Lane Change Control Research Articles

Collision-Avoidance Lane Change Control Method for Connected Vehicle Platoon Under Mixed Traffic Environment

Collision-Avoidance Lane Change Control Method for Connected Vehicle Platoon Under Mixed Traffic Environment

Lane Change Control Combined with Ramp Metering: A Strategy to Manage Delays at On-Ramp Merging Sections

Control measures at merging locations aimed at either the mainline traffic or on-ramp traffic do not lead to a fairness in the distribution of total delay across the two streams. This paper presents a control strategy of combining a lane change control with a ramp metering system at motorway merges. The control strategy presents the opportunity to control the delays incurred at the two traffic streams of the merge. An optimization problem is formulated for a multilane motorway with an on-ramp with the aim to minimize the total travel time of the system. The proposed strategy is tested using an incentive-based lane-specific traffic flow model. Results revealed a 17% reduction in the total travel time due to the proposed strategy. Moreover, it was shown that the distribution of delays over the mainline and on-ramp could be controlled via the proposed strategy. The performance of the combined control was also compared to the individual control measures. It was observed that the individual control measures (lane change only and ramp metering only) lead to high delays on either the mainline or on-ramp compared to the combined control, where the balance between the delay for the drivers on the mainline and on-ramp could be regulated. The combined lane change and ramp metering control presents opportunities for the road authorities to manage the total delay distribution across the two traffic streams.

Hybrid MPC System for Platoon based Cooperative Lane change Control Using Machine Learning Aided Distributed Optimization

This study is devoted to developing a platoon-based cooperative lane-change control (PB-CLC). It coordinates the trajectories of a CAV platoon under a platoon-centered platooning control to accommodate the CAV lane-change requests from its adjacent lane, aiming to reduce the negative traffic impacts on the platoon resulting from lane-change maneuvers, on the premise of ensuring CAVs’ safety and mobility. Mathematically, the PB-CLC control is established using a hybrid model predictive control (MPC) system. The hybrid MPC system involves an MPC-based mixed integer nonlinear programming optimizer (MINLP-MPC) for optimal lane-change decisions, which considers multiple objectives such as traffic smoothness, driving comfort and lane-change response promptness subject to vehicle dynamics and safety constraints. To ensure the feasible lane-change, this study investigates and provides a lower bound of the lane-change time window by analyzing the MINLP-MPC model feasibility. Apart from the optimal lane-change decision consideration, the hybrid MPC system is well designed to ensure the control continuity and smoothness. In particular, the hybrid MPC system control feasibility and stability are proved to enable the platoon's back-and-forth state switchings between car-following and lane-change accommodation states. Next, we developed a machine learning aided distributed branch and bound algorithm (ML-DBB) to solve the MINLP-MPC model within a control sampling time interval (< 1 second). Specifically, built upon computer simulation and the c-LHS sampling technique, supervised machine learning models are developed offline to predict a reduced solution space of the integer variables, which is further integrated into the distributed branch and bound method to solve the MINLP-MPC model efficiently online. Extensive numerical experiments validate the effectiveness and applicability of the ML-DBB algorithm and the PB-CLC control.

Optimization of lane-changing advisory of connected and autonomous vehicles at a multi-lane work zone

The work zone with lane closure will be an active bottleneck due to vehicles’ mandatory lane-changing conflicts. The emerging Connected Autonomous Vehicle (CAV) technology provides opportunities for vehicle motion planning to improve traffic performance. However, the literature using CAV technology mainly focuses on single-lane lane-changing control in the merging area. The algorithm dealing with multi-lane lane-changing control is absent. In this paper, a simulation system with a lane-changing optimal strategy embedded for the multi-lane work zone is presented under the heterogeneous traffic flow. First, the road upstream of the work zone is divided into several segments, and an optimal multi-lane lane-changing algorithm is designed. It is recommended that CAVs, on the closure lane and the merged lane, change lanes on each segment to balance traffic distribution and minimize traffic delay. Second, to validate the algorithm proposed, a typical three-lane freeway with one-lane closed for the work zone is researched, and the simulation platform based on cellular automata is developed. Third, the advantages of multi-lane control strategies are studied and discussed in traffic efficiency improvement and collision risk reduction by comparing previous lane-changing control algorithms.

Research on intelligent vehicle lane changing and obstacle avoidance control based on road adhesion coefficient

Aimed at the safety and stability problems of intelligent vehicles under extreme conditions such as low adhesion road surface and emergency lane change and obstacle avoidance, this article designs a lane change and obstacle avoidance controller based on road adhesion coefficient. Using the nonlinear vehicle dynamics model as the prediction model, using the recursive least squares method to identify the road adhesion coefficient, considering the road adhesion coefficient to plan and adjust in the obstacle avoidance path as well as limit constraint conditions of the model predictive control controller, using model predictive control method for the expectation of intelligent vehicle trajectory tracking, travels tremendously guarantee the security and stability of driving. The joint CarSim–Simulink simulations results show that under poor road conditions, the trajectory tracking accuracy after optimization is higher and the vehicle is less prone to sideslip and instability. The lane change controller designed in this article has strong adaptability to different road surface adhesion coefficient, and all parameters can be controlled within a reasonable safety range at different speeds, with good robustness.

Lane-Change Gaming Decision Control Based on Multiple Targets Evaluation for Autonomous Vehicle

Automatic lane change is one of the most important highway operations. It seriously affects traffic efficiency and safety. It is also an important driving technology for automatic driving. To achieve the best automatic lane-change control, it is necessary to achieve the control from the perspective of multi-objective evaluation. In this paper, to make it applicable for a hybrid condition of car following and lane change, the traditional car-following model is modified by regarding the longitudinal motion during the lane-changing process as a transition of the car-following behavior in the two lanes before and after a certain lane-change behavior. A hyperbolic tangent transition function is introduced to connect the model to achieve a smooth transition of the model output. Then, the discretionary lane-change decision process of highway autonomous vehicles is modeled into a two-vehicle game model, and a comprehensive loss function concerning safety, efficiency, and ride comfort is proposed for the evaluation of the strategies. The optimal strategy is obtained by minimizing the expectation of losses. Finally, to verify the performance of the proposed new model, simulations of different car-following and lane-changing models are carried out, which is for multi-target simulation conditions. The results of the simulation show that the new model exhibits higher traffic efficiency, better homogeneity, and stability.

Personalized Trajectory Planning and Control of Lane-Change Maneuvers for Autonomous Driving

With the aims of safe, smart and sustainable future mobility, a personalized approach of trajectory planning and control based on user preferences is developed for lane-change of autonomous vehicles in this paper. First, a safe area during the lane change process is identified by using constraint Delaunay triangulation. Then, an improved rapidly-exploring Random Trees (i-RRT) is developed with B-spline to generate the feasible trajectory cluster, which is subject to the safe area boundaries and the vehicle dynamics. To extract a personalized trajectory from this cluster, we firstly adopt the fuzzy linguistic preference relation (FLPR) method to identify users' preferences on driving, which can be reflected by their subjective objectives including driving safety, ride comfort and vehicle stability. Then, the technique for order preference by similarity to ideal situation (TOPSIS) is utilized to solve the multi-objective optimisation problem formulated by considering the user preferences. The algorithms proposed above are integrated, and both simulation and experimental validation are conducted under lane-change scenarios of autonomous driving. Simulation and experiment results show that proposed approach is able to successfully realize personalized trajectory planning and lane-change control, satisfying users' various preferences and simultaneously ensure vehicle safety, demonstrating its feasibility and effectiveness.

Intelligent vehicle lane change trajectory control algorithm based on weight coefficient adaptive adjustment

In order to improve the trajectory smoothness and the accuracy of lane change control, an adaptive control algorithm based on weight coefficient was proposed. According to lane change trajectory constraint conditions, the sixth-order polynomial lane change trajectory applied to intelligent vehicles was constructed. Based on the vehicle model and the model predictive control theory, the time-varying linear variable path vehicle predictive model was derived by combining soft constraint of the side slip angle. Combined with fuzzy control algorithm, the weight coefficient of the deviation of the lateral displacement was dynamically adjusted. Finally, the FMPC (model predictive controller based on fuzzy control) and MPC controller were compared and analyzed by co-simulation of CarSim and Simulink under different speeds. The simulation results show that the designed FMPC controller can track the lane change trajectory better, and the controller has better robustness when the vehicle changes lanes at different speeds.

Integrated optimal control strategies for freeway traffic mixed with connected automated vehicles: A model-based reinforcement learning approach

This paper proposes an integrated freeway traffic flow control framework that aims to minimize the total travel cost, improve greenness and safety for freeway traffic mixed with connected automated vehicles (CAVs) and regular human-piloted vehicles (RHVs). The proposed framework devises an integrated action of several control strategies such as ramp metering, lane changing control (LCC) for CAVs and lane changing recommendation (LCR) for RHVs, variable speed limit control (VSLC) for CAVs and variable speed limit recommendation (VSLR) for RHVs with minimum safety gap control measures for lane changing and merging maneuvers. The CAVs are assumed to follow the system control instructions fully and immediately. In contrast, the RHVs would make decisions in response to the recommendations disseminated and also the behaviors of CAVs. The compliance rate of drivers to the LCR is captured by the underlying traffic flow model. A set of constraints is imposed to restrict VSLC/VSLR and LCC/LCR measures from changing too frequently or too sharply on both temporal and spatial dimensions to avoid excessive nuisance to passengers and traffic flow instability. A reinforcement learning based solution algorithm is proposed. First, a control parameterization technique is adopted to reduce the dimension of the original optimal control problem to increase computational efficiency. Then, a gradient-free Cross-Entropy-Method based algorithm is used to search the optimal parameters to circumvent the non-differentiability of the traffic flow model. The feasibility and effectiveness of the proposed framework are illustrated via numerical examples for a variety of penetration rates of CAVs under various traffic conditions. A sensitivity analysis is conducted to demonstrate the impacts of several important parameters such as the reaction time of the CAVs. It is found that the integrated control strategy can reduce the total travel cost by reducing the lane changing maneuvers and vehicles queuing at the bottleneck meanwhile smooth the traffic flow and suppress the adverse impact of shockwaves. The effect of ramp metering is not significant when the penetration rate of CAVs is high enough. Speed harmonization (with minimum gap control) in conjunction with LCC/LCR would be a better integrated control strategy under high penetration rate of CAVs.

Evaluation of Integrated Variable Speed Limit and Lane Change Control for Highway Traffic Flow

A sudden lane drop on a freeway with high volume of vehicles can cause severe traffic congestion and safety issues. Forced lane changes near the bottleneck reduce the traffic mobility on all lanes and trigger the capacity drop phenomenon. Numerous studies have explored the effect of Variable Speed Limit (VSL) and Lane Change (LC) control on solving the bottleneck congestion, but only a few have taken the measurement or model uncertainties into considerations. In this paper, we design an integrated VSL and LC controller based on a modified multi-section Cell Transmission Model (CTM) to alleviate the freeway bottleneck congestion and reject uncertainties. The location of the most upstream speed sign is treated as a parameter and optimized using simulation data. We first evaluate the proposed controller without uncertainty to demonstrate its effectiveness in improving traffic flow. Then we incorporate the uncertainties in measurements and model parameters to examine the robustness of the control. The proposed combined VSL and LC controllers are demonstrated to be robust with respect to uncertainties. The bounds of allowable uncertainties are more sensitive when uncertainties lead to a reduction of speed limit commands than an increase.

Decoupled Longitudinal and Lateral Vehicle Control Based Autonomous Lane Change System Adaptable to Driving Surroundings

The purpose of this study is to develop an autonomous lane change control system that adapts to variable surrounding conditions, to ensure vehicle safety and traffic flow stability. In this paper, we propose decision-making and control procedures for realizing autonomous lane changing; to this end, we consider not only behaviors for changing lanes but also those involved in approaching the lane changing state, with a focus on the controller design. A decoupled control structure and longitudinal trajectory-free control approach are suggested. We design a novel inter-vehicle spacing policy and a 3DOF lateral error vehicle dynamics model. To verify the effectiveness of our system, simulation experiments are performed for 12 scenarios, and system assessments are conducted based on four evaluation perspectives. The results confirm that our system can safely control the vehicle amidst various surrounding vehicle conditions and can also ensure vehicle motion stability. Furthermore, we solve the existing dynamic instability problem of lateral control, which arises through longitudinal acceleration variability. Another significant advantage of this model is that the controlled vehicle does not interfere with the target-lane traffic flow and smoothly synchronizes with the flow during lane changing.

Autonomous-Driving Vehicle Control With Composite Velocity Profile Planning

This article develops autonomous-driving vehicle control with a composite velocity profile (CVP) planning. An innovative technique of path planning is presented in the vehicle coordinates to allow complete lane change from any position at the origin lane to the center of the target lane. A technique of control is also presented to allow lane-change control design with vehicle speed planning. The CVP consists of longitudinal and lateral velocity profiles in time and planar geometric domains in the vehicle coordinates, respectively. This article also develops an autonomous-driving control framework with decentralized speed/lateral control using the CVP. The stability of the autonomous-driving control system is mathematically proved. Some experimental results on a test vehicle are also presented to demonstrate the utility of the proposed autonomous-driving vehicle control with CVP planning.

Design of variable curvature lane changing control system based on inverse system decoupling

At present, the lane changing technology for a single driving environment has been relatively mature, but the actual situations are much more complicated, such as the changing curvature and the changes in road conditions. In this paper, a new lane changing obstacle avoidance control strategy for the variable curvature road was proposed. It has two stages including path planning for variable curvature lane changing and inverse system decoupling (ISD) control for path tracking. The first stage established the lane changing path models for the variable curvature road. A longitudinal-lateral safe distance model was proposed to constrain the safe boundary of the lane changing path. The second stage proposed a dynamic decoupling method for longitudinal and lateral motion based on the inverse system decoupling, a direct yaw moment and active front wheel steering coordinated control method was designed. The inverse system decoupling algorithm can correct the single point preview (SPP) method to improve the stability and the path tracking accuracy in the lane changing obstacle avoidance process. The strategy was simulated by CarSim/Simulink co-simulation, and the experiments were carried out on the hardware-in-the-loop platform. The results show that the proposed control strategy can effectively avoid obstacles when the vehicle is driving on the variable curvature road. Besides, for the different road conditions and the strong non-linearity generated during the lane changing process, the control strategy can reduce the tracking error by a maximum of 32.7%, both the yaw rate and side slip angle can be controlled in a smaller range.

Gain‐scheduling control of dynamic lateral lane change for automated and connected vehicles based on the multipoint preview

Dynamic lateral lane change (DLLC) control of automated and connected vehicles (ACVs) is challenging because of the time-varying and complex properties of the traffic environment. This study proposes a DLLC control strategy combining dynamic trajectory planning and tracking. According to the real-time longitudinal accelerations and velocities of multiple surrounding vehicles, as well as the real-time states of the ACVs, the safe trajectory reference of DLLC is obtained by solving a case-dependent constrained optimisation problem. The lane changing efficiency, vehicle stability and passenger comfort are considered jointly in the trajectory planning. Then, the dynamic trajectory reference is tracked through a gain-scheduling control algorithm combining previewed trajectory feed-forward and ACVs states feedback. Gain-scheduling control algorithm based on a linear time-varying form is utilised to achieve the precise control of the different velocities and improve the real-time ability of the algorithm. The proposed strategy is tested through software and hardware-in-loop experiments, and in different test scenarios. The results of simulations and experiments show that the proposed control strategy can achieve a satisfactory performance of DLLC. The lane changing efficiency, safety, passenger comfort and vehicle stability are verified in complex traffic environments.

Simulation and evaluation of speed and lane-changing advisory of CAVS at work zones in heterogeneous traffic flow

Emerging connected autonomous vehicle (CAV) technologies provide an opportunity to the vehicle motion control to improve the traffic performance. This study simulated and evaluated the CAV-based speed and lane-changing (LC) control strategies at the expressway work zone in heterogeneous traffic flow. The control strategies of CAV are optimized by the multi-layer control structure based on model predictive control. The heterogeneous traffic flow composed of human-driven vehicles and CAVs is constructed based on cellular automata by the proposed Expected Distance-based Symmetric Two-lane Cellular Automate (ED-STCA) LC model and CAV car-following model. The six control strategies composed of variable speed limits (VSL), LC and their coordinated control strategies are experimented. The average travel time and throughput are selected to assess the advantages and disadvantages of each strategy under each combination of vehicles’ arrival rates and CAV mixed ratios. The numerical results show that: (i) the effect of the control strategy on the traffic is not obvious under free flow, and the control strategy may worsen the traffic under medium traffic. (ii) Early lane-changing control (ELC) is better than late lane-changing control (LLC) under medium traffic, and LLC is better under heavy traffic. (iii) [Formula: see text] is the best choice under heavy traffic and the mixed rate of CAVs is high. The simulation results obtained in the paper would provide some practical references for transportation agencies to manage the traffic in work zone under networking environment in the future.

Design and implementation of human driving data–based active lane change control for autonomous vehicles

This article describes the design, implementation, and evaluation of an active lane change control algorithm for autonomous vehicles with human factor considerations. Lane changes need to be performed considering both driver acceptance and safety with surrounding vehicles. Therefore, autonomous driving systems need to be designed based on an analysis of human driving behavior. In this article, manual driving characteristics are investigated using real-world driving test data. In lane change situations, interactions with surrounding vehicles were mainly investigated. And safety indices were developed with kinematic analysis. A safety indices–based lane change decision and control algorithm has been developed. In order to improve safety, stochastic predictions of both the ego vehicle and surrounding vehicles have been conducted with consideration of sensor noise and model uncertainties. The desired driving mode is decided to cope with all lane changes on highway. To obtain desired reference and constraints, motion planning for lane changes has been designed taking stochastic prediction-based safety indices into account. A stochastic model predictive control with constraints has been adopted to determine vehicle control inputs: the steering angle and the longitudinal acceleration. The proposed active lane change algorithm has been successfully implemented on an autonomous vehicle and evaluated via real-world driving tests. Safe and comfortable lane changes in high-speed driving on highways have been demonstrated using our autonomous test vehicle.

A hierarchical hybrid system of integrated longitudinal and lateral control for intelligent vehicles

A hierarchical hybrid control system is proposed to cope with highly automated driving in highway environments with multiple lanes and surrounding vehicles. In the high-level layer, the discrete driving decisions are coordinated by the finite-state machine (FSM) based on the relative position identification and predictive longitudinal distance of the surrounding vehicles. The low-level layer is responsible for the vehicle motion control, where the model predictive control (MPC) approach is utilized to integrate the longitudinal and lateral control mainly including car-following control and lane changing control. The proposed control system focuses on two issues regarding safe driving on highways. On one hand, the subject vehicle must always keep a safe distance with its leading vehicle to avoid the rear-end collision. On the other hand, the subject vehicle should also overtake the preceding vehicle by safe lane changes if the desired speed is not achieved. The effectiveness of the hybrid control is tested in the simulation, whose results verify that the driving decisions are made reasonably and the vehicle motion control obeys stability and comfort requirements. Moreover, it is also indicated by the simulations in random scenarios that the control strategy is able to deal with most of ordinary situations on highways although some emergency situations or critical driving maneuvers of other vehicles are not considered.

Sensitivity of a vehicle lane change control system to disturbances and measurement signal errors – Modeling and numerical investigations

Automatic control of the lane change process forms a basis to automate more complex vehicle maneuvers. The lane change controller model presented in the paper has a mixed structure. In the open-loop structure, the controller works as a reference signals generator supplying a time course of the steering system angle (reference input signal), and two time courses of the lateral and angular shifts of the vehicle (reference output signals). In the closed-loop structure it works as a steering signal corrector (regulation based on the error signals between the reference and measured signals expressing vehicle trajectory). For the validation of the controller algorithm, extensive simulation investigations have been carried out. In these investigations, as a virtual control object, a very detailed and experimentally verified mathematical model of a medium-duty truck has been used. The control system model and its simulation investigations were already presented in several papers by the Authors. This paper presents the results of the research focused on the sensitivity of the control system to disturbances and measurement signal errors (noises, offsets, delays).

Design of Real-Time Dynamic Reversible Lane in Intelligent Cooperative Vehicle Infrastructure System

The rapidly growing traffic demand and the slowly increasing traffic supply have produced an mounting contradiction, which is mainly manifested in cities as road congestion and unbalanced bidirectional traffic flow. Most of the reversible lanes are implemented on fixed sections and fixed times and are mainly guided by ground markings, road signs, railings, and traffic police officer. It requires a lot of human and material costs. And, the control effect is lagging and inaccurate. Aiming at these problems, a real-time dynamic reversible lane scheme in the Intelligent Cooperative Vehicle Infrastructure System (CVIS) was proposed. Traffic information was collected in real time through the CVIS, and a reversible lane scheme was established based on the real-time service level V/C and BRP functions. A lane change control model was applied to determine the number of lanes and the timing of lane changes. Then, the reversible lanes were managed in real time through intelligent road stud lights and light curtain walls. Buffer sections and no-entry sections were set to ensure reversible lanes operating safely and efficiently. VISSIM simulation was used for case analysis, and the results showed that compared with the traditional time-controlled reversible lane scheme, the real-time dynamic reversible lane scheme could reduce the average vehicle delay by 27.4% and decrease the vehicle VOC, CO and NOX emissions by 13.5%.

Trajectory Planning with Time-Variant Safety Margin for Autonomous Vehicle Lane Change

A lane change is one of the most important driving scenarios for autonomous driving vehicles. This paper proposes a safe and comfort-oriented algorithm for an autonomous vehicle to perform lane changes on a straight and level road. A simplified Gray Prediction Model is designed to estimate the driving status of surrounding vehicles, and time-variant safety margins are employed during the trajectory planning to ensure a safe maneuver. The algorithm is able to adapt its lane changing strategy based on traffic situation and passenger demands, and features condition-triggered rerouting to handle unexpected traffic situations. The concept of dynamic safety margins with different settings of parameters gives a customizable feature for the autonomous lane changing control. The effect of the algorithm is verified within a self-developed traffic simulation system.