Vehicle Lane Change Research Articles

Vehicle Lane Changing Game Model Based on Improved SVM Algorithm

In order to improve the autonomous lane-changing performance of unmanned vehicles, this paper aims to solve the problem of inaccurate decision classification in traditional support vector machine (SVM) algorithms applied to the lane-changing decision-making stage of intelligent driving vehicles. By using game theory-related theories and combining the improved support vector machine (SSA-SVM) method, a vehicle autonomous lane-changing strategy based on game theory is established. The optimized SVM method has certain advantages for vehicle lane-changing decision-making with a small sample size in actual production processes. The lane-changing decision judgment accuracy rate of the SSA-SVM algorithm model can reach 93.6% compared with the SVM algorithm model without algorithm optimization; the SSA-SVM algorithm model has obvious advantages in decision performance and running speed. Therefore, the proposed new algorithm can effectively solve the problem of the objective consideration of the payoff function in conventional decision game theory.

Human-like Decision Making for autonomous lane change driving: a hybrid inverse reinforcement learning with game-theoretical vehicle interaction model

The development of automated driving vehicles aims to provide safer, comfortable, and more efficient mobility options. However, the decision-making control of autonomous vehicles still embraces limitations on human performance mimicry. These limitations become particularly evident in complex and unfamiliar driving scenarios, where weak decisionmaking abilities and poor adaptation of vehicle behaviour are prominent issues. This paper proposes a game-theoretic decisionmaking algorithm for human-like driving in the vehicle lane change scenario. Firstly, an inverse reinforcement learning (IRL) model is used to quantitatively analyse the lane change trajectories of the natural driving dataset, establishing the human-like human cost function. Subsequently, joint safety, comfort to build the comprehensive decision cost function. Use the combined decision cost function to conduct a non-cooperative game of vehicle lane changing decision to solve the optimal decision of host vehicle lane changing. The host vehicle lane-changing decision problem is formulated as a Stackelberg game optimization problem. To verify the feasibility and effectiveness of the algorithm proposed in this study, a lane change test scenario has been established. Firstly, we analyse the human-like decision-making model derived by the maximum entropy inverse reinforcement learning algorithm to verify the effectiveness and robustness of the IRL algorithm. Secondly, the human-like game decisionmaking algorithm in this paper is validated by conducting an interactive lane-changing experiment with obstacle vehicles of different driving styles. The experimental results prove that the human-like driving decision-making model proposed in this study can make lane-changing behaviours in line with human driving patterns in lane-changing scenarios of expressway.

A Study on the Impact of Overtaking Lane-Changing Behaviour in Expressway Interchange Weaving Areas

This study investigates the overtaking lane-changing (OLC) behaviour in expressway interchange weaving areas, aiming to analyse these behaviours’ causes and potential impacts. Field data are utilised to analyse the statistical characteristics of lane-changing points and spatio-temporal utilisation in weaving areas. A modified NS model, which considers the distribution pattern of vehicle speeds, and a rigid lane-changing rule based on Gaussian distribution are proposed. Additionally, a cellular automaton simulation model is constructed to quantify the influence of OLC behaviour on traffic efficiency and spatio-temporal utilisation based on simulated data. The findings indicate that the imbalanced distribution of lane-changing points and spatio-temporal utilisation in weaving segments, caused by rigid lane-changing behaviour, is an objective factor that triggers OLC behaviour. When the traffic volume in weaving areas ranges from 500 to 1,100 pcu/5 min and the proportion of OLC behaviour is between 0.35 and 0.7, the behaviour will significantly enhance the average vehicle speeds of the outermost lane of the main road and normal rigid lane-changing (NRLC) vehicles, with increases of up to 48% and 51%, respectively. Moreover, OLC behaviour also improves the balance of spatio-temporal utilisation in weaving areas and reduces the average spatio-temporal utilisation. This study clarifies the positive impact of OLC behaviour on expressway interchange weaving areas and provides new research ideas for enhancing the efficiency of these areas.

An Improved RRT Path-Planning Algorithm Based on Vehicle Lane-Change Trajectory Data

The Rapidly-exploring Random Tree (RRT) algorithm faces issues in path planning, including low search efficiency, high randomness, and suboptimal path quality. To overcome these issues, this paper proposes an improved RRT planning algorithm based on vehicle lane change trajectory data. This algorithm dynamically adjusts the sampling area based on road environment and trajectory change laws, allowing the random tree to sample within an effective area, thereby improving the algorithm’s sampling efficiency. After determining the sampling area, a sampling point optimization strategy is used to enhance sampling quality, resulting in a smoother and more executable path. Finally, the vehicle is processed in a standardized manner to further improve path safety. Simulation results indicate that, compared to the original RRT algorithm, the improved version reduces nodes, planning time, and path length by 12.77%, 64.79%, and 12.87%, respectively. It also improves path smoothness and more closely aligns with actual lane-change trajectories, demonstrating the effectiveness and executability of the improved algorithm.

Multi-lane cellular automata model considering bus-HOV lane

To quantify the impacts of high occupancy vehicles (HOV) entering the exclusive bus lane on road traffic efficiency, the car following and lane changing characteristics of bus, HOV and non-HOV were analyzed when setting the bus-HOV lane. Using the theory of cellular automata (CA) and combining the control rules of the bus-HOV lane, the lane occupied by a vehicle and the type of a vehicle regarded as constraints were introduced into the asymmetric lane changing rules, and then a multi-lane CA model was established for the car following and lane changing of multi-type vehicles considering the bus-HOV lane. MATLAB software was used to accomplish numerical simulation to analyze the specific effects of the proportion of HOV, bus departure frequency and bus stop spacing on the traffic flow parameters and their relationships. The results show that: when the proportion of HOV is less than 0.5 on a three-lane road segment, setting the bus-HOV lane can not only improve road traffic efficiency but also ensure the speed of bus operation under the condition of high initial space occupancy; when the proportion of HOV exceeds 0.5 , it is difficult to improve road traffic efficiency by restricting non-HOV from entering the bus-HOV lane; when the initial space occupancy is greater than 0.2 , the higher the bus departure frequency is, the greater the decline of traffic volume of the bus-HOV lane is; when there are 5 bus routes to dispatch a bus every 3 minutes, compared with the low initial space occupancy, the traffic volume of the bus-HOV lane under the condition of the high initial space occupancy reduces by about 30%; the bus stop spacing more significantly affects the traffic efficiency of the bus-HOV lane, the longer the bus stop spacing is, the higher the frequency of HOV lane changing is, the more the increase of the congestion segments on the bus-HOV lane and the adjacent normal lane is, thus the traffic efficiency of the whole road is influenced.

Road section traffic risk propagation model based on macro traffic flow energy consumption

Traffic risk is an important source of road traffic accidents. Effectively predicting the propagation of accident risks on the road after an accident is of great significance for preventing secondary accidents. Therefore, this paper establishes a road section accident risk propagation model based on macro traffic flow energy consumption. Firstly, the influence of lane occupation and vehicle lane changing after the accident on traffic flow is analyzed, and a macro traffic flow model under the risk after the accident is constructed. Secondly, combined with the definition of traffic flow energy dissipation, the movement of the whole vehicle is abstracted as a fluid movement process, and a traffic flow energy consumption model under the risk after the accident is proposed to quantify the accident risk. Finally, according to the spatiotemporal correlation of risk propagation, a risk impact range and duration calculation model is proposed to describe the risk propagation process. In order to verify the effectiveness of the risk propagation model proposed in this paper, MATLAB and VISSIM are used to carry out numerical simulation and simulation verification of traffic flow operation under different initial traffic flow densities. The experimental results show that the greater the initial density of the road section when the accident occurs, the wider the risk impact range and the longer the duration. The absolute value of the prediction deviation rate of the proposed model is below 8%, which has a good prediction effect.

Analysis of Highway Vehicle Lane Change Duration Based on Survival Model

To investigate highway vehicle lane-changing behavior, we utilized the publicly available naturalistic driving dataset, HighD, to extract the movement data of vehicles involved in lane changes and their proximate counterparts. We employed univariate and multivariate Cox proportional hazards models alongside random survival forest models to analyze the influence of various factors on lane change duration, assess their statistical significance, and compare the performance of multiple random survival forest models. Our findings indicate that several variables significantly impact lane change duration, including the standard deviation of lane-changing vehicles, lane-changing vehicle speed, distance to the following vehicle in the target lane, lane-changing vehicle length, and distance to the following vehicle in the current lane. Notably, the standard deviation and vehicle length act as protective factors, with increases in these variables correlating with longer lane change durations. Conversely, higher lane-changing vehicle speeds and shorter distances to following vehicles in both the current and target lanes are associated with shorter lane change durations, indicating their role as risk factors. Feature variable selection did not substantially improve the training performance of the random survival forest model based on our findings. However, validation set evaluation showed that careful feature variable selection can enhance model accuracy, leading to improved AUC values. These insights lay the groundwork for advancing research in predicting lane-changing behaviors, understanding lane-changing intentions, and developing pre-emptive safety measures against hazardous lane changes.

An improved hierarchical deep reinforcement learning algorithm for multi-intelligent vehicle lane change

Lane change is a pivotal technology in autonomous driving, playing a significant role in improving traffic conditions. The control task of lane change becomes exceptionally complex for coordination of multiple intelligent vehicles, especially in unpredictable situations. To address this issue, the paper proposes a novel multi-intelligent vehicle lane change method (MVLC) based on Deep Reinforcement Learning (DRL). The MVLC features a hierarchical framework that includes a lower-layer lane change controller and an upper-layer reinforcement learning decision-making method. In the lower layer, a lane change controller is designed to execute motion control for a single vehicle, consisting of a lateral controller utilizing dueling double deep Q-network to make lane change decision and a longitudinal controller that employs Intelligent Driver Model (IDM) to follow the preceding vehicle. In the upper layer, a model-free DRL method is used for simultaneous control of multiple intelligent vehicles. To learn the optimal policy, a comprehensive reward function is designed. Finally, experiments are conducted in mixed traffic to valid the effectiveness of the proposed method. Results show that MVLC achieves secure and timely lane changes for multiple vehicles. Therefore, the method is expected to have significant potential for improving overall traffic efficiency and mitigating traffic congestion.

Improved Taillight Detection Model for Intelligent Vehicle Lane-Change Decision-Making Based on YOLOv8

With the rapid advancement of autonomous driving technology, the recognition of vehicle lane-changing can provide effective environmental parameters for vehicle motion planning, decision-making and control, and has become a key task for intelligent vehicles. In this paper, an improved method for vehicle taillight detection and intent recognition based on YOLOv8 (You Only Look Once version 8) is proposed. Firstly, the CARAFE (Context-Aware Reassembly Operator) module is introduced to address fine perception issues of small targets, enhancing taillight detection accuracy. Secondly, the TriAtt (Triplet Attention Mechanism) module is employed to improve the model’s focus on key features, particularly in the identification of positive samples, thereby increasing model robustness. Finally, by optimizing the EfficientP2Head (a small object auxiliary head based on depth-wise separable convolutions) module, the detection capability for small targets is further strengthened while maintaining the model’s practicality and lightweight characteristics. Upon evaluation, the enhanced algorithm demonstrates impressive results, achieving a precision rate of 93.27%, a recall rate of 79.86%, and a mean average precision (mAP) of 85.48%, which shows that the proposed method could effectively achieve taillight detection.

Modeling risk potential fields for mandatory lane changes in intelligent connected vehicle environment

In the environment of Intelligent Connected Vehicles (ICVs), the ICV system is capable of gathering, processing, and transmitting the majority of traffic information to each vehicle, thereby empowering drivers to make more informed and secure decisions. Nevertheless, ICVs are still incapable of completely avoiding traffic conflicts and accidents. Consequently, developing a risk assessment model for ICVs is crucial for their deployment. To investigate the dynamic patterns of lane-changing vehicle risks within ICV environments, this study developed a quantitative relationship model based on the Artificial Potential Field (APF) theory. The model correlates the Total Potential Field (TPF) of mandatory lane-changing (MLC) vehicle with the longitudinal/transverse gravitational potential field (LGPF/TGPF) and the longitudinal/transverse repulsion potential field (LRPF/TRPF). Subsequently, three sets of Human Machine Interface (HMI) systems were developed to delineate distinct ICV environments: baseline, warning group, and guidance group. The baseline furnishes fundamental data, the warning group supplements preceding vehicle icons and real-time headway information, whereas the guidance group additionally augments speed and voice guidance functionalities. Following this, a simulated driving experiment involving 43 participants was carried out to simulate MLC scenarios in highway merging sections. Using enumeration and gradient descent methods, the quantitative relationship between different types of risk fields was determined. The findings indicated that the ICV environment significantly impacts the mean values of LGPF, TGPF, LRPF (Leading Vehicle, LV), and TRPF, while insignificantly affecting the mean value of LRPF (Following Vehicle, FV). Nevertheless, a notable discrepancy in the mean value of LRPF (FV) was observed between the warning and guidance groups. Comparative results with lane positions demonstrate that the proposed TPF accurately depicts vehicle lane-changing behaviors. Comparative results with classical risk indicators indicate that TPF more accurately depicts the actual driving risks and variation trends faced by vehicles in different MLC processes. The research findings will be pivotal in improving real-time control and guidance strategies for vehicles in highway merging sections under ICV environments.

Influence of surrounding traffic on lane change dynamics: Insights from a video-based laboratory study

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.

Prototype Network for Few-Shot Hazard Assessment of Vehicle Lane Changes in Risk Field

Prototype Network for Few-Shot Hazard Assessment of Vehicle Lane Changes in Risk Field

Modeling coupled driving behavior during lane change: A multi-agent Transformer reinforcement learning approach

In a lane change (LC) scenario, the lane change vehicle interacts with surrounding vehicles. The interactions not only affect their driving behaviors but also influence the traffic flow. This study aims to model the coupled behavior of the lane changer and the follower in the target lane during LC. Large-scale real-world connected vehicle (CV) data from the Safety Pilot Model Deployment (SPMD) program are used to extract LCs and study vehicle interactions. A multi-agent Transformer-based deep deterministic policy gradient (MA-TDDPG) method is proposed to model the coupled behaviors during LC. The multi-agent framework can handle the multiple agents’ behaviors with interactions, and the Transformer can process the observation-action memory accurately and efficiently. The MA-TDDPG algorithm can learn the sequential decision-making process over continuous action space during LC with the accommodation of the multi-vehicle interaction and the driver’s memory effect. Compared to traditional supervised learning and reinforcement learning methods, it demonstrates superior performance in imitating the longitudinal and lateral actions of the lane changer and the follower. The findings of this study provide insights into the development of microscopic simulations by producing realistic LC behaviors, and assistance/automation LC systems by generating LC motions and responses conforming to human driving habits. The model also creates an interactive simulation environment and lays the foundation for optimizing driving strategies.

Multi-lane traffic flow model based on cellular automaton fine-scale under cooperative vehicle infrastructure system

Microscopic traffic flow modeling is crucial for predicting future traffic demand and optimizing the design of transportation systems. At the microscopic level, a well-crafted model is capable of describing the interactions and dynamic variations among vehicles with greater accuracy. The cell scale in the classical cellular automaton (CA) multi-lane traffic flow model is a critical parameter to accurately express the location relationship of vehicles. In this paper, a new method based on Symmetric Two-lane Cellular Automaton (STCA), entitled STCA-X, is proposed for the cell scale selection. Firstly, the position, velocity, acceleration, and interaction of vehicle operation in the urban multi-lane environment are analyzed, and a feature model is built based on CA. One important drawback of the existing models is that they do not match the vehicle movement in the actual lane. To handle this problem, a fine-scale CA multi-lane model is developed. Secondly, a new traffic flow model is created by redefining several key behaviors such as road following, lane changing, and blocking in the STCA model. The experimental results are analyzed and compared with several of the state-of-the-art methods. Analysis of the simulation results proves that STCA-X improves vehicle lane change frequency and lane utilization efficiency. However, the enhancements in the model have resulted in increased computational complexity. Consequently, future research will delve into novel approaches to boost computational efficiency, thereby propelling the progress of traffic flow modeling technology.

Modeling of Lane Changing Behavior of Connected Driving Based on Vehicle Interaction Potential

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.

Research on the vehicle lane-changing decision-making system in complex traffic environments

In order to ensure the safety of vehicles changing lanes, there should be a certain degree of interaction and dynamics between vehicles on the way. In this paper, the problems of non-interactive road information and imprecise path planning are investigated. Through the research of the road condition decision-making system, autonomous lane-changing assistance system and intelligent traffic system, vehicle wireless networking technology, vehicle-road cooperation technology, and lane-changing decision-making and planning technology are used to increase the frequency of information interaction between vehicles and the external environment in order to realize information interaction between vehicles and collaborative driving. In this paper, simulation experiments of vehicle lane-changing are carried out by using MATLAB Simulink and compared with other research models. The results show that the lane change planning data based on the road condition decision system is more accurate, which leads to fast lane change driving in complex traffic scenarios.

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.

Multi-head attention-based intelligent vehicle lane change decision and trajectory prediction model in highways

With the aim to improve the interaction between intelligent vehicles and human drivers, this article proposes the MCLG (multi-head attention + convolutional social pooling + long short-term memory + Gaussian mixture model) lane change decision and trajectory prediction model, which includes a lane-changing intention decision module. The model comprises a lane change decision module responsible for determining three lane change intentions: left lane change, right lane change, and car-following. Subsequently, a multi-head attention mechanism processes complex vehicle interaction information to enhance modeling accuracy and intelligence. In addition, uncertainty in trajectory prediction is considered by using multimodal trajectory prediction and Gaussian mixture model, and diversity and uncertainty are combined by combining trajectory prediction from several different modalities through probabilistic combinatorial sampling patterns. Test results indicate that the MCLG model, based on the multi-head attention module, outperforms existing methods in trajectory prediction. The decision module, which takes interactive information into account, exhibits higher predictability and accuracy. Furthermore, the MCLG model, considering the lane-changing decision module, significantly enhances trajectory prediction accuracy, providing robust decision-making support for autonomous driving systems.

Optimal Mandatory Lane-Changing Location Planning for CAV Based on Cell Transmission Model

If dedicate a lane to connected autonomous vehicle (CAV) on a multilane road, the traffic congestion and safety risks remain a major problem but in a different style. Random and disorderly mandatory lane-changing behaviour before approaching the next ramp or intersection would have a disturbing effect on the following vehicles of the traffic flow. This paper mainly establishes the optimal mandatory lane-changing location matching model for each target vehicle in the dedicated CAV lane environment. The aim is to minimizing the total travel time, which could take the disturbing effect into account. This model nests the cell transmission model (CTM) to describe vehicle running. The constraints include the relation between target CAV lane-changing cell and the corresponding behaviour start time, the updating of the flow, and occupancy for varied cells. We use the Ant Colony Optimization (ACO) algorithm to solve the problem. Through the case study of a basic two-lane road scenario in Ningbo, we acquire the convergence results based on the ACO algorithm. Our optimal lane-changing location matching scheme can save 5.9% total travel time when compared to the near-end location lane-changing scheme. We test our model by increasing the total number of upstream input vehicles with 4%, 11%, 15%, and the mandatory lane-changing vehicles with 60%, 200%, respectively. The testing results prove that out optimization method could deal with varied road traffic flow situations. Specifically, when the traffics and mandatory lane-changing vehicles increase, our method could perform better.

An effective lane changing behaviour prediction model using optimized CNN and game theory

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.