A personalized social force-based lane-changing model with a new optimizer

Driver behaviors, particularly lane-changing behaviors, have an important effect on the safety and throughput of the roadway-vehicle-based transportation system. Lane-changing models are a vital component of various microscopic traffic simulation tools, which are extensively used and playing an increasingly important role in Intelligent Transportation Systems studies. The authors conducted a detailed review and systematic comparison of existing microscopic lane-changing models that are related to roadway traffic simulation to provide a better understanding of respective properties, including strengths and weaknesses of the lane-changing models, and to identify potential for model improvement using existing and emerging data collection technologies. Many models have been developed in the last few decades to capture the uncertainty in lane change modeling; however, lane-changing behavior in the real world is very complex due to driver distraction (e.g., texting and cellphone or smartphone use) and environmental (e.g., pavement and lighting conditions) and geometric (e.g., horizontal and vertical curves) factors of the roadway, which have not been adequately considered in existing models. Therefore, large and detailed microscopic vehicle trajectory data sets are needed to develop new lane changing models that address these issues, and to calibrate and validate lane-changing models for representing the real world reliably. Possible measures to improve the accuracy and reliability of lane-changing models are also discussed in this paper.

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.

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.

This paper presents a driver lane change model that adapts to individuals with different driving behavior. For lane change assistance systems, its acceptance by customers greatly relies on the assistant algorithm which can accurately reflect drivers' individual characteristics. By analyzing the real world vehicle data and drivers' characteristic parameters, such as time to lane change (TTLC), the steering angle and lateral offset were obtained, and consequently a lane change model was proposed. The inputs of the model are the vehicles' current state (e.g. velocity) and drivers' characteristic parameters (e.g. TTLC, lateral offset), and the output is the steering angle. Drivers' individual differences can be demonstrated by the variation of model parameters. Comparing the simulation results to the real world vehicle data indicates that the developed model is able to imitate driver operations during lane changes, and it can be integrated into the control algorithm of the lane change assistance system embodying drivers' characteristics.

A new two-lane changing model with inter-vehicle communication based on intelligent transportation system for traffic flow is proposed. By taking the information of several front vehicles in both self-lane and objective lane into consideration, drivers can predict the velocity tendency of next time steps on both lanes, thus decide whether to change lanes or not. Simulations with both periodic boundary and open boundary under the new lane changing model and Rickert's lane changing model [17] are conducted. Compared with previous lane changing models, the lane changing model in this paper can reduce ping pang lane changing frequency effectively. When there is a bottleneck on the one lane, the new lane changing mechanism performs better than the traditional model.

Microscopic traffic simulation tools are becoming increasingly popular in evaluating transport options. Driving behaviour models (e.g. route choice models, lane-changing models, etc.) are essential components of these tools. The state-of-the-art driving behaviour models assume that drivers make instantaneous decisions. However, in reality, many of the driving decisions are based on a specific plan. The plan is however unobserved or latent and only the manifestations of the plan through actions are observed. Examples include selection of a target lane before execution of the lane change, choice of a merging tactic before execution of the merge. Ignoring the effect of plans in the decision framework can lead to incorrect representation of congestion in traffic simulation tools. In this article, we present a modelling methodology to address the effects of unobserved plans in the decisions of the drivers. The actions of the driver are conditional on the current plan and can be influenced by anticipation of downstream traffic conditions. The heterogeneity in decision making and planning capabilities of drivers are explicitly addressed. The methodology has been applied in developing lane-changing behaviour models with disaggregate trajectory data extracted from video recordings of an urban road using the maximum likelihood technique. Estimation results show that the latent plan models have a significantly better goodness-of-fit compared to the ‘reduced form’ models where the latent plans are ignored. The latent plan models were also found to outperform the reduced form models in validation case studies within the microscopic traffic simulator MITSIMLab.

In general, drivers tend to change lane to have a convenient journey. Lane changing process give notable impact in traffic flow especially in congested road. Over the last decades, driving models are being develop with the intention to illustrate the behavior of vehicles. This paper intends to review the process of lane changing in the context of vehicle communication. In addition, existing lane changing models are reviewed and classified based on their characteristic. For this section, few studies on development of the models are summarized and to build new behavior model that can manage lane changes. The limitations of earlier lane-changing models are then addressed. Last, the findings and conclusions are presented, and future work are proposed.

Expressway lane change strategy of autonomous driving based on prior knowledge and data-driven

Lane changes significantly affect the characteristics of traffic flow. Lane-changing models are therefore important in microscopic traffic simulation. Existing lane-changing models emphasize the decision-making aspects of the task but generally neglect the detailed modeling of the lane-changing action itself and model it only as an instantaneous event. However, research indicates that lane-changing durations are on average in the range of 5 to 6 s. The omission of lane-changing durations from simulation models may have a significant impact on simulation outputs. Models of the duration of lane changes are presented. These models are estimated by using detailed vehicle trajectory data that were collected in naturalistic driving with high-mounted video cameras. Separate models are presented for passenger cars and for heavy vehicles and statistical tests are conducted for the similarity between the lane-change durations of the two vehicle types.

This chapter summarizes a series of advances in lane changing models aiming at providing a more complete and integrated representation of drivers' behaviors. These advances include the integration of mandatory and discretionary lane changes in a single framework, the inclusion of an explicit target lane choice in the decision process and the incorporation of various types of lane-changing mechanisms, such as cooperative lane changing and forced merging. In the specifications of these models, heterogeneity in the driver population and correlations among the various decisions a single driver makes across choice dimensions and time are addressed. These model enhancements were implemented in the open source microscopic traffic simulator of MITSIMLab, and their impact was demonstrated in validation case studies where their performance was compared to that of existing models. In all cases, a substantial improvement in simulation capability was observed.

A unified modeling framework for lane change intention recognition and vehicle status prediction

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

Prevailing traffic conditions affect highway safety and the processes by which drivers perceive a stimulus, evaluate it, and execute a corresponding driving maneuver. Several efforts have been made to use microscopic traffic simulation for evaluating highway safety. However, these efforts faced serious challenges because previous acceleration and lane-changing models had been built in an accident-free environment with different layers of safety constraints. A new approach relies on a cognitive risk-based microscopic model to study the relationship between prevailing traffic conditions and the risk experienced by drivers in a traffic stream. The model can consider accidents endogenously through lane-changing logic and provide an indicator of relative roadway safety as experienced by drivers. Six scenarios are simulated. The results show the importance of lane changing to understanding accident and near-accident occurrence in simulation models. A risk value comparison reveals that work zone bottlenecks have a greater impact on drivers’ risk-taking tendencies than bottlenecks caused by uphill grades.

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-changing behavior is one of the most common driving behaviors while driving. Due to the complexity of its operation, vehicle collision accidents are prone to occur when changing lanes. Under the environment of vehicle networking, drivers can obtain more accurate traffic information in time, which can be of great help in terms of improving lane-changing safety. This paper analyzes the core factors that affect the safety of vehicles changing lanes, establishes the weight model of influencing factors of lane-changing behavior using the analytic hierarchy process (AHP), and obtains the calculation method of lane-changing behavior factors (LCBFs). Based on the fuzzy reasoning theory, the headway between the lane-changing vehicle and adjacent vehicles in the target lane was examined, and fuzzy logic lane-changing models were established for both situations (i.e., change to the left and change to the right lane). The fuzzy logic lane-changing models were tested via simulation experiments, and the test results showed that the models have a better warning effect on lane changing (LCBF = 1.5), with an accuracy of more than 90%. Thus, the established model in this paper can provide theoretical support for safety warnings when changing lanes and theoretical support for the sustainable development of transportation safety.