Investigating model and impacts of lane-changing execution process based on CA 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.

<div>Single lane changing is one of the typical scenarios in vehicle driving. Planning an appropriate lane change trajectory is crucial in autonomous and semi-autonomous vehicle research. Existing polynomial trajectory planning mostly uses cubic or quintic polynomials, neglecting the lateral jerk constraints during lane changes. This study uses seventh-degree polynomials for lane change trajectory planning by considering the vehicle lateral jerk constraints. Simulation results show that the utilization of the seventh-degree method results in a 41% reduction in jerk compared to the fifth-degree polynomial. Furthermore, this study also proposes lane change trajectory schemes that can cater to different driving styles (e.g., safety, efficiency, comfort, and balanced performance). Depending on the driving style, the planned lane change trajectory ensures that the vehicle achieves optimal performance in one or more aspects during the lane change process. For example, with the trajectory that provides the best comprehensive performance under given constraints (initial speed of 20 m/s, lane width of 3.5 m, and a longitudinal distance of 50 m to the obstacle in front), the four-wheel steering model predictive control can effectively track the planned trajectory, with the maximum jerk value being 6.4 m/s<sup>3</sup> and the longitudinal speed after lane change being approximately 12.6 m/s. Although this study assumes specific longitudinal displacement before and after the lane change, the methodology is applicable to other scenarios. For example, it can determine the shortest longitudinal displacement and the optimal lane change trajectory given predefined vehicle speeds and maximum lateral acceleration conditions. The lane change trajectories developed in this study can be directly applied to the system design of autonomous vehicles.</div>

Two-lane traffic flow model based on regular hexagonal cells with realistic lane changing behavior

As a dynamic driving process, lane change behavior involves the longitudinal and lateral motion of lane changing vehicles. There is a strong coupling effect between longitudinal and lateral motion, so it is difficult to achieve ideal control effect by single control. This paper constructs a scene in which vehicles accelerate to change lanes on slippery roads, and longitudinal safety model is introduced to measure the longitudinal safety of vehicle following lane changing process. Considering the side-slip phenomenon of lane change vehicles on slippery roads, the classical vehicle “bicycle” model and fixed tire cornering-stiffness are used to evaluate the tire force of vehicles on slippery roads to ensure the lateral safety of lane change vehicle. The nonlinear programing problem of lane-change trajectory planning is established, and the comparison of three solutions is given to plan the optimal lateral and longitudinal lane change acceleration for the vehicle. The simulation results show that the planned lane change trajectory meet the longitudinal and lateral safety objective. By considering the lateral and longitudinal coupling effects, the safety and rationality of lane changing on slippery pavement can be improved, and the deficiency of the existing lane changing trajectory planning model that does not consider the risk of sideslip can be made up.

Objective: The aim of this study is to examine the effect of traffic density on drivers’ lane change and overtaking maneuvers. The differences between drivers’ left and right lane changing/overtaking maneuvers were also investigated.Method: A driving simulator experiment was conducted and 24 participants took part in this experiment. Based on the driving simulation data, lane change frequency, time duration, average speed, and acceleration were extracted as key variables of lane change maneuvers; overtaking frequency, overtaking duration, initial overtaking distance and headway, instantaneous speed, and acceleration before overtaking were analyzed as the key overtaking variables. One-way repeated measures analysis of variance (ANOVA), the Friedman test, and the Wilcoxon signed rank test were adopted for hypothesis tests with a significance level of .05. Further pairwise comparisons were performed with a Bonferroni correction for multiple comparisons.Results: Some significant differences in lane change and overtake behaviors were observed among different traffic densities: (1) both lane change and overtaking frequencies significantly increase with traffic density; (2) the average lane change acceleration and instantaneous overtaking acceleration significantly increase with traffic density; (3) as the traffic density increases, the initial overtaking distance and headway decrease. As for the effect of the directions of maneuvers, the (1) time duration of lane change and overtaking from the right side was significantly shorter than that from the left side; (2) right initial overtaking distance/headway was smaller than that of the left side; (3) right instantaneous overtaking acceleration was significantly higher than the left instantaneous acceleration.Conclusions: The results showed that as traffic density increases, drivers’ lane changing and overtaking intentions are enhanced. Both initial overtaking distance and headway decrease with traffic density, which might influence road safety. In addition, drivers do not show a preference on the directions of lane change or overtaking according to frequency. However, drivers tend to be more decisive and reckless when overtaking on the right because of a smaller distance/headway before overtaking, higher instantaneous acceleration, and a more restricted field of view compared with overtaking on the left.

Energy-efficient lane-change motion planning for personalized autonomous driving

The expressway traffic flow characteristics of off-ramp have an important impact on traffic congestion. By analyzing the vehicle car-following and lane-changing behavior, the cellular automata model of off-ramp area was built. When analyzing lane-changing behavior, the behavior of vehicle changing lane from main lane to off-ramp buffer lane was considered, and the information interaction process between the vehicle ready to changing lane and the rear vehicle on the target lane was considered, the new lane-changing rules were built. In the new lane-changing rules, free lane-changing and compulsory lane-changing were included. By analyzing the simulation results, when traffic flow density is lower, the behavior of vehicles in the main lane changing lane into off-ramp buffer lane has less effect on traffic capacity of the road section, but when traffic flow density rising to a certain extent, the behavior of vehicles in the main lane changing lane into off-ramp buffer lane will bring an adverse effect on traffic capacity of the road section.

Along with the rapid development of autonomous driving technology, autonomous vehicles are showing a trend of practicality and popularity. Autonomous vehicles perceive environmental information through sensors to provide a basis for the decision making of vehicles. Based on this, this paper investigates the lane-changing decision-making behavior of autonomous vehicles. First, the similarity between autonomous vehicles and moving molecules is sought based on a system-similarity analysis. The microscopic lane-changing behavior of vehicles is analyzed by the molecular-dynamics theory. Based on the objective quantification of the lane-changing intention, the interaction potential is further introduced to establish the molecular-dynamics lane-changing model. Second, the relationship between the lane-changing initial time and lane-changing completed time, and the dynamic influencing factors of the lane changing, were systematically analyzed to explore the influence of the microscopic lane-changing behavior on the macroscopic traffic flow. Finally, the SL2015 lane-changing model was compared with the molecular-dynamics lane-changing model using the SUMO platform. SUMO is an open-source and multimodal traffic experimental platform that can realize and evaluate traffic research. The results show that the speed fluctuation of autonomous vehicles under the molecular-dynamics lane-changing model was reduced by 15.45%, and the number of passed vehicles was increased by 5.93%, on average, which means that it has better safety, stability, and efficiency. The molecular-dynamics lane-changing model of autonomous vehicles takes into account the dynamic factors in the traffic scene, and it reasonably shows the characteristics of the lane-changing behavior for autonomous vehicles.

The driver lane change process is divided into two parts: lane change awareness formation and behavior implementation. Considering that the intrinsic factors of assessing the status and ability of drivers and the external traffic environment factors will affect the entire lane changing process independently rather than being independent and non-interfering, this paper chooses network AHP to analyze and identify the lane changing behaviors. The result shows that the accuracy of this method can reach 92.37% and the effect is better.

Lane changing poses a great threat to traffic safety in the work zone areas. This study aims to conduct an in-depth investigation of drivers' lane-changing behavior and to propose and verify the efficacy of an intelligent in-vehicle audio prompt through a driving simulator experiment. Forty-two participants, comprising 22 males (12 professional drivers and 10 normal drivers) and 20 females (10 professional drivers and 10 normal drivers), took part in the study. Three traffic density levels (20pcu/km, 40pcu/km, and 60pcu/km) and five locations (baseline, 250m, 500m, 750m, and 1000m) to release the audio prompt information were set to collect eye movement and driving behavior data. A multistage lane-changing process including the perception stage, preparation stage, and action stage was developed, and three types of variables, namely experiment variable, behavioral variable, and risk assessment variable, were defined. The results indicated that the audio prompt set at 500m, 750m, and 1000m could noticeably improve lane-changing behavior (i.e., triggering lane changes more in advance and providing drivers with more preparation time). However, the audio prompt set at 250m upstream of the work zone showed limited efficacy, which is not recommended in practical application. Male drivers and professional drivers performed better during lane changing, and they had a more active observation of the surrounding environment and tended to adopt safer vehicle operations than their counterparts. A high traffic density could increase the collision risks even if drivers had made sufficient preparations, while in low traffic density conditions, drivers who failed to change lanes tended to apply hard braking when they approached the work zone.

Recent research on the prediction of driver’s lane-changing behaviour requires vehicle surrounding information, as it is believed that driver’s decision on lane changing is made consciously based on those information. However, current research has shown that the usage of such surrounding information leads to high false alarm rate of lane-changing predict system [1]. Therefore this paper contributes to developing a lane-changing prediction method which uses vehicle state information only. From the perspective of the observer’s daily experience, this paper selects vehicle’s lateral trajectory and the spectrum of its lateral trajectory as input to predict drivers’ lane-changing intention. A Direction Convolutional LSTM (DCLSTM) network has been developed to predict drivers’ lane-changing behaviours. Recent pure LSTM methods proposed by researchers provide high accuracy when predicting the generation of drivers’ lane-changing intentions, but they have relatively low accuracy in predicting drivers’ lane-changing direction. DCLSTM retains pure LSTM network’s high accuracy in the prediction of drivers’ lane-changing intentions, while its prediction of drivers’ lane-changing directions is also accurate. All the training and testing data are extracted from the NGSIM dataset.

The intention forecasting of the human driver plays an essential role in improving the safety and driver-machine coordination of the co-piloting vehicle, which is the decision-making foundation for driver assistance system (DAS). However, the quantitative prediction of the lane change decision, which determines the occasion and shape of the lane change path, has drawn little attention by the prior works. In this paper, a novel convolutional neural network (CNN)-based predictor that forecasts the driver’s lane change path is proposed. By including the driver’s eye-gazing and head-heading information, an earlier and more accurate prediction of driver’s time to lane change (TLC) and lane change duration (LCD), and we use the positive TLC and negative TLC to represent the classification results LC and RC, respectively. The classification and regression results validated that the inclusion of driver’s gazing information achieved an earlier and more precise detection of driver’s lane change intention. Furthermore, owning to the hierarchical structure of the CNN model and the parallel computing devices, the execution time of the proposed model is shorter and exhibits its powerful real-time computing performance. The estimated TLC and LCD are adopted to predict the driver’s target lane change trajectory with satisfactory accuracy. Therefore, the proposed model enables the DAS to generate a human-like lane change decision to minimize the driver-machine conflicts during lane change.

<div class="section abstract"><div class="htmlview paragraph">Segment with lane drops are very important in freeway systems since they are major constrains to traffic flow and safety. The frequency of capacity reductions and higher safety risks is proportional to an increase in lane-changing actions, which worsen traffic congestion, decrease road capacity, and increase the risk of an accident. Traditional traffic management strategies that rely on physical structures and driver’s decision making often fail under such conditions. This paper provides a detailed lane change control strategy specific to freeway segments with lane reduction in the connected and autonomous vehicle (CAV) environment. The strategy combines both centralized and decentralized techniques to improve the vehicle’s lane-changing behavior and density. A cellular transmission model of lane-level is proposed for the centralized control of the linked vehicles based on the ratio of the driver compliance. The model derives the density equation and transforms the lane-changing problem before the work zone into merging traffic flow problem. The optimization model is developed based on the total trip time, density deviation, and total lane changes, with constraints on the cell reception capacity and lane changing ratios. Control parameters for lane change distribution are identified using genetic algorithms to solve the problem. For the decentralized control, a reinforcement learning solution is introduced which uses deep Q-networks (DQN) to improve lane-changing actions. The reward function takes into account the traffic efficiency and the impact of lane changing, and the continuous action space is discretized for application. The control mechanism is evaluated by the simulation of a work zone scenario that includes two restricted lanes on the Shanghai-Nanjing Expressway. It also shows that there is an improvement of 3% to 6% in traffic flow and velocity as compared to single-strategy approaches. The collaborative control strategy significantly enhances traffic flow and reduces congestion at bottlenecks and offers valuable information for future traffic control in CAV environments.</div></div>

Lane change (LC) behavior has critical effects on traffic flows and safety due to its complex interactions with surrounding vehicles. To ensure safe lane changes and prevent potential crashes, it is important to recognize the potential crash risk of lane change in real time. This study proposes an improved risk estimation (IRE) model to evaluate the potential collision risk of lane change (LCR) vehicle groups. The safety margin is introduced to consider the deceleration capability of vehicles to measure the reaction time of drivers during the LC. Then the IRE model is established, incorporating the collision probability and collision severity measured based on the safety margin. The trajectory data, extracted from the highD dataset, are used and 1536 LC samples are investigated. We compare the LCR under different contextual factors, including vehicle types (cars and trucks), two lane change directions (left and right lane change, LLC and RLC), and traffic flows (low and high traffic). It was found that truck drivers keep higher LCR compared with car drivers due to limited brake capacity, and the left lane change results in higher LCR compared with the right lane change. Additionally, lane change is associated with higher crash risk in high traffic flow, as compared to low traffic flow. The understanding of the crash risk of lane change behavior under different contextual factors, can be useful for real-time crash prediction and devising traffic management strategies.

Enhancing lane changing trajectory prediction on highways: A heuristic attention-based encoder-decoder model