This paper presents a hierarchical hybrid predictive control framework for an autonomously controlled road vehicle. At the top, an assigner module is designed as a finite state machine for decision-making. Based on the current information of the controlled vehicle and its environment (obstacles, and lane markings, etc), the assigner selects discrete maneuver states through pre-defined switching rules. The several maneuver states are related to different setups for the underlying model predictive trajectory guidance module. The guidance module uses a reduced-order curvilinear particle motion description of the controlled vehicle and obstacle objects as well as a corresponding description of the reference path, lane and traffic limits. The output of the guidance module interfaces with the lower level controller of the continuous vehicle dynamics. The performance of the proposed framework is demonstrated via simulations of highway-driving scenarios.
- Dynamic Systems and Control Division
Hierarchical Hybrid Predictive Control of an Autonomous Road Vehicle
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Wang, Q, Weiskircher, T, & Ayalew, B. "Hierarchical Hybrid Predictive Control of an Autonomous Road Vehicle." Proceedings of the ASME 2015 Dynamic Systems and Control Conference. Volume 3: Multiagent Network Systems; Natural Gas and Heat Exchangers; Path Planning and Motion Control; Powertrain Systems; Rehab Robotics; Robot Manipulators; Rollover Prevention (AVS); Sensors and Actuators; Time Delay Systems; Tracking Control Systems; Uncertain Systems and Robustness; Unmanned, Ground and Surface Robotics; Vehicle Dynamics Control; Vibration and Control of Smart Structures/Mech Systems; Vibration Issues in Mechanical Systems. Columbus, Ohio, USA. October 28–30, 2015. V003T50A006. ASME. https://doi.org/10.1115/DSCC2015-9773
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