Automated Lane Change Research Articles

Automatic lane change based on dynamic occupancy of an adaptive gird zone

This paper presents adaptive and occupancy grid map algorithms for automatic lane change technology, a core technology in autonomous vehicles. The objectives are to improve driver safety and convenience with technology that automatically changes lanes at the request of the driver. The algorithms construct nine grids on the basis of ego vehicles and generate adaptive and occupancy grid maps by using the relative speeds of ego and target vehicles. When a driver requests a lane change, the adaptive grid map reduces the number of cases where the target vehicles may exist around the ego vehicle from 256 to 32, thus decreasing the calculation amount. Therefore, the algorithms are suitable for use in autonomous vehicles that require real-time calculations. An occupancy grid map is formed in accordance with the location of the target vehicles, and whether lane changes are possible is determined. The algorithms generate a virtual simulation environment with the CarMaker and are simulated using Matlab/Simulink. An experiment is conducted in a real driving environment with real vehicles to prove the validity of the algorithms.© 2017 Elsevier Inc. All rights reserved.

Automatic Lane-Changing System on Congested Highway

This study proposes an autonomous lane-changing system for congested merging areas. Manual and autonomous vehicles are expected to coexist until all vehicles are substituted by autonomous vehicles. Therefore, interactions between humans and autonomous driving systems should be discussed. This study assumed a scenario in which an autonomous vehicle performed a lane change to a congested main lane, where all vehicles were manual. The proposed system estimated the possibility of changing lanes without collisions. A driving simulator was used to measure the lane-changing operations of human drivers in a congested merging area, and the proposed method was developed based on the experimental results. Simulations demonstrated that the proposed method could safely change lanes.

Automated lane change behavior prediction and environmental perception based on SLAM technology

In the automatic driving system, the external environment of the vehicle is perceived, in fact, there is also a perception sensor that has been silently dedicated in the system, that is, the positioning module. This paper explores the role of SLAM (Simultaneous Localization and Mapping) technology in autonomous vehicles, particularly in automatic lane change behavior prediction and environment perception. It emphasizes the limitations of traditional methods and the advantages of SLAM, especially visual SLAM, for accurate positioning and mapping. The discussion covers SLAM fundamentals, challenges, and the significance of visual SLAM's higher perception ability. Case studies on Waymo and Tesla illustrate the application of visual SLAM in achieving high-precision navigation and lane change prediction. The paper concludes by highlighting future research directions to enhance the intelligence and adaptability of automated lane change systems through advancements in AI and sensor technology, alongside optimizing SLAM algorithms for reliable driving in various scenarios.

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

Automatic driving in expressways is generally considered to be the easiest for commercial landing, and vehicle behavior decision-making is the core of automatic driving technology, which directly affects the safety and comfort of vehicle driving. In this paper, an automatic lane change strategy based on prior knowledge and data-driven is proposed for expressway. Our method decouples autonomous driving decisions into two processes. Firstly, we propose an optimization model of lane change features based on prior knowledge, which not only provides a basis for the selection of vehicle features but also effectively reduces the interference of invalid features. The driving risk field is used to screen the vehicle targets that have potential influence on the lane change of the ego vehicle, and the maximal information coefficient evaluates the effectiveness of the features of the vehicle targets through the maximal information coefficient to optimize the lane change features. Then, a data-driven lane-changing model is proposed based on Attention-BiLSTM (Bi-directional Long Short-Term Memory), which transforms vehicle lane-changing into time series prediction, fully explores the relationship between lane-changing features in the context information, and effectively improves the anti-interference ability of the system against accidental errors. In addition, the attention mechanism can adaptively adjust the weight of lane-changing features and effectively capture the important information that needs attention in different lane-changing states, further improving the accuracy of the model. Finally, the validity of the model is verified by remote sensing data sets and real vehicle experiments. The test results show that the test accuracy of our method is 95.1% in data sets and 94.2% in real vehicle experiments, which fully meets the requirements of lane change decisions about automatic driving.

A comprehensive high‐level automated driving assistance system with integrated multi‐functionality

AbstractAdvanced Driver Assistance Systems (ADAS) have gained substantial attention in recent years. However, the integration mechanism of multiple functions within ADAS remains unexplored, and the full potential of its functionality remains underutilised. This paper presents a novel multi‐functional integrated High‐level Automated Driving Assistance System that combines the Cruise Control (CC), Adaptive Cruise Control (ACC), Automated Emergency Brake (AEB), and Automated Lane Change (ALC) functions. The presented system utilises a hierarchical framework. The extension multi‐mode switch strategy is established as the superior module and the Event‐Triggered Model Predictive Controller (ETMPC) is designed as the inferior controller. The CC, ACC, and ALC functions are effectively utilised to enhance traffic efficiency, while the AEB function ensures driving safety. To address the time constraints of conventional Model Predictive Control, an event‐trigger mechanism is proposed to reduce computational load. Simulations are conducted using the CarSim and Matlab platforms. The study results demonstrate significant improvements in both safety and traffic efficiency compared to conventional ADAS strategies. Furthermore, the proposed ETMPC method significantly reduces the frequency of solving Optimisation Problems and decreases online computation costs.

Automated Lane Change via Adaptive Interactive MPC: Human-in-the-Loop Experiments

Automated Lane Change via Adaptive Interactive MPC: Human-in-the-Loop Experiments

Efficient motion planning and control for automated lane change considering road adhesion coefficient

Efficient motion planning and control for automated lane change considering road adhesion coefficient

Efficient motion planning and control for automated lane change considering road adhesion coefficient

Efficient motion planning and control for automated lane change considering road adhesion coefficient

Study on Anthropomorphic Lane Changing Decision Making for Smart Trucks Based on Driving Behavior

In order to improve the applicability of the lane change decision model to different styles of drivers, especially for trucks with less flexibility and stability of their own, safe and reasonable lane change decisions are more important. In this paper, based on the research of integrating personalized driver styles in automatic lane change control technology, a vehicle lane change decision model considering driver styles is established. Firstly, the HighD dataset is screened and the drivers are classified by driving style using principal component analysis and K-means (K-means) cluster analysis. Secondly, we propose a lane change decision model that takes into account the rationality and safety of lane change by learning human drivers' driving behavior experience through Long Short-Term Memory (LSTM) method, so as to improve drivers' acceptance and satisfaction of smart cars. Finally, the joint simulation by Simulink/CarSim/PreScan software proves that the lane change decision method based on the driver's style proposed in this study can realize the autonomous lane change task of smart cars.

An Objective Evaluation Method for Driver/Passenger Acceptance of an Autonomous Driving System for Lane Changes

Reliable assessment methods of driver acceptance are needed due to increased interest in high levels of autonomous driving systems. Subjective evaluation methods have mostly been utilized to assess the acceptance of newly developed advanced driver assistance systems because acceptance varies depending on the individual. In this paper, an objective evaluation methodology of driver acceptance for an autonomous driving system was proposed based on objective measurable parameters in the case of automatic lane change situations. To this end, a massive driver–vehicle interaction database was utilized, constructed by a specially designed experimental program. The experiment was carried out with 19 selected drivers (9 experts and 10 novices), supposed as an autonomous driving system. The database consisted of not only various measurable parameters on control commands, vehicle behaviors, and relations with other vehicles but also subjective acceptances. To interpret the driver acceptance, objective parameter sets were derived by two different methods: a statistical significance test and an acceptance sensitivity analysis. Then, a modeling method based on stochastic estimation to evaluate driver acceptance was suggested as an objective evaluation method for the driver acceptance of an automatic lane change system. The data set of the expert drivers was only used for the acceptance evaluation modeling; the other data sets of the novice drivers were used for verifications for the suggested model. The estimation accuracies of the two different models using a significance test and sensitivity analysis were 90.2% and 99.5%, respectively. This objective method for acceptance evaluation can not only be expanded to other functions of an autonomous driving system but also to an entirely autonomous driving vehicle.

A Trajectory Planning Method of Automatic Lane Change Based on Dynamic Safety Domain

A Trajectory Planning Method of Automatic Lane Change Based on Dynamic Safety Domain

Longitudinal and Lateral Control Strategies for Automatic Lane Change to Avoid Collision in Vehicle High-Speed Driving.

The vehicle particle model was built to compare and analyze the effectiveness of three different collision avoidance methods. The results show that during vehicle high-speed emergency collision avoidance, lane change collision avoidance requires a smaller longitudinal distance than braking collision avoidance and is closer to that with a combination of lane change and braking collision avoidance. Based on the above, a double-layer control strategy is proposed to avoid collision when vehicles change lanes at high speed. The quintic polynomial is chosen as the reference path after comparing and analyzing three polynomial reference trajectories. The multiobjective optimized model predictive control is used to track the lateral displacement, and the optimization objective is to minimize the lateral position deviation, yaw rate tracking deviation, and control increment. The lower longitudinal speed tracking control strategy is to control the vehicle drive system and brake system to track the expected speed. Finally, the lane changing conditions and other speed conditions of the vehicle at 120 km/h are verified. The results show that the control strategy can track the longitudinal and lateral trajectories well and achieve effective lane change and collision avoidance.

Level-$K$ Reasoning, Deep Reinforcement Learning, and Monte Carlo Decision Process for Fast and Safe Automated Lane Change and Speed Management

This paper presents a decision process model for real-time automated lane change and speed management in highway traffic. The presented algorithm is developed based on level- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> game theory to model and predict the interaction between the vehicles. Using deep reinforcement learning, this algorithm encodes and memorizes the past experiences that are recurrently used to reduce the computations and speed up motion planning. Also, we use Monte Carlo Tree Search (MCTS) as an effective tool that is employed nowadays for fast planning in complex and dynamic game environments. This development leverages the computation power efficiently and showcases promising outcomes for maneuver planning and predicting the environment's dynamics. In the absence of traffic connectivity that may be due to either passenger's choice of privacy or the vehicle's lack of technology, this development can be extended and employed in fully-automated vehicles for real-world and practical applications.

Fault Tolerance analysis of an Adaptive Neuro-Fuzzy Inference System for mandatory lane changing decisions in automated driving

Past research has developed a binary decision model for mandatory lane changes based on the Adaptive Neuro-Fuzzy Inference System (ANFIS). This ANFIS Decision Model (simply called ADEM), developed and tested with the Next Generation Simulation (NGSIM) data, mimics the sensory inputs and decisions of human drivers. This research assumed that ADEM will be implemented as part of the automated lane changing system in Automated Vehicles (AVs). The system in AVs will depend on active radar sensors to make measurements. The sensor outputs will be converted into the input parameter values of ADEM. This research tested ADEM’s performance when the sensors could only measure the distance of surrounding vehicles within 50 m , and when one of the sensors malfunctions. The original NGSIM test data set was modified to simulate the sensors’ detection range limit in Scenario 0, plus six other scenarios in which each sensor took turns to fail and assumed either the minimum or maximum possible output values. The results show that: (i) ADEM performs in a safer manner when considering the sensors’ limited detection range; (ii) the minimum value of 0 m should be used as the default sensor output when a sensor fails, so that ADEM makes safer mandatory lane changing decisions; and (iii) the most critical sensors, by which failure of any of them would cause the greatest degradation to ADEM’s performance, are the two sensors that measure the distances to the preceding and the following vehicles in the target lane.

Neural network optimization algorithm based non-singular fast terminal sliding-mode control for an uncertain autonomous ground vehicle subjected to disturbances

As computer computing capabilities increase, optimization algorithms are becoming more useful for solving engineering problems. Up to now, several metaheuristic algorithms have been exploited in control engineering. However, this effort remains weak in addressing the autonomous ground vehicles (AGVs) trajectory tracking problem. This research presents a novel optimal approach merging the robust non-singular fast terminal sliding-mode control method (NFTSMC) and the neural network optimization algorithm (NNA) for automatic lane changing. First, a reference double lane-change path (DLC) is designed, and the robust non-singular fast terminal sliding-mode steering controller is developed, according to Lyapunov stability theory, to suppress the lateral deviation and ensure the yaw stability. Then, the control strategy is optimized by the NNA algorithm to adjust the steering controller optimally while avoiding local optimums. A comparison, under the same conditions, with the particle swarm optimization algorithm (PSO) revealed the superiority of the control law resulting from the NNA-based optimization. Furthermore, the proposed approach shows its excellent tracking performance versus the integrated backstepping sliding-mode controller (IBSMC) and the adaptive sliding-mode control (ASMC) under severe conditions typical of real-world lane changes.

Study on automatic lane change trajectory control of intelligent vehicle on expressway

Aiming at the dangerous working condition of the front car driving at low speed, a safety distance model using the vehicle braking process is proposed to determine the best time for intelligent vehicles to change lanes. Based on the fitting method of quintic polynomial, an automatic lane change fitting trajectory is constructed, and the LQR algorithm is used to achieve accurate tracking. Through the joint simulation and experiment of CarSim, PreScan, and MATLAB/Simulink, it is shown that the established lane change trajectory control model can determine the best time for intelligent vehicles to change lanes, plan safe and comfortable lane change trajectory, and achieve good tracking effect.

Optimal Lane Change Path Planning Based on the NSGA-II and TOPSIS Algorithms

Among so many autonomous driving technologies, autonomous lane changing is an important application scenario, which has been gaining increasing amounts of attention from both industry and academic communities because it can effectively reduce traffic congestion and improve road safety. However, most of the existing researchers transform the multi-objective optimization problem of lane changing trajectory into a single objective problem, but how to determine the weight of the objective function is relatively fuzzy. Therefore, an optimization method based on the combination of the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), which provides a new idea for solving the multi-objective optimization problem of lane change trajectory algorithm, is proposed in this paper. Firstly, considering the constraints of lane changing and combining with the collision detection algorithm, the feasible lane changing trajectory cluster is obtained based on the quintic polynomial. In order to ensure the comfort, stability and high efficiency of the lane changing process, the NSGA-II Algorithm is used to optimize the longitudinal displacement and time of lane changing. The continuous ordered weighted averaging (COWA) operator is introduced to calculate the weights of three objective optimization functions. Finally, the TOPSIS Algorithm is applied to obtain the optimal lane change trajectory. The simulations are conducted, and the results demonstrate that the proposed method can generate a satisfactory trajectory for automatic lane changing actions.

Research on Automatic Lane Changing Method for Electric Vehicles Based on Deep Deterministic Policy Gradient Algorithm

Intelligent driving is an important feature of electric vehicles, and automatic lane-changing is an important auxiliary operation in the driving process. It involves complex data such as the external environment, vehicle status, and relationship characteristics of other vehicles. This paper proposes research on automatic lane-changing methods for electric vehicles based on a deep deterministic policy gradient algorithm aiming at the complexity of automatic lane-changing for electric vehicles. First, use the actor-critic model of deep reinforcement learning to realize the design of an automatic lane-changing algorithm. On this basis, the actor-critic model is further improved, and a deep deterministic policy gradient algorithm is proposed and applied to the automatic lane-changing strategy of electric vehicles, which improves the accuracy of automatic lane-changing and thus ensures the safety of vehicle driving.

Research and Analysis on Motion Planning Method of Intelligent Network Connected Vehicles

Article Research and Analysis on Motion Planning Method of Intelligent Connected Vehicles Heng Wang 1,2, Xianyi Yang 1,2, Zhengbai Liu 3, and Zhenfeng Wang 1,2, * 1 College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou 450002, China 2 Henan Provincial Cold Chain Information and Equipment Laboratory for Logistics of Agricultural Products, Zhengzhou 450002, China 3 School of Innovation and Entrepreneurship, Southern University of Science and Technology, Shenzhen 518055, China * Correspondence: zfking@henau.edu.cn Received: 23 September 2022 Accepted: 21 November 2022 Published: 18 December 2022 Abstract: Combined with the tire model, a two-degree-of-freedom model is proposed in this study. At the same time, a turning control model is analyzed, which lays the foundation for the subsequent local path planning, that is, the simulation control of lane changing and obstacle avoidance. The co-simulation of CarSim and Simulink is used to realize the tracking control of trajectories, and the given reference trajectory can be tracked with high precision in the serpentine rod penetration test, so as to verify the stability of the control algorithm control. A set of optimal trajectories of straight lane change and curved lane change are selected as the reference trajectories of the controller, and the target speed of the lane change is given in the CarSim speed control module. The simulation results show that the motion planning error is extremely small that completes the high-precision automatic control of steering, and this helps realize the tracking of a given trajectory, reflecting the good trackability of the planned lane-changing trajectory from the side. Aiming at the automatic lane changing process of the intelligent connected vehicle on different straight roads and curved roads, the V2V method is used to obtain the status information of the surrounding traffic vehicles where the multi-objective optimization method is used to determine the optimal trajectory. Driving with reference to the trajectory realizes the coordination and unity of planning and control.

A Novel Simultaneous Planning and Control Scheme of Automated Lane Change on Slippery Roads

Existing hierarchical planning and control architectures can cause the upper-level target trajectory passed to the lower-level tracking controller to be too conservative or impossible to track on slippery roads. To solve this problem, this paper proposes a new simultaneous planning and control scheme that determines the control inputs without explicit path planning and requires only information about the control objectives and safety constraints. First, we establish the vehicle stability boundary and the safety distance constraints to ensure that the vehicle avoids drifting and collisions on slippery roads. Moreover, real-time adaptive model predictive control (MPC) with online model linearization is designed to approximate the nonlinear programming as a quadratic program (QP), which allows the use of fast convex optimization tools. The steering angle and longitudinal acceleration are thus obtained. Finally, we design the controller to convert the longitudinal acceleration into an actuatable drive torque to avoid tire skidding on slippery surfaces. The simulation results show that under the conditions of low adhesion road and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> -split roads, the proposed algorithm makes the sideslip angle of the vehicle within 1 degree. In contrast, the sideslip angle of the hierarchical algorithm reaches 6 degrees, and the vehicle has a noticeable drift. The proposed algorithm dramatically improves stability and driving comfort.