Simulation-Based Evaluation of Variation in Left-Turn Paths in the Coordinated Intersection Management

Coordinated intersection management (CIM) has gained more attention with the advance of connected and autonomous vehicle technology. The optimization of passing schedules and conflict separation between conflicting vehicles are usually conducted based on the predefined travelling paths through the intersection area in the CIM. In real-world implementation, however, the diversity of turn paths exists due to multiple factors such as various vehicle sizes and automation control algorithms. The aim of this paper is to investigate how the variation in left-turn paths affects the feasibility and viability of optimal passing schedules, as well as the safety and efficiency of intersection operation. To do this, we start with identifying six typical left-turn paths to represent the variation. A scenario-based simulation is first conducted by using each of the paths as the nominal path. The optimal schedules and the corresponding alternative schedules are generated to calculate indicators for nominal performance, average performance, and robustness. The best path is selected in terms of schedule optimality and robustness. With schedules obtained by solving CIM models using the selected path, the left-turning CAVs are assumed to travel along one of the six paths randomly to simulate the path divergence. A surrogate safety measure, PET, is utilized to assess the safety of the intersection under CIM. The theoretical PET with the nominal path and the actual PET with the random path are calculated for each conflict event. Comparisons of two PET sets show the increase in conflict risk and vehicle delay. The conclusion can be drawn that the variation in left-turn paths causes the decline in safety level and travelling efficiency and should be considered in the CIM model to ensure safe and efficient implementation in the intersection.

The Human—Unmanned Aerial Vehicle System Based on SSVEP—Brain Computer Interface

The brain–computer interface (BCI) is a mechanism for extracting information from the brain, with this information used for various applications. This study proposes a method to control an unmanned aerial vehicle (UAV) flying through a BCI system using the steady-state visual evoked potential (SSVEP) approach. The UAV’s screen emits three frequencies for visual stimulation: 15, 23, and 31 Hz for the UAV’s left-turn, forward-flight, and right-turn functions. Due to the requirement of immediate response to the UAV flight, this paper proposes a method to improve the accuracy rate and reduce the time required to correct instruction errors in the resolution of brainwave signals received by UAVs. This study tested ten subjects and verified that the proposed method has a 10% improvement inaccuracy. While the traditional method can take 8 s to correct an error, the proposed method requires only 1 s, making it more suitable for practical applications in UAVs. Furthermore, such a BCI application for UAV systems can achieve the same experience of using the remote control for physically challenged patients.

Left-Turn Driving Behavior Based On Situational Anger: Day Or Night

Anger is a key factor affecting drivers' subjective judgment and driving skills. The influence of anger on driving behavior has been widely studied, but there is a lack of comparative research under different lighting conditions. Through driving simulation experiment, this paper studies the influence of anger on left-turn driving behavior under two light conditions of day and night. In the experiment, 32 licensed participants were divided into two groups, one in emotional neutrality and the other in anger. Among them, the emotional state of anger is induced by a traffic related video. The results showed that compared with daytime participants, participants at night had higher anger intensity, shorter gap acceptance and post encroachment time (PET) when left-turn driving. In addition, compared with emotion neutral participants, angry participants tended to accept shorter gap acceptance and post encroachment time (PET) when turning left. This indicates that participants failed to respond correctly to left-turn driving behavior in a state of anger. However, the response of gender differences to situational driving anger was not affected by light conditions. The anger intensity of male participants during the day and night was higher than that of female participants, and the gap acceptance and post encroachment time (PET) during left-turn were shorter than that of female participants. This shows that male participants are more likely to produce high-intensity anger and are more likely to make dangerous driving decisions in a state of anger. This paper puts forward some suggestions on the identification of anger and the prevention of angry driving.

Improve urban traffic efficiency with prohibiting left-turn policy

Enhancing traffic flow plays an important role in the traffic management of urban arterial networks. The policy of prohibiting left-turn (PLT) at selected highly demanded intersections has been adopted as an attempt to increase the efficiency at these intersections. In this paper, we study the impact of PLT by mathematical analysis and simulations based on the cellular automaton model. Using the flow-density relation, three system performance indexes are examined: the average trip completion rate, the average traffic flow, and the average velocity of vehicles. Different route guidance strategies, including the shortest path and the quickest path, are investigated. We show that when left turn is prohibited, vehicles are distributed more homogeneously in the road network, and the system performs better and reaches a higher capacity. We also derive a critical length of link, above which the benefit of PLT will decrease.