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This study presents the effectiveness of an anti-jerk predictive controller (AJPC) based on active aerodynamic surfaces to handle upcoming road maneuvers and enhance vehicle ride quality by mitigating external jerks operating on the body of the vehicle. In order to eliminate body jerk and improve ride comfort and road holding during turning, accelerating, or braking, the proposed control approach assists the vehicle in tracking the desired attitude position and achieving a realistic operation of the active aerodynamic surface. Vehicle speed and upcoming road data are used to calculate the desired attitude (roll or pitch) angles. The simulation results are performed for AJPC and predictive control strategies without jerk using MATLAB. The simulation results and comparison based on root-mean-square (rms) values show that compared to the predictive control strategy without jerk, the proposed control strategy significantly reduces the effects of vehicle body jerks transmitted to the passengers, improving ride comfort without degrading vehicle handling at the cost of slow desired angle tracking.

This paper explores the application of Model Predictive Control (MPC) to enhance safety and efficiency in autonomous vehicle (AV) navigation through optimized path planning. The evolution of AV technology has progressed rapidly, moving from basic driver-assistance systems (Level 1) to fully autonomous capabilities (Level 5). Central to this advancement are two key functionalities: Lane-Change Maneuvers (LCM) and Adaptive Cruise Control (ACC). In this study, a detailed simulation environment is created to replicate the road network between Nantun and Wuri on National Freeway No. 1 in Taiwan. The MPC controller is deployed to optimize vehicle trajectories, ensuring safe and efficient navigation. Simulated onboard sensors, including vehicle cameras and millimeter-wave radar, are used to detect and respond to dynamic changes in the surrounding environment, enabling real-time decision-making for LCM and ACC. The simulation results highlight the superiority of the MPC-based approach in maintaining safe distances, executing controlled lane changes, and optimizing fuel efficiency. Specifically, the MPC controller effectively manages collision avoidance, reduces travel time, and contributes to smoother traffic flow compared to traditional path planning methods. These findings underscore the potential of MPC to enhance the reliability and safety of autonomous driving in complex traffic scenarios. Future research will focus on validating these results through real-world testing, addressing computational challenges for real-time implementation, and exploring the adaptability of MPC under various environmental conditions. This study provides a significant step towards achieving safer and more efficient autonomous vehicle navigation, paving the way for broader adoption of MPC in AV systems.

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.

An intelligent vehicle must face a wide variety of situations ranging from safe and comfortable to more aggressive ones. Smooth maneuvers are adequately addressed by means of linear control, whereas more aggressive maneuvers are tackled by nonlinear techniques. Likewise, there exist intermediate scenarios where the required responses are smooth but constrained in some way (rise time, settling time, overshoot). Due to the existence of the fundamental linear limitations, which impose restrictions on the attainable time-domain and frequency-domain performance, linear systems cannot provide smoothness while operating in compliance with the previous restrictions. For this reason, this article aims to explore the effects of reset control on the alleviation of these limitations for a lane change maneuver under a set of demanding design conditions to guarantee a suitable ride quality and a swift response. To this end, several reset strategies are considered, determining the best reset condition to apply as well as the magnitude thereto. Concerning the reset condition that triggers the reset action, three strategies are considered: zero crossing of the controller input, fixed reset band and variable reset band. As far as the magnitude of the reset action is concerned, a full-reset technique is compared to a Lyapunov-based error minimization method to calculate the optimal reset percentage. The base linear controller subject to the reset action is searched via genetic algorithms. The proposed controllers are validated by means of CarSim.

Lane change maneuver is one of most riskiest driving tasks. In order to increase the safety level of the vehicles during this maneuver, design of lane change assist systems which are based on dynamics behavior of driver-vehicle unit is necessary. Therefore, modeling of the maneuver is the first step to design the driver assistance system. In this paper, a novel method for modeling of lateral motion of vehicles in the standard double-lane-change (DLC) maneuver is proposed. A neuro-fuzzy model is suggested consisting of both the vehicle orientation and its lateral position. The inputs of the model are the current orientation, lateral position and steering wheel angle, while the predicted lateral position and orientation of the vehicle are the outputs. The efficiency of the proposed method is verified using both simulation results and experimental tests. The simulation and experimental maneuvers are performed in different velocities. It is shown that the proposed method can effectively reduce the undesirable effects of environmental disturbances and is significantly more accurate in comparisons with the results in the recent available papers. This method can be used to personalize the advanced driver assistance systems.

The dynamic behavior of a car can be modeled as a hybrid system involving continuous state changes and discrete state transitions. We show that the control of safe (collision free) lane change maneuvers in multi-lane traffic on highways can be described by finite state machines extended with continuous variables coming from the environment. We use standard theory for controller synthesis to derive the dynamic behavior of a lane-change controller. Thereby, we contrast the setting of interleaving semantics and synchronous concurrent semantics. We also consider the possibility of exchanging knowledge between neighboring cars in order to come up with the right decisions. Finally, we address compositional verification using an assumption-guarantee paradigm.

The paper demonstrates the validity of sensors and the model in the algorithm for a lane change controller. The paper presents the systematic derivation of the chosen model from the ground up and the important role played by the sensors used in this system. The whole concept of the system on which the tests were carried out is presented step by step. Simulations were realised in the Matlab and Simulink environments. Preliminary tests were performed to confirm the need for the controller in a closed-loop system. On the other hand, sensitivity (the influence of noise and offset) studies showed the advantages and disadvantages of the developed algorithm. This allowed us to create a research path for future work with the aim of improving the operation of the proposed system.

PurposeFeature 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/approachIn 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.FindingsIt 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/valueIn 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.

PurposeThe purpose of this paper is to investigate problems in performing stable lane changes and to find a solution to reduce energy consumption of autonomous electric vehicles.Design/methodology/approachAn optimization algorithm, model predictive control (MPC) and Karush–Kuhn–Tucker (KKT) conditions are adopted to resolve the problems of obtaining optimal lane time, tracking dynamic reference and energy-efficient allocation. In this paper, the dynamic constraints of vehicles during lane change are first established based on the longitudinal and lateral force coupling characteristics and the nominal reference trajectory. Then, by optimizing the lane change time, the yaw rate and lateral acceleration that connect with the lane change time are limed. Furthermore, to assure the dynamic properties of autonomous vehicles, the real system inputs under the restraints are obtained by using the MPC method. Based on the gained inputs and the efficient map of brushless direct-current in-wheel motors (BLDC IWMs), the nonlinear cost function which combines vehicle dynamic and energy consumption is given and the KKT-based method is adopted.FindingsThe effectiveness of the proposed control system is verified by numerical simulations. Consequently, the proposed control system can successfully achieve stable trajectory planning, which means that the yaw rate and longitudinal and lateral acceleration of vehicle are within stability boundaries, which accomplishes accurate tracking control and decreases obvious energy consumption.Originality/valueThis paper proposes a solution to simultaneously satisfy stable lane change maneuvering and reduction of energy consumption for autonomous electric vehicles. Different from previous path planning researches in which only the geometric constraints are involved, this paper considers vehicle dynamics, and stability boundaries are established in path planning to ensure the feasibility of the generated reference path.

In the future, car-to-car communication and car-to-infrastructure communication will be a central part of automated driving experience. Cooperative interactive driving is seen as a promising approach, in which cars interact cooperatively with drivers and the environment. However, to ensure drivers’ acceptance and their trust in such systems, it is important to understand the underlying mechanisms of human cooperation in traffic context. Therefore, this study investigated potential influencing parameters for cooperative behaviour in a lane change situation on a highway. As central influencing parameters the situation’s criticality and the distance in time and space to the driver asking for cooperation were manipulated. This was done by selecting appropriate levels for the time to collision (TTC) in conjunction with the variation of distances to other involved agents. In a video-based experiment with the perspective of driving on the left lane, 43 participants (M = 23.2 years; SD = 4.26 years) had to decide if they would give way to a driver in the right lane situated behind a slower truck. The results showed that the willingness to cooperate was strongly influenced by aspects of the situation: the driver’s costs (operationalized by the distance in time and space to the driver asking for cooperation) and the criticality of the situation for the other driver. A large distance in time and space to the driver asking for cooperation and, therefore, low costs of cooperation facilitate the driver’s willingness to cooperate via accelerating and decelerating. The results also indicated that in situations with high criticality drivers seemed to show strong uncertainty about how to behave or solve this situation. Consequently, cooperatively interacting systems with well-developed user interfaces might support drivers’ cooperative behaviour in critical situations.