The rapid advancement of autonomous driving and ADAS technologies has increased the demand for high-definition (HD) lane-level maps that accurately preserve rich geometric information while scaling to city-wide coverage. While traditional polyline-based formats are widely used, they struggle to provide continuous representations of key geometric properties such as curvature and heading angle, which are essential for autonomous driving applications. Curve-based representations have been introduced to address these limitations, but existing methods are often restricted to simplified or sparsely connected road networks, limiting their effectiveness in large-scale, real-world environments. This study presents an arc-spline-based lane representation framework that efficiently models complex, large-sized lane-level maps while preserving continuous road geometry. To achieve this, we introduce a novel road network decomposition and merging method that enables structured parameterization without requiring full map-scale optimization. Instead, optimization is localized to cluster connection regions, significantly enhancing computational efficiency. Validation using lanelet maps from the nuScenes dataset demonstrates that our approach maintains an average approximation error of 4.3 cm while preserving detailed lane topology and global tangential continuity, while also achieving a significant reduction in storage requirements compared to conventional polyline formats.

Collision avoidance systems that utilize both steering and braking have been widely studied. However, information about the tire-road friction coefficient, which plays a critical role in collision avoidance, is assumed to be a known constant value in many studies. Because of this, it is difficult to properly respond to changes in road surface conditions that commonly occur in practice. In this paper, a collision avoidance system that estimates and applies the tire-road friction coefficient in real-time is proposed to overcome these limitations. First, a path planner and tracking controller are designed. It applies friction coefficient changes to the algorithm and provides prompt feedback of the updated vehicle state variables. Next, a friction coefficient estimator is proposed that enables rapid and highly accurate estimation in a short period of time, even in collision avoidance situations where sufficient excitation required for estimation may not be continuously achieved for a long time. Finally, by integrating the collision avoidance system and the friction coefficient estimator, the estimated tire-road friction coefficient is applied to the collision avoidance system in real-time, and the reactions are used to estimate the tire-road friction coefficient again. This demonstrates the excellent collision avoidance performance despite changes in road friction and also proves the virtuous cycle interaction of the integrated system.

This paper proposes a backlash control algorithm using Terminal Iterative Learning Control (TILC) in the angle domain. The proposed method addresses the issue of control time variation in the Iterative Learning Control (ILC) method, which makes it impractical for vehicle control. By controlling backlash in the angle domain, the control interval remains the same for each iteration. The backlash impact is proportional to the velocity at the end of the backlash mode. However, by bringing the reference value nearer to zero, the impact was mitigated. Additionally, the utilization of TILC enhances its resilience to sensor noise. The proposed method is evaluated through simulations and experimental results, demonstrating its practical applicability to vehicles and high accuracy in various initial conditions. This paper provides a novel approach to backlash control in-vehicle systems, contributing to the advancement of control methods for improved ride comfort and safety.

The dual-clutch transmission (DCT) was developed to increase the transmission efficiency and the shift performance. However, in a DCT, due to uncertainty related to the actuator, the tie-up phenomenon can arise, in which two clutches engage together or the clutch torque control performance deteriorates. Especially, the actuator uncertainty increased when using a special mechanism to increase controllability and efficiency. Among them, the ball-ramp DCT (BR-DCT), which uses a self-energizing mechanism, can reduce the consumption of actuator energy while also reducing the tie-up effect. However, the nonlinearity of the actuator must be considered, such as friction between parts and change in friction coefficient. Among the methods by which this type of uncertainty is estimated, the nonlinear disturbance observer is most effective when used to estimate the unmodeled nonlinearity of an actuator and time-varying uncertainty. In order to execute disturbance rejection control to improve the performance of the shift controller using the estimated uncertainty, it is necessary to configure disturbance compensation input. However, because the BR-DCT powertrain includes mismatched disturbances and nonlinearity of the actuator, it is difficult to apply the existing methods on the composite control law to shift controller. Therefore, in this study, we propose a composite control law to guarantee integrated stability in nonlinear systems such as a BR-DCT powertrain. The proposed method was verified through a powertrain test bench equipped with a BR-DCT. Finally, the proposed composite control law was able to converge both the tracking error and the disturbance estimation error.

In the transition period, autonomous vehicles are mixed with unconnected traffic occupants, such as non-autonomous vehicles and pedestrians, resulting in a major hurdle toward autonomy in urban areas, especially at intersections. In this context, the cooperative-intelligent transportation system (C-ITS) affords a promising solution to achieve a breakthrough with its omniscient sensors network and computing capability. From the perspective of a C-ITS-based service, the trajectory of non-autonomous vehicle is a critical uncertainty that resides at the intersection. Therefore, this paper proposes a unique interactive framework, which is installed in the edge server of C-ITS and can estimate the present trajectories and predict the future trajectories of the non-autonomous vehicles at intersections. The proposed framework was based on multiple hypotheses of possible maneuvers that formed the confined prior set to reduce the high uncertainties posed by the complicated environment of the urban intersection. The resulting all-in-one framework provided a stable long-term trajectory prediction with intrinsic maneuver classification and improved tracking in an integrated way by incorporating the interactions between the multiple hypotheses. This situation awareness can assist autonomous vehicles to drive safely and defensively. The proposed framework was verified using a dataset collected at a real urban intersection.

This paper focused on improving a model predictive control (MPC) using Laguerre functions. This study was conducted to achieve high performance while reducing the computational complexity of MPC for autonomous vehicle tracking control. Previous studies have used a conventional linear time-varying MPC (LTV-CMPC) for the linear time-varying (LTV) vehicle model. For LTV-CMPC, the computational complexity increases exponentially as a predictive horizon and control horizon increase. Real-time implementation of LTV-CMPC with long horizons was difficult due to limited computational resources. For reducing computational complexity, we proposed LTV-MPC using Laguerre functions (LTV-LMPC). Considering the vehicle system, LTV-LMPC used a non-augmented model and described the input rate as Laguerre functions. The proposed LTV-LMPC significantly reduced the number of optimization variables. The number of Laguerre functions and the Laguerre pole determined the performance of the LTV-LMPC. In this study, we derived and proved propositions for analyzing the performance change of LTV-LMPC according to the Laguerre pole. LTV-LMPC with pole optimization (LTV-OLMPC) was proposed based on these propositions. The performance of the proposed algorithm was verified by simulation. The LTV-OLMPC guarantees low computational complexity and high performance.

This paper presents an autonomous vehicle’s path planning and tracking system optimized for collision avoidance on slippery roads. During path planning, a path that can induce the maximum possible lateral acceleration is generated through the fifth-order spline in consideration of the tire-road friction. The generated path is tracked based on the model predictive control (MPC), and the nonlinearity generated by the tire and low friction surface is reflected through the extended bicycle model and the combined brushed tire model. Inside the controller, a new type of yaw rate constraint considering side-slip angles is utilized to prevent the vehicle from becoming unstable on slippery roads and maximize lateral maneuver of the vehicle at the same time. The proposed system is verified by the vehicle dynamics software CarSim, and the simulation results show that it significantly increases the possibility of collision avoidance in slippery road conditions.

This paper proposes a new control strategy to improve vehicle cornering performance in a model predictive control framework. The most distinguishing feature of the proposed method is that the natural handling characteristics of the production vehicle is exploited to reduce the complexity of the conventional control methods. For safety's sake, most production vehicles are built to exhibit an understeer handling characteristics to some extent. By monitoring how much the vehicle is biased into the understeer state, the controller attempts to adjust this amount in a way that improves the vehicle cornering performance. With this particular strategy, an innovative controller can be designed without road friction information, which complicates the conventional control methods. In addition, unlike the conventional controllers, the reference yaw rate that is highly dependent on road friction need not be defined due to the proposed control structure. The optimal control problem is formulated in a model predictive control framework to handle the constraints efficiently, and simulations in various test scenarios illustrate the effectiveness of the proposed approach.

This paper introduces a new integrated observer for estimating the vehicle center of gravity (CG) height in real time. It can assist the vehicle in monitoring the real-time rollover risk and improving the performance of vehicle safety control systems. The proposed integrated observer consists of three parts: a linearized recursive least square (LRLS) algorithm on vehicle longitudinal motion, an adaptation law on vehicle lateral motion, and observer synthesis. First, the LRLS algorithm performs estimation of vehicle mass and CG height during longitudinal braking and exploits the characteristic that normalized longitudinal tire stiffness is the same in the front and rear axles. Second, the adaptation law, accompanied by a roll angle observer, estimates CG height on the vehicle lateral motion based on Lyapunov stability analysis. It includes the following contributions: verification of robustness to the vehicle mass estimation error and prevention of integration drift. Finally, in the observer synthesis, the final estimation of CG height combining the above two results is derived. The overall estimation algorithm has high practicality due to the following features. 1) CG height can be obtained on both longitudinal and lateral motions of the vehicle. This point leads to a fast convergence rate in CG height estimation. 2) The fact that it does not cause any computational burden issues in real-time implementation is also a great advantage in terms of practicality. 3) It utilizes only readily available sensors. An experimental study with various driving scenarios evaluates the effectiveness of the proposed algorithm in real-car application.

Technical development with the goal of achieving zero accidents and zero fatalities is ongoing. The autonomous emergency braking systems that debuted in the late 2000s have proven their value regarding improved safety. However, the technology still presents many challenges because it is not easy to ensure that the system will operate as intended in any environment and at any time. Any system that is unaware of its environment is prone to be excessively conservative, which could adversely affect the efficacy of said system. Situation awareness is a key to resolving this problem. The present study suggests the use of warning braking to gain an awareness of the level of road friction, which is one of the major uncertainties faced on the road. During warning braking, the tire-road maximum friction coefficient is estimated in real time, and a threat assessment is performed adaptively based on the friction information. Because warning braking is momentary and applied with limited dynamics due to issues related to human factors, this study discusses the major considerations and requirements for the key parameters related to warning braking. The performance of the suggested adaptive collision avoidance scheme is verified by means of simulation and experiments.

In many industrial fields, the mass information of a moving system is important and necessary to prevent undesired motion or failure and to control the system in its desired trajectory. One simple solution could be direct measurement of the mass using a sensor such as force sensor and accelerometer. However, it requires additional cost increase. In addition, it is not easy to measure the mass of a moving part in many cases. For those reasons, in this research, an online varying mass estimation algorithm is designed using an Extended Kalman Filter (EKF) without any additional sensors. Furthermore, the lumped disturbance compensating algorithm, which was designed by the authors in the previous research using EKF, is combined to obtain further position tracking performance. The effectiveness of the suggested method is validated through simulations. Additional verification with experiments is planned for future work.

This paper proposes a periodic adaptive disturbance observer (PADOB) for a precision position control of a PMLSM (Permanent Magnet Linear Synchronous Motor), which is based on a periodic adaptive learning control(PALC). It updates the output of a classical linear DOB to compensate modeling errors between a nominal plant and actual plant and external disturbance forces such as the friction and detent force. Therefore, it can improve problems occured by inaccurate parameters of the nominal plant and the instability problem occurred by updating parameters of nominal model in the DOB directly. Also, the complicated procedures to design the initial conditions in PALC are not needed. Through simulation test and experiments of the PMLSM, the validity of PADOB is illustrated.

This paper mainly focuses on the accurate estimation of the vehicle velocities of all axes, using the data received from a low-cost 6-D inertial measurement unit. The data include the vehicle linear acceleration and angular rates of all axes. In addition, the observer uses the wheel speed sensors and steering wheel angle information, which are already available on most recent production cars. Utilizing the aforementioned information, based on the combination of a bicycle model and a kinematic model, a multiple-observer system that computes the weighted sum estimation that is dependent on cornering stiffness adaptation is adopted to observe the lateral vehicle velocity, as well as longitudinal and vertical velocities. The stability of each component of the proposed observer is investigated, and a set of assessments to confirm the performance of the entire system is arranged through experiments using a real production sport utility vehicle.