Interaction-aware model predictive control for autonomous highway driving

Abstract

Automated driving technology has developed rapidly over the past decades, profoundly transforming traditional modes of transportation. It offers significant conveniences by replacing human drivers in simple scenarios and improves safety through driver assistance systems. However, despite these advances, achieving full automation still faces substantial challenges. One critical challenge is accurately predicting the motion of surrounding vehicles in real time and incorporating these predictions into the control of autonomous vehicles to ensure safety. The diversity in individual driving behaviors, coupled with the complexity of modeling interactions among traffic participants under uncertainties, makes this problem particularly difficult. To provide insights into these challenges, this thesis focuses on interaction-aware traffic prediction and the safe control of autonomous vehicles on highways. Highways are chosen as the study environment due to their structured lanes and lower traffic density. The aim is to establish a research foundation in this setting that can later be extended to more complex traffic environments, such as urban roads. Specifically, this thesis investigates two primary research directions to address these challenges. The first direction separates the tasks of interaction-aware traffic prediction and safe control of autonomous vehicles. The second direction aims to integrate these tasks into a unified control architecture for greater simplicity and efficiency. In the first research direction, diverse intention-based models along with the Interacting Multiple Model Kalman Filter (IMM-KF) algorithm are employed to estimate and predict vehicle motion states while considering interactions. Possible vehicle motion maneuvers are represented by a finite number of normal scenarios. Additionally, a “worst-case” scenario is considered along with normal scenarios in a Scenario-based Model Predictive Control (SCMPC) architecture to ensure the safety of the autonomous vehicle. Moreover, to reduce the conservatism of the control strategy associated with considering the “worst-case” scenario, a new Contingency Model Predictive Control (CMPC) scheme is explored with time-varying prediction horizons and an enlarged terminal set. In the second research direction, the Minimizing Overall Braking Induced by Lane Change (MOBIL) model is employed to find out the possible lanes that vehicles may occupy based on vehicle interactions. With this lateral traffic prediction, the longitudinal states of surrounding vehicles are modeled simultaneously with those of the autonomous vehicle within a Model Predictive Control (MPC) structure. They are determined by minimizing the collective control costs of all vehicles through dynamic interaction-aware mechanisms. All proposed control architectures are validated in a high-fidelity IPG CarMaker simulation environment. Simulation results demonstrate the effectiveness of these approaches in safely controlling autonomous vehicles while considering vehicles’ interactions on highways. The findings of this thesis provide new perspectives on addressing challenges in high way autonomous driving and establish the foundation for extending safe control strate gies to more complex traffic scenarios involving interactive behaviors. This research advances the understanding of interaction modeling in multi-agent traffic environments, contributing structured approaches to represent and leverage vehicle interactions for safer autonomous control. The modular design of the proposed frameworks facilitates their integration with broader automated driving systems, such as perception and planning modules. Moreover, validation in a high-fidelity simulation environment offers a practical reference for a potential real-world implementation.