Lane Change Scenario Research Articles

A Cooperative Driving Strategy Based on Velocity Prediction for Connected Vehicles With Robust Path-Following Control

The autonomous vehicles need to cooperate with the nearby vehicles to ensure driving safety, however, it is challenging to plan and follow the desired trajectory considering the nearby vehicles. This article proposes a cooperative driving strategy for the connected vehicles by integrating vehicle velocity prediction, motion planning, and robust fuzzy path-following control. The system uncertainties are considered to enhance the cooperation between the autonomous vehicle and the nearby vehicle. With the driving information obtained from the connected vehicles technique, the recurrent neural network is used to predict the nearby vehicle velocity. A motion planner is developed to provide the reference trajectory considering the velocity prediction errors. Then, a robust fuzzy path-following controller is designed to track the planned trajectory. The CarSim simulations are conducted to validate the proposed cooperative driving strategy. The simulation results show that the autonomous vehicle can avoid collisions with the nearby vehicle by applying the proposed driving strategy in the overtaking and the lane-changing scenarios.

Research on driving behavior decision making system of autonomous driving vehicle based on benefit evaluation model

Autonomous driving vehicle could increase driving efficiency, reduce traffic congestion and improve driving safety, it is considered as the solution of current traffic problems. Decision making systems for autonomous driving vehicles have significant effects on driving performance. The performance of decision making system is affected by its framework and decision making model. In real traffic scenarios, the driving condition of autonomous driving vehicle faced is random and time-varying, the performance of current decision making system is unable to meet the full scene autonomous driving requirements. For autonomous driving vehicle, the division between different driving behaviors needs clear boundary conditions. Typically, in lane change scenario, multiple reasonable driving behavior choices cause conflict of driving state. The fundamental cause of conflict lies in overlapping boundary conditions. To design a decision making system for autonomous driving vehicles, firstly, based on the decomposition of human driver operation process, five basic driving behavior modes are constructed, a driving behavior decision making framework for autonomous driving vehicle based on finite state machine is proposed. Then, to achieve lane change decision making for autonomous driving vehicle, lane change behavior characteristics of human driver lane change maneuver are analyzed and extracted. Based on the analysis, multiple attributes such as driving efficiency and safety are considered, all attributes benefits are quantified and the driving behavior benefit evaluation model is established. By evaluating the benefits of all alternative driving behaviors, the optimal driving behavior for current driving scenario is output. Finally, to verify the performances of the proposed decision making model, a series of real vehicle tests are implemented in different scenarios, the real time performance, effectiveness, and feasibility performance of the proposed method is accessed. The results show that the proposed driving behavior decision making model has good feasibility, real-time performance and multi-choice filtering performance in dynamic traffic scenarios.

HFO-LADRC Lateral Motion Controller for Autonomous Road Sweeper.

How to make a controller robust and stable to reject the disturbance of uncertainty is an inevitable challenge. Aiming at addressing the lateral control problem for an autonomous road sweeper, a heading-error-based first order linear active disturbance rejective controller (HFO-LADRC) is proposed in this paper. To eliminate the lateral error and the heading error at the same time, a new model, called the heading-error-based model, is proposed for lateral motion, and the Lyapunov function was employed to explore the convergence ability of the heading error and lateral error. Since the heading-error-based model is first order, the ADRC is designed as first order and linear, and each module of the HFO-LADRC has been devised in detail. To ensure solution accuracy, the fourth order Runge–Kutta method was adopted as the differential system solver, and a typical ring scenario and a double lane-changing scenario were designed referencing the standard. Considering the obvious influence, wheelbase uncertainty, steering ratio uncertainty and Gaussian white noise disturbance were taken into account for the tests. The results illustrate that, in the case of both wheelbase uncertainty and steer ratio uncertainty, the HFO-LADRC has strong robustness and stability compared with a typical pure pursuit controller and classical SO-LADRC.

Dynamic Trajectory Planning of Autonomous Lane Change at Medium and Low Speeds Based on Elastic Soft Constraint of the Safety Domain

Most current research on the trajectory planning of the autonomous lane change focuses on high-speed scenarios and assumes that the states of the surrounding vehicles keep stable during the lane change. The methods based on geometric-curve are mostly used for trajectory planning. In this paper, considering the inevitable development of the autonomous driving, the surrounding vehicles are assumed to be driven by human drivers, while the ego vehicles are able to autonomously change lanes. Representative local lane-change scenarios are then designed and analyzed in detail aiming at medium- and low-speed lane-change conditions. Additionally, in contrast with most research, dynamic trajectory planning which considers the possible state variations of the surrounding vehicles and the driver characteristics is studied and described by a fifth-order polynomial function. The safety and comfort of the dynamic trajectory planning are validated through simulation. Moreover, the elastic soft constraint of the safety domain is designed, whereby the sensitivity of the studied dynamic trajectory planning system is reduced under the premise of ensuring safety. The effectiveness of the elastic soft constraint in terms of improving comfort during the lane change is verified through simulation. The availability of the dynamic trajectory planning system with the elastic soft constraint is demonstrated with the addition of trajectory tracking based on model predictive control, showing its potential in practical applications.

Managing Lane Changing of Algorithm-Assisted Drivers

Theoretical models of vehicular traffic ascribe the fundamental cause of velocity oscillations and stop-and-go waves to suboptimal or unpredictable human driving behavior. In this paper we ask: if vehicles were controlled or assisted by algorithms, and hence driven optimally, would these phenomena simply go away? If not, how should a regulator manage algorithm-assisted vehicular traffic for a smooth flow? We study these questions in the context of a mandatory lane-changing scenario with mixed traffic, i.e., an algorithm-assisted driver on a curtailed lane that has to merge to an adjacent free lane with a relatively dense platoon of human drivers. In a stylized model of algorithm-assisted driving, we liken the blocked-lane driver to a rational self-interested agent, whose objective is to minimize her expected travel time through the blockage, deciding (a) at what velocity to move, and (b) whether to merge to the free lane, if given the opportunity (adequate gap). Moving at higher velocities reduces travel time, but also reduces the probability of finding a large enough gap to merge. We analyze the problem via dynamic programming, and we show that the optimal policy has a surprising structure: in the presence of uncertainty on adequate-sized gaps in the target lane, it may be optimal for the blocked-lane driver, in certain parameter regimes, to oscillate between high and low velocities while attempting to merge. Hence, traffic oscillations can arise not just due to suboptimal or unpredictable human driving behavior, but also endogenously, as the outcome of a driver’s rational, optimizing behavior. We draw out structural similarities to bang-bang control. As velocity oscillations are known to be detrimental to a smooth traffic flow, we provide sufficient conditions on the velocity limits so that traffic oscillations while merging, due to such optimizing behavior, do not arise at optimality. Finally, we investigate the fundamental flow-density and travel time-density diagrams through traffic micro-simulations. We establish that the proposed approach exhibits consistently near-optimal performance, in a broad variety of traffic conditions.

Autonomous-Vehicle Maneuver Planning Using Segmentation and the Alternating Augmented Lagrangian Method

Abstract Segmenting a motion-planning problem into smaller subproblems could be beneficial in terms of computational complexity. This observation is used as a basis for a new sub-maneuver decomposition approach investigated in this paper in the context of optimal evasive maneuvers for autonomous ground vehicles. The recently published alternating augmented Lagrangian method is adopted and leveraged on, which turns out to fit the problem formulation with several attractive properties of the solution procedure. The decomposition is based on moving the coupling constraints between the sub-maneuvers into a separate coordination problem, which is possible to solve analytically. The remaining constraints and the objective function are decomposed into subproblems, one for each segment, which means that parallel computation is possible and beneficial. The method is implemented and evaluated in a safety-critical double lane-change scenario. By using the solution of a low-complexity initialization problem and applying warm-start techniques in the optimization, a solution is possible to obtain after just a few alternating iterations using the developed approach. The resulting computational time is lower than solving one optimization problem for the full maneuver.

Predictive Cruise Control Using Radial Basis Function Network-Based Vehicle Motion Prediction and Chance Constrained Model Predictive Control

Predicting future motions of surrounding vehicles and driver’s intentions are essential to avoid future potential risks. The predicting future motions, however, is very challenging because the future cannot be deterministically known a priori and there are infinitely many possible future trajectories. Prediction becomes far more challenging when trying to foresee distant future. This paper proposes a probabilistic motion prediction algorithm that can accurately compute the likelihood of multiple target lanes and trajectories of surrounding vehicles by using the artificial neural network; more specifically radial base function network (RBFN). The RBFN prediction algorithm estimates the likelihood of each lane being the driver’s target lane in categorical distributions and the corresponding future trajectories in parallel. In order to demonstrate the effectiveness of the proposed prediction algorithm, it is applied for the predictive cruise control problem. Chance-constrained model predictive control (CCMPC) is utilized because the chance constraints in CCMPC can handle collision uncertainties associated with future uncertainties from the proposed prediction algorithm. The RBFN-based CCMPC simulation is conducted for several risky cut-in scenarios and compared with the state-of-the-art Interactive Multiple Model (IMM)-based prediction algorithm. The simulation results show that the RBFN-based CCMPC achieves higher collision avoidance success rate than that of the IMM-based CCMPC while using smaller actuator inputs and providing higher passenger comforts. Furthermore, the RBFN-based CCMPC showed high robustness to false braking during near lane-change (lane-keeping) scenarios.

A new safe lane-change trajectory model and collision avoidance control method for automatic driving vehicles

Lane change maneuvers, are important contributors to road traffic accidents on highway. In this paper, we propose a new safe lane change trajectory model and collision avoidance control method for Society of Automotive Engineers (SAE)-level 2 automatic driving vehicles. First, a Gaussian distribution is used to describe the new trajectory model that uses pure steering and combined braking. According to regional and progressive states, a new safe lane change meaning is defined. Second, we design a new four-level automatic driving mode and an effective decision mechanism that considers safety and ergonomics. Moreover, a new trajectory tracking controller combined with a decision mechanism was designed using feedback linearization, verified using typical lane-change scenarios. Finally, based on a physical simulation platform, PreScan, the hardware-in-the-loop simulation result demonstrate the feasibility and effectiveness of our method. This paper provides a valuable reference on an expert and intelligent system methodology for automatic driving vehicles, which will be helpful for improving highway traffic safety and efficiency.

Estimating Driver’s Lane-Change Intent Considering Driving Style and Contextual Traffic

Estimating a driver’s lane-change (LC) intent is very important so as to avoid traffic accidents caused by improper LC maneuvers. This paper proposes a lane-change Bayesian network (LCBN) incorporated with a Gaussian mixture model (GMM), termed as LCBN-GMM, to estimate a driver’s LC intent considering a driver’s driving style over varying scenarios. According to the scores made by participates with a behavioral-psychological questionnaire, three driving styles are classified. In order to get more effective labeled LC and lane-keep (LK) data for model training, we propose a gaze-based labeling (GBL) method by monitoring a drivers’s gaze behavior, instead of using a time-window labeling method. The capability of LCBN-GMM to estimate a driver’s lane-change intent is evaluated in different LC scenarios and driving styles, in comparison to support vector machine and Naive Bayes. Data are collected in a seat-box-based driving simulator where 32 drivers, consisting of 9 aggressive, 15 neutral, and 8 conservative drivers, participated. Experimental results demonstrate that the LCBN-GMM with GBL achieves the best performance, estimating a driver’s LC intent an average of 4.5 s ahead of actual LC maneuvers with 78.2% accuracy considering both driving style and contextual traffic, compared with other approaches.

Transfer Learning for Driver Model Adaptation in Lane-Changing Scenarios Using Manifold Alignment

Driver model adaptation (DMA) provides a way to model the target driver when sufficient data are not available. Traditional DMA methods running at the model level are restricted by the specific model structures and cannot make full use of the historical data. In this paper, a novel DMA framework based on transfer learning (TL) is proposed to deal with the adaptation of driver models in lane-changing scenarios at the data level. Under the proposed DMA framework, a new TL approach named DTW-LPA that combines dynamic time warping (DTW) and local Procrustes analysis (LPA) is developed. Using the DTW, the relationship between the datasets for different drivers can be found automatically. Based on this relationship, the LPA can transfer the data in the historical dataset to the dataset of a newly-involved driver (target driver). In this way, sufficient data can be obtained for the target driver. After the data transferring process, a proper modeling method, such as the Gaussian mixture regression (GMR), can be applied to train the model for the target driver. Data collected from a driving simulator and realistic driving scenes are used to validate the proposed method in various experiments. Compared with the GMR-only and GMR-MAP methods, the DTW-LPA shows better performance on the model accuracy with much lower predicting errors in most cases.

Virtual-to-Real Knowledge Transfer for Driving Behavior Recognition: Framework and a Case Study

Considering the difficulty and high cost of collecting sufficient data in the real world, driving simulators are used in many studies as an alternative data source, which can provide a much easier and safer way to collect driving data. However, because of the inherent differences between the virtual and real world, the recognition model for driving behavior trained using simulation-based data cannot fit the real driving scenes well. To fill the gap between simulation and real data, a knowledge transfer framework is proposed in this paper. Two transfer learning (TL) methods namely semi-supervised manifold alignment (SMA) and kernel manifold alignment (KEMA) are used in the proposed framework to map the data collected from the virtual and real world to one latent common space. A typical lane-changing scenario is selected for a case study. Three classifiers are trained in the latent space and used to do the lane-changing behavior recognition in the real world. In this way, sufficient simulation data are transferred to supplement the training set with few labeled real data, and thus improve the performance of behavior recognition in the real world. Compared with the traditional methods without knowledge transfer, classifiers combined with TL can reduce the error rate of recognition from around 30% (when only the real data are used) or higher than 50% (when only the simulation data are used) to as low as 11%.

Longitudinal and lateral dynamics control of automatic lane change system

The urgency of extending functionalities of current advanced driver assistance systems (ADAS), and eventually progressing to highly automated highway driving necessitates the design of automatic lane change system. This paper presents an automatic lane change system targeting on discretional lane change scenario in highway driving. Assuming motion signals of all participants to be available, the MOBIL model is employed as the decision module. The functionality of the adaptive cruise controller is extended to realize dual-target tracking, so as to prevent abrupt acceleration change provoked by the sudden switch of the leading vehicle during the lane change. For the lateral part, a hierarchical trajectory planning algorithm, which combines parametric function and learning-based technique, is proposed to account for uncertainties of driver characteristics in different traffic situations. The trajectory is then tracked by a low-level controller based on model predictive control (MPC) theory, which employs a force input model to predict the motion of the vehicle, and formulates environment envelope and handling limits as constraints. The proposed algorithm is validated through simulations of typical scenarios. Overall, this paper lays a solid foundation for the prototype of ADAS regarding lane change.

Model predictive control–based steering control algorithm for steering efficiency of a human driver in all-terrain cranes

All-terrain cranes with multi-axles have large inertia and long distances between the axles that lead to a slower dynamic response than normal vehicles. This has a significant effect on the dynamic behavior and steering performance of the crane. Therefore, the purpose of this study is to develop an optimal steering control algorithm with a reduced driver steering effort for an all-terrain crane and to evaluate the performance of the algorithm. For this, a model predictive control technique was applied to an all-terrain crane, and a steering control algorithm for the crane was proposed that could reduce the driver’s steering effort. The steering performances of the existing steering system and the steering system applied with the newly developed algorithm were compared using MATLAB/Simulink and ADAMS with a human driver model for reasonable performance evaluation. The simulation was performed with both a double lane change scenario and a curved-path scenario that are expected to happen in road-steering mode.

Manoeuvre prediction and planning for automated and connected vehicles based on interaction and gaming awareness under uncertainty

The complex and mixed traffic environment makes it a challenge for the widespread use of automated and connected vehicles (ACVs). It is necessary for these systems to have a better understanding of the traffic environment including interaction and gaming between multiple vehicles. In this study, a manoeuvre prediction and planning framework is proposed on the basis of game theories for complex and mixed traffic scenarios via vehicle-to-everything communication. In this framework, the interaction and gaming between multiple vehicles are considered by employing the extensive form game theories. In the payoff function, the risk assessment model based on trajectory prediction under uncertainty is employed to assess collision risks. Driving efficiency and preference are also combined in the payoff function. Uncertainty elements, including estimation and prediction, are considered to predict and plan by using Nash equilibrium of the extensive form game theory in mixed and behavioural strategies. Finally, this framework is applied and proved in different lane-change scenarios. The results show that this framework could predict other vehicles’ driving manoeuvres and plan manoeuvres for ego vehicles by considering interaction and gaming between multiple vehicles, which helps ACVs understand the environment better and make the cooperative manoeuvre planning in complex traffic scenarios.

Three Years CoInCar: What Cooperatively Interacting Cars Might Learn from Human Drivers

Abstract The CoInCar project’s overall aim is to contribute to the development of cooperative driving. Cooperatively interacting cars will conduct maneuvers based on information perceived by and shared with other road users as well as the infrastructure. However, drivers might face problems understanding the vehicles’ behavior, which could lead to critical situations. In the last three years, we investigated human behavior in potentially cooperative traffic situations, like in lane change scenarios and at intersections. Results showed that cooperative behavior in lane changing scenarios on highways depends beside other factors on the way the other drivers are indicating their intention to change lanes. In inner-city intersection scenarios, a comparison between an equal narrow passage and a complex T-junction scenario showed that human drivers prefer to drive firstly in simple situations but to give priority to other cooperation partners in complex situations. Furthermore, the high-frequency compound (HFC) of the steering angle could be identified as the relevant driving parameter for intention recognition. Moreover, for further understanding of the decision-making process, we analyzed cooperative situations with the Natural Decision Making approach. In situations in which the behavior of an automated vehicle differs from the human’s expectations, an explanation will be needed. Therefore, knowledge about human behavior in cooperative situations helps understanding human expectations, which will be relevant in the process of designing interfaces for cooperatively interacting vehicles.

Effects of feature selection on lane-change maneuver recognition: an analysis of naturalistic driving data

Purpose Feature selection is crucial for machine learning to recognize lane-change (LC) maneuver as there exist a large number of feature candidates. Blindly using feature could take up large storage and excessive computation time, while insufficient feature selection would cause poor performance. Selecting high contributive features to classify LC and lane-keep behavior is effective for maneuver recognition. This paper aims to propose a feature selection method from a statistical view based on an analysis from naturalistic driving data. Design/methodology/approach In total, 1,375 LC cases are analyzed. To comprehensively select features, the authors extract the feature candidates from both time and frequency domains with various LC scenarios segmented by an occupancy schedule grid. Then the effect size (Cohen’s d) and p-value of every feature are computed to assess their contribution for each scenario. Findings It has been found that the common lateral features, e.g. yaw rate, lateral acceleration and time-to-lane crossing, are not strong features for recognition of LC maneuver as empirical knowledge. Finally, cross-validation tests are conducted to evaluate model performance using metrics of receiver operating characteristic. Experimental results show that the selected features can achieve better recognition performance than using all the features without purification. Originality/value In this paper, the authors investigate the contributions of each feature from the perspective of statistics based on big naturalistic driving data. The aim is to comprehensively figure out different types of features in LC maneuvers and select the most contributive features over various LC scenarios.

“What Makes a Cooperative Driver?” Identifying parameters of implicit and explicit forms of communication in a lane change scenario

An automated vehicle needs to learn how human road users communicate with each other in order to avoid misunderstandings and prevent giving a negative outward image during interactions. The aim of the present work is to develop an autonomous driving system which communicates its intentions to change lanes based on implicit and explicit rules used by human drivers. To reach this goal, we aimed at gaining a deeper understanding of which aspects of lane change behaviour makes them cooperative from the perspective of other drivers. Therefore a vehicle used various lane change announcement strategies by varying combinations of driving parameters in a static driving simulator. (First study: Start indicator signal, Waittime, lane change duration; Second study: Longitudinal acceleration). It’s impact on the perception and behaviour of other road users was observed in two studies (N = 25 per study). The results showed that the earlier the merging vehicle was indicating its intentions, the more cooperative it was perceived. When turning on the indicator at a later time participants considered it as more cooperative to merge with a slower or faster lane change duration or to wait longer in the lane before starting to move to the other lane. An early longitudinal acceleration when starting to change lanes is perceived more cooperative. These findings can be used to model a lane change strategy based on human behaviour, which will eventually lead to more acceptable and safer interactions between automated and non-automated road users.

Vehicle Trajectory Prediction by Integrating Physics- and Maneuver-Based Approaches Using Interactive Multiple Models

Vehicle trajectory prediction helps automated vehicles and advanced driver-assistance systems have a better understanding of traffic environment and perform tasks such as criticality assessment in advance. In this study, an integrated vehicle trajectory prediction method is proposed by combining physics- and maneuver-based approaches. These two methods were combined for the reason that the physics-based trajectory prediction method could ensure accuracy in the short term with the consideration of vehicle running dynamic parameters, and the maneuver-based prediction approach has a long-term insight into future trajectories with maneuver estimation. In this study, the interactive multiple model trajectory prediction (IMMTP) method is proposed by combining the two predicting models. The probability of each model in the interactive multiple models could recursively adjust according to the predicting variance of each model. In addition, prediction uncertainty is considered by employing unscented Kalman filters in the physics-based prediction model. To the maneuver-based method, random elements for uncertainty are introduced to the trajectory of each maneuver inferred by using the dynamic Bayesian network. The approach is applied and analyzed in the lane-changing scenario by using naturalistic driving data. Comparison results indicate that IMMTP could achieve a more accurate prediction trajectory with a long prediction horizon.

Stochastic Model-Predictive Control for Lane Change Decision of Automated Driving Vehicles

This paper describes lane change motion planning with a combination of probabilistic and deterministic prediction for automated driving under complex driving circumstances. The autonomous lane change should arrive safely at the destination. The subject vehicle needs to perceive and predict the behaviors of other vehicles with sensors. From the information of other vehicles, a collision probability is defined using a reachable set of uncertainty propagation. In addition, the lane change risk is monitored using predicted time-to-collision and safety distance to guarantee safety in lane change behavior. A safe driving envelope is defined as constraints based on the combinatorial prediction (probabilistic and deterministic) of the behavior of surrounding vehicles. To obtain the desired steering angle and longitudinal acceleration to maintain the automated driving vehicle under constraints, a stochastic model-predictive control problem is formulated. The proposed model has been evaluated by performing lane change simulations in MATLAB/Simulink, while considering the effect of combination prediction. Also, the proposed algorithm has been implemented on a test vehicle. The simulation and test results show that the proposed algorithm can handle complicated lane change scenarios, while guaranteeing safety.

Input Design for Nonlinear Model Discrimination via Affine Abstraction

This paper considers the design of separating input signals in order to discriminate among a finite number of uncertain nonlinear models. Each nonlinear model corresponds to a system operating mode, unobserved intents of other drivers or robots, or to fault types or attack strategies, etc., and the separating inputs are designed such that the output trajectories of all the nonlinear models are guaranteed to be distinguishable from each other under any realization of uncertainties in the initial condition, model discrepancies or noise. We propose a two-step approach. First, using an optimization-based approach, we over-approximate nonlinear dynamics by uncertain affine models, as abstractions that preserve all its system behaviors such that any discrimination guarantees for the affine abstraction also hold for the original nonlinear system. Then, we propose a novel solution in the form of a mixed-integer linear program (MILP) to the active model discrimination problem for uncertain affine models, which includes the affine abstraction and thus, the nonlinear models. Finally, we demonstrate the effectiveness of our approach for identifying the intention of other vehicles in a highway lane changing scenario.