Human-driven Vehicles Research Articles

Cooperative lane-changing for connected autonomous vehicles merging into dedicated lanes in mixed traffic flow

Connected and automated vehicles (CAVs) have enormous potential to enhance traffic safety, efficiency, and emissions reduction. However, in the initial phases of CAV development, mixed traffic comprising CAVs and human-driven vehicles (HDVs) will inevitably coexist in the traffic system. To fully exploit the benefits of CAVs, dedicated lanes with independent rights of way will be established. This paper proposes an optimal control strategy for coordinating the mandatory lane-changing of CAVs from ordinary lanes to dedicated lanes. The strategy develops a centralized two-stage cooperative optimal control model to optimize the lane-changing sequence and trajectories of CAVs. In the first stage, a dynamic programming formulation is designed to determine the lane-changing sequence decisions. The model predictive control (MPC) controller is adopted to dynamically solve the optimal control problem with a fixed terminal state. In the second stage, we dynamically and cooperatively designed the longitudinal trajectories of related CAVs. The lateral trajectories of lane-changing CAVs are planned with a cubic polynomial. The objective function considers driving comfort and state tracking to ensure traffic smoothness. Simulation results show that: (1) the proposed strategy can improve the negative impact of lane-changing behavior under different traffic demand levels. (2) Compared to the benchmark approach, the proposed strategy can significantly enhance traffic efficiency and driving comfort, particularly in medium-traffic demand. The strategy can improve the average speed of CAVs by approximately 12 % and decrease the average acceleration by over 45 %. (3) The average fuel consumption is positively correlated with traffic demands and the difference in arrival speeds between lane-changing and dedicated lane CAVs. (4) The effectiveness of the strategy increases with the length of the lane-changing segment. However, the marginal benefit becomes negligible when the segment exceeds 300 m. Therefore, the findings of this paper can provide theoretical support for the cooperative control of CAVs in dedicated lanes of highways in the future.

A distributed simulation study to investigate pedestrians’ road-crossing decisions and head movements in response to different vehicle kinematics in mixed traffic

The impending deployment of automated vehicles (AVs) will lead to mixed traffic conditions, where pedestrians will be required to interact with both AVs and human-driven vehicles. For traffic flow to be safe and efficient, AVs’ understanding of pedestrians’ behaviour and intention is as important as pedestrians’ perception of AVs’ status and intent. To investigate pedestrians’ road-crossing decisions and interactive behaviour in mixed traffic, a distributed simulation study was developed by linking a CAVE-based pedestrian simulator and a desktop driving simulator. Twenty-five pairs of pedestrians and drivers were recruited, and each pair experienced 32 trials, where pedestrians decided to cross (or not) before an approaching vehicle at an un-signalised, single-lane, road. The driving pattern of the approaching vehicle (controlled by either a predefined program or human driver) and braking mode (braking/non-braking) were manipulated. For the predefined vehicles, the braking pattern was subdivided into hard braking and soft braking to provide more kinematic variability. Human drivers were also instructed to yield, or not, in different trials. Pedestrians’ road-crossing decisions and head movements were recorded and analysed. Results revealed a significant difference in head-turning patterns between crossing and non-crossing manoeuvres, demonstrating pedestrians’ head movements as a valid indicator of their road-crossing intentions. Moreover, results identified a ‘last moment check’ behaviour before pedestrians’ crossing initiation, with a significant increase in head-turning during the last 2 s. Finally, pedestrians made a similar percentage of road crossings and displayed a similar pattern of head movements, in response to human-driven and predefined vehicles, suggesting that the difference of implicit cues in the current mixed traffic setup does not impact their behaviour prior to road crossings. The findings from this study extend our knowledge of how pedestrians behave when crossing the road in mixed traffic, particularly in terms of their head-turning behaviour. We hope this information can be used by future AVs to better predict pedestrians’ road-crossing intentions in urban settings.

Traffic state estimation from vehicle trajectories with anisotropic Gaussian processes

Accurately monitoring road traffic state is crucial for various applications, including travel time prediction, traffic control, and traffic safety. However, the lack of sensors often results in incomplete traffic state data, making it challenging to obtain reliable information for decision-making. This paper proposes a novel method for imputing traffic state data using Gaussian processes (GP) to address this issue. We propose a kernel rotation re-parametrization scheme that transforms a standard isotropic GP kernel into an anisotropic kernel, which can better model the congestion propagation in traffic flow data. The model parameters can be estimated by statistical inference using data from sparse probe vehicles or loop detectors. Moreover, the rotated GP method provides statistical uncertainty quantification for the imputed traffic state, making it more reliable. We also extend our approach to a multi-output GP, which allows for simultaneously estimating the traffic state for multiple lanes. We evaluate our method using real-world traffic data from the Next Generation simulation (NGSIM) and HighD programs, along with simulated data representing a traffic bottleneck scenario. Considering current and future mixed traffic of connected vehicles (CVs) and human-driven vehicles (HVs), we experiment with the traffic state estimation (TSE) scheme from 5% to 50% available trajectories, mimicking different CV penetration rates in a mixed traffic environment. We also test the traffic state estimation when traffic flow information is obtained from loop detectors. The results demonstrate the adaptability of our TSE method across different CV penetration rates and types of detectors, achieving state-of-the-art accuracy in scenarios with sparse observations.

Hydroacoustic optimization with using Noise-GAN

In recent years, noise pollution has a significant impact on marine organisms, hence requiring some restrictions and safety protocols. The primary objective of these restrictions is to minimize the noise generated by human-driven vehicles in aquatic environments. As a result, hydroacoustical studies are becoming increasingly integrated into design studies. The objective of this study is to provide an innovative approach to the design of hydrofoils, are considered a crucial component in the field of hydroacoustic engineering. The objective of this approach is to create a cutting-edge optimization tool through the integration of machine learning and hydroacoustic performance calculations. This article presents the development of a novel method called Noise-GAN, combines Generative Adversarial Networks (GAN) algorithms with hydroacoustic optimization capabilities. The GAN algorithm is used as a dimensionality reduction method in the optimization process, allowing for quick exploration of the design space and obtaining unique profile geometries during optimization. This method is used to generate optimal hydrofoil geometry for various conditions. The hydroacoustic and hydrodynamic performances of the generated geometry are analyzed to determine the applicability and performance of the method. The results are compared to two existing dimensionality reduction method to enhance comprehension of the performance. In order to gain insight into the performance of generated geometries compared to traditional geometries, the results are also compared to the performance data of the profiles available in the literature. This article also includes comparing the performance results of the NACA0009 profile, which is typically employed in hydrofoil research, with the optimal geometries to evaluate of the method and the performance of the generated geometries. In order to determine the real-life applicability of the hydrofoil geometries generated by the method, optimization studies were performed at the five different Angle of Attack (AoA) conditions within the range of 0o - 15o. In this way, the feasibility of the method at different AoA values was also examined. The results of all these comparisons demonstrate that the Noise-GAN method, which was developed to generate hydroacoustically optimized hydrofoil geometry, is highly effective in a variety of respects. The optimization tool developed in this study generates hydrodynamically efficient and unique hydrofoil profiles while optimizing for hydroacoustical performance. The results of this study are presented in this paper.

Distributed Model Predictive Control for Heterogeneous Platoon With Leading Human-Driven Vehicle Acceleration Prediction

Distributed Model Predictive Control for Heterogeneous Platoon With Leading Human-Driven Vehicle Acceleration Prediction

Towards Safe Autonomy in Hybrid Traffic: Detecting Unpredictable Abnormal Behaviors of Human Drivers via Information Sharing

Hybrid traffic which involves both autonomous and human-driven vehicles would be the norm of the autonomous vehicles’ practice for a while. On the one hand, unlike autonomous vehicles, human-driven vehicles could exhibit sudden abnormal behaviors such as unpredictably switching to dangerous driving modes—putting its neighboring vehicles under risks; such undesired mode switching could arise from numbers of human driver factors, including fatigue, drunkenness, distraction, aggressiveness, and so on. On the other hand, modern vehicle-to-vehicle (V2V) communication technologies enable the autonomous vehicles to efficiently and reliably share the scarce run-time information with each other [ 1 ]. In this article, we propose, to the best of our knowledge, the first efficient algorithm that can (1) significantly improve trajectory prediction by effectively fusing the run-time information shared by surrounding autonomous vehicles, and can (2) accurately and quickly detect abnormal human driving mode switches or abnormal driving behavior with formal assurance without hurting human drivers’ privacy. To validate our proposed algorithm, we first evaluate our proposed trajectory predictor on NGSIM and Argoverse datasets and show that our proposed predictor outperforms the baseline methods. Then through extensive experiments on SUMO simulator, we show that our proposed algorithm has great detection performance in both highway and urban traffic. The best performance achieves detection rate of 97.3%, average detection delay of 1.2 s, and 0 false alarm.

Distance headway-based safety evaluation of emerging mixed traffic flow under snowy weather

The advent of emerging mixed traffic flow composed of human-driven vehicles (HDVs) and connected and automated vehicles (CAVs) is poised to revolutionize traffic operation mechanisms. To fill the gaps in dynamic threshold of surrogate safety measures (SSMs) and snow weather safety evaluation in emerging mixed traffic flow, this study develops a novel dynamic threshold of distance headway, safety warning distance (SWD), and tests it using a proposed snow weather traffic flow simulation analysis framework. The SWD extends the distance headway to a type of SSM that can simultaneously identify longitudinal and lateral conflicts. In addition, to overcome the problem of lateral conflict identification caused by the deficiency of simulation software, a method for estimating the lane-changing angle is proposed. The results indicate that: (i) CAVs have the potential to significantly enhance the safety of mixed traffic flow, particularly under snowy weather conditions; (ii) there are disparities in the identification of longitudinal conflicts between the commonly used time-to-collision (TTC) and distance headway, which is likely due to discrepancies in the speed and space distributions between the leading and following vehicles under different weather conditions; (iii) differences exist in the identification of lateral conflicts between the conventional post-encroachment time (PET) and distance headway, which is probably attributed to variations in their respective conflict identification patterns; and (iv) Compared to TTC and PET, distance headway with SWD performs better in effectively identifying conflicts within mixed traffic flow under snowy weather conditions. The findings can offer a theoretical foundation for identifying longitudinal and lateral conflicts in emerging mixed traffic flow and quantifying crash risks under both favorable and adverse weather conditions.

Cooperative Lane-Change Control Method for Freeways Considering Dynamic Intelligent Connected Dedicated Lanes

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.

The Influence of Incentives on Drivers’ Aggressive Behaviors Toward Automated Vehicles

In the process of merging into the mixed traffic flow composed of human driven vehicles, automated vehicles may be attacked by human drivers. The purpose of this study was to compare the inhibitory effect of four incentive measures, including reward, punishment, education, and warning, on aggressive behaviors of drivers in automated vehicles and to explore their impact mechanism. In this study, 410 participants were recruited and divided into five groups, one of which was the control group, and the other four groups were given an incentive measure. The study found that reward and punishment significantly inhibit aggressive behaviors, although their influence mechanisms are different, while education and warning have no significant inhibition effect. Our research expands the theory of human-vehicle interaction, enriches the specific measures to inhibit the aggressive behavior of humans to autonomous driving, and provides some empirical suggestions for the future hybrid traffic situation of humans and vehicles. In the face of the upcoming mixed traffic of people and vehicles in the future, in order to ensure the good order of the road, it is necessary to take effective measures against human drivers to reduce their attacks on automated vehicles. On the one hand, relevant government departments need to play a guiding role in educating human drivers; on the other hand, manufacturers in related industries need to consider the above factors in the design of automated vehicles functions to make human-vehicle hybrid traffic safe and efficient.

Cooperative lane-changing in mixed traffic: a deep reinforcement learning approach

Deep Reinforcement Learning (DRL) has made remarkable progress in autonomous vehicle decision-making and execution control to improve traffic performance. This paper introduces a DRL-based mechanism for cooperative lane changing in mixed traffic (CLCMT) for connected and automated vehicles (CAVs). The uncertainty of human-driven vehicles (HVs) and the microscopic interactions between HVs and CAVs are explicitly modelled, and different leader-follower compositions are considered in CLCMT, which provides a high-fidelity DRL learning environment. A feedback module is established to enable interactions between the decision-making layer and the manoeuvre control layer. Simulation results show that the increase in CAV penetration leads to safer, more comfort, and eco-friendly lane-changing behaviours. A CAV-CAV lane-changing scenario can enhance safety by 24.5%–35.8%, improve comfort by 8%–9%, and reduce fuel consumption and emissions by 5.2%–12.9%. The proposed CLCMT promises advantages in the lateral decision-making and motion control of CAVs.

Linear internal stability for mixed traffic flow of CAVs with different automation levels

With the development of autonomous driving and vehicle-to-infrastructure technologies, connected automated vehicles (CAVs) will gradually replace human-driven vehicles (HDVs). However, during the widespread adoption of CAVs, the roads will experience a new type of mixed traffic flow composed of vehicles with different automation levels for an extended period. To analyze the impact of different automation levels on mixed traffic flow, this paper proposes a stability analysis framework for mixed traffic flow that considers different automation levels. The influence of automation level classification on the composition of mixed traffic flow is analyzed first, and the degradation of CAVs in mixed traffic flow is further explored. Then, a stability analysis framework for mixed traffic flow considering different automation levels is proposed, and the impact of key parameters on the stability is analyzed. Finally, simulation experiments are designed to validate the theoretical analysis results. The research findings indicate that (1) the stability of mixed traffic flow improves with the increase in steady-state speed. When vehicle speed exceeds 20 m/s, mixed traffic flow remains stable. (2) When the CAV penetration rate is low, the stability of mixed traffic flow is poor due to the degradation of CAVs. However, with the increase in the penetration rate and automation level of CAVs, mixed traffic flow rapidly tends toward a stable state. (3) Numerous factors influence the stability of mixed traffic flow, with the order of importance being the CAV penetration rate, automation level, headway, and maximum acceleration. Therefore, this study can provide theoretical support for managing and controlling mixed traffic flow with different automation levels in the future.

Connected Automated and Human-Driven Vehicle Mixed Traffic in Urban Freeway Interchanges: Safety Analysis and Design Assumptions

The introduction of connected automated vehicles (CAVs) on freeways raises significant challenges, particularly in interactions with human-driven vehicles, impacting traffic flow and safety. This study employs traffic microsimulation and surrogate safety assessment measures software to delve into CAV–human driver interactions, estimating potential conflicts. While previous research acknowledges that human drivers adjust their behavior when sharing the road with CAVs, the underlying reasons and the extent of associated risks are not fully understood yet. The study focuses on how CAV presence can diminish conflicts, employing surrogate safety measures and real-world mixed traffic data, and assesses the safety and performance of freeway interchange configurations in Italy and the US across diverse urban contexts. This research proposes tools for optimizing urban layouts to minimize conflicts in mixed traffic environments. Results reveal that adding auxiliary lanes enhances safety, particularly for CAVs and rear-end collisions. Along interchange ramps, an exclusive CAV stream performs similarly to human-driven ones in terms of longitudinal conflicts, but mixed traffic flows, consisting of both CAVs and human-driven vehicles, may result in more conflicts. Notably, when CAVs follow human-driven vehicles in near-identical conditions, more conflicts arise, emphasizing the complexity of CAV integration and the need for careful safety measures and roadway design considerations.

Assessment of automated vehicles’ freeway exit distances in mixed and managed lane traffic environments

Vehicles planning to exit at an upcoming freeway off-ramp require adequate exit distance to execute lane change maneuvers, otherwise, traffic disturbances may be experienced. This research assesses the changes in exit distances for a mixed traffic environment comprising automated vehicles (AVs) and human-driven vehicles (HDVs) on freeways with and without a managed lane (ML). A left-side continuous ML was designed and eligible vehicles were AVs. Traffic microsimulation exercises were conducted on a 3.5 km freeway segment, and scenarios varied based on traffic demand, the number of freeway lanes, and AV adoption rates. Traffic demand was set relative to the queue discharge flow rate ( qQ). The results indicated an increase in qQ as the AV adoption rate increased. Also, the exit distances were influenced by the traffic environment, the freeway configuration, and the traffic demand. The optimal exit distance increased when an ML was implemented at 25% and 50% AV adoption rates.

Model-data-driven control for human-leading vehicle platoon

This paper proposes a model-data-driven control method for a human-leading vehicle platoon, comprising a human-driven vehicle (HDV) as the leader and connected automated vehicles (CAVs) as followers. Initially, a representative trajectory of HDVs is constructed using principal component analysis and the K-means clustering algorithm, which is utilized as training dataset. Subsequently, we propose a novel platooning method, named deep reinforcement learning with model-based guidance (DRLMG). The output of model predictive control (MPC) is integrated into the input state and reward function of the deep reinforcement learning (DRL) algorithm. The DRL algorithm benefits from guidance provided by MPC, leading to more optimal decision-making. To ensure safety and stability, a safety filter is designed using control barrier function and the control Lyapunov function. Simulation experiments with real-world driving data show that DRLMG outperforms MPC, reducing speed error, spacing error, and acceleration change rate by 17.9%, 53.7%, and 47.1%, respectively. In comparison to pure DRL, DRLMG increases spacing error by 6.5% but reduces speed error by 15.4% and acceleration change rate by 14.3%. The proposed method enhances DRL’s generalization capability, dampens traffic oscillations caused by the leading HDV, and guarantees driving safety and stability.

Investigating autonomous vehicle discretionary lane-changing execution behaviour: Similarities, differences, and insights from Waymo dataset

Recently released autonomous vehicle datasets like Waymo can provide rich information (and unprecedented opportunities) to investigate lane-changing behaviour of autonomous vehicles, requiring data from multiple drivers and lanes with different objectives. As such, the study investigates the discretionary lane-changing execution behaviour of autonomous vehicles and compares its behaviour with human-driven vehicles from Waymo and Next Generation Simulation (NGSIM) datasets. Several behavioural factors are statistically analysed and compared, whereas the discretionary lane-changing execution time (or duration) is modelled by a random parameters hazard-based duration modelling approach, which accounts for unobserved heterogeneity. Descriptive analyses suggest that autonomous vehicles maintain larger lead and lag gaps, longer discretionary lane-changing execution time, and lower acceleration variation than human-driven vehicles. The random parameters duration model reveals heterogeneity in discretionary lane-changing execution behaviour, which is higher in human-driven vehicles but decreases significantly for autonomous vehicles. Whilst contradictory to a general hypothesis in the literature that autonomous vehicles will eliminate heterogeneity, our finding indicates that heterogeneous behaviour also exists in autonomous vehicles (although to a lesser extent than in human-driven vehicles), which can be contextual to prevailing traffic conditions. Overall, autonomous vehicles show safer discretionary lane-changing behaviour compared to human-driven vehicles.

An improved eco-driving strategy for mixed platoons of autonomous and human-driven vehicles

The emergence of autonomous vehicles (AVs) could transform traffic flow characteristics and affect energy consumption subsequently. The primary objective of this study is to propose an improved eco-driving strategy for the mixed platoons by exploring the impacts of AVs on energy consumption characteristics. Initially, leveraging precise AV control, this study utilizes a Model Predictive Control (MPC) system to optimize AV trajectories, aiming to minimize energy consumption within mixed platoons of AVs and human-driven vehicles (HDVs). Given the differing collaborative capabilities of HDVs and AVs in mixed platoons, exceptional situations may arise at intersections where not all platoon vehicles cross the stop line during the same green light cycle (GLC). To address these challenges, we then propose a splitting approach to optimize the platoon operation mode. This approach allows for flexible splitting decisions based on AVs market penetration and current traffic conditions. The simulation results indicate that the energy consumption reduction ranges from 15.35% to 35.39% and 2.75–3.64% compared to the traditional mode and the eco-driving mode, respectively. In addition, through an analysis of the energy consumption of various vehicle orders within a mixed platoon, we determined that positioning the second AV in the middle platoon maximizes stability, resulting in a more consistent energy consumption pattern. These findings highlight the advantages of the strategy in terms of energy consumption, providing theoretical support management of mixed platoons.

Robust H∞ control of heterogeneous connected vehicles platoon with random time-varying communication delay

As an essential component of intelligent transportation systems (ITS), connected cruise control (CCC) has attracted much attention due to its potential to improve vehicle safety and reduce traffic congestion. However, besides the disturbance acting on the head vehicle of the platoon, the time-varying communication delay in vehicle-to-vehicle (V2V) also seriously impacts the CCC system’s stability. In this paper, we investigate a robust control scheme utilizing the Lyapunov-Krasovskii functional stability theory and [Formula: see text] technique to ensure the stability of the CCC system consisting of the connected and automated vehicle (CAV) and connected human-driven vehicles (CHVs) under random time-varying communication delay. The effectiveness and advantage of the designed robust controller are verified by MATLAB/Simulink simulation and comparison with the existing result of the literature.

Adaptive cruise control design for collision risk avoidance

With advanced technologies such as perception and automatic control, automated vehicles (AVs) are gradually replacing human-driven vehicles (HDVs). They will become a significant component of transportation systems. As the crucial longitudinal control model for AVs, the existing adaptive cruise control (ACC) model faces challenges such as poor linear stability and compromised safety. To solve the problem, this paper proposes an adaptive cruise control model to avoid collision risk. First, the delay-considerate collision risk control strategy (DCRCS) is developed to consider vehicles' perception and control delay. Then, a proposed adaptive cruise control (PACC) model is designed by incorporating DCRCS into the ACC model based on the control gain coefficient. Finally, the effectiveness of the PACC model is discussed and analyzed in terms of stability, safety, efficiency, energy consumption, and pollutant emissions through a combined approach of theoretical analysis and simulation experiments. The result shows that (1) compared to the classical ACC model, the PACC model demonstrates significant improvements in system stability, reduced safety risks, enhanced efficiency, and decreased energy consumption and pollutant emissions. These enhancements are observed when the control gain coefficient is set to a positive value. (2) The equilibrium speed minimally influences the effectiveness of the PACC model. Under varying speed conditions, the PACC model consistently maintains stable performance. (3) The effectiveness of the PACC model is closely related to the selection of the control gain coefficient. Considering the effects of the control gain coefficient on stability, safety, efficiency, energy consumption, and pollutant emissions of the PACC model, the optimal range for the control gain coefficient is in the range of [0.8, 1.0]. In summary, compared to the classical ACC model, the PACC model exhibits outstanding performance in various aspects. It provides theoretical support for the longitudinal control of AVs in the future.

Exploration on relation between vehicle oscillation type and platoon oscillation evolution based on multi-scenario field experiment

In order to explore the categorization of vehicle oscillation and the relationship between individual vehicle oscillation and platoon oscillation in traffic flow, this paper conducts field experiments with both human driving vehicles(HDVs) and autonomous vehicles(AVs). The objective is to utilize experimental data to categorize and quantify traffic oscillation, delve into the impact of different degrees of vehicle oscillation on platoon oscillation, identify patterns between vehicle oscillation types and platoon oscillation evolution, and study the influence of AVs on platoon oscillation. The paper commences with car-following experiments involving HDVs and AVs, sets the platoon into three scenarios of HDV’s platoon, AV’s platoon, and mixed platoon experimental datasets. Subsequently, nine oscillation features reflecting the oscillation characteristics of vehicle traveling, such as volatility and trend, etc., are extracted from the experimental datasets. Then the CLS deep clustering model is constructed, trained, and outputs high-dimensional features. The model's effectiveness is assessed using both clustering error and deep learning error simultaneously. Finally, the paper analyzes the classification results of the experimental dataset and explores the relationship between vehicle oscillation type and platoon oscillation. The research finding indicates that the oscillation classification effectiveness of the CLS model is significantly superior to other algorithms. Using the CLS model, vehicle oscillations are categorized into four most suitable types: I, II, III, and IV, representing slight oscillation, general oscillation, relatively serious oscillation, and severe oscillation respectively. In HDVs’ platoon, as the vehicle oscillation type increases, the following vehicle does not fully adhere to the front vehicle, the amplitude trend of the vehicle changes, leading to oscillation in the following vehicle platoon. In the AVs’ platoon, AVs suppress the backward propagation of oscillations in the platoon, and the oscillation types are all type I, indicating mild oscillation. In the mixed platoon, it is observed that AVs, through swift perception of the state of preceding vehicle, promptly respond to adjust their own driving condition to alleviate platoon oscillation. The higher the proportion of AVs in the platoon, the stronger the inhibitory effect on platoon oscillation.

Phase diagram in multi-phase heterogeneous traffic flow model integrating the perceptual range difference under human-driven and connected vehicles environment

With the gradual development of autonomous driving and connected communication technology, urban road networks will be shared by a combination of Human-driven vehicles (HDVs) and Connected vehicles (CVs) for a long era. Besides, as the actual vehicle operator, the driver regulates the vehicle state with an accelerator, gear lever, or brake, whereas the stepwise acceleration process is overlooked in the literature. Motivated by this, comprehensive kinetic difference between the two types of vehicles, we propose a novel heterogeneous multi-phase traffic flow accounting for the HDVs and CVs to close this hole. In the section on linear stability analysis, the reductive perturbation approach was applied to figure out the stability norm of the new model. The findings show that, on the one hand, inhibiting traffic bottlenecks is positively impacted by the penetration rate of CVs and the number of preceding vehicles taken into account; on the other hand, by varying the headway between following vehicles, the multiple-phase transition occurs; consequently, the number of turning points in the optimal velocity function is the sole factor that affects the number of stages in multi-phase transitions. Subsequently, the modified Korteweg-de Vries (mKdV) equation corresponding to the new model is deduced to investigate the nonlinear phenomenon of traffic flow at the vicinity of the critical point, and the kink-antikink soliton wave solution by solving the above characteristic equation can describe the “stop-and-go” phenomenon in real traffic flow. Finally, the conclusion of the numerical experiments is consistent with the above theoretical analysis. This research can provide insight into the dynamic evolution of road traffic flow during the transition from HDVs to semi-CVs and eventually to CVs.