Vehicles today have many advanced driver assistance control systems that improve vehicle safety and comfort. With the development of more sophisticated vehicle electronic control and autonomous driving technology, the need and effort to estimate road surface conditions is increasing. In this paper, a real-time road surface classification algorithm, based on a deep neural network, is developed using a database collected through an intelligent tire sensor system with a three-axis accelerometer installed inside the tire. Two representative types of network, fully connected neural network (FCNN) and convolutional neural network (CNN), are learned with each of the three-axis acceleration sensor signals, and their performances were compared to obtain an optimal learning network result. The learning results show that the road surface type can be classified in real-time with sufficient accuracy when the longitudinal and vertical axis acceleration signals are trained with the CNN. In order to improve classification accuracy, a CNN with multiple input that can simultaneously learn 2-axis or 3-axis acceleration signals is suggested. In addition, by analyzing how the accuracy of the network is affected by number of classes and length of input data, which is related to delay of classification, the appropriate network can be selected according to the application. The proposed real-time road surface classification algorithm is expected to be utilized with various vehicle electronic control systems and makes a contribution to improving vehicle performance.

Feasible local motion planning for autonomous mobile robots in dynamic environments requires predicting how the scene evolves. Conventional navigation stakes rely on a local map to represent how a dynamic scene changes over time. However, these navigation stakes depend highly on the accuracy of the environmental map and the number of obstacles. This study uses semantic segmentation-based drivable area estimation as an alternative representation to assist with local motion planning. Notably, a realistic 3D simulator based on an Unreal Engine was created to generate a synthetic dataset under different weather conditions. A transfer learning technique was used to train the encoder-decoder model to segment free space from the occupied sidewalk environment. The local planner uses a nonlinear model predictive control (NMPC) scheme that inputs the estimated drivable space, the state of the robot, and a global plan to produce safe velocity commands that minimize the tracking cost and actuator effort while avoiding collisions with dynamic and static obstacles. The proposed approach achieves zero-shot transfer from a simulation to real-world environments that have never been experienced during training. Several intensive experiments were conducted and compared with the dynamic window approach (DWA) to demonstrate the effectiveness of our system in dynamic sidewalk environments.

Efficient path planning for dual crane cooperative systems is a complex challenge due to high-dimensional configuration spaces and the volatile nature of dynamic construction environments. Conventional sampling-based algorithms often suffer from computational delays and lack the ability to adapt to environmental changes without restarting the search process from scratch. This paper proposes a fast path planning and re-planning framework that integrates a 4-DOF reduced-space sampling strategy with a tree-reuse multiple-RRT mechanism. By constraining the sampling space to critical kinematic degrees of freedom and maintaining pre-explored valid nodes as a persistent knowledge base, the framework enables rapid initial planning and near-real-time response to unperceived obstacles. Experimental results demonstrate that the 4-DOF sampling significantly reduces total planning time compared to conventional 6-DOF methods, with superior performance particularly in restricted narrow-path scenarios. Furthermore, the tree-reuse strategy facilitates efficient path re-initialization, substantially reducing re-planning time and exhibiting a notable learning effect in sequential transport tasks through accelerated convergence. These findings demonstrate that the proposed framework effectively mitigates the “new node exploration” bottleneck, offering a robust and practical solution for autonomous dual crane coordination in complex construction sites.