This paper discusses the design of a human-aware cooperative adaptive cruise control (CACC) system that (i) takes into account driver comfort in autonomous cruise control, and (ii) provides assistive corrections to avoid driver errors. To incorporate driver characteristics into system controller design, two self-learning algorithms are used to estimate driver’s preferred time headway. We then develop a human-like blending control for CACC based on a model predictive control (MPC)-type method, which integrates the driver comfort, traffic efficiency, and fuel economy criteria. Furthermore, a driving assistance controller is developed to help human driver to maintain string stability in platoon. Simulation results show that (i) the human-like CACC design can significantly improve driving experience, and (ii) with the help of the assistive controller, string stability is satisfied for both exclusively autonomous CACC and when the CACC switches to manual driving in a platoon.
- Dynamic Systems and Control Division
Human-Aware Autonomous Control for Cooperative Adaptive Cruise Control (CACC) Systems
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Wang, X, & Wang, Y. "Human-Aware Autonomous Control for Cooperative Adaptive Cruise Control (CACC) Systems." Proceedings of the ASME 2015 Dynamic Systems and Control Conference. Volume 2: Diagnostics and Detection; Drilling; Dynamics and Control of Wind Energy Systems; Energy Harvesting; Estimation and Identification; Flexible and Smart Structure Control; Fuels Cells/Energy Storage; Human Robot Interaction; HVAC Building Energy Management; Industrial Applications; Intelligent Transportation Systems; Manufacturing; Mechatronics; Modelling and Validation; Motion and Vibration Control Applications. Columbus, Ohio, USA. October 28–30, 2015. V002T31A001. ASME. https://doi.org/10.1115/DSCC2015-9625
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