Complex Traffic Conditions Research Articles

Dynamic Path Planning Method for Unmanned Surface Vessels in Complex Traffic Conditions of Island Reefs Waters

Unmanned Surface Vehicles (USVs) operating in complex traffic conditions in island reef waters often require different types of algorithms. Therefore, selecting a dynamic path-planning algorithm with strong adaptability has become a new challenge. This paper proposes a dynamic adaptive path planning algorithm for USVs, incorporating an improved Dynamic Window Approach (DWA) with fuzzy logic and the International Regulations for Preventing Collisions at Sea (COLREGS). The algorithm is designed by integrating three key aspects: evaluation function, fuzzy control, and COLREGS. First, to enable USVs to approach the target point more safely and quickly during navigation, an additional target point attraction sub-function is introduced, extending the original evaluation function. Furthermore, to ensure robust dynamic path planning for USVs across various water environments, such as narrow channels, reef-laden waters, and open seas, fuzzy logic is integrated with the improved DWA algorithm. Since USVs must comply with COLREGS during navigation, the algorithm incorporates these regulations, enhancing the DWA algorithm with fuzzy logic to ensure compliance. Finally, simulation experiments validate the proposed algorithm, demonstrating that the planned paths are safer and more stable, ensuring the safe navigation of USVs in compliance with COLREGS.

Intelligent Fuzzy Traffic Signal Control System for Complex Intersections Using Fuzzy Rule Base Reduction

In this study, the concept of symmetry is employed to implement an intelligent fuzzy traffic signal control system for complex intersections. This approach suggests that the implementation of reduced fuzzy rules through the reduction method, without compromising the performance of the original fuzzy rule base, constitutes a symmetrical approach. In recent decades, urban and city traffic congestion has become a significant issue because of the time lost as a result of heavy traffic, which negatively affects economic productivity and efficiency and leads to energy loss, and also because of the heavy environmental pollution effect. In addition, traffic congestion prevents an immediate response by the ambulance, police, and fire brigades to urgent events. To mitigate these problems, a three-stage intelligent and flexible fuzzy traffic control system for complex intersections, using a novel hybrid reduction approach was proposed. The three-stage fuzzy traffic control system performs four primary functions. The first stage prioritizes emergency car(s) and identifies the degree of urgency of the traffic conditions in the red-light phase. The second stage guarantees a fair distribution of green-light durations even for periods of extremely unbalanced traffic with long vehicle queues in certain directions and, especially, when heavy traffic is loaded for an extended period in one direction and the short vehicle queues in the conflicting directions require passing in a reasonable time. The third stage adjusts the green-light time to the traffic conditions, to the appearance of one or more emergency car(s), and to the overall waiting times of the other vehicles by using a fuzzy inference engine. The original complete fuzzy rule base set up by listing all possible input combinations was reduced using a novel hybrid reduction algorithm for fuzzy rule bases, which resulted in a significant reduction of the original base, namely, by 72.1%. The proposed novel approach, including the model and the hybrid reduction algorithm, were implemented and simulated using Python 3.9 and SUMO (version 1.14.1). Subsequently, the obtained fuzzy rule system was compared in terms of running time and efficiency with a traffic control system using the original fuzzy rules. The results showed that the reduced fuzzy rule base had better results in terms of the average waiting time, calculated fuel consumption, and CO2 emission. Furthermore, the fuzzy traffic control system with reduced fuzzy rules performed better as it required less execution time and thus lower computational costs. Summarizing the above results, it may be stated that this new approach to intersection traffic light control is a practical solution for managing complex traffic conditions at lower computational costs.

A New Accurate Aircraft Trajectory Prediction in Terminal Airspace Based on Spatio-Temporal Attention Mechanism

Trajectory prediction serves as a prerequisite for future trajectory-based operation, significantly reducing the uncertainty of aircraft movement information within airspace by scientifically forecasting the three-dimensional positions of aircraft over a certain period. As convergence points in the aviation network, airport terminal airspace exhibits the most complex traffic conditions in the entire air route network. It has stronger mutual influences and interactions among aircraft compared to the en-route phase. Current research typically uses the trajectory time series information of a single aircraft as input for subsequent predictions. However, it often lacks consideration of the close-range spatial interactions between multiple aircraft in the terminal airspace. This results in a gap in the study of aircraft trajectory prediction that couples spatiotemporal features. This paper aims to predict the four-dimensional trajectories of aircraft in terminal airspace, constructing a Spatio-Temporal Transformer (ST-Transformer) prediction model based on temporal and spatial attention mechanisms. Using radar aircraft trajectory data from the Guangzhou Baiyun Airport terminal airspace, the results indicate that the proposed ST-Transformer model has a smaller prediction error compared to mainstream deep learning prediction models. This demonstrates that the model can better integrate the temporal sequence correlation of trajectory features and the potential spatial interaction information among trajectories for accurate prediction.

A discrete-event simulation study for pre-hospital emergency systems

ABSTRACT Ambulance dispatch, deployment, and relocation are crucial decisions in pre-hospital emergency medical services (EMS) that significantly impact response times. In metropolitan areas, the rapid movement of people and complex traffic conditions create a challenging environment for effective decision-making. This paper presents a discrete-event simulation framework designed to evaluate and improve pre-hospital emergency systems by capturing core EMS workflows and incorporating comprehensive, dynamic data. To demonstrate the practical application of this framework, a simulation instance is implemented and validated in the context of downtown Shanghai, with an analysis of key performance indicators for EMS in this area. Additionally, we propose a dynamic ambulance relocation model to improve the alignment between ambulance deployment plans and emergency calls. Simulation experiments show that dynamic ambulance relocation effectively reduces response times. Due to its ability to integrate real-time data, this framework has the potential to support immediate decision-making and evaluate proposed strategies in the real world.

Application of Large Language Models and Assessment of Their Ship-Handling Theory Knowledge and Skills for Connected Maritime Autonomous Surface Ships

Maritime transport plays a critical role in global logistics. Compared to road transport, the pace of research and development is much slower for maritime transport. It faces many major challenges, such as busy ports, long journeys, significant accidents, and greenhouse gas emissions. The problems have been exacerbated by recent regional conflicts and increasing international shipping demands. Maritime Autonomous Surface Ships (MASSs) are widely regarded as a promising solution to addressing maritime transport problems with improved safety and efficiency. With advanced sensing and path-planning technologies, MASSs can autonomously understand environments and navigate without human intervention. However, the complex traffic and water conditions and the corner cases are large barriers in the way of MASSs being practically deployed. In this paper, to address the above issues, we investigated the application of Large Language Models (LLMs), which have demonstrated strong generalization abilities. Given the substantial computational demands of LLMs, we propose a framework for LLM-assisted navigation in connected MASSs. In this framework, LLMs are deployed onshore or in remote clouds, to facilitate navigation and provide guidance services for MASSs. Additionally, certain large oceangoing vessels can deploy LLMs locally, to obtain real-time navigation recommendations. To the best of our knowledge, this is the first attempt to apply LLMs to assist with ship navigation. Specifically, MASSs transmit assistance requests to LLMs, which then process these requests and return assistance guidance. A crucial aspect, which has not been investigated in the literature, of this safety-critical LLM-assisted guidance system is the knowledge and safety performance of the LLMs, in regard to ship handling, navigation rules, and skills. To assess LLMs’ knowledge of navigation rules and their qualifications for navigation assistance systems, we designed and conducted navigation theory tests for LLMs, which consisted of more than 1500 multiple-choice questions. These questions were similar to the official theory exams that are used to award the Officer Of the Watch (OOW) certificate based on the Standards of Training, Certification, and Watchkeeping (STCW) for Seafarers. A wide range of LLMs were tested, which included commercial ones from OpenAI and Baidu and an open-source one called ChatGLM, from Tsinghua. Our experimental results indicated that among all the tested LLMs, only GPT-4o passed the tests, with an accuracy of 86%. This suggests that, while the current LLMs possess significant potential in regard to navigation and guidance systems for connected MASSs, further improvements are needed.

Understanding the Interaction between Delivery Robots and Other Road and Sidewalk Users: A Study of User-generated Online Videos

The deployment of autonomous delivery robots in urban environments presents unique challenges in navigating complex traffic conditions and interacting with diverse road and sidewalk users. Effective communication between robots and road and sidewalk users is crucial to address these challenges. This study investigates real-world encounter scenarios where delivery robots and road and sidewalk users interact, seeking to understand the essential role of communication in ensuring seamless encounters. Following an online ethnography approach, we collected 117 user-generated videos from TikTok and their associated 2067 comments. Our systematic analysis revealed several design opportunities to augment communication between delivery robots and road and sidewalk users, which include facilitating multi-party path negotiation, managing unexpected robot behaviour via transparency information, and expressing robot limitations to request human assistance. Moreover, the triangulation of video and comments analysis provides a set of design considerations to realise these opportunities. The findings contribute to understanding the operational context of delivery robots and offer insights for designing interactions with road and sidewalk users, facilitating their integration into urban spaces.

Z-YOLOv8s-based approach for road object recognition in complex traffic scenarios

Object detection in road scenarios is crucial for intelligent transport systems and autonomous driving, but complex traffic conditions pose significant challenges. This paper introduces Z-You Only Look Once version 8 small (Z-YOLOv8s), designed to improve both accuracy and real-time efficiency under real-world uncertainties. By incorporating Revisiting Perspective Vision Transformer (RepViT) and C2f into the YOLOv8s framework, and integrating the Large Selective Kernel Network (LSKNet), the model enhances spatial feature extraction. Additionally, the YOLOv8s backbone is optimized with Space-to-Depth Convolution (SPD-Conv) for better small object detection. The Softpool-Spatial Pyramid Pooling Fast (SoftPool-SPPF) module ensures precise characteristic information preservation. Z-YOLOv8s improves mean average precision (mAP)@0.5 on the Berkeley Deep Drive 100 K (BDD100K) and Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) datasets by 7.3 % and 3.8 %, respectively. It also achieves accuracy increases of 5.7 % and 6.5 % in Average Precision (AP)-Small, and a real-time detection speed of 78.41 frames per second (FPS) on the BDD100K. Z-YOLOv8s balances detection precision and processing speed more effectively than other detectors, as demonstrated by experimental results and comparisons.

Predictive analytics for traffic flow optimization in urban logistics: A transformer-based time series approach.

In this study, we focus on the analysis and prediction of urban logistics traffic flow, a field that is gaining increasing attention due to the acceleration of global urbanization and heightened environmental awareness. Existing forecasting methods face challenges in processing large and complex datasets, particularly when extracting and analyzing valid information from these data, often hindered by noise and outliers. In this context, time series analysis, as a key technique for predicting future trends, becomes crucial for supporting real-time traffic management and long-term traffic planning. To this end, we propose a composite network model that integrates gated recurrent unit (GRU), autoregressive integrated moving average (ARIMA), and temporal fusion transformer (TFT), namely the GRU-ARIMA-TFT network model, to enhance prediction accuracy and efficiency. Through the analysis of experimental results on different datasets, we demonstrate the significant advantages of this model in improving prediction accuracy and understanding complex traffic patterns. This research not only theoretically expands the boundaries of urban logistics traffic flow prediction but also holds substantial practical significance in real-world applications, especially in optimizing urban traffic planning and logistics distribution strategies during peak periods and under complex traffic conditions. Our study provides a robust tool for addressing real-world issues in the urban logistics domain and offers new perspectives and methodologies for future urban traffic management and logistics system planning.

A dynamic exploratory hybrid modelling framework for simulating complex and uncertain system

Complex disaster systems involve various components and mechanisms that could interact in complex ways and change over time, leading to significant deep uncertainty. Due to deep uncertainty, decision-makers have severe inadequacy of knowledge and often encounter unpredictable surprises that may emerge in the future, thus making it difficult to specify appropriate models and parameters to describe the system of interest. In this paper, we propose a dynamic exploratory hybrid modeling framework that fits data, models, and computational experiments together to simulate complex systems with deep uncertainty. In the framework, one needs to develop multiple plausible models from a hybrid modeling perspective and perform enormous computational experiments to explore the diversity of future scenarios. Real-time data is then incorporated into diverse forecasts to dynamically adjust the simulation system. This ultimately enables an ongoing modeling and analysis process in which deep uncertainty would be gradually mitigated. Our approach has been applied to a human-involved car-following system simulation under complex traffic conditions. The results show that the proposed approach can improve the prediction accuracy while enhancing the sensitivity of the simulation system to uncertain changes in the system of interest.

Does the priority of ambulance guarantee no delay? a MIPSSTW model of emergency vehicle routing optimization considering complex traffic conditions for highway incidents.

Globally, traffic accidents on the highway network contribute significantly to a high fatality rate, drawing considerable attention from health institutions. The efficiency of transportation plays a vital role in mitigating the severe consequences of these incidents. This study delves into the issues of emergency vehicles experiencing delays despite having priority. Therefore, we construct mixed-integer linear programming with semi-soft time windows (MIPSSTW) model for optimizing emergency vehicle routing in highway incidents. We analyze the time-varying and complex traffic situations and respectively propose corresponding estimation approaches for the travel time of road segments, intersections on the urban road network, and ramp-weave sections on the highway network. Furthermore, we developed a modified cuckoo search(MCS) algorithm to solve this combinatorial problem. Optimization strategies of Lévy flight and dynamic inertial weight strategy are introduced to strengthen the exploration capability and the diversity of solution space of the CS algorithm. Computational experiments based on the Chinese emergency medical system data are designed to validate the efficacy and effectiveness of the MIPSSTW model and MCS algorithm. The results show that our works succeed in searching for high-quality solutions for emergency vehicle routing problems and enhance the efficacy of strategic decision-making processes in the realm of incident management and emergency response systems.

Hierarchical optimization control strategy for intelligent fuel cell hybrid electric vehicles platoon in complex operation conditions

Encountering complex traffic conditions such as the dynamic interference of preceding and rear vehicles and gradients, a control strategy that simultaneously considers inter-vehicle cooperative control and energy economy is one of the key technologies, that improves traffic efficiency and exploits the energy-saving potential of platoon vehicles. In this paper, a hierarchical optimization control strategy is proposed for the intelligent fuel cell hybrid electric vehicles (FCHEV) platoon in a network-connected environment. The hierarchical control framework consists of upper speed control and lower power distribution. In the upper layer, the improved particle swarm optimization (PSO) algorithm is applied to calculate the global optimal speed trajectory, and then the model predictive control (MPC) is adopted for global speed trajectory tracking and self-adaptation, which can ensure the ego vehicle tracks the pre-calculated speed trajectory, and can re-plan the vehicle speed under the condition of safety priority when sudden disturbances occur in the foreground. The lower layer utilizes Q-learning (QL) to achieve power distribution between hybrid power sources, reducing the number of fuel cell starts and stops, slowing battery degradation and improving vehicle economy. Simulation results based on complex road conditions show that the proposed controller has good energy economy and tracking performance. Under the condition of the slope, the total platoon cost of the proposed strategy is reduced by 3.99% compared with the constant speed cruise strategy, and under the interference condition, the total platoon consumption cost of the proposed strategy decreased by 6.79% compared with adaptive cruise control (ACC).

Beyond Basics: Can a Driving Simulator Reliably Reproduce Real Vehicle Dynamics?

Despite constant technological innovation, road transport remains a significant source of pollutant emissions, and effective driver-behaviour changes can be considered as solutions that can increase the sustainability of road traffic in a short period. Thus, understanding driver behaviour plays a key role in assessing traffic-related impacts. Since real-world experiments entail some risks and are often not flexible, simulator-based experiments can be relevant to studying vehicle dynamics and driver behaviour. However, the reliability of the simulation results' accuracy must be ensured. The primary objective of this paper is to present an exploratory analysis focused on the study of the reliability of a driving simulator to reproduce driving parameters that can then be used for emission estimation. For that purpose, tests were conducted by two drivers for urban and highway scenarios performed on a driving simulator and in real-world environments. Different road singularities composed events that were microscopically analysed. Second-by-second vehicle dynamic variables were recorded, and the pollutant emissions were estimated using the vehicle specific power (VSP) methodology. The results of this exploratory validation analysis showed that the total average emissions of all events were not significantly different (958.39 g for simulated and 998.06 g for empirical tests). Overall, the driving simulator can replicate vehicle dynamics from a microscopic perspective, especially for the urban scenario. This may be due to the more complex traffic conditions and road specificities that require more restrained driving behaviour. Nevertheless, VSP mode distributions did not follow the same pattern in 4 out of 10 events, meaning that the drivers displayed different behaviours in the simulated and empirical tests for those events. The relative errors range between 4 and 29% for carbon dioxide emissions and between 2 and 33% for nitrogen oxides emissions.

Toward safer highway work zones: An empirical analysis of crash risks using improved safety potential field and machine learning techniques

Due to complex traffic conditions, transition areas in highway work zones are associated with a higher crash risk than other highway areas. Understanding risk-contributing features in transition areas is essential for ensuring traffic safety on highways. However, conventional surrogate safety measures (SSMs) are quite limited in identifying the crash risk in transition areas due to the complex traffic environment. To this end, this study proposes an improved safety potential field, named the Work-Zone Crash Risk Field (WCRF). The WCRF force can be used to measure the crash risk of individual vehicles that enter a work zone considering the influence of multiple features, upon which the overall crash risk of the road segment in a specific time window can be estimated. With the overall crash risk used as a label, the time-window-based traffic data are used to train and validate an eXtreme Gradient Boosting (XGBoost) classifier, and the Shapley Additive Explanations (SHAP) method is integrated with the XGBoost classifier to identify the key risk-contributing traffic features. To assess the proposed approach, a case study is conducted using real-time vehicle trajectory data collected in two work zones along a highway in China. The results demonstrate that the WCRF-based SSM outperforms conventional SSMs in identifying crash risks in work zone transition areas on highways. In addition, we perform lane-based analysis regarding the impact of setting up work zones on highway safety and investigate the heterogeneity in risk-contributing features across different work zones. Several interesting findings from the analysis are reported in this paper. Compared to existing SSMs, the WCRF-based SSM offers a more practical and comprehensive way to describe the crash risk in work zones. The approach using the developed WCRF technique offers improved capabilities in identifying key risk-contributing features, which is expected to facilitate the development of safety management strategies for work zones.

Data Mining-Based Collision Scenarios of Vehicles and Two Wheelers for the Safety Assessment of Intelligent Driving Functions

The safety performance test of intelligent driving vehicles needs to rely on the collision scenarios in a real road traffic environment. In order to study the collision scenarios and accident characteristics of vehicles and two wheelers (TWs) in line with the complex traffic conditions in China, this paper proposes using clustering analysis to initially cluster traffic accident data to obtain the base scenarios and then applying the association rule algorithm to each base scenario to obtain the potential connection of its accident attributes and describe the collision scenarios in more detail. This study is based on data from 335 vehicle and two-wheeler crashes in the National Automobile Accident In-Depth Investigation System (NAIS). It used clustering analysis to cluster the crash data into different partitions to obtain eight clusters of vehicle and two-wheeler base scenarios and applied association rules to analyze the rest of the accident attributes, revealing common crash characteristics to describe the base scenarios in more detail. In the end, it constructed eleven types of detailed vehicle and two-wheeler collision scenarios covering straight roads, intersections, and T-junctions. The results provide richer and more suitable crash scenarios of vehicles and two wheelers in China’s complex traffic and is an important reference for the development of intelligent driving testing scenarios in the future.

Design of Active Disturbance Rejection Controller for Trajectory-Following of Autonomous Ground Electric Vehicles

In this paper, the concept of symmetry is utilized in the promising trajectory-following control design of autonomous ground electric vehicles—that is, the construction and the solution of active disturbance rejection controllers are symmetrical. This paper presents an active disturbance rejection controller (ADRC) for improving the trajectory-following performance of autonomous ground electric vehicles (AGEV) with an advanced active front steering system. Since AGEV trajectory dynamics are inherently affected by complex traffic conditions, various driving maneuvers, and other road environment, the main control objective is to deal with the AGEV trajectory control challenges of system uncertainties, system nonlinearities, and external disturbance. First, the vehicle dynamics trajectory-following model and its state space representation system are established. Then, the augmented control-oriented vehicle-trajectory-following system with dynamic error is developed. The resulting active disturbance rejection controller of the vehicle-trajectory-following system is finally designed using the trajectory performance index and active disturbance compensation, and the stability of the active disturbance rejection controller is also analyzed and derived via Lyapunov stability theory. The effectiveness of the proposed controller is validated through double lane change and serpentine maneuvers under the co-simulation platform of MATLAB/Simulink-Carsim®. Simulation results show that the designed controller provides enhanced vehicle-trajectory-following performance compared to the linear quadratic regulator controller (LQR) and model predictive controller (MPC). It will provide a certain guidance for the controller engineering design of the AGEV trajectory-following system.

Energetic Impacts of Autonomous Vehicles in Real-World Traffic Conditions From Nine Open-Source Datasets

Potential increases or decreases in energy consumption for various traffic operation modes is a research focus in light-duty autonomous vehicle (AV) studies. However, AVs have unclear energy consumption patterns compared with human-driving vehicles (HV), especially under diverse real-world traffic conditions. Considering nine open-source AV activity datasets from six nations, three nontrivial characteristics of light-duty AVs under real traffic conditions were found. 1) The energy consumption of AVs was similar to that of HVs in the low-speed range and was lower than HVs in the high-speed range. 2) The turning point between low-speed and high-speed was a variable parameter for different data sources. 3) The energy consumption of AVs may be higher than that of HVs under complex traffic conditions in the high-speed range. Based on a vehicle-specific power distribution model, this study developed an AV energy consumption model under real traffic conditions at average speeds of 10 to 100 km/h. Potential energy consumption savings and losses were determined using two parameters: the turning point between low-speed and high-speed and the rate of change in vehicle-specific power standard deviation in the high-speed range. Under real traffic conditions, the results implied that light-duty AVs had the potential to reduce energy consumption by 3.78% to 26.47% under stable conditions but could also increase energy consumption by 3.25% to 48.93% compared with light-duty HVs under unstable conditions.

Study of fatigue performance of fiber reinforced high-viscosity asphalt mastic based on rheology

Open-graded asphalt mixture pavement, with its functional benefits of drainage and noise reduction, holds promising engineering application potential. The high-viscosity asphalt mastic significantly influences the performance of this open-graded asphalt mixture. In this study, the fatigue performance of fiber reinforced high-viscosity asphalt mastic was investigated based on rheology. Shear rheometer was used to perform temperature sweep tests, time sweep tests, and linear amplitude sweep tests on the fiber reinforced high-viscosity asphalt mastic. The influences of filler/binder ratio, filler properties, fiber type, and content on the fatigue performance of the mastic were analyzed. Time sweep test data was used to establish a prediction equation for temperature-stress fatigue life of the mastic. The interconnectivity and differences between various fatigue life indicators were studied based on phenomenology and energy dissipation theory. The fatigue characteristics and damage mechanism of the mastic were investigated using a viscoelastic damage model, and the fatigue lives at different strain levels were observed. The research results indicate that fatigue damage of high-viscosity asphalt mastic mainly occurs at low temperatures. An increase in filler/binder ratio at the same temperature reduces the anti-fatigue performance of the mastic. Compared with dust, mineral powder has a larger specific surface area and better fatigue resistance performance. The established temperature-stress equation can accurately predict the fatigue life of mastic under different temperatures and stresses. The fatigue indicators Np20 and Nfm, based on energy dissipation theory, show a favorable linear relation with the fatigue indicator Nf50 based on phenomenology, and these three indicators are consistent in evaluating the fatigue performance and life of the mastic. Np20 and Nfm are found to be more applicable for evaluating the fatigue performance of fiber reinforced high-viscosity asphalt mastic. The fatigue life of the mastic decreases with increasing strain levels, and high-viscosity asphalt mastic containing basalt fiber shows better adaptability to complex traffic conditions.

Driver behaviors assisted by different human machine interfaces to avoid rear-end collisions during level 2 automated driving

Objective To practically apply level 2 automated driving in complex traffic conditions, it is necessary to prompt driver behaviors to prevent potential accidents in areas where manual interventions are frequently required. Methods A driving simulator experiment with 20 participants was conducted to evaluate the impact of different human machine interfaces (HMIs) on drivers’ interventions in terms of braking to avoid rear-end collisions during level 2 automated driving when a motorcycle abruptly cut in near intersections. Two types of HMIs were tested: a static HMI that informed drivers about approaching intersections, and a sensor HMI that displayed real-time object recognition results. Each driver participated in five experimental conditions, which varied the presence or absence of the static and sensor HMIs during level 2 automated driving, with manual driving serving as the baseline condition. Results The maximum deceleration in terms of braking to avoid rear-end collisions was significantly larger when level 2 automated driving was used without any HMI, compared to that of manual driving. However, when the sensor HMI was applied together with the static HMI during level 2 automated driving, a comparable time to collision could be achieved with a significantly smaller deceleration, compared to that without any HMI. Drivers’ eye-gaze behaviors revealed that no significant difference existed in the percentages of gaze to the road center area, indicating that they were not distracted by the HMIs. Finally, drivers’ attention levels to surrounding traffic and feeling of safety were significantly higher when level 2 automated driving was used in combination with the static and sensor HMIs. Conclusions The results demonstrated that the combination of static and sensor HMIs successfully aided drivers in ensuring driving safety with a significantly smaller deceleration to avoid rear-end collisions during level 2 automated driving. Furthermore, drivers’ attention levels were maintained, and their feeling of safety was improved when both HMIs were used in combination.

Pedestrians’ road-crossing behavior towards eHMI-equipped autonomous vehicles driving in segregated and mixed traffic conditions

Pedestrians’ road-crossing behavior can be influenced by eHMIs (external Human-Machine Interfaces) on autonomous vehicles (AVs). In this research, we developed a novel eHMI concept that aimed to support pedestrians’ risk evaluation by displaying predicted real-time risk levels. In a virtual reality environment, we measured pedestrians’ road-crossing behavior when they encountered AVs with this eHMI and manual-driven vehicles (MVs) in the same lane. Results showed that pedestrians exhibited typical crossing behaviors based on gap size for both vehicle types. In segregated traffic conditions, compared to MVs, eHMI-equipped AVs made pedestrians more sensitive to the changes in gap size by rejecting more small gaps and accepting more large gaps. Pedestrians also walked faster and kept larger safety margins for smaller gaps. Similar results were observed for AVs in mixed traffic conditions. However, in mixed traffic conditions, pedestrians faced more challenges when interacting with MVs as they tended to accept smaller gaps, walk more slowly, and maintain smaller safety margins. These findings indicate that dynamic risk information could be conducive to pedestrians’ road-crossing behavior, but the use of eHMIs on AVs might disrupt pedestrians’ interactions with MVs in complex traffic conditions. This potential risk shift among vehicles also poses the question of whether AVs should use segregated lanes to reduce their indirect impacts on pedestrian-MV interactions.

QSD-LSTM: Vessel trajectory prediction using long short-term memory with quaternion ship domain

Vessel trajectory prediction is a critical aspect of ensuring maritime traffic safety and avoiding collisions. The long short-term memory (LSTM) network and its extensions have represented powerful ability of vessel trajectory prediction. However, the previous studies often did not take dynamic interactions between neighboring vessels into account. Additionally, in complex traffic conditions, trajectory prediction will acquire uncertainty, and these potential negative factors can limit the prediction of future trajectory. To enhance the prediction performance, we propose an interactive vessel trajectory prediction framework (i.e., QSD-LSTM) based on LSTM, which is embedded with the quaternion ship domain (QSD). The QSD is beneficial for avoiding unwanted collision between neighboring vessels. In addition, the operation of trajectory clustering is further incorporated into our trajectory prediction framework, potentially leading to more robust prediction results. Numerous experiments have been implemented on realistic automatic identification system (AIS)-based vessel trajectories to compare our QSD-LSTM with several state-of-the-art prediction methods. The prediction results have demonstrated the superior performance of our method in terms of both quantitative and qualitative evaluations.