Multimodel Approach to Personalized Autonomous Adaptive Cruise Control

Autonomous vehicles are gaining increased attention but surveys have shown that a large percentage of people are wary of adopting the new technology. One possible explanation for the hesitancy is that the occupant would not be comfortable with the driving style as a result of the control models and their parameters as set by the manufacture. Comfort level is subjective in nature and therefore varies between individuals. To combat this issue, autonomous vehicles must be able to adapt to the driving style preference of the user. If we assume drivers are more comfortable with their own driving style, we can choose to have the vehicle learn and incorporate the driver's style into the control models. However, there is still no widely accepted “best” model. One model may prove to better represent a particular driver than other models though a driver may choose unsafe driving conditions, the replication of which should not take precedence over the safety of the occupant. In this paper, we propose a multimodel approach to find the best driver model for describing an individual's longitudinal driving style on highway. A method for extracting the indicators of an individual's driving style is proposed first. Then, a multimodel-based evaluation method is described in detail. Chandler, Herman, & Montroll, General Motors Nonlinear, Tampère, Addison & Low, Optimal Velocity Model, and Neural Network models are trained and compared in this paper. The model with the best performance in replicating driving style is further coupled with a model predictive controller to include safety constraints for safer driving. Finally, the proposed multimodel approach is tested with driving data collected from five different drivers. The test results show that our multimodel-based approach is showing advantage over a single model approach in imitating an individual's longitudinal driving style.