Personalized Speed Planning Algorithm Using a Statistical Driver Model in Car-following Situations

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Personalized Speed Planning Algorithm Using a Statistical Driver Model in Car-following Situations

Baek, Seung Eon / Kim, Hak Su / Han, Manbae

Advanced driving assistance systems (ADAS) such as adaptive cruise control (ACC), traffic jam assistance, and collision warning have been developed to enhance driving comfort and reduce the driving burden in car-following situations. Although these systems provide automated driving to ensure safety, those do not harmonize the intentions of the driver by reflecting individual drivers’ characteristics. To ensure that system reflects driver intention, we propose a personalized longitudinal speed planning algorithm in car-following situations, which system mimics personal driving styles. Individual driving styles were characterized by designing a pedal behavior prediction model and time headway distribution prediction model. The pedal behavior prediction model is an ensemble tree-based classifier that estimates the driver’s current driving state, i.e., accelerating, cruising, or braking. Then, the driver-specific time headway distribution is estimated based on the polynomial model. These two prediction models were applied to the existing sampling-based speed planning algorithm and implemented with MATLAB/Simulink. The entire speed planning algorithm was simulated using vehicle simulation software. The simulation results showed that the actual driver’s driving style was successfully reproduced.