Lane Change Maneuver Research Articles

A closed-loop directional dynamics control with LQR active trailer steering for articulated heavy vehicle

To improve the directional performance of articulated heavy vehicles (AHV), three previewed points driver model and LQR active trailer steering (ATS) strategy are proposed and examined using closed-loop driver/vehicle dynamics simulation. Firstly, a simplified linear three-degree-of-freedom (3-DOF) yaw plane model for AHV is established and validated. Secondly, based on the multi-point preview-predictor following strategy, the weighted-average error of three previewed points of the tractor is defined as the final lateral previewed displacement error. According to the assumption of constant yaw rate and the trajectory prediction of AHV, and three previewed points driver model has been configured, it’s path-following performance is verified. Thirdly, instead of using the idea of minimization, a linear quadratic regulator (LQR) controller is proposed based on the linear 3-DOF yaw plane model of AHV to make the control variables to keep following the desired responses. Through joint simulations using MATLAB/Simulink and TruckSim, the effectiveness of the built-in driver model, three-previewed point driver model and three-previewed point driver model + LQR ATS control methods on improving vehicle orientation performance were compared and analyzed through closed-loop driver/vehicle dynamics simulations. The significant effectiveness of the proposed method in improving maneuverability and stability is verified through low-speed 360° trajectory tracking tests and high-speed double lane change maneuver simulations. Finally, a hardware-in-the-loop (HIL) platform is used to further verify the effectiveness of the designed strategy.

An enhanced eco-driving strategy based on reinforcement learning for connected electric vehicles: cooperative velocity and lane-changing control

Purpose This study aims to propose an enhanced eco-driving strategy based on reinforcement learning (RL) to alleviate the mileage anxiety of electric vehicles (EVs) in the connected environment. Design/methodology/approach In this paper, an enhanced eco-driving control strategy based on an advanced RL algorithm in hybrid action space (EEDC-HRL) is proposed for connected EVs. The EEDC-HRL simultaneously controls longitudinal velocity and lateral lane-changing maneuvers to achieve more potential eco-driving. Moreover, this study redesigns an all-purpose and efficient-training reward function with the aim to achieve energy-saving on the premise of ensuring other driving performance. Findings To illustrate the performance for the EEDC-HRL, the controlled EV was trained and tested in various traffic flow states. The experimental results demonstrate that the proposed technique can effectively improve energy efficiency, without sacrificing travel efficiency, comfort, safety and lane-changing performance in different traffic flow states. Originality/value In light of the aforementioned discussion, the contributions of this paper are two-fold. An enhanced eco-driving strategy based an advanced RL algorithm in hybrid action space (EEDC-HRL) is proposed to jointly optimize longitudinal velocity and lateral lane-changing for connected EVs. A full-scale reward function consisting of multiple sub-rewards with a safety control constraint is redesigned to achieve eco-driving while ensuring other driving performance.

Lane change maneuver detection considering real-time vehicle dynamic features via V2X communication

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.

Simultaneous Feasible Local Planning and Path-Following Control for Autonomous Driving

In this paper, a new approach for lane change and double-lane change planning and following for autonomous driving is proposed. Herein, we introduce a novel technique, based on exponential functions, to generate online and feasible lane change maneuvers; these maneuvers satisfy the constraints on the maximum allowable curvature a given vehicle can handle. In addition, a simultaneous local path planning and path-following control framework is adapted. The framework utilizes a multi-threading architecture to run the local planner module concurrently with the control module. The planning module generates parametric reference paths based on the proposed lane change and double-lane change maneuvers. The control module is based on a Model Predictive Path-following Control (MPFC) scheme, which achieves the path following objective while satisfying vehicle’s state and control limits. To validate the proposed framework, several real-time simulation scenarios are designed and tested on CARLA soft real-time vehicle simulator. The results show the effectiveness of the proposed framework in generating and smoothly following lane change maneuvers.

Combining Decision Making and Trajectory Planning for Lane Changing Using Deep Reinforcement Learning

In the context of Automated Vehicles, the Automated Lane Change system, is fundamentally based upon the separate constructs of Perception, Decision making, Trajectory Planning, and Execution. However, in existing works there are many simplistic and unplausible assumptions in applying these constructs that severely restrict their operational effectiveness in realistic and complex driving scenarios. For instance, there are rigid assumptions about the disposition of vehicles and that lane-changing maneuvers can occur instantaneously, but that highly desirable features such as the ability for real-time trajectory re-planning are lacking. In this paper, we address these limitations through an integrated methodology for lane-change decision making and trajectory planning, in which a deep Reinforcement Learning algorithm with a safe action set technique is employed in decision making that is effectively coupled to a specially devised trajectory planning model. The proposed new methodology is computationally efficient, supporting real-time implementation, and provides for lane-changing maneuvers that can be made simultaneously with other vehicles and can be dynamically re-planned; thus, enabling flexible, robust, and safe lane-changing maneuvers under the guidance of a new decision-making module. Finally, the veracity of the proposed methodology in guiding a vehicle to improve travel times and accomplish high-level driving behaviors such as overtaking and desired-speed maintenance in a range of road traffic scenarios is demonstrated in a number of numerical experiments.

Exploring Behavioral Patterns of Lane Change Maneuvers for Human-Like Autonomous Driving

Due to the growing interest in automated driving, a deep understanding on the characteristics of human driving behavior is critical for human-like autonomous vehicles. Among various driving behaviors, lane change is the most important one for vehicle lateral driving safety. This study proposes an unsupervised method to extract and discover the behavioral patterns of lane change maneuvers for the purpose of exploring the composed behavioral patterns during lane change. This method involves two phases: Firstly, the lane change sequences will be segmented into blocks using time-series segmentation algorithms. Three segmentation algorithms were utilized in this study. In the second phase, the segments will be clustered to find the corresponding behavioral pattern of each segment. Two extended latent Dirichlet allocation (LDA) models were adopted to cluster the segments. The combination of different segmentation and clustering algorithms were evaluated and compared by employing entropy and perplexity as the evaluation criteria. Collected lane change data from naturalistic driving were applied to examine its effectiveness. The results show that this method could effectively mine descriptive behavioral patterns from lane change data. This study provides a promising data mining solution to facilitating deep and comprehensive understanding on driver lane change behaviors, which will promote the development of human-like autonomous vehicles.

Lateral Stability Control of a Tractor-Semitrailer at High Speed

To improve the high-speed lateral stability of the tractor-semitrailer, a lateral stability control strategy based on the additional yaw moment caused by differential braking is proposed and investigated based on the co-simulation environment. First of all, a five-degree-of-freedom (5-DOF) yaw-roll dynamic model of the tractor-semitrailer is established, and the model accuracy is verified. Secondly, the lateral stability control strategy of the tractor-semitrailer is proposed, two yaw moment controllers and the braking torque distributor are designed. Then, the effectiveness of the proposed control strategy and the influence of the yaw moment controller on the lateral stability of the tractor-semitrailer are investigated under the high-speed lane-change maneuvers. Finally, the controller robustness is discussed. Research results show that the proposed high-speed lateral stability control strategy can ensure the tractor-semitrailer to perform safely the single lane-change (SLC) maneuver at 110 km/h and the double lane-change (DLC) maneuver at 88 km/h; the yaw moment controller has significant influence on the lateral dynamic performance of the tractor-semitrailer; compared with the proportional-derivative (PD) control, the model predictive control (MPC) can make the tractor-semitrailer obtain better lateral stability under high-speed lane-change maneuvers; MPC and PD controllers exhibit good robustness to the considered vehicle parameter uncertainties.

Distributed cooperative trajectory and lane changing optimization of connected automated vehicles: Freeway segments with lane drop

This study presents a methodology for optimal control of connected automated vehicles (CAVs) in freeway segments with a lane drop. Lane drops can create bottlenecks with a considerable number of mandatory and discretionary lane-changing maneuvers when traffic volume is high, which can eventually lead to stop-and-go conditions. Proper motion planning aligned with optimal lane changing upstream of a lane drop can increase capacity and reduce the number of stops and the risk of collision. This paper introduces a vehicle-level mixed-integer program to control longitudinal and lateral movement of CAVs, provide a smooth flow of traffic, and avoid congestion in freeway segments with lane drops. To ensure the feasibility of vehicle-level decisions and promote system-level optimality, a cooperative distributed algorithm is established, where CAVs coordinate their decisions to find the optimal longitudinal and lateral maneuvers that avoid collisions among all vehicles. The proposed coordination scheme lets CAVs find their optimal trajectories based on predictive information from surrounding vehicles (i.e., future locations and speeds) and coordinate their lane-changing decisions to avoid collisions. The results show that optimal lane changing of CAVs smoothens the traffic flow and increases freeway capacity in congested traffic conditions. Compared with all-knowing CAVs simulated in Vissim, the proposed methodology reduced the average travel time by up to 86.4%. It increased the number of completed trips by up to 134.3% based on various traffic demands and lane drop layout combinations.

Torque vectoring algorithm for distributed drive electric vehicle considering coordination of stability and economy

Working of in-wheel motors (IWMs) in high-efficiency areas and minimum tire slip should be considered when driving distributed drive electric vehicles (DDEVs). Therefore, a novel torque vectoring control algorithm is proposed to lower energy dissipation and ensure lateral stability, which consists of a linear quadratic regulator and a proportion integration control module in upper controller to calculate desired additional yaw moment and total driving torque, respectively, for following desired yaw rate, side slip angle, and longitudinal velocity. In addition, the stability objective function considering tire working load and the economic objective function considering working efficiency of IWMs and tire slip energy are established separately in lower controller. The fitness function of coordinating lateral stability and economy is obtained by phase plane method. Particle swarm optimization (PSO) algorithm with a superior initial population (SIP-PSO) is proposed to solve torque distribution coefficients for torque distribution of DDEVs. Finally, simulation and hardware-in-the-loop test results under double lane change and snake lane change maneuvers on lower adhesion road indicate that the proposed algorithm can effectively lower the energy loss of IWM working and tire slip while ensuring lateral stability under different working conditions.

Improved Model Predictive Control Path Tracking Strategy Based an Online Updating Algorithm With Cosine Similarity and a Horizon Factor

Path tracking plays an essential role in autonomous vehicles. To ensure tracking accuracy and improve tracking adaptability in different velocities, a path tracking strategy based on an improved model predictive control (MPC) method is presented in this research. First, a path tracking controller based on improved MPC based an online Updating algorithm is constructed. The update mechanism is triggered by using the cosine similarity, when the cosine similarity is lower than the predefined threshold value, making the state space and cost function of MPC match real-time conditions to rectify the sensitivity of MPC to vehicle speed. Additionally, to further enhance the controller’s performance, a fuzzy control is employed to determine the horizon factor for optimizing the prediction horizon and control horizon online. Also, the weighting factors of the prediction horizon and control horizon are discussed to improve the adaptability at varying velocities. Next, the improved MPC controller is compared with the traditional MPC controller for a double lane change maneuver. The validation results demonstrate that the proposed strategy achieves good adaptability with satisfactory tracking accuracy at various velocities. Finally, the feasibility of the proposed strategy is verified in a real prototype vehicle test.

Impact of Variability of Car-Following Parameters on Road Capacity: The Simple Case of Newell’s Model

This paper contributes to the vehicle-level analysis of two macroscopic features of the road traffic: capacity variability and capacity drop. This paper focuses only on car-following (CF) behavior and leaves the part related to lane-change maneuvers for future research. In particular, a simplistic CF model (Newell’s with bounded acceleration) for a single-lane scenario is studied. In this work, by introducing a speed limitation across a zone, a bottleneck with variable nominal capacity has been created. A continuous event-based numerical resolution method is used. Consequently, it is possible to vary the three Newell’s model parameters: maximal acceleration, minimal distance, and reaction time. It has been shown that the variability of those CF parameters (e.g., reaction time, minimal distance, and maximal acceleration) has a strong impact on pre-breakdown capacity variation and also on queue discharge flow. It has been concluded that this parameters variability does affect the drop (provided that the maximal acceleration has a relatively high mean value). Various distribution shapes (uniform, truncated Gaussian, and Gamma) have been explored. It has been realized that this does not have any significant impact on the capacity distribution. Concerning the amplitude of the capacity distribution, it is demonstrated that reaction time is the parameter with the highest impact followed by minimal distance. If all parameters vary with an amplitude of 30%, it is shown that the capacity standard deviation, in this scenario without lane changes, is about half the experimental values reported in the literature.

Deep Reinforcement Learning Approach for Automated Vehicle Mandatory Lane Changing

This paper proposes a reinforcement learning-based framework for mandatory lane changing of automated vehicles in a non-cooperative environment. The objective is to create a reinforcement learning (RL) agent that is able to perform lane-changing maneuvers successfully and efficiently and with minimal impact on traffic flow in the target lane. For this purpose, this study utilizes the double deep Q-learning algorithm structure, which takes relevant traffic states as input and outputs the optimal actions (policy) for the automated vehicle. We put forward a realistic approach for dealing with this problem where, for instance, actions selected by the automated vehicle include steering angles and acceleration/deceleration values. We show that the RL agent is able to learn optimal policies for the different scenarios it encounters and performs the lane-changing task safely and efficiently. This work illustrates the potential of RL as a flexible framework for developing superior and more comprehensive lane-changing models that take into consideration multiple aspects of the road environment and seek to improve traffic flow as a whole.

Deep understanding of big geospatial data for self-driving: Data, technologies, and systems

With the continued development of Autonomous Vehicle System (AVS), self-driving related technologies have attracted much attention over the past decade. In this light, we survey existing literature regarding self-driving related data, technologies, and systems. We present details of representative studies regarding collision avoidance, automatic lane-changing maneuver, object detection (including pedestrian detection and obstacle detection), and vehicle trajectory prediction, respectively. This survey summarizes the findings of existing self-driving studies, thus uncovering new insights that may guide researchers and software engineers in fields of self-driving data management systems and autonomous vehicle systems.

Determining the trajectory of the vehicle motion on the basis of the recorded physical quantities describing its dynamics during typical maneuvers in road traffic

The paper compares the results of the vehicle motion trajectory calculations determined on the basis of the recorded waveforms of the acceleration vector and angular velocity components during the double lane change maneuver, taking into account the errors of acceleration sensors reset (zeroing). The differences in the results of the calculations were determined and possible causes of these differences were indicated. The test results indicate that special attention should be paid to zeroing the acceleration sensors, in particular the ay component.

The bicycle model of a 4WS car lateral dynamics for lane change controller

The article shows the validity of four-wheel steering (4WS) bicycle model. At the beginning it is presented in which tasks, connected with 4WS vehicles, the bicycle model is applied. Then the equations in local coordinate system and their forms in global coordinate system are presented. The research tested the response of the model to a lane change manoeuvre, using transfer functions and a “bang-bang” control signal. The transfer functions was connected with static ratio characteristic to construct full four-wheel steering model. A reference signal generator and vehicle model were constructed in analytical form and checked in Matlab&Simulink using sample vehicle’s data from another literature item. One example of simulations was shown. The results presented led to interesting conclusions and indicated directions for further research. This research will include the issue of regulators derivation. This will allow for the construction of a fully functioning control system. The effects of this activities will be tested again in simulation environment.

Determining the trajectory of the vehicle motion on the basis of the recorded physical quantities describing its dynamics during double lane change maneuvers

The paper indicated the results of the vehicle motion trajectory calculations determined on the basis of the recorded waveforms of the acceleration vector and angular velocity components during the double lane change maneuver, taking into account the errors of acceleration sensors reset (zeroing). For the sake of simplicity, it was assumed that the zeroing errors apply only to the linear acceleration sensors, while the angular velocity sensors show exact values at the time t = 0 s (when starting the double lane change maneuver). During the analysis of the results of the experimental research, the vehicle kinematics model was used, in which right-handed coordinate systems were used: global and two local coordinate systems. The differences in the results of the calculations were determined and possible causes of these differences were indicated. The test results indicate that special attention should be paid to zeroing the acceleration sensors, in particular the ay component.

The effect of information from dash-based human-machine interfaces on drivers' gaze patterns and lane-change manoeuvres after conditionally automated driving

The goal of this paper was to measure the effect of Human-Machine Interface (HMI) information and guidance on drivers' gaze and takeover behaviour during transitions of control from automation. The motivation for this study came from a gap in the literature, where previous research reports improved performance of drivers’ takeover based on HMI information, without considering its effect on drivers’ visual attention distribution, and how drivers also use the information available in the environment to guide their response. This driving simulator study investigated drivers’ lane-changing behaviour after resumption of control from automation. Different levels of information were provided on a dash-based HMI, prior to each lane change, to investigate how drivers distribute their attention between the surrounding environment and the HMI. The difficulty of the lane change was also manipulated by controlling the position of approaching vehicles in drivers’ offside lane. Results indicated that drivers' decision-making time was sensitive to the presence of nearby vehicles in the offside lane, but not directly influenced by the information on the HMI. In terms of gaze behaviour, the closer the position of vehicles in the offside lane, the longer drivers looked in that direction. Drivers looked more at the HMI, and less towards the road centre, when the HMI presented information about automation status, and included an advisory message indicating it was safe to change lane. Machine learning techniques showed a strong relationship between drivers' gaze to the information presented on the HMI, and decision-making time (DMT). These results contribute to our understanding of HMI design for automated vehicles, by demonstrating the attentional costs of an overly-informative HMI, and that drivers still rely on environmental information to perform a lane-change, even when the same information can be acquired by the HMI of the vehicle.

Research on Lane Change Motion Planning Steering Input Based on Optimal Control Theory

Aiming at traditional control methods are not suitable for solving the multiconstraint problem of the vehicle steering and lane-change maneuvering process, a hybrid optimization scheme based on dynamic adaptive salp swarm algorithm and Gaussian pseudospectral method (DASSA-GPM) is proposed for its strong global search capability. Based on a steering inverse dynamics model, the lane change motion planning steering problem is converted into an optimal control problem which is then converted into a nonlinear programming problem by applying the adaptive salp group algorithm which is solved through the sequential quadratic programming (SQP) method finally. The simulation results verify the effectiveness and feasibility of the proposed dynamic adaptive salp swarm algorithm and hybrid optimization scheme. In addition, compared with GPM which the absolute error of steering wheel angle is 8.2 × 10−4 degree, the proposed method has higher computational accuracy, which absolute error of steering wheel angle is 5.5 × 10−4 degree. At the same time, under the same calculation accuracy (10−3), compared with GPM which CPU time is 4.81 s, the proposed method has higher calculation efficiency which CPU time is 3.86 s when solving non-smooth problems. The proposed method can provide a reference value into the active safety of manned and unmanned vehicles.

Lane-Change Maneuver on Overtaking Moving Vehicle on a Two-Way Street

This article discusses a safe and collision-free decision for an autonomous car to make when passing another vehicle on a congested two-way roadway. The proposed algorithm considers both the car going the other way and the vehicle in front of the "ego" car that could be an obstacle. The LIDAR, the medium-range radar, and the long-range radar are the three types of sensors that the ego car will use to observe its surroundings. The second sensor's job is to keep an eye on the vehicle blocking the ego lane. Once that car is detected, the ego car will start to pass it, but only if the first sensor has found that the obstruction has been removed. With the help of the third sensor, the ego car can determine if there is a vehicle approaching from the other direction. After that, the algorithm will determine the time needed to pass both automobiles and their final positions and the distance between them. If there is enough space, the ego vehicle will switch lanes and perform the lane-change movements. If there is not enough space, the ego car will continue driving in the same lane. MATLAB simulation confirms the method, which demonstrates significant improvement.

An integrated lane change prediction model incorporating traffic context based on trajectory data

Predicting lane change maneuvers is critical for autonomous vehicles and traffic management as lane change may cause conflict in traffic flow. Most existing studies do not consider the effect of traffic context (i.e., traffic level and vehicle type) on lane change maneuvers. Therefore, these models cannot adapt to different traffic environments. This study aims to address this problem and establish an integrated lane change prediction model incorporating traffic context using machine learning algorithms. In addition, lane change decisions and lane change trajectories are both predicted to capture the whole process, which have been less studied. The framework of the proposed model contains two parts: the traffic context classification model, which is used to predict traffic level and vehicle type, and the integrated lane change prediction model, which is used to predict lane change decision with XGBoost and lane change trajectories with LSTM incorporating context information. Instead of considering lane change, we establish trajectory prediction models for left lane change and right lane change, further improving the prediction accuracy. The naturalistic trajectories of the highD dataset are used to train and validate the model. The results show that the proposed model improves the accuracy from 97.02% to 98.20% when predicting lane change decision that incorporate traffic context. In addition, the MSE decreases from 11.21 to 6.62 when predicting trajectories. The proposed models are also validated on NGSIM dataset, proving the adaptability of the model. The proposed model can be applied to different environments to reduce collision risks caused by lane change maneuvers and improve traffic management and driving safety.