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In this paper, some new lane-changing rules for one-way two-lane traffic with one work zone are proposed. Considering their velocities difference between the vehicle in the current lane and some vehicle behind/ahead in the target lane, and its own velocity, driver will choose to change lanes if necessary. Unlike some previous studies, one-way two-lane with one work zone is divided into three sections: symmetrical zone, conversion zone and work zone. In the conversion zone, the maximum velocity of vehicles in merge lane depends on the number of unsuccessful lane-changing. Based on the refined Nagel–Schreckenberg model, all vehicles update their corresponding velocities and positions in own respective lanes, employing open boundary conditions. The computer results show that the system has two states: unsaturated state and saturated state. In the saturated state, these vehicles on these two lanes have the same occupancy, which change steadily without inflection point when approaching the conversion zone, especially for upstream of the work zone. As one of the results of vehicle self-organization, the system can reach the saturated state in a short period of time as the injection probability increases. This saturated state can also spread across the conversion zone to the symmetrical zone. In the unsaturated state, one also finds that the velocity distribution can be consistent with the actual traffic conditions for upstream of the work zone. We selected three road sections with work zone to carry out field test, and the flow of these three road sections is basically consistent with the flow of the saturated state for the present model. [ABSTRACT FROM AUTHOR]

Trajectory-following control is the basis for the practical application of an articulated virtual rail train transportation system. In this paper, a planar nonlinear dynamics model of an articulated vehicle is derived using the Euler–Lagrange method. A trajectory-following control strategy based on the first following point is proposed, and a feedback linearization control algorithm is designed based on the vehicle dynamics model to achieve the trajectory following of the rear vehicle. Based on the target trajectory formed by the first following point and measured by virtual sensors, a vector analysis method grounded in geometric relationships is proposed to solve in real time for the desired position, velocity, and acceleration of the vehicle. Finally, a MATLAB/SIMPACK dynamics virtual prototype is established to test the vehicle's trajectory-following effectiveness and dynamics performance under lane change and circular curve routes. The results indicate that the control algorithm can achieve trajectory following while maintaining good vehicle dynamics performance. It is robust to variations in vehicle mass, vehicle speed, tire cornering stiffness, and road friction coefficient. [ABSTRACT FROM AUTHOR]

• Developed mandatory lane change models to various intelligent connected vehicles environments employing game theory. • Model demonstrated exceptional predictive accuracy for both lane changes and waiting events. • Predicted lane change positions varied significantly based on different intelligent connected vehicles environments. • Discrepancies in lane change duration were notably influenced by diverse game strategies. In the environment of intelligent connected vehicles, drivers are capable of making wiser and safer decisions. However, the interaction between drivers and vehicle systems has undergone changes in the intelligent connected vehicles environment, leading to a decrease in the applicability of existing microscopic driving models, such as the mandatory lane change model, which requires reevaluation or improvement. Therefore, to investigate the influence of different intelligent connected vehicles environments on the microscopic mandatory lane-changing model, this study developed three interaction systems to characterize different intelligent connected vehicles environments: the baseline, warning group, and guidance group. The Baseline provides basic information, the warning group adds icons of preceding vehicles and real-time headway information, while the guidance group further includes speed and voice guidance functions. The baseline describes the traditional environment, while the other two groups describe the intelligent connected vehicles environment. Using a self-developed intelligent connected vehicle testing platform, we conducted driving simulation experiments with 43 participants at the interchange merging area of a highway. This study, grounded in game theory, establishes function models for participants, strategies, and payoff functions in the mandatory lane-changing process. Utilizing data from driving simulation experiments, the parameters of the dual-layered planning model are calibrated. Evaluation of the constructed model is conducted through confusion matrices and lane-changing spatiotemporal characteristic indicators. The results demonstrate satisfactory predictive performance of the baseline group model, warning group model, and guidance group model across different intelligent connected vehicles environments. Specifically, compared to existing literature, the baseline group model exhibits improvements of 7 % and 2 % respectively in overall lane-changing detection accuracy by drivers. The warning group model shows improvements of 2.9 % and 1.7 %, while the guidance group model exhibits improvements of 5.1 % and 4.3 %. Additionally, the baseline group model reduces the mean absolute error in predicting different game strategies by 16.7 % and 5.6 % respectively compared to existing literature. Concerning lane-changing position prediction, the warning and guidance group models demonstrate minimal errors, whereas the baseline group model exhibits good consistency in predicting lane-changing duration. Furthermore, both the warning and guidance group models show some delay in predicting lane-changing duration. While intelligent connected vehicles environments significantly influence the prediction of lane-changing positions, they do not significantly affect the prediction of lane-changing duration. However, game strategies significantly impact the prediction of lane-changing duration but do not significantly affect the prediction of lane-changing positions. The study findings offer valuable insights into micro lane-changing behaviors of drivers in intelligent connected vehicles environments, bearing crucial significance for the in-depth investigation of real-time control and guidance strategies for vehicles in merge areas of highways under intelligent connected vehicles conditions. [ABSTRACT FROM AUTHOR]

The mixed traffic flow of various types of vehicles at bus station is severe, which has an impact on the overall safety of traffic. Especially upstream of bus station, since buses need to change lane in order to stop at the bus station, it interacts with social vehicles. Thus, in this paper, a two-lane CA model considering the mixed traffic flow composing of buses, cars and nonmotorized vehicles upstream of a bus station is proposed. Through several field surveys, the movement characteristics of the vehicles are obtained and the basic parameters are calibrated. For comparison, five cases about bus stops are studied. Several indexes (i.e. parallel time, blind area dwell time, time to collision, potential collision energy) are analyzed to assess the safety upstream of the bus station. Four safety indices are used to examine, based on simulation findings, the impact of bus station on vehicle safety and the significance of establishing exclusive nonmotorized vehicle lanes and exclusive bus lanes. Finally, several safety-related recommendations for drivers and management are provided. These findings offer a theoretical basis for driver management and optimal design of bus stations. [ABSTRACT FROM AUTHOR]

• We investigated a motion sickness mitigation method during a real car drive. • Vibrotactile cues announcing upcoming driving manoeuvres mitigate motion sickness. • Auditory cues are less effective than vibrotactile cues in mitigating sickness. • Passengers express greater preference for vibrotactile cues. Car passengers experience much more car sickness than car drivers. We assume that this is because drivers can better anticipate the car's motions. Does helping passengers to anticipate the car's motions then mitigate car sickness? Indeed, laboratory studies have shown that anticipatory cues which announce one-dimensional motions of a linear sled mitigate sickness to a small extent. Does this mitigation generalize to real car driving? We tested this in a car ride on a test track along a trajectory involving lane changes, accelerations, and decelerations. We show that vibrotactile cues mitigated car sickness in passengers. Auditory cues were less effective. The mitigating effect of the vibrotactile cue was considerable: a 40% decrease in car sickness symptoms, a larger effect than we found in the laboratory. Automated vehicles can predict their own motion very well. They could thus provide vibrotactile cues to mitigate car sickness in their passengers. [ABSTRACT FROM AUTHOR]

The inherent complexity associated with lane-changing manoeuvres can significantly disrupt traffic flow and increase the risk of collisions. While the lane-changing vehicle influences the surrounding traffic, it is also simultaneously influenced by it. This study aims to examine how the presence and behaviour of surrounding vehicles affect the lane changer's behaviour. A study was conducted in the laboratory using video stimuli of various simulated lane-changing scenarios to achieve the research aim. The study used a two-block design. In the first block, the impact of the lag vehicle was evaluated by varying its gap and behaviour (acceleration, deceleration or maintaining speed) relative to the lane-changing vehicle. In the second block, both the lead and lag gaps were manipulated with respect to the lane-changing vehicle. Data from the participants (n=29) were collected on dependent variables, including lane change decisions (gap acceptance or rejection), perceived cooperation, and reaction time. The analysis was conducted using an Aligned Rank Transform ANOVA. The findings of this laboratory-based study suggest that the lag vehicle, specifically its behaviour, has more influence on lane change decisions than the lead vehicle in the target lane. Additionally, when a lag vehicle decelerates to create a gap in response to a lane changer's request, its actions are perceived as more cooperative, compared to when it accelerates or maintains speed. Furthermore, decisions to change lanes are made faster when the lag vehicle shows a deceleration behaviour. The results of this laboratory-based study provide valuable insights for improving current lane-changing models. We also discuss the implications of findings for improving algorithms governing autonomous vehicle interactions in mixed traffic. Finally, we discuss the benefits and limitations of laboratory-based approaches in studying causal relationships among different factors, as well as the generalizability of our findings. • Investigated lane change behaviour using video-based lab study. • Lag vehicle's behaviour significantly influence lane change decisions. • Lag vehicle deceleration seen as cooperative, aiding decision-making. • Lane change decisions are made faster with clear lag vehicle deceleration. [ABSTRACT FROM AUTHOR]

Accurately predicting lane changes, a crucial driving activity for preventing accidents and ensuring driver safety, is addressed in this study. An innovative predictive model that integrates game theory for precise lane change intention detection and an optimized Convolutional Neural Network (CNN) for trajectory prediction is proposed in this study. The CNN’s efficiency is enhanced through metaheuristic optimization of both the convolution and fully connected layers using the Whale Optimization Algorithm (WOA). Emphasizing robust data processing, a Wiener filter is applied for pre-processing, and the Cascaded Fuzzy C means (CFCM) technique is employed for segmentation. The resulting Whale Optimization Algorithm-based CNN (WOA-CNN) effectively forecasts the trajectory of lane-changing vehicles. Validation of the proposed approach in Python demonstrates exceptional accuracy, reaching 96.5%. This study showcases the effectiveness of the WOA-CNN model in advancing the prediction accuracy of lane-changing behaviour, contributing to enhanced driver safety and accident prevention. [ABSTRACT FROM AUTHOR]

In warehouses with vast quantities of heavy goods, heavy-duty forklift Automated Guided Vehicles (AGVs) play a key role in facilitating efficient warehouse automation. Due to their large load capacity and high inertia, heavy-duty forklift AGVs struggle to automatically navigate optimized routes. Additionally, rapid acceleration and deceleration can pose safety hazards. This paper proposes a velocity-adaptive model predictive control (MPC)-based path tracking method for heavy-duty forklift AGVs. The movement of heavy-duty forklift-type AGVs is categorized into straight-line and curve-turning motions, corresponding to the straight and curved sections of the reference path, respectively. These sections are segmented based on their curvature. The best driving speeds for straight and curved sections were 1.5 m/s and 0.3 m/s, respectively, while the optimal acceleration rates were 0.2 m/s2 for acceleration and −0.2 m/s2 for deceleration in straight paths and 0.3 m/s2 for acceleration with −0.15 m/s2 for deceleration in curves. Moreover, preferred sampling times, prediction domain, and control domain were determined through simulations at various speeds. Four path tracking methods, including pure tracking, Linear Quadratic Regulator (LQR), MPC, and the velocity-adaptive MPC, were simulated and evaluated under straight-line, turning, and complex double lane change conditions. Field experiments conducted in a warehouse environment demonstrated the effectiveness of the proposed path tracking method. Findings have implications for advancing path tracking control in narrow aisles. [ABSTRACT FROM AUTHOR]

Aiming at the problem of blind field of view caused by the change in the vehicle's yaw angle when the self-driving vehicle is turning or changing lanes, this paper proposes a camera rotation strategy based on monocular active environment sensing, which realizes the detection of the blind field of view when the vehicle's yaw angle changes in the self-driving vehicle. Based on the two-degrees-of-freedom dynamic model, the camera rotation angle control is achieved by controlling the front-wheel angle of the vehicle. A camera control module is designed using Simulink to control the camera in real-time, allowing it to rotate based on different driving scenes. The effect of obstacle detection by traditional vision sensors and active vision sensors is tested under different vehicle driving scenes. The results demonstrate that the obstacle detection effect of the camera rotation strategy based on monocular active environment perception, as designed in this paper, is better than the traditional monocular vision. [ABSTRACT FROM AUTHOR]

To enhance the safety of autonomous vehicles in mixed traffic and connected environment, it is crucial to recognize the lane-changing intentions (LCIs) of human-driven vehicles for autonomous vehicles. This paper presents a novel method for LCI recognition, which extracts features from the driving state and relative motion of the target vehicle and its neighbors. The method applies short-time Fourier transform, Gramian angular summation field, and Gramian angular difference field to the time-series data, and generates three grayscale images, which are merged into one information fusion image (IFI) by image processing techniques. The IFIs are then classified into three categories: lane keeping, lane-changing left, and lane-changing right, using the Vision Transformer model with transfer learning to speed up convergence and reduce training cost. The experimental results demonstrate that the proposed method outperforms the traditional methods, achieving an accuracy of 95.65% for recognizing LCI 3s before the lane change point. [ABSTRACT FROM AUTHOR]

This study aims to investigate left- and right-side merging sections on urban underground roads based on virtual reality driving simulator experiments. The behaviors investigated were changed by acceleration lane in the merging section, including 100, 120, and 140 m, considering current design guidelines. Typically, lane changing behavior was studied based on experiments using speed and lateral placement on driving. The behavior of more speed reduction in merging sections occurred in left-side merging than in right-side merging sections. In the left-side merging sections, speed reduction and acceleration rate decreased with the length of the acceleration lane. In the cases with relatively long acceleration lanes, lane changing locations for left-side merging sections were more sensitive than those of right-side merging sections. Some results from the driving simulator experiments show that road geometric design based on left-side merging sections might have more risk situations due to driver expectation and behaviors. This article provides technical knowledge to be applied to the acceleration lanes of left-side merging sections that extend 1.4 times longer than the usual road designs. [ABSTRACT FROM AUTHOR]

When a ground vehicle runs at high speeds, even a slight excess in the wheel steering angle can immediately cause the vehicle to slide sideways and lose control. In this study, we propose an active safety control system designed to address emergency situations where the driver applies excessive steering input and the vehicle speed varies significantly during control. The system combines the direct yaw moment (DYM) method with a steering saturation scheme that prevents excessive driver steering input from adversely influencing the front-wheel steering. Consequently, the control system allows the DYM to focus more on other stabilization tasks and maintain tire/road friction within its workable linear range. The implementation relies on a reference steering angle and a reference vehicle state, derived from a linear vehicle model considering tire/road friction limitations. When the driver's steering angle and the system state deviate from these reference values, the control system intervenes by applying both the steering saturation scheme and DYM method. This ensures the front-wheel steering angle and system state remain close to the reference values. The control strategy is developed using the polytopic Linear Parameter Varying (LPV) technique and Linear Matrix Inequality (LMI) to account for the changes in vehicle speed. It is further enhanced with an input saturation technique based on a high-gain approach, which improves control utilization and system response during emergency situations. The advantages of the proposed control strategy are demonstrated through simulation results. [ABSTRACT FROM AUTHOR]

Reinforcement Learning (RL) methods are regarded as effective for designing autonomous driving policies. However, even when RL policies are trained to convergence, ensuring their robust safety remains a challenge, particularly in long-tail data. Therefore, decision-making based on RL must adequately consider potential variations in data distribution. This paper presents a framework for highway autonomous driving decisions that prioritizes both safety and robustness. Utilizing the proposed Replay Buffer Constrained Policy Optimization (RECPO) method, this framework updates RL strategies to maximize rewards while ensuring that the policies always remain within safety constraints. We incorporate importance sampling techniques to collect and store data in a Replay buffer during agent operation, allowing the reutilization of data from old policies for training new policy models, thus mitigating potential catastrophic forgetting. Additionally, we transform the highway autonomous driving decision problem into a Constrained Markov Decision Process (CMDP) and apply our proposed RECPO for training, optimizing highway driving policies. Finally, we deploy our method in the CARLA simulation environment and compare its performance in typical highway scenarios against traditional CPO, current advanced strategies based on Deep Deterministic Policy Gradient (DDPG), and IDM + MOBIL (Intelligent Driver Model and the model for minimizing overall braking induced by lane changes). The results show that our framework significantly enhances model convergence speed, safety, and decision-making stability, achieving a zero-collision rate in highway autonomous driving. [ABSTRACT FROM AUTHOR]

Under the context of rapid development of the Internet of vehicles and vehicle-road collaboration system, active traffic management (ATM) becoming the mainstream means of road traffic control and developing toward refinement. In this paper, to study the high-precision lane-level dynamic induction control strategy in different scenarios, based on the NaSch model of cellular automata and combined with the characteristics of the failure section area, a fuzzy lane-changing bypass vehicle-following model considering lane-changing pressure in multi-lane failure scenarios was built. The simulation results show that (i) if the lane failure occurs on the middle lane, the lane should be induced in advance, and the induced lane change effect is the best at about 100 m. When the lane failure occurs in the left lane and right lane, the prompt is best at about 250 m. (ii) The induced distance should be based on actual traffic conditions, free combination of different early warning distances between 100 and 300 m can save about 20–30 s congestion time. (iii) The lane-level dynamic coordinated guidance control measures can effectively improve the road traffic efficiency compared with the static unified control measures, improve the traffic efficiency of road performance, and alleviate traffic congestion time. The conclusion of this paper can provide some reference for dynamic active control management and achieve higher accuracy of traffic flow lane-level control. [ABSTRACT FROM AUTHOR]

This paper presents a design of lane change decision and control algorithm in highly dense traffic situation for self-driving vehicles using motion-based adaptive uncertainty propagation and Stochastic Model Predictive Control (SMPC). Essential ideas of the proposed algorithm are introduced; i) an optimal motion in a current situation with multiple criteria decision making (MCDM), ii) four steps to change lane successfully in the dense traffic situation which is modeled as a simple acceleration model based on real driving data, iii) motion-based adaptive uncertainty propagation to consider a model error. The proposed algorithm has been evaluated via simulation studies in MATLAB/Simulink and CARSIM. The simulation results show the effectiveness of the proposed algorithm and its performance for changing lane in the highly-dense traffic situation. [ABSTRACT FROM AUTHOR]

Drivers of Level 3 automated vehicles are relieved from the driving task in specific circumstances but are required to take over control once the takeover request is prompted. Previous studies have investigated drivers' takeover performance in non-critical car-following. However, little is known about drivers' takeover behaviors in critical car-following, especially in low-visibility weather, which remarkably increases the risk of car-following. A driving simulator experiment with a 2 × 3 × 3 factor within-group design was conducted. The design matrix contained two weather conditions (clear and foggy), three car-following time headways (2 s, 3 s, 4 s) and three deceleration rates of the lead vehicle (LV) (0, 2 m/s2, 4 m/s2). A total of 30 participants completed the experiment. The results showed that in critical car-following situations, drivers faced greater challenges in negotiating with adjacent vehicles rather than the LV itself. Urgency and visibility did not significantly impact the likelihood of a crash with the LV due to drivers' adoption of stronger braking. However, decreased visibility and higher LV's deceleration increased the crash rate when drivers attempted lane changes. As urgency increased, drivers tended to change lanes earlier, leading to higher lane-changing risks and compromised lateral stability. This study can provide some insights for the car-following strategies of automated driving vehicles and the design of dedicated takeover schemes in various transportation environments. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Automobile Technology is the property of Automobile Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study proposes a guidance strategy based on human–machine transition (HMT) for cooperative adaptive cruise control (CACC) truck platoon actuator failures. Existing research on the CACC platoon mainly focuses on upper-level planning and rarely considers platoon planning failures caused by actuator failures. This study proposes that the truck in the platoon creates sufficient space on the target lane through HMT when the actuator fails, thereby promoting lane changes for the entire team. The effectiveness of the proposed strategy is evaluated using the Simulation of Urban Mobility (SUMO) simulation. The results demonstrate that under conditions ensuring the normal operation of traffic flow, this guidance strategy enhances the platoon's lane-changing capability. In addition, this strategy exhibits stronger robustness and efficiency in different traffic densities. This guidance strategy provides valuable insights into improving the driving efficiency of CACC truck platoons in complex road environments. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Guangdong University of Technology is the property of Journal of Guangdong University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Arctic is particularly vulnerable to the impacts of climate change, of which vessel-source black carbon aerosols serving as a prominent catalyst for these changes. This situation is poised to worsen as sea ice melts and shipping lanes change. Rapid action aimed at mitigating short-term climate forcing factors can yield almost immediate climate benefits in the Arctic. This article provides an overview of the legal framework governing black carbon in the Arctic, considering three distinct perspectives: the global, regional, and national dimensions. These perspectives encompass global forums represented by the International Maritime Organization (IMO) and the United Nations Framework Convention on Climate Change (UNFCCC), with a focus on recent developments concerning black carbon governance, notably the amendments to MARPOL Annex VI and Annex I. Regionally, forums represented by the Arctic Council and the European Union are examined. Black carbon emissions exhibit migratory characteristics, yet the primary legal responsibilities for emission reduction are concentrated within Arctic states. Therefore, this article also delves into the laws and practices of Arctic coastal states in their efforts to combat black carbon emissions, using Canada and Norway as examples. The analysis of institutional effectiveness in this article indicates that, at present, specialized legislation on black carbon is either vague or non-existent. The current Arctic ship-source black carbon governance system faces issues related to leadership ambiguity in its institutional structure, a limited scope of responsible entities, and a lack of diverse implementation measures. Simultaneously, the governance system is questioned for having weak or non-legally binding regulations at the level of legal enforcement. The article anticipates the introduction of more mandatory regulations while also encouraging the selection of non-coercive policy tools. Accordingly, this article argues that a coordinated governance system centered on IMO and the Arctic Council needs to be established. Such a framework should adopt a more inclusive approach to stimulate positive interactions between regulations, aiming to create a broader winning alliance based on the existing foundations. [ABSTRACT FROM AUTHOR]

Accurate and proactive lane-changing (LC) intention recognition can assist drivers in making LC decisions to improve driving safety. However, the mechanism of drivers’ LC decisions in dynamically changing environments is still not fully understood, which makes it difficult for advanced driver-assistance systems (ADASs) to make accurate LC decisions under different working conditions. To accurately capture the dynamic features before the generation of LC intention, relying on the multivehicle interaction capability of the connected environment, a LC intention recognition framework using graph theory to model the interaction relationship among multiple vehicles, i.e., the Multivehicle Interaction Dynamic Time Graph (MIDTG) framework, is proposed. First, the interaction relationship between LC vehicles and their surrounding vehicles in the connected communication range is modeled by graph theory. Second, the graph convolutional network (GCN) is used to extract spatial features of multivehicle interactions, and a long short-term memory (LSTM) neural network is used to learn the association of multivehicle interaction graphs in a time series. Finally, LC intentions are output through the Softmax function. The highD data set is used to validate the proposed model. Results show that the model can accurately extract the dynamic features of multivehicle interactions within a time window of 2.5 to 3.5 s, and the accuracy of LC intention recognition reaches 98%, which is an average improvement of 3.5% compared with other baseline models. The study provides a new way to model multivehicle interactions in the connected environment, which can be helpful for ADASs’ LC decision-making. Practical Applications: The significance of this study is to provide guidance for advanced driver-assistance systems (ADASs) to better assist drivers in making lane-changing decisions. In this study, a novel multivehicle interaction modeling method based on graph theory is proposed, combined with an advanced deep learning method to extract the spatiotemporal characteristics of multivehicle interaction, and finally applied to lane-changing intention recognition. The results show that the proposed method achieves good lane-changing intention recognition accuracy and can accurately extract key information from multivehicle interactions. This will provide a new way to predict driving behavior in a connected environment. This study will be meaningful for traffic operators and can spur a new wave of innovative applications in ADASs, such as lane-changing alerts. [ABSTRACT FROM AUTHOR]

In Japan, small agricultural tractors without axle suspensions used on bumpy farm roads experience severe vibrations. Excessive vibrations often cause tyres to lose contact with the supporting ground or decrease the vertical tyre force such that the cornering force exceeds the static friction limit. The resulting bouncing and sliding motions induce steering instability, resulting in overturning accidents. This study investigated the effectiveness of axle suspension in alleviating bump disturbance-induced steering instability in small tractors. A step lane change test involving bump disturbances was newly proposed for the steering stability testing of small tractors. The numerical experiments compared four axle suspension structures: no-, front-, rear-, and full-suspensions. The results showed that bump disturbances induce steering instability in the no- and rear-suspension tractors. In contrast, the front- and full-suspension structures significantly improved the steering stability of the tractor by keeping the steering wheel in contact with the ground. The proposed testing method helped evaluate improvements in the steering stability brought about by increasing the ground contact of the steering wheel. Our results can aid in designing and testing suspension systems for small tractors. • Tractor bouncing, sliding, and driver models were newly coupled for simulations. • Step lane change involving bump disturbances was proposed for stability test. • Steering instability was found in the no- and rear-suspension tractors. • Front- and full-suspensions could significantly alleviate steering instability. [ABSTRACT FROM AUTHOR]

The symmetry principle has significant guiding value in vehicle dynamics modeling and motion control. In complex driving scenarios, there are problems of low accuracy and large time delay in the trajectory tracking control of unmanned ground vehicles. In order to solve this problem and improve the motion control of unmanned ground vehicles, a vehicle coordination control method based on chaotic particle swarm optimization (CPSO) and model predictive control (MPC) algorithms is proposed. To achieve coordinated control of vehicle trajectory tracking and yaw stability, a model predictive controller was designed with the objective of minimizing trajectory tracking errors and yaw stability tracking errors. The required front wheel angle and yaw torque control variables were obtained by solving nonlinear constraint optimization. At the same time, considering the problems of low computational efficiency, high solving time, and local optimization in model predictive control, a chaotic particle swarm optimization algorithm is introduced to solve the optimization constraint problem within model predictive control, thereby effectively improving the computational efficiency and accuracy of the model predictive trajectory tracking controller. The results show that compared with MPC, the multi-objective function optimization solution time and vehicle lane changing time of CPSOMPC improved by 24.51% and 7.21%, respectively, which indicates the coordinated control method that combines the CPSO and MPC algorithms can effectively improve trajectory tracking performance while ensuring vehicle lateral stability. [ABSTRACT FROM AUTHOR]

• Driving cohorts such as young drivers, male drivers, passenger cars, and pickups appear to have higher propensities of engaging in speeding driving. • The propensity of speeding is higher when the driver is drinking, distracted, changing lanes, negotiating a curve, driving in lighted condition, and on curved roads. • The random parameter logit model with heterogeneity in means has better performance as opposed to that without heterogeneity in means. Introduction: Speeding behavior is a major threat to road traffic safety, which can increase crash risks and result in severe injury outcomes. Although several studies have been conducted to analyze speeding crashes and relevant influential factors, the heterogeneity of variables has not been fully explored. Based on the traffic crash data extracted from the Crash Report Sampling System, the study aims to identify the factors that influence speeding driving with the consideration of variable heterogeneity. Method: Quasi-induced exposure technique is adopted to identify the disparities in the propensities of speeding for various driving cohorts. The random parameter logit model with heterogeneity in means is employed to examine the factors impacting speeding behavior. Results: Results indicate that: (a) driving cohorts such as young drivers, male drivers, passenger cars, and pickups appear to have higher propensities of engaging in speeding driving; (b) the propensity of speeding is higher when the driver is drinking, distracted, changing lanes, negotiating a curve, driving in lighted condition, and on curved roads; and (c) the random parameter logit model with heterogeneity in means has better performance as opposed to that without heterogeneity in means. Conclusions: Speeding behavior can be influenced by various factors in terms of driver-vehicle characteristics, physical condition, driving actions, and environmental conditions. Practical Applications: The findings could serve to develop effective countermeasures to reduce speeding behavior and improve traffic safety. [ABSTRACT FROM AUTHOR]

Connected Autonomous vehicles (CAVs) are expected to improve the safety and efficiency of traffic by automating driving tasks. Amongst those, lane changing is particularly challenging, as it requires the vehicle to be aware of its highly-dynamic surrounding environment, make decisions, and enact them within very short time windows. As CAVs need to optimise their actions based on a large set of data collected from the environment, Reinforcement Learning (RL) has been widely used to develop CAV motion controllers. These controllers learn to make efficient and safe lane changing decisions using on-board sensors and inter-vehicle communication. This paper, first presents four overlapping fields that are key to the future of safe self-driving cars: CAVs, motion control, RL, and safe control. It then defines the requirements for a safe CAV controller. These are used firstly to compare applications of Multi-Agent Reinforcement Learning (MARL) to CAV lane change controllers. The requirements are then used to evaluate state-of-the-art safety methods used for RL-based motion controllers. The final section summarises research gaps and possible opportunities for the future development of safe MARL-based CAV motion controllers. In particular, it highlights the requirement to design MARL controllers with continuous control for lane changing. Moreover, as RL algorithms by themselves do not guarantee the level of safety required for such safety-critical applications, it offers insights and challenges to integrate safe RL methods with MARL-based CAV motion controllers. [ABSTRACT FROM AUTHOR]

Existing vehicle-road models used for road tracking do not take into account the side slip angle, which leads to a reduction in road tracking accuracy in scenarios where the vehicle is at a large side slip angle, such as an emergency lane change. Consequently, this study presents a path-tracking control technique based on the T-S fuzzy model of heading angle vehicle autonomy. In this paper, based on the yaw angle-based vehicle tracking model, a heading angle-based tracking model considering the side slip angle is constructed. Second, since the vehicle speed varies with time, this paper selects the membership function of the vehicle speed to establish the T-S fuzzy model of autonomous vehicle based on the yaw angle and heading angle, respectively, and ensures the robustness and stability over the whole parameter space by the linear parameter variation robust H ∞ controller. Then, cost functions based on the yaw angle and heading angle augmented error systems are created separately to optimize the system's overall performance. Ultimately, simulation and experimentation confirm that the algorithm for control, which is based on the fuzzy model of the heading angle vehicle, has superior autonomous trajectory performance. [ABSTRACT FROM AUTHOR]

Aiming at the interactive behavior of vehicles changing lanes in the intelligent network traffic environment, the relative position of vehicle, speed, acceleration, and steering angle are introduced to correct the molecular interaction potential, and the vehicle interaction potential model is established. The spatial distribution map of the vehicle interaction potential field under different motion states was drawn, and the changing law of the vehicle interaction potential field was described. By analyzing the longitudinal critical distance of the lane changing safe critical time, the expression of acceptable clearance for lane changing is defined, and the relationship between acceptable clearance and the speed and acceleration of the interactive vehicle is verified by simulation. Combined with the acceptable clearance of lane changing, a lane changing behavior model based on the vehicle interaction potential is established, and the situation of lane changing vehicles subjected to the interaction potential and potential field forces generated by the surrounding vehicles is analyzed, and the driving decision of lane changing vehicles under the action of different potential field forces is given. Compared with the molecular dynamics lane changing model and LS2015 lane changing model, the results show that the vehicle action potential lane changing model can effectively improve the running speed and stability of traffic flow. The research has theoretical significance for the lane changing behavior decisions of networked autonomous vehicles in the future. [ABSTRACT FROM AUTHOR]

Fast detection of the trajectory is the key point to improve the further emergency proposal. Especially for ultra-long highway, prompt detection is labor-intensive. However, automatic detection relies on the accuracy and speed of vehicle detection, and tracking. In multi-camera surveillance system for ultra-long highways, it is often difficult to capture the same vehicle without intervals, which makes vehicle re-recognition crucial as well. In this paper, we present a framework that includes vehicle detection and tracking using improved DeepSORT, vehicle re-identification, feature extraction based on trajectory rules, and behavior recognition based on trajectory analysis. In particular, we design a network architecture based on DeepSORT with YOLOv5s to address the need for real-time vehicle detection and tracking in real-world traffic management. We further design an attribute recognition module to generate matching individuality attributes for vehicles to improve vehicle re-identification performance under multiple neighboring cameras. Besides, the use of bidirectional LSTM improves the accuracy of trajectory prediction, demonstrating its robustness to noise and fluctuations. The proposed model has a high advantage from the cumulative matching characteristic (CMC) curve shown and even improves above 15.38% compared to other state-of-the-art methods. The model developed on the local highway vehicle dataset is comprehensively evaluated, including abnormal trajectory recognition, lane change detection, and speed anomaly recognition. Experimental results demonstrate the effectiveness of the proposed method in accurately identifying various vehicle behaviors, including lane changes, stops, and even other dangerous driving behavior. [ABSTRACT FROM AUTHOR]

Articulated heavy vehicles (AHVs) play a vital role in the economy of freight transport. Lateral stability control of AHVs during the immediate vicinity to loss of control (LOC) is a significant issue that has not been effectively addressed in the literature. This paper presents a novel approach to the lateral stability control of tractor semi-trailers under conditions leading to LOC. An active auxiliary axle is proposed to prevent trailer swing and snaking during severe lane change maneuvers. A linear 3-DOF model was developed to represent the effectiveness of the active auxiliary axle and tire cornering stiffness in yaw-rate control to provide an analytical basis. Nonlinear modeling of the axle and vehicle system was performed in TruckSIM. A Fuzzy Logic Controller (FLC) was developed to identify the required rate (magnitude per unit time) of actuation force, and a PID controller was introduced to regulate the magnitude of actuation force at the axle-wheel interface. Co-simulation was performed in MATLAB/SIMULINK in combination with TruckSIM. To simulate an LOC on a dry road with a coefficient of friction of 0.85, a double lane change (DLC) maneuver at 90 km/h was conducted. This resulted in a combined state of relative roll-over and trailer swing, facilitating the evaluation of the semi-active auxiliary axle's performance in regaining stability and eliminating transient overshoots in the vehicle combination's lateral response. Yaw rate rearward amplification was effectively controlled, and articulation angle oscillations were significantly diminished. This approach suggests a systematically minimal yet practicable retrofit to the trailer, contributing to a remarkable improvement in the traffic safety of AHVs. [ABSTRACT FROM AUTHOR]

This research presents an experimental electric vehicle developed at the Tecnológico Nacional de México Celaya campus. It was decided to use a golf cart-type gasoline vehicle as a starting point. Initially, the body was removed, and the vehicle was electrified, meaning its engine was replaced with an electric one. Subsequently, sensors used to measure the vehicle states were placed, calibrated, and instrumented. Additionally, a mathematical model was developed along with a strategy for the parametric identification of this model. A communication scheme was implemented consisting of four slave devices responsible for controlling the accelerator, brake, steering wheel, and measuring the sensors related to odometry. The master device is responsible for communicating with the slaves, displaying information on a screen, creating a log, and implementing trajectory tracking techniques based on classical, geometric, and predictive control. Finally, the performance of the control algorithms implemented on the experimental prototype was compared in terms of tracking error and control input across three different types of trajectories: lane change, right-angle curve, and U-turn. [ABSTRACT FROM AUTHOR]

This paper considers the vehicle control process in emergency lane change (ELC). The control method is divided into two parts: before crossing the obstacle and after crossing it. The first part is done by a feed-forward controller inspired by the driver's behavior pattern in the ELC maneuver. This controller is compensated by a neural network system and two proportional-integral (PI) and proportional-derivative (PD) controllers, which correct the output of the feed-forward controller to follow the path more accurately. The second part consists of two PD controllers responsible for controlling and steering the vehicle in the main path after crossing the obstacle. The main novelty of the proposed method is that the computational burden is low because of using a feed-forward controller, which is the main source of the calculation's burden. So as a result, the controller system can cover the ELC maneuver in under 2 seconds in various lateral displacements to bypass the obstacle and prevent collisions. The co-simulations are performed in CarSim software and MATLAB/Simulink, which shows that the control system can appropriately steer the vehicle and follow the desired at different speeds, in the presence of road friction and parameter uncertainty. [ABSTRACT FROM AUTHOR]

• The effect of driving aggression on steering behavior, lane change duration, and crash probability during lane changes was analyzed. • Lane change durations were reduced by 59% for aggressive drivers due to their tendency for hasty lane changing. • Aggressive drivers increased their steering reversal rates by 60% and their maximum steering swerve by 62%. • The risk-taking behavior of aggressive drivers increased their crash rate by 5.10 times. The current study aimed to investigate the influence of driving aggression on lane change behavior. Drivers belonging to three levels of aggressive driving styles (aggressive, moderately aggressive, and non-aggressive) participated in lane change experiments, and 232 lane change maneuvers were recorded. Steering behavior during lane change maneuvers was investigated using three steering performance measures, including the standard deviations of steering angle, steering reversal rates, and maximum steering swerve. Three separate generalized estimating equation models were developed to analyze the effects of aggressive driving behavior on the standard deviation of steering angle, steering reversal rates, and maximum steering swerve. A Weibull accelerated failure time model with clustered heterogeneity was developed to analyze the effect of aggressive driving behavior on lane change execution duration. Additionally, to model the crash probability during lane change, a random effects logistic regression model was developed. The findings indicate that aggressive and moderately aggressive drivers completed lane changes 59% and 38% faster, respectively, compared to non-aggressive drivers. This implies a propensity for aggressive and moderately aggressive drivers to execute lane changes more swiftly than non-aggressive drivers. Aggressive drivers increased their standard deviation of steering angle, steering reversal rates, and maximum steering swerves by 180%, 60%, and 62%, respectively, compared to non-aggressive drivers. This implies that aggressive drivers engaged in extreme steering behavior as a compensatory measure for their reckless driving in order to avoid a collision during lane change maneuvering. However, despite their vigorous compensatory steering behavior, the crash rate (number of crashes observed) increased by 5.10 and 2.83 times for aggressive and moderately aggressive drivers, respectively, compared to non-aggressive drivers. The findings have implications for developing driving assistance systems and lane change simulation models. [ABSTRACT FROM AUTHOR]

• Dedicated lanes on motorways enable platooning of connected and automated vehicles. • Impact of dedicated lanes on drivers' automation use and behaviour is investigated. • A dedicated lane does not increase drivers' preference to use automation. • Drivers keep larger time headways and accept larger gaps in automated driving mode. • Drivers perform closer car-following and merge into smaller gaps on dedicated lanes. Dedicated Lanes (DLs) have been proposed as a potential alternative for the deployment of Connected and Automated Vehicles (CAVs) to facilitate platooning and increase motorway capacity. However, the impact of the presence and utilization policy of such a lane on drivers' preference to use automation and their behaviour has not yet been thoroughly investigated. In this study, a driving simulator experiment is conducted, where participants drive a CAV in the presence of a DL with different utilization policies. Drivers have the possibility to choose between driving in an automated mode or in a manual mode. In automated mode they could adjust the driving speed and time headway and initiate automated lane changes. Two utilization policies were examined: mandatory versus optional use of DLs when driving in an automated mode. The impact of the presence and utilization policy of the DL on drivers' preference to use automation and their behaviour in car-following and lane changing are investigated. The study found that while the presence of a DL does not increase drivers' preference for automation use, it encourages drivers to utilize the DL more when the utilization policy is mandatory (i.e., drivers can only use automation mode when driving on this lane). Furthermore, drivers are more conservative in automated mode and when driving in mixed traffic. However, they perform closer car-following and merge into smaller gaps when driving on DLs which on one hand can increase the capacity of the DLs, but on the other hand can increase the risk of collisions. These results are useful for road operators, and in setting-up a more realistically traffic simulation studies. [ABSTRACT FROM AUTHOR]

• The study investigates the effects of user interface on trust, perceived risk, and acceptance in partially automated driving using a driving simulator. • The study designs and evaluates four UIs combining surrounding and manoeuvre information with visual and auditory modalities. • The user interface, delivering both surrounding and manoeuvre information via visual and auditory modalities, showed the highest trust and acceptance and the lowest perceived risk. • Criticality of event types and individual differences of participants were found to have a more substantial influence on drivers' behaviour, trust, and perceived risk compared to the user interfaces. • Eye-tracking results demonstrated that drivers checked the centre-console user interface when present, with no difference between the four user interfaces. Trust and perceived safety are pivotal in the acceptance of automated vehicles and can be enhanced by providing users with automation information on the (safe) operation of the vehicle. This study aims to identify how user interfaces (UI) can enhance drivers' trust and acceptance and reduce perceived risk in partially automated vehicles. Four interfaces were designed with different levels of complexity. These levels were achieved by combining automation information (surrounding information vs surrounding and manoeuvre information) and modality (visual vs visual and auditory). These interfaces were evaluated in a driving simulator in which a partially automated vehicle reacted to an event of a merging and braking vehicle in its front. The criticality of the events was manipulated by the factors merging gap (in meters) and deceleration (m/s2) of the vehicle in front. The reaction of the automation was either to brake or to change lanes. The results show that an optimal combination of automation information and modality enhances drivers' trust and acceptance. More specifically, the most advanced UI, which provided surrounding and manoeuvre information via the visual and auditory modalities , was associated with the highest trust and acceptance ranking and the lowest perceived risk. Manoeuvre information delivered through the auditory modality was particularly effective in enhancing trust and acceptance. The benefits of the UIs were consistent over events. However, in the most critical events, drivers did not feel entirely safe and did not trust the automation completely. This study suggests that the design of UIs for partially automated vehicles shall include automation information via visual and auditory modalities. [ABSTRACT FROM AUTHOR]

This paper considers an integrated control method combining autonomous driving and active steering. By using artificial flow guidance (AFG), the steering at all five axles on a tractor-semitrailer are synchronized for precise low-speed path tracking and improved lateral stability. AFG is a motion planning method which uses velocity vectors to guide the motion of the vehicle; here, two modification on AFG are proposed to form the integrated-AFG which improves the transient performance and generalizes the choice of the tracking points. Simulations rendered in TruckMaker show submillimeter level steady-state off-tracking for a broad speed range, with maximum deviation being 1 cm for a high-speed lane change; amplification of trailer lateral motion is also suppressed, with rearward amplification (RWA) < 1 maintained throughout. This control method is robust to changes in key parameters such as trailer mass and road friction coefficient. The methods presented provide a new foundation for implementing automated freight transportation on highways. [ABSTRACT FROM AUTHOR]

The weaving section on the freeway will be an active bottleneck due to vehicles' mandatory or discretionary lane-changing (LC) conflicts. In the early stages of the development of intelligent connected transportation, destination information collection of weaving vehicles is possible, which provides an opportunity to improve performance in weaving traffic. In this paper, a simulation system with coordinated strategies embedded to satisfy the LC needs is presented under the weaving traffic flow. First, an algorithm is proposed to evaluate the balance between the demand for LC vehicles and the available gaps provided in weaving traffic, which can be adjusted by variable speed limit (VSL) and ramp metering strategies. Second, to validate the proposed algorithm, a typical weave section with a merge and a diverge connected through a single auxiliary lane is researched, and a simulation platform based on cellular automata has been developed. The optimal control strategies are obtained by model predictive control (MPC) and taking the minimum travel time as an objective. Third, the advantages of coordinated control strategies are studied and discussed in traffic efficiency improvement and the success of LC proportions under different combinations of weaving ratios and large vehicle traffic mix. [ABSTRACT FROM AUTHOR]

The systems that are currently installed in autonomous vehicles are not designed to avoid obstacles on the road by manoeuvring at the limits of the vehicle and the road surface – the goal of the research presented was to develop such system. For these purposes, a universal test track based on ISO 3888-1 and ISO 3888-2 standards was adopted, which can be used to represent any situation in which there is an obstacle on the road in front of the vehicle that needs to be avoided. An analysis of the curves for generating the paths through the test track was carried out, on the basis of which the Bézier curves were chosen. In addition to them, the results of simulations in Adams Car software were used to generate the paths, which can be considered equivalent to real-world trials with a professional driver behind the wheel. A control model was developed for longitudinal and transverse control of the vehicle based on PID controllers, with the selection of optimal parameters – the ones that define PID controllers, and the others that define other characteristics of the model. The model proved to be successful in guiding the vehicle through the test track at all speeds at which manoeuvres are possible. Bézier curves are shown to be a better choice at lower speeds, while paths based on Adams Car simulations are a better choice at higher speeds. [ABSTRACT FROM AUTHOR]

With the development of intelligent driving assistance systems, the evaluation of driving behavior risk has shifted from traditional single-vehicle studies to multi-vehicle studies. This study aimed to investigate the interaction mechanism between vehicles and to study the microscopic laws of traffic flow operation. Firstly, the concept of "driving interaction field" was proposed. The virtual interaction quality and distance were used to define the driving interaction field. The interaction angle distinguished the vehicle interaction between different lanes. Then, the risk mechanism in the interaction process was analyzed by driving risk index. Corresponding thresholds of 50% and 85% quantile values were determined. Finally, the process of the lane-changing simulation experiments was divided into three phases (preparation, execution and adjustment). The driving risk index of the execution phase was larger than the other phases. Meanwhile, the comparison with the classical driving risk indexes revealed that the proposed index was more accurate and intuitive in describing the interaction risks. The driving interaction model proposed in this study quantified the overall environmental pressure on the vehicle. It overcomes the previous limitation of kinetic interaction parameters. The research provides a new idea for the ITS and autonomous driving systems, contributing to the enhancement of traffic safety and efficiency. [ABSTRACT FROM AUTHOR]

Tunnel–interchange connecting sections pose significant safety challenges on mountainous expressways due to their high incidence of accidents. Improving road safety necessitates a comprehensive understanding of driver behavior in such areas. This study explores the influences of road characteristics, signage information volume, and traffic conditions on drivers' car-following and lane-changing behavior in tunnel–interchange diverging areas. Utilizing driving data from 25 subjects of 72 simulated road models, driving performance is assessed using the Friedman rank test and multivariate variance analysis. The results highlight the significant influence of both connection distance and signage information load on driving behavior. In tunnel–interchange scenarios, the reduction in velocity increased by 62.61%, and speed variability surged by 61.11%, indicating potential adverse effects on driving stability due to the environmental transitions. Decreased connection distances are associated with reduced lane-changing durations, larger steering angles, and increased failure rates. Furthermore, every two units of increase in signage information leads to a 13.16% rise in maximum deceleration and a 5% increase in time headway. Notably, the signage information volume shows a significant interaction with connection distance (F > 1.60, p < 0.045) for most car-following indicators. Hence, the study recommends a maximum connection distance of 700 m and signage information not exceeding nine units for optimal safety and stability. [ABSTRACT FROM AUTHOR]

Connected Autonomous Vehicle (CAV) dedicated lanes can spatially eliminate the disturbance from Human-Driven Vehicles (HDVs) and increase the probability of vehicle cooperative platooning, thereby enhancing road capacity. However, when the penetration rate of CAVs is low, CAV dedicated lanes may lead to a waste of road resources. This paper proposes a cooperative lane-changing control method for multiple vehicles considering Dynamic Intelligent Connected (DIC) dedicated lanes. Initially, inspired by the study of dedicated bus lanes, the paper elucidates the traffic regulations for DIC dedicated lanes, and two decision-making approaches are presented based on the type of lane-change vehicle and the target lane: CAV autonomous cooperative lane change and HDV mandatory cooperative lane change. Subsequently, considering constraints such as acceleration, speed, and safe headway, cooperative lane-change control models are proposed with the goal of minimizing the weighted sum of vehicle acceleration and lane-change duration. The proposed model is solved by the TOPSIS multi-objective optimization algorithm. Finally, the effectiveness and advancement of the proposed cooperative lane-changing method are validated through simulation using the SUMO software (Version 1.19.0). Simulation results demonstrate that compared to traditional lane-changing models, the autonomous cooperative lane-changing model for CAVs significantly improves the success rate of lane changing, reduces lane-changing time, and causes less speed disturbance to surrounding vehicles. The mandatory cooperative lane-changing model for HDVs results in shorter travel times and higher lane-changing success rates, especially under high traffic demand. The methods presented in this paper can notably enhance the lane-changing success rate and traffic efficiency while ensuring lane-changing safety. [ABSTRACT FROM AUTHOR]

Lane changing behavior, a pivotal aspect of vehicular dynamics, significantly influences traffic flow, safety, and overall road network efficiency. This review article synthesizes the current body of knowledge surrounding lane changing behavior, encompassing both mandatory and discretionary lane changes. The intricate interplay between various factors, including speed differentials, driver characteristics, road geometry, and external stimuli, shape the decision-making processes underlying lane changes. The article explores the underlying psychological, cognitive, and socio-demographic influences on lane changing behavior, highlighting the role of emerging technologies such as advanced driver assistance systems (ADAS) and vehicle-to-vehicle (V2V) communication. Furthermore, the review delves into existing models and methodologies employed to analyze lane changing behavior, addressing their limitations and potential for advancement. Realworld applications, such as work zones and incident management, are examined to underline the practical implications of understanding and predicting lane changing behavior. By presenting a comprehensive overview of the subject, this review aims to deepen the understanding of the complexities inherent in lane changing behavior, facilitating informed decision-making for traffic management, safety enhancement, and future research endeavors. [ABSTRACT FROM AUTHOR]

Physiological signal monitoring and driver behavior analysis have gained increasing attention in both fundamental research and applied research. This study involved the analysis of driving behavior using multimodal physiological data collected from 35 participants. The data included 59-channel EEG, single-channel ECG, 4-channel EMG, single-channel GSR, and eye movement data obtained via a six-degree-of-freedom driving simulator. We categorized driving behavior into five groups: smooth driving, acceleration, deceleration, lane changing, and turning. Through extensive experiments, we confirmed that both physiological and vehicle data met the requirements. Subsequently, we developed classification models, including linear discriminant analysis (LDA), MMPNet, and EEGNet, to demonstrate the correlation between physiological data and driving behaviors. Notably, we propose a multimodal physiological dataset for analyzing driving behavior(MPDB). The MPDB dataset's scale, accuracy, and multimodality provide unprecedented opportunities for researchers in the autonomous driving field and beyond. With this dataset, we will contribute to the field of traffic psychology and behavior. [ABSTRACT FROM AUTHOR]

To reduce the risk of traffic conflicts in merging area, driver's behavior pattern was analyzed to provide a theoretical basis for traffic control and conflict risk warning. The unmanned aerial vehicle (UAV) was used to collect the videos in two different types of merging zones: freeway interchange and service area. A vehicle tracking detection model based on YOLOv5 (the fifth version of You Only Look Once) and Deep SORT was constructed to extract traffic flow, speed, vehicle type, and driving trajectory. Acceleration/deceleration distribution and vehicle lane-changing behavior were analyzed. The influence of different vehicle models on vehicle speed and lane-changing behavior was summarized. Based on this data, the mean and standard deviation of velocity, acceleration, and variable acceleration were selected as the characteristic variables for driving style clustering. To avoid redundant information between features, principal component dimensionality reduction was performed, and the dimensionality reduction data was used for K-means and K-means++ clustering to obtain three driving styles. The results show that there are obvious differences in the driving behaviors of vehicles in different types of merging areas, and the characteristics of different areas should be fully considered when conducting traffic conflict warnings. [ABSTRACT FROM AUTHOR]

In this paper, a typical motion-based modelling and tracking issue is investigated for vehicle targets in linear road segment by a variable structure multiple model (VSMM) method. Vehicle target motions in a road with multiple lanes are described by typical lane keeping and lane changing manoeuvres. To describe trajectories of typical manoeuvres, an Ornstein–Uhlenbeck process and a sine half-cycle are used to model lane keeping and lane changing motions in lateral direction, respectively. Note that starting point and length of lane changing motion are unknown in target tracking. Time-varying model sets are designed based on the current motion model with different motion parameters. A VSMM tracking frame is constructed to obtain target state estimates with time-varying model set. The effectiveness of the proposed typical motion-based tracking scheme is displayed by simulation results on road vehicle tracking. [ABSTRACT FROM AUTHOR]

Delivery e-bicyclists are vulnerable traffic participants, and delivery e-scooter crashes annually cause great losses. There are limited empirical analyses performed to characterize the endogenous and exogenous influences of potential factors on delivery e-scooter crash severities. This study focused on identifying the heterogeneous impacts of contributing factors. Based on 3064 police-reported delivery e-scooter crashes, different potential factors selected from seven perspectives were examined. Combining the latent class clustering analysis and the partial proportional odds model, an unsupervised learning approach was developed to modeling the heterogeneities as well as the corresponding marginal effects. According to the AIC, BIC, and entropy-based approach, the most appropriate number of clusters was identified as three. The model results revealed that the top six factors significantly influencing the injury severities were running red light, reverse riding, speeding, bike lane, changing lane, and e-bicyclist age. Their maximum absolute values of the marginal effects were more than 29%. The sub-models owned a better goodness of fit in characterizing heterogeneities and avoiding the model bias. It was easy to find the remarkable heterogeneities within each factor and across different clusters. The findings could provide insightful guidance and effective recommendations for alleviating the crash losses and promoting rider safety. [ABSTRACT FROM AUTHOR]