results on usability indicated the existence of discomfort during the use of VR tools, suggesting deeper studies on digital-divide issues.

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.

The number of vehicles on the road is increasing across all vehicle types, especially in developing countries. The rise of heterogeneity in traffic causes greater mixed traffic congestion. This study focuses on 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 references multiple leaders, these works did not consider whether anticipatory driving occurred in mixed traffic. Using data collected in experiments with groups of two and three vehicles, we found that the operational delay and the maximum acceleration and deceleration of the followers were affected by the presence of next-nearest leaders. In addition, we also found that the height of the next-nearest leading vehicles affected followers’ deceleration. These findings imply that model parameters for determining the deceleration of following vehicles should take the height of the next-nearest leading vehicle into account.

We quantitatively clarified that, in an actual mixed traffic, the distributions of the types of leaders and followers for each vehicle type have biases toward the distributions predicted based on the number of each type of vehicle. Vehicular traffic in some countries is heterogeneous, having weak lane-discipline, i.e., 2D mixed traffic. Previous studies have clarified that the order of vehicle types in a vehicle platoon influences the traffic properties. It has been further suggested that a certain vehicle type, e.g., motorcycles, exhibits herding or grouping behavior. However, such grouping phenomena have not been quantitatively investigated. In this study, we aim to extract the vehicle combinations frequently observed in an actual 2D mixed traffic. By utilizing video footage that was shot overlooking a road in Mumbai, India, the extended vehicle order, i.e., leader–follower network, was detected first. Then, we compared the observed distribution of leader–follower combinations and the estimated distribution based on expectations calculated using the number of each type of vehicle, which revealed discrepancies between these two distributions in the leader–follower network. For example, motorcycles more frequently became leaders and followers of themselves than estimated. Moreover, they less frequently had leader–follower relationships with other types of vehicles. Specifically, auto-rickshaws and motorcycles are mutually exclusive. Based on these obtained results, two groups appear to exist: a group of motorcycles and another group consisting of auto-rickshaws, cars, and heavy vehicles. Our results implied that segregation phenomena comprises “isolation,” as well as filtering, gathering, and dispersion, as proposed in a previous study. The results also showed that not only size but even agility (i.e., quickness in longitudinal acceleration or deceleration) and speed play different roles for each type of vehicle in the respective segregation process, though the cause of the mutual exclusiveness between motorcycles and auto-rickshaws is still unclear. From the asymmetry of the frequency of leader–follower relationships, we proposed two types of preference, i.e., “potentially desiring” and “unintentionally attracted.”