Identification of Lane-Changing Behavior Oriented to Behavioral Early Warning

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.

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.

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

A major aspect in the development of advanced driving assistance systems (ADASs) is the research in developing human driving behavior prediction and recognition models. Recent contributions focus on developing these models for estimating different driving behaviors like lane or speed change. Thus, the models are incorporated into the ADAS to generate warnings and hints for safe maneuvers. Driving behavior recognition and prediction models are generally developed based on machine learning (ML) algorithms and are proven to generate accurate estimations. Previous review research contributions tend to focus on ML-based models for the prediction and recognition of speed change, trajectory change, and even driving styles. Due to high number of driving errors occurring during a lane change, a state-of-art review of different ML-based models for lane-changing behavior prediction and recognition is helpful to present a comparison between different models in terms of structure, influencing input variables, and performance. This enables the integration of the most efficient model for the development of ADASs to avoid accidents during a lane change. First, definitions and terms related to the model’s task and evaluation metrics used to evaluate the model’s performance are described to improve the readability. Then, the different input variables of the models affecting the lane-changing behaviors are presented. Next, a review of the models developed based on well-known approaches, such as artificial neural network (ANN), hidden Markov model (HMM), and support vector machine (SVM), using different input variables is given. Three lane-changing behaviors are focused on here: left/right lane change and lane keeping. The advantages and disadvantages of the different ML models with a comparison are summarized as well. Finally, the improvements required in the future are discussed.

Key feature selection and risk prediction for lane-changing behaviors based on vehicles’ trajectory data

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.

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.

Key findings are discussed regarding characteristics of lane-changing behavior based on observations of an urban street network. An in-depth exploration of observed lane-changing behavior and its modeling were conducted using vehicle trajectory data extracted from video observations using VEVID, a software package developed by the authors, integrated with a video-capture system. As a result, rules for modeling lane-changing behavior are proposed with respect to various types of lane changes. A lane-changing model consists of three components: a decision model, a condition model, and a maneuver model. Drivers’ decisions to change lanes depend on travel maneuver plans, the current lane type (i.e., the relationship between the current lane and the driver’s planned route), and traffic conditions in the current and adjacent lanes. A lane-changing condition model is the description of acceptable conditions for different types of lane changes. A lane-changing maneuver model describes a vehicle’s speed and duration when a certain type of lane change occurs. All of these models are established in a heuristic structure.

Lane change detection is crucial for intelligent transportation systems, as it affects traffic flow on both macroscopic and microscopic levels. Lane change models are widely used in traffic and transportation studies, making it important to understand the factors that affect drivers’ lane changing behavior. In this context, we proposed a novel model for detecting lane changes by applying wavelet transform to high-resolution data from unmanned aerial vehicles. The model was trained and tested using empirical lane changing data from pNEUMA. Firstly, the azimuth angle was calculated on WGS-84 coordinates of each vehicle found in the specified road segment. Next, a multi-level wavelet transform was applied to the azimuth series using mother wavelets such as Haar, Daubechies, and Symlet for each vehicle. Machine learning method was applied to extracted features to detect lane changing. Additionally, the lane changing style of drivers was classified as sudden or normal using the same model. The results indicate that the proposed data-driven model is able to accurately detect lane changes and the type of lane change.

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.

More than 90% of traffic accidents are caused by driver behavior, with lane change behavior being a major contributor. Recently, driving assistance systems are being introduced on vehicles to reduce traffic accidents, and a reliable vehicle lane change collision detection system is a key component of these systems. Besides, the foundation of the vehicle lane change detection system is the effective vehicle lane change detection model. In this paper, based on the support vector machine, we propose a model for detecting driver lane change maneuvers and take into account the real-time vehicle dynamic features transmitted via Vehicle to X (V2X) Communication. The accuracy is ideal for lane keep and lane change situations, and it is also robust for zigzag driving situations, according to tests conducted using the NGSIM real traffic dataset. The detection accuracy for left and right lane change maneuvers is 97.5% and 99.09%, respectively, while the false alarm rate is 8.56%. Additionally, the average advance detection time is 1.7 s, which is suitable for actual driving application scenarios.

<div class="section abstract"><div class="htmlview paragraph">The recent progress in camera-based technologies has prompted the development of prototype camera-based video systems, intended to replace conventional passenger vehicle mirrors. Given that a significant number of collisions during lane changes stem from drivers being unaware of nearby vehicles, these camera-based systems offer the potential to enhance safety. By affording drivers a broader field of view, they facilitate the detection of potential conflicts. This project was focused on analyzing naturalistic driving data in support of the Federal Motor Vehicle Safety Standard 111 regulatory endeavors. The goal was to assess the effectiveness and safety compatibility of prototype camera-based side-view systems as potential replacements for traditional side-view mirrors. The method employed involved extracting radar data from instances of lane changes conducted by 12 drivers for two pick-up trucks includes 10018 signal-indicated lane changes performed at speeds consistent with highway travel in a naturalistic driving study. Among these, 5029 lane changes were carried out using camera-based displays. The study delved into the influence of camera-based systems on lane change behavior, particularly examining lane change conflicts using time-to-collision data obtained from radar readings. The objective was to assess their impact on safety measures, such as vehicle detection, distance, closing speeds, and conflicts. These assessments spanned a range of conditions, including both daylight and nighttime operations. Upon analyzing the results, no compelling evidence emerged to indicate that the adoption of camera-based systems had a detrimental effect on lane change performance, leading to riskier lane changes in comparison to the traditional mirror systems.</div></div>

<div class="section abstract"><div class="htmlview paragraph">As a basic link of driving behavior in urban roads, vehicle lane changing has a significant impact on traffic flow characteristics and traffic safety, and the automation of lane change is also a key issue to be solved in the field of intelligent driving. In this paper, the research on the automatic lane change control for intelligent vehicles is carried out. The main work is to build the overall structure of the vehicle's automatic lane change behavior, of which the planning and tracking are focused. The strategy of Constant Time Headway (CTH) is used in the lane change decision. The lane change trajectory adopts the model of constant velocity offset plus sine function, and the longitudinal displacement is determined by the vehicle speed when changing lanes. Model Predictive Control (MPC) theory is used to track the trajectory, which optimizes tracking accuracy and vehicle stability and constrains the range and rate of change of vehicle speed and steering angle. By using weighted quadratic cost function, linearity matrix inequality constraints and upper and lower bound constraints, the multi-objective trajectory tracking problem is eventually transformed into a constrained online convex quadratic programming problem. The results of simulation and HIL test show that the scheme of automatic lane change can make the vehicle smoothly complete the lane changing behavior, and the errors can meet the error requirements of lane change. Compared with other controller, the method shows smaller lateral acceleration, stronger robustness and higher control precision during the test. Moreover, the computational time of the proposed MPC controller, implemented using the PXI, is 47.994ms during one sampling period, which can satisfy the real-time requirement.</div></div>

Advanced driver assistance systems are required to detect latent hazards posed by surrounding vehicles and generate an appropriate response to enhance safety. Lane changes constitute potentially risky maneuvers, as drivers involved encounter latent hazards due to surrounding vehicles. A careful study of lane change behavior is therefore essential in identifying potential abnormalities that may lead to various hazards, during the process of a lane change. In this study, an anomaly detection technique is used to compare snapshots of normal and dangerous lane change maneuvers, to identify the abnormal instances. A one-class support vector machine is used and tested for novelty identification of naturalistic driving study data. The results show that the technique is able to detect dangerous lane changes with high accuracy. In addition, results suggest that dangerous behavior could occur before, after or during a lane change maneuver.

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.