Lane Change Performance Research Articles

Fuzzy Logic-Based Autonomous Lane Changing Strategy for Intelligent Internet of Vehicles: A Trajectory Planning Approach

The autonomous lane change maneuver is a critical component in the advancement of intelligent transportation systems (ITS). To enhance safety and efficiency in dynamic traffic environments, this study introduces a novel autonomous lane change strategy leveraging a quintic polynomial function. To optimize the trajectory, we formulate an objective function that balances the time required for lane changes with the peak acceleration experienced during the maneuver. The proposed method addresses key challenges such as driver discomfort and prolonged lane change durations by considering the entire lane change process rather than just the initiation point. Utilizing a fifth-order polynomial for trajectory planning, the strategy ensures smooth and continuous vehicle movement, reducing the risk of collisions. The effectiveness of the method is validated through comprehensive simulations and real-world vehicle tests, demonstrating significant improvements in lane change performance. Despite its advantages, the model requires further refinement to address limitations in mixed traffic conditions. This research provides a foundation for developing intelligent vehicle systems that prioritize safety and adaptability.

Modeling the impact of driving aggression on lane change performance Measures: Steering compensatory Behavior, lane change execution duration and crash probability

The current study aimed to investigate the influence of driving aggression on lane change behavior. Drivers belonging to three levels of aggressive driving styles (aggressive, moderately aggressive, and non-aggressive) participated in lane change experiments, and 232 lane change maneuvers were recorded. Steering behavior during lane change maneuvers was investigated using three steering performance measures, including the standard deviations of steering angle, steering reversal rates, and maximum steering swerve. Three separate generalized estimating equation models were developed to analyze the effects of aggressive driving behavior on the standard deviation of steering angle, steering reversal rates, and maximum steering swerve. A Weibull accelerated failure time model with clustered heterogeneity was developed to analyze the effect of aggressive driving behavior on lane change execution duration. Additionally, to model the crash probability during lane change, a random effects logistic regression model was developed. The findings indicate that aggressive and moderately aggressive drivers completed lane changes 59% and 38% faster, respectively, compared to non-aggressive drivers. This implies a propensity for aggressive and moderately aggressive drivers to execute lane changes more swiftly than non-aggressive drivers. Aggressive drivers increased their standard deviation of steering angle, steering reversal rates, and maximum steering swerves by 180%, 60%, and 62%, respectively, compared to non-aggressive drivers. This implies that aggressive drivers engaged in extreme steering behavior as a compensatory measure for their reckless driving in order to avoid a collision during lane change maneuvering. However, despite their vigorous compensatory steering behavior, the crash rate (number of crashes observed) increased by 5.10 and 2.83 times for aggressive and moderately aggressive drivers, respectively, compared to non-aggressive drivers. The findings have implications for developing driving assistance systems and lane change simulation models.

Inverse Reinforcement Learning Based: Segmented Lane-Change Trajectory Planning With Consideration of Interactive Driving Intention

One of the most challenging problems in autonomous driving is trajectory planning for lane changes. Conventional trajectory planning is generally realized by optimizing a specific cost function. However, the model performance required for different scenarios makes it repetitive and time-consuming to tune the cost function. Furthermore, to ensure driving safety, comfort as well as traffic efficiency, it is essential to interact with other vehicles by predicting their driving intentions. Therefore, this paper proposes a segmented-updated lane-change trajectory planning method for autonomous vehicles on closed highways. The maximum entropy inverse reinforcement learning (IRL) algorithm for human demonstration learning and the long short-term memory and Bayesian network (LSTM-BN)-based model for target vehicles' driving intentions are applied. Three contributions are made in this work: 1) The LSTM-BN is used to judge the intentions of surrounding vehicles based on its historical information to inform lane-change trajectory planning. 2) Different feature-based cost functions of the IRL algorithm are designed according to different lane-change scenarios, which enhances its adaptability to different scenarios. 3) Segmented-updated trajectory planning is used to dynamically optimize trajectories via an online optimization process, which reduces the model's limitations and improves overall lane-change performance. Finally, simulation experiments demonstrate that the proposed method has strong generalization ability, reliable accuracy, and can adjust lane-change trajectories reasonably according to the driving intentions of surrounding vehicles.

Autonomous Driving using Linear Model Predictive Control with a Koopman Operator based Bilinear Vehicle Model

This paper presents a real-time Model Predictive Control (MPC) formulation for autonomous driving based on a lifted bilinear vehicle model developed using the Koopman operator. Koopman operator based models can closely mimic the original nonlinear behaviors with a higher dimensional linear structure, which is attractive for computationally efficient linear MPC formulations for controlling nonlinear systems. However, current linear models based on linear Koopman realizations cannot capture the control-affine dynamics in nonlinear systems. This may result in large discrepancies between the original nonlinear system and the data-driven linear model, hindering its use in MPC. To address this gap, first, a novel Koopman bilinear vehicle model that takes control-affine dynamics into consideration is constructed and tested in open-loop simulations. This bilinear Koopman model is then linearized to serve as a prediction model in MPC, and is shown to have higher accuracy compared to the state-of-the-art linear models. The model is then used to develop a linear MPC formulation for simultaneous planning and control of an autonomous vehicle. The formulation is tested on lane change scenarios with obstacles against the nonlinear MPC and standard linear MPC benchmarks. The results show that the new formulation can achieve a lane change performance closer to the nonlinear MPC with a computational performance similar to the standard linear MPC. The new formulation is observed to be successful in handling high speeds where the standard linear MPC fails.

A Case for Raising the Camera: A Driving Simulator Test of Camera-Monitor Systems.

This experiment provides a first-of-its-kind driving-simulator study to investigate the feasibility of camera-monitor systems (CMS) with displaced side-mounted cameras in sedans. Among the increasing number of studies investigating the replacement of side-mounted rearview mirrors with CMS, the placement of side-mounted cameras has been largely neglected. Moreover, user preferences with respect to camera placement have not been validated in a driving simulator. Past research merely has shown that the vertical camera position can affect distance perception. In a driving simulator experiment, we investigated the effects of rearward camera placement on driver acceptance and performance. Thirty-six participants performed multiple lane changes in a last safe-gap paradigm. The camera position, ego-velocity, and velocity of the approaching vehicle varied across the experiment. The results suggest a clear preference for a high rearward perspective, whereas participants disliked the lower viewpoint. However, these stark differences were only marginally mirrored in lane change performance. Average safety margins tended to decrease and their variation tended to increase for the low camera position. Even if the impact of the camera position on driving behavior seems to be small in sedans, driver expectations show clear-cut preferences. When designing CMS, this should be taken into account, as these preferences could promote the use of CMS and thus their positive impact on safety. Designers should place side-mounted cameras as high as possible to increase acceptance of CMS. Low camera positions are not recommended, as they might decrease safety margins and are not appreciated by drivers.

Safe and optimal lane-change path planning for automated driving

This paper proposes an integrated lane-change trajectory planning method for advanced driver assistance system of connected and automated vehicles. First, the time-based quintic polynomial automated lane-change model is presented, which could adjust longitudinal and lateral velocity simultaneously. By tuning the lane-change duration and longitudinal displacement in the lane-change model, the lane-change reference trajectories satisfying the demands of safety, lane-change duration, travel distance, and comfort were derived under traffic-free condition. All feasible reference trajectories compose a trajectory map, which includes different driving situations, such as quick and comfortable or sudden and safe lane change. Second, the lane-change constraints induced by surrounding vehicles are introduced. The effects of surrounding vehicles on the lane-change performance are investigated by adjusting the speeds and initial gaps of preceding and rear vehicles. In addition, the initial velocity of the host vehicle is optimized to maximize the area of the trajectory map to enable a safer lane change. Finally, within the derived trajectory map, an optimal lane-change trajectory eliminating potential collisions is calculated by minimizing the lane-change duration, travel distance, driving comfort, and fuel consumption.

Multiobjective Optimization of Lane-Changing Strategy for Intelligent Vehicles in Complex Driving Environments

This paper describes an optimal lane-changing strategy for intelligent vehicles under the constraints of collision avoidance in complex driving environments. The key technique is optimization in a collision-free lane-changing trajectory cluster. To achieve this cluster, a tuning factor is first derived by optimizing a cubic polynomial. Then, a feasible trajectory cluster is generated by adjusting the tuning factor in a stable handling envelope defined from vehicle dynamics limits. Furthermore, considering the motions of surrounding vehicles, a collision avoidance algorithm is employed in the feasible cluster to select the collision-free trajectory cluster. To extract the optimal trajectory from this cluster, the TOPSIS algorithm is utilized to solve a multiobjective optimization problem that is subject to lane change performance indices, i.e., trajectory following, comfort, lateral slip and lane-changing efficiency. In this way, the collision risk is eliminated, and the lane change performance is improved. Simulation results show that the strategy is able to plan suitable lane-changing trajectories while avoiding collisions in complex environments.

Investigating the Effects of Visuospatial Memory Secondary Tasks on LCT Driving Performance

Memory demand is associated with increased mental workload. The objective of the present study was to examine the effects of visuospatial memory secondary tasks on driving performance. Memory tasks for the unknown word-figure pairs and recognition tasks for word-figure pairs at two-level difficulties were employed separately to represent working memory’s process and long-term memory’s process. A simulator study was conducted based on the simulation of the standard environment of Lane change test (LCT). The performance of lane keeping, lane change, and secondary tasks was measured by statistical methods. The comprehensive appraisal model was constructed to quantify total driving performance. The results showed that the mean path deviation, steering angle, and lane excursion times increased, and the proportion of correct lane change decreased, with the perceived workload increasing and the total driving performance decreasing in dual-task driving condition. Compared with the simple working memory group, as the difficulty of tasks increased in difficult working memory group, lane change performance degraded and the perceived workload increased. In contrast to difficult working memory group, the performance of lane keeping and lane change increased, while the perceived workload decreased and the total performance increased by about 50% in difficult recognition group. There were few differences between the simple working memory group and simple recognition group. The difficult working memory group had the lowest total driving performance. The results indicate that as the secondary task’s difficulty increases, driving performance will degrade. Performance improves significantly when the working memory process is converted to the recognition process. This trend is more obvious when the memory task assumes to be more difficult.

Study of Mandatory Lane Change Execution Behavior Model for Heavy Vehicles and Passenger Cars

Lane changing is one of the basic driver behavior interactions in microscopic traffic simulations for traffic, safety, and transportation system analysis. Although many traffic simulations pay attention to the decision process for lane changing, the execution process is often simplified or even ignored. This paper presents a study of lane change execution of heavy vehicles and passenger cars in a traffic situation with a blocked lane. A video camera was used to collect data from an arterial road in Melbourne, Australia. This paper investigates the behavior of heavy vehicles and passenger cars in mandatory lane changes and proposes a model framework for the execution of lane changes with regard to the emergency status and the impact of surrounding traffic for individual drivers who are changing lanes. The distance to the blockage was used to denote the emergency status. For the impact of the surrounding traffic, it was assumed that the driver would adjust the execution if a traffic conflict were detected and would continue the lane change if there were no conflict. A probability model was developed to interpret the driver’s choice of whether to continue the lane change. The data analysis found that the lane change performance of heavy vehicles differed from that of passenger cars. Therefore, the model was calibrated for heavy vehicles and passenger cars separately. In conclusion, the paper provides a framework of the future work of lane change execution models on traffic simulation to assess the traffic safety and road efficiency.

Effects of Driver and Secondary Task Characteristics on Lane Change Test Performance

AbstractThe main objective of this study was to examine the sensitivity of the Lane Change Test (LCT) as proposed by International Organization of Standardization by evaluating LCT performance between primary and dual‐task conditions in simulated driving conditions. The study involved four different secondary tasks that involved tracking, visual search, memory, and data entry, each under two different difficulty levels. The primary task involved a series of lane changes on a three‐lane straight roadway where the actual lane change trajectory was compared with a normative model of the trajectory. Thus, the lane change performance was measured by the mean deviation of the actual driving trajectory from the normative trajectory. Twenty‐four participants within three age groups (25–34, 35–45, and >55 years) and equally distributed between male and female took part in the study. Thus, the study also investigated the effect of age and gender on driving performance. The results showed that secondary tasks that require visual attention and psychomotor coordination deteriorated driving performance the most, whereas tasks that required memory scanning and utilization of the auditory modality least affected driving performance. The study also found differences in LCT performances with respect to three different age categories and gender. © 2012 Wiley Periodicals, Inc.

Relationships between lane change performance and open-loop vehicle handling metrics

This paper deals with the question of interpreting open-loop vehicle handling metrics with a driver in the loop. A vehicle model was developed and validated with track test data and subsequently coupled to an optimised driver model. Multiple vehicle configurations were simulated to identify relationships between four commonly used open-loop handling performance metrics and some quadratic measures of closed-loop performance indicative of driver mental and physical workload and overall path tracking ability. Qualitative statements are made regarding the relationships between some of the open-loop handling metrics and the objective closed-loop performance measures.

Forensic Engineering Analysisf A Truck Accident

Forensic Engineering Analyses Of Collision Events Often Focus On Physics Models And Calculation Methods Governing The Forces Occurring During An Impact And After The Separation Of The Vehicles. However, Physics Models And Calculation Methods Governing The Motion Of Vehicles In Normal Linearnd Rotational Dynamics, Outside Of The Impact Phase, Can Be Applied To A Forensic Engineering Analysis To Gain Insight Into The Collision Events. Techniques From The Fields Of Automotive Engineering And Highway Engineering, When Applied By The Forensic Engineer, Can Answer Critical Questions About Aarticular Case, Or May Be Able To Eliminate Hypothetical Scenarios Proposed By Others. He Mechanical Systems And Dynamics Of Commercial Vehicles, Especially Articulated Vehicles, Differreatly From The Dynamics Of Typical Utomobiles. The Limitations And Performance Capabilities Ofommercial Vehicles In Acceleration, Lateral Motion, Rotational Yaw, Roll And Pitch, And Handling Characteristicsan Be Used To Analyze Events Leading Up To A Collision Or To Evaluate Avoidance Capabilities. Annderstanding Of Factors Such As Articulation, High Enters Of Gravity, Rollover Propensity, Off-Tracking Inurns, Jack-Knifes, Trailer Swingouts, Acceleration Rates, And Lane Change Performance Is Essential In Thenvestigation And Reconstruction Of Commercial Vehicle Collisions. This Paper Will Provide An Introductiono Vehicle Dynamics Concepts Applicable To Commercial Vehicle Collisions, With A Combination Ofcientific Theories That Are Unique To Heavy Vehicles.

Lane change steering manoeuvre using model predictive control theory

This paper is a fundamental study on steering manoeuvre (driver model) when the steering angle input is obtained via Model Predictive Control (MPC). Conventional front steering angle for the lane change manoeuvre uses the sine wave input. This is very simple but this feed forward control input does not have adequate performance for mounting on real vehicle. For this reason, the steering input using MPC is developed. For example, the collision avoidance of lane-change steering manoeuvre has to deal with many constraints, so that the servo control like an LQI theory is not suitable for this problem. In addition, offline nonlinear programming calculation for optimal steering input of vehicle trajectory involves too heavy computational complexity to apply with real-time controllers. Desired control of the lane-change performance is conducted by the MPC approach with the constraint conditions. Potential advantage of the collision avoidance steering compared to the hard braking is estimated.

538 人間-自動車系における可変特性操舵装置による車線変更性能の向上可能性

538 人間-自動車系における可変特性操舵装置による車線変更性能の向上可能性