Driving safety in tunnels has always been an issue of great concern. Establishing delineators to improve drivers’ instantaneous cognition of the surrounding environment in tunnels can effectively enhance driver safety. Through a simulation study, this paper explored how delineators affect drivers’ gaze behavior (including fixation and scanpath) in tunnels. In addition to analyzing typical parameters, such as fixation position and fixation duration in areas of interest (AOIs), by modeling drivers’ switching process as Markov chains and calculating Shannon’s entropy of the fit Markov model, this paper quantified the complexity of individual switching patterns between AOIs under different delineator configurations and with different road alignments. A total of 25 subjects participated in this research. The results show that setting delineators in tunnels can attract drivers’ attention and make them focus on the pavement. When driving in tunnels equipped with delineators, especially tunnels with both wall delineators and pavement delineators, the participants exhibited a smaller transition entropy $H_t$ and stationary entropy $H_s$ , which can greatly reduce drivers’ visual fatigue. Compared with left curve and right curve, participants obtained higher $H_t$ and $H_s$ values in the straight section.

The impact of work zones on traffic is a common problem encountered in traffic management. The reconstruction of roads is inevitable, and it is necessary and urgent to reduce the impact of the work zone on the operation of traffic. There are many existing research results on the influence of highway work zones, including management strategies, traffic flow control strategies, and various corresponding model theories. There are also many research results on the impacts of urban road and subway construction on traffic operation, including construction efficiency, economic impact, and travel matrix. However, there are few studies concerning the choice of work zone location, and most previous studies have assumed that the work zone choice was scientific and reasonable. Therefore, it is reasonable to choose the location of the work zone and to assess whether there is room for improvement in the road form of the work zone, but this remains a research gap. Therefore, we studied a seven-lane main road T-intersection in Xi’an, China, and investigated a work zone located at this intersection that caused a road offset, leading to the non-aligned flow of main traffic. We designed two road improvement schemes and multiple transition schemes, used VISSIM software to evaluate the traffic operation of the two schemes, and used the entropy method to choose the suitability of the two schemes under different conditions. According to the results, in the best case, the driving time, delay, and number of stops are reduced by 44%, 66%, and 92%.

Poor visual conditions in tunnels can easily cause traffic accidents, and it is difficult for emergency services to reach these areas. As an economical and effective visual guiding device, delineators have attracted wide attention. Based on the actual alignment of the Qinling Mountain No.1, No.2 and No.3 tunnels of the G5 Expressway in Xi’an City (Shaanxi Province, China), this paper designs a simulation experiment. Through a simulator study and a questionnaire survey, this paper discusses how delineators affect drivers’ visual characteristics (including fixation area and pupil size) in different settings and with different road alignments. Twenty-five subjects participated in this research. The results show that setting delineators in tunnels can continuously guide drivers’ vision and attract their attention to focus on the pavement. Compared with setting only pavement delineators, setting wall delineators and pavement delineators together can provide better guiding effects and ensure driving safety in both straight and curved sections. In addition, when driving in tunnels equipped with delineators, especially tunnels with both wall delineators and pavement delineators, the participants exhibited a smaller pupil diameter and lower pupil diameter change rate. In terms of the relationship between pupil size and road alignment, the results indicated that regardless of what type of delineator was used, the drivers exhibited the smallest pupil size and lowest pupil change rate when driving on the straight section compared with the curved sections.

Direct left turns (DLTs) could cause traffic slowdown, delay, stops, and even accidents on intersections, especially on no-median roads. Channelization and signalization can significantly diminish negative impact of DLTs. In China, a total of 56 large and medium-sized cities, including 17 provincial capitals, have adopted vehicle restriction policies due to traffic congestion, vehicle energy conservation and emission reduction, which cause travel inconvenience for citizens. This paper mainly studies signalization and channelization selections at intersections based on an entropy method. Based on the commonly used three evaluation indexes, the number of vehicles, CO emissions and fuel consumption have been added. The entropy evaluation method (EEM) method is innovatively used to objectively calculate the weight of the six indexes, which carry out the optimal traffic volume combinations for intersections of present situation, channelization and signalization. A VISSIM simulation is also used to evaluate the operating status of three conditions. The results show that EEM could help enormously in choosing different methods at a certain intersection. With the EEM, six indexes decrease by 20–70% at most.

In complex environments, autonomous navigation for quadrotor drones presents challenges in terms of obstacle avoidance and path planning. Traditional artificial potential field (APF) methods are plagued by issues such as getting stuck in local minima and inadequate handling of dynamic obstacles. This paper introduces a layered obstacle avoidance structure that merges vortex artificial potential (VAPF) fields with reinforcement learning (RL) for motion control. This approach dynamically adjusts the target position through VAPF, strategically guiding the drone to avoid obstacles indirectly. Additionally, it employs the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm to facilitate the training of the motion controller. Simulation experiments demonstrate that the incorporation of the VAPF effectively mitigates the issue of local minima and significantly enhances the success rate of drone navigation, reduces the average arrival time and the number of sharp turns, and results in smoother paths. This solution harmoniously combines the flexibility of VAPF methods with the precision of RL for motion control, offering an effective strategy for autonomous navigation of quadrotor drones in complex environments.

Consensus tracking control for multiple UAVs demonstrates critical theoretical value and application potential, improving system robustness and addressing challenges in complex operational environments. This paper addresses the challenge of event-triggered prescribed-time synchronization tracking control for 6-DOF fixed-wing UAVs with state constraints. We propose a novel prescribed-time command filtered backstepping approach to effectively tackle the issues of complexity explosion and singularities. By utilizing a state-transition function, we manage asymmetric time-varying state constraints, including limitations on speed, roll, yaw, and pitch angles in UAVs. The theoretical analysis demonstrates that all signals in the 6-DOF UAV system remain bounded, with tracking errors converging to the origin within the prescribed time. Finally, simulation results validate the effectiveness of the proposed control strategy.