This paper proposes a novel model predictive control (MPC) algorithm that increases the path tracking performance according to the control input. The proposed algorithm reduces the path tracking errors of MPC by updating the sampling time of the next step according to the control inputs (i.e., the lateral velocity and front steering angle) calculated in each step of the MPC algorithm. The scenarios of a mixture of straight and curved driving paths were constructed, and the optimal control input was calculated in each step. In the experiment, a scenario was created with the Automated Driving Toolbox of MATLAB, and the path-following performance characteristics and computation times of the existing and proposed MPC algorithms were verified and compared with simulations. The results prove that the proposed MPC algorithm has improved path-following performance compared to those of the existing MPC algorithm.

The vision transformer (ViT) with global self-attention exhibits quadratic computational complexity that depends on the image size. To address this issue, window-based self-attention ViT limits attention area to a specific window, thereby mitigating the computational complexity. However, it cannot effectively capture the relationships between windows. The Swin Transformer, a representative window-based self-attention ViT, introduces shifted-window multi-head self-attention (SW-MSA) to capture the cross-window information. However, SW-MSA groups tokens that are close to each other in the image into one window and thus cannot capture relationships between distant tokens. Therefore, this paper introduces a feature-space window attention transformer (FSwin Transformer) that includes distant but similar tokens in one window. The proposed FSwin Transformer clusters similar tokens based on the feature space and conducts self-attention within the cluster. Thus, this approach helps understand the global context of the image by compensating for interactions between long-distance tokens, which cannot be captured when windows are set based on the image space. In addition, we incorporate a feature-space refinement method with channel and spatial attention to emphasize key parts and suppress non-essential parts. The refined feature map improves the representation power of the model, resulting in improved classification performance. Consequently, in classification tasks for ImagaNet-1K, FSwin Transformer outperforms existing transformer-based backbones, including the Swin Transformer.

The concern over safety features in autonomous vehicles is increasing due to the rapid development and increasing use of autonomous driving technology. The safety evaluations performed for an autonomous driving system cannot depend only on existing safety verification methods, due to the lack of scenario reproducibility and the dynamic characteristics of the vehicle. Vehicle-In-the-Loop Simulation (VILS) utilizes both real vehicles and virtual simulations for the driving environment to overcome these drawbacks and is a suitable candidate for ensuring reproducibility. However, there may be differences between the behavior of the vehicle in the VILS and vehicle tests due to the implementation level of the virtual environment. This study proposes a novel VILS system that displays consistency with the vehicle tests. The proposed VILS system comprises virtual road generation, synchronization, virtual traffic manager generation, and perception sensor modeling, and implements a virtual driving environment similar to the vehicle test environment. Additionally, the effectiveness of the proposed VILS system and its consistency with the vehicle test is demonstrated using various verification methods. The proposed VILS system can be applied to various speeds, road types, and surrounding environments.

Vehicle-in-the-loop simulation (VILS) is a vehicle-testing technique that integrates high-fidelity simulation environments with real-world vehicles. Among existing simulation approaches for evaluating autonomous driving systems (ADS), VILS is particularly noteworthy because it faithfully reflects the dynamic characteristics of real-world vehicles and ensures repeatable and reproducible testing in diverse virtual scenarios. While researchers strive to implement a VILS platform that closely approximates real-world vehicle-testing environments, the performance of vehicles in VILS testing may differ from that observed in real-world testing, depending on the platform’s reliability. Therefore, methods must be established to validate the reliability of VILS platforms. Herein, we present the essential components of a VILS platform for evaluating ADS (AD-VILS) and propose a metho dology to validate the reliability of the implemented AD-VILS platform. This methodology includes scenario definition, techniques for VILS testing and real-world vehicle testing, and procedures for evaluating consistency and correlation based on statistical and mathematical comparisons between the datasets from virtual and real-world tests. Moreover, we empirically derive reliability evaluation criteria through iterative testing. This methodology aims to enhance the precision and reliability of ADS evaluations conducted on AD-VILS platforms.

In-wheel motor (IWM) vehicles offer a more sporty driving experience but suffer from left-right torque differences that impair straight-line driving stability. This paper proposes an integrated yaw-rate control strategy for IWM vehicle based on adaptive sliding mode control (ASMC) to enhance both agility in cornering and stability in straight-line driving situations. Given that 1) internal and external driving conditions such as driving maneuvers, road surfaces, and unwanted external disturbances affect vehicle yaw dynamics and 2) the tire cornering stiffness and understeer gradient are decisive parameters in yaw dynamics, the proposed strategy adapts online to these two parameters. Moreover, the proposed strategy adopts an adaptive update rate during the adaptation process to ensure robust disturbance rejection performance under various driving conditions. All control laws are defined solely based on measurable information in mass-production vehicles, without any knowledge of road-tire conditions or uncertainty bounds. In experimental tests, the proposed strategy shows improved control precision, accuracy, and robustness compared to PD controller and non-adaptive SMC. As a result, the proposed adaptation strategy improves the maneuverability of IWM vehicles in both dynamic cornering and straight-line driving situations.

With the advancement of autonomous driving systems, the need for effective emergency avoidance path planning has become increasingly important. To enhance safety, the predicted paths of surrounding vehicles anticipate risks and incorporate them into avoidance strategies, enabling more efficient and stable driving. Although the artificial potential field (APF) method is commonly employed for path planning due to its simplicity and effectiveness, it can suffer from the local minimum problem when using gradient descent, causing the vehicle to become stuck before reaching the target. To address this issue and improve the efficiency and stability of path planning, this study proposes integrating prediction data into the APF and optimizing the control points of the quintic Bézier curve using sequential quadratic planning. The validity of the proposed method was confirmed through simulation using IPG CarMaker 12.0.1 and MATLAB/Simulink 2022b.