In this paper, a lane-keeping system is proposed for the tractor with two articulated trailers. For Tractor with Multi-Unit Trailer (T-MUT) vehicles, the motions of each unit of the vehicle are quite different from those of ordinary single-unit vehicles. Based on the dynamic model of the T-MUT vehicle, the lane-keeping control system of a T-MUT vehicle is designed with Model Predictive Control (MPC). In order to consider the uncertainty of the weight change at each trailer, a model uncertainty analysis is performed through the transfer functions for each hitch angle. For lane-keeping performance, the geometry constraints on each unit of the T-MUT vehicle are described to prevent the vehicle from lane invasion. Also, by applying the constraint tightening method to the output of the controller, a robust MPC is designed against model uncertainty, such as weight changes at each trailer. The performance of the proposed controller is validated in simulations with various curvature scenarios.

In this paper, a new approach of state observer is proposed for the tractor with multi unit trailers. In the case of tractor with two articulated trailers, the dynamic characteristics of the trailers are dominantly determined by the two hitch angles connecting each trailer. A novel estimation system for the hitch angle is introduced by combining the Kalman filter with the deep learning network and transfer function techniques. The Gated Recurrent Unit (GRU) network is constructed to calculate hitch angles and these values are used as the virtual measurement in the Kalman filter. The dynamic characteristics of two trailers with respect to the hitch angles are expressed as the transfer function models and these models are used to calculate the hitch angles as the virtual measurement. The virtual measurements from the two methods are integrated separately into the Kalman filter design. The estimation performance of the hitch angle with the proposed hybrid observer is validated in simulations with various curvature scenarios.

A robust and high-performance lateral control system is necessary for autonomous bus driving in complex downtown roads. In this paper, a Sliding Mode (SM) controller with an uncertainty observer is proposed to reduce the chattering phenomenon while ensuring robustness and maintaining the control performance. A stiffness gain is introduced in the cornering stiffness of rear wheels to compensate for the model uncertainty and external disturbance. For lane keeping and changing maneuvers, an optimal path planning algorithm is designed by considering dynamic constraints of the bus. The lane departure of bus is prevented with minimizing the lateral jerk through the sampling-based path regeneration process. The proposed lateral control system is compared to other conventional SM based controllers in the TRUCKSIM simulator. In an experiment with a real bus, lane keeping maneuver is conducted and compared with experienced human driver in the same driving course. Lane change maneuver is also tested with real bus to perform smooth lane transition while satisfying the lateral acceleration and yaw rate limitations.

In this paper, a novel Enhanced Kinematic (E-Kinematic) model for Heavy-Duty Buses (HDBs) is suggested and employed to construct a robust Model Predictive Control (MPC) system for efficient path tracking. The E-Kinematic model is derived from actual vehicle yaw motion data, with its efficacy validated at both low and high speeds through frequency domain analysis. This model is then utilized in the development of a robust MPC system for the improved path tracking performance. To enhance the path tracking performance of HDBs, a new reference trajectory is introduced within the delay frame to account for the vehicle's inherent lateral motion delay. This approach prompts the vehicle to adhere to the reference trajectory based on its anticipated future state, effectively considering lateral motion delay of the vehicle. Furthermore, the output constraints of the MPC controller are designed to take into account the geometric characteristics of the vehicle, such as sidewalls and overhangs, to prevent potential lane boundary intrusion. Techniques of constraint tightening and smoothing are applied to ensure robust control in the presence of model uncertainty. The performance of the proposed system is comprehensively validated through simulations and real vehicle experiments over various curvatures.

This paper proposes a behavior planning algorithm for self-driving vehicles to handle lane keeping, speed control considering inter-vehicle space, and collision avoidance under uncertainty. The behavior planning approach is structured as a hierarchically organized Markov Decision Process (MDP) comprising two components: the path planning MDP and the speed profiling MDP. The path planning MDP generates multiple path candidates using lane-change path data collected from human drivers. Evaluation of each path candidate is based on reward and penalty terms. The path planning MDP spans and updates considering the predicted simulation time of the path candidate. Subsequently, the speed profiling MDP determines the optimal sequence of speeds for the host vehicle on the planned path. Evaluation of the path and speed profile utility is performed using reward and penalty terms based on the current states of vehicles and road structure. A unique aspect of this approach is the incorporation of uncertainty-aware collision risk and interaction-aware gap penalty, which account for the uncertainty of perception and traffic motion. Various cut-in scenarios are presented in simulations to demonstrate the effectiveness of the proposed algorithm.

Accurately estimating the road slope and mass of a tractor-trailer is crucial from a control perspective as it significantly impacts the dynamic behavior. However, achieving accurate estimation of these variables poses a challenge, especially in scenarios involving mass variations due to loading on the semi-trailer. This difficulty arises from the need to appropriately decouple among the longitudinal acceleration, the acceleration due to road slope and acceleration sensor bias due to pitch angle. To solve this difficulty, this study proposes a road slope and mass estimation framework that accounts for changes in pitch angle. Firstly, a kinematic model is introduced including the longitudinal acceleration and the pitch angle. Secondly, a physically feasible neural network-based pitch angle estimator is designed to determine the pitching effect in the longitudinal acceleration sensor data. The input features are derived based on the pitch dynamics, and pitch bias is additionally introduced to consider the mass variation during weight loading/unloading. Then, a Kalman Filter (KF) is designed to estimate the longitudinal acceleration and slope angle with considering the estimated pitch angle. Finally, a Recursive Least Squares (RLS)-based mass estimation method is presented utilizing the estimated longitudinal acceleration and the estimated road slope. Outlier data are filtered using the Random Sample Consensus (RANSAC) algorithm to improve the performance of the RLS. The proposed estimation method is validated through simulations.