PV
P.A. Velchev
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Master thesis
(2025)
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P.A. Velchev, A.J.J. van den Boom, B. Shyrokau, M. Khosravi, A. Bertipaglia, Felipe Santafe
This paper presents a novel data-driven Reference Governor with Model Predictive Control integrating local motion replanning and path following for collision avoidance. Employing a model-free Reference Governor, the proposed solution utilises system knowledge through Bayesian Optimisation to augment predetermined evasive trajectories, minimising path-following errors and simultaneously ensuring obstacle safety margins. A single-track vehicle model in combination with non-linear tyre models is used to capture the vehicle’s dynamics. The optimised control action is the vehicle steering angle, whilst the Reference Governor optimises parameters of a sigmoid reference signal to minimise the tracking error and guarantee safety with respect to obstacles in emergency manoeuvres. The proposed approach is evaluated on a single lane change using a high-fidelity simulation environment and its performance is compared to a baseline controller integrating path following and obstacle avoidance. The results show a 14% reduction of safety critical overshoot, maximising obstacle safety distance and a four times lower controller cycle time compared to the baseline. Furthermore, through a robustness analysis, it is demonstrated that the proposed approach is more robust towards model mismatches and perception-based errors, as seen by average 30% and 40% reductions in near-miss and collision rates.
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This paper presents a novel data-driven Reference Governor with Model Predictive Control integrating local motion replanning and path following for collision avoidance. Employing a model-free Reference Governor, the proposed solution utilises system knowledge through Bayesian Optimisation to augment predetermined evasive trajectories, minimising path-following errors and simultaneously ensuring obstacle safety margins. A single-track vehicle model in combination with non-linear tyre models is used to capture the vehicle’s dynamics. The optimised control action is the vehicle steering angle, whilst the Reference Governor optimises parameters of a sigmoid reference signal to minimise the tracking error and guarantee safety with respect to obstacles in emergency manoeuvres. The proposed approach is evaluated on a single lane change using a high-fidelity simulation environment and its performance is compared to a baseline controller integrating path following and obstacle avoidance. The results show a 14% reduction of safety critical overshoot, maximising obstacle safety distance and a four times lower controller cycle time compared to the baseline. Furthermore, through a robustness analysis, it is demonstrated that the proposed approach is more robust towards model mismatches and perception-based errors, as seen by average 30% and 40% reductions in near-miss and collision rates.