This paper presents a probabilistic trajectory prediction of cut-in vehicles exploiting the information of interacting vehicles. First, a probability distribution of behavioral parameters, which represents the characteristics of lane-change motion, is obtained via Gaussian Process Regression (GPR). For this purpose, Gaussian Process (GP) models are trained using real-world trajectories of lane-changing vehicles and adjacent vehicles. Subsequently, the future states of the lane-change vehicle are probabilistically estimated using a path-following model, which introduces virtual measurements based on the information of behavioral parameters. The proposed predictor is applied to the motion planning and control of autonomous vehicles. A Model Predictive Control (MPC) is designed to achieve predictive maneuvering of autonomous vehicles against cut-in preceding vehicles. The proposed predictor has been evaluated in terms of its prediction accuracy. Also, the performance of the proposed predictor-based control has been validated via computer simulations and autonomous driving vehicle tests. Compared to conventional prediction methods, it is shown that the interaction-aware proposed predictor provides improved prediction of cut-in vehicles' motion in multi-vehicle scenarios. Furthermore, the control results indicate that the proposed predictor helps the autonomous vehicle to reduce the control effort and improve ride quality for passengers in cut-in scenarios, while guaranteeing safety.

This paper presents a trajectory planning and control algorithm of autonomous vehicles for static traffic agent avoidance in multi vehicle urban environments. In urban autonomous driving, the subject vehicle encounters diverse traffic scenes including lane changing, intersection driving, and illegally parked static vehicle avoidance. Among these, dealing with illegally parked static target vehicle is a major challenge to urban autonomous driving due to large velocity difference between ego and target vehicles and interactions with surrounding vehicles. In order to tackle this problem, we introduce a decision making and motion planning framework for static vehicle avoidance considering both the preceding static vehicles and surrounding vehicles. Among the surrounding vehicles, the set of objects with potential collision risk is selected based on the lane boundaries and road geometry. Then, the driving status of the selected target vehicles are classified as normal driving vehicles or parked vehicles based on their longitudinal speed, lateral position and lateral space occupancy. For the preceding parked vehicles, the motion planner generates lateral and longitudinal evasive motion, by taking side lane traffic flow and risk into account. The desired motion is executed by applying optimized control inputs computed by lateral and longitudinal model predictive controllers. The performance validation of the proposed algorithm has been conducted with actual autonomous test vehicles. The test results confirmed that the proposed algorithm can successfully perform evasive maneuvers on urban roads to ensure safety and mitigate collision risk with the surrounding traffic agents.

This paper describes design, implementation, and evaluation of human driving data-based Lane Keeping Assistance System (LKAS) for electric bus equipped with a hybrid electric power steering system. The hybrid electric-power steering system used in this study means a steering system in which an Electric Power Steering (EPS) system and an Electro-Hydraulic Power Steering (EHPS) system are integrated into a ball-nut. A dynamic model of hybrid EPS system including EHPS system and EPS system has been developed to generate EPS torque and EHPS force corresponding to the input torque. In order to determine proper timing of LKAS intervention, driving data of electric bus drivers were collected and driving patterns were analyzed using a 2-D normal distribution probability density function. Lane information necessary for the lane-keeping assistance system is obtained from a vision camera mounted on the electric bus. Sliding mode control is used to get a Steering Wheel Angle (SWA) required for LKAS. A Proportional–Integral (PI) control is used to obtain an overlay torque required to track the target SWA. A proposed DLC threshold has been validated using vehicle simulation software, TruckSim, and MATLAB/Simulink. It is shown that the proposed DLC threshold shows good performance in both cases of slow lane departure and fast lane departure. The proposed algorithm has been successfully implemented on the electric bus and evaluated via real-world driving tests. Test scenario setting and the evaluation of performance were carried out by ISO 11270 criteria. It is shown that the algorithm successfully prevented the electric bus from unintended lane departure satisfying ISO 11270 criteria.